DataFramed

Mastering the technical side of data and AI is one thing, but communicating those insights effectively is a whole different challenge. How do you make sure your data is understood, acted upon, and influences decisions? It’s not just about presenting the right numbers—it’s about framing them in a way that resonates with different audiences. But how do you tailor your communication to different stakeholders and ensure your message cuts through? What strategies can you use to make your insights truly impactful?

Wes Kao is an entrepreneur, marketer, coach, and advisor who writes at newsletter.weskao.com. She is co-founder of Maven, an edtech company that raised $25M from First Round and Andreessen Horowitz. Previously, she co-founded the altMBA with bestselling author Seth Godin.

In the episode, Richie and Wes explore communication skills, tailoring to your audience, persuasion vs information, feedback and behavioral change, intellectual honesty, judgement and analytical thinking, management and ownership, dealing with mistakes, conflict management, career advice for data practitioners and much more. 

Links Mentioned in the Show:

• Wes’ Website
• Connect with Wes
• 10,000 Hours Concept by Malcolm Gladwell
• Course: Data Communication Concepts
• Related Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision Scientist
• Sign up to RADAR: Forward Edition

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Building a robust data infrastructure is crucial for any organization looking to leverage AI and data-driven insights. But as your data ecosystem grows, so do the challenges of managing, securing, and scaling it. How do you ensure that your data infrastructure not only meets today’s needs but is also prepared for the rapid changes in technology tomorrow? What strategies can you adopt to keep your organization agile, while ensuring that your data investments continue to deliver value and support business goals?

Saad Siddiqui is a venture capitalist for Titanium Ventures. Titanium focus on enterprise technology investments, particularly focusing on next generation enterprise infrastructure and applications. In his career, Saad has deployed over $100M in venture capital in over a dozen companies. In previous roles as a corporate development executive, he has executed M&A transactions valued at over $7 billion in aggregate. Prior to Titanium Ventures he was in corporate development at Informatica and was a member of Cisco's venture investing and acquisitions team covering cloud, big data and virtualization. 

In the episode, Richie and Saad explore the business impacts of data infrastructure, getting started with data infrastructure, the roles and teams you need to get started, scalability and future-proofing, implementation challenges, continuous education and flexibility, automation and modernization, trends in data infrastructure, and much more. 

Links Mentioned in the Show:

• Titanium Ventures
• Connect with Saad
• Course - Artificial Intelligence (AI) Strategy
• Related Episode: How are Businesses Really Using AI? With Tathagat Varma, Global TechOps Leader at Walmart Global Tech
• Rewatch sessions from RADAR: AI Edition

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Businesses are collecting more data than ever before. But is bigger always better? Many companies are starting to question whether massive datasets and complex infrastructure are truly delivering results or just adding unnecessary costs and complications. How can you make sure your data strategy is aligned with your actual needs? What if focusing on smaller, more manageable datasets could improve your efficiency and save resources, all while delivering the same insights?

Ryan Boyd is the Co-Founder & VP, Marketing + DevRel at MotherDuck. Ryan started his career as a software engineer, but since has led DevRel teams for 15+ years at Google, Databricks and Neo4j, where he developed and executed numerous marketing and DevRel programs. Prior to MotherDuck, Ryan worked at Databricks and focussed the team on building an online community during the pandemic, helping to organize the content and experience for an online Data + AI Summit, establishing a regular cadence of video and blog content, launching the Databricks Beacons ambassador program, improving the time to an “aha” moment in the online trial and launching a University Alliance program to help professors teach the latest in data science, machine learning and data engineering.

In the episode, Richie and Ryan explore data growth and computation, the data 1%, the small data movement, data storage and usage, the shift to local and hybrid computing, modern data tools, the challenges of big data, transactional vs analytical databases, SQL language enhancements, simple and ergonomic data solutions and much more. 

Links Mentioned in the Show:

• MotherDuck
• The Small Data Manifesto
• Connect with Ryan
• Small DataSF conference
• Related Episode: Effective Data Engineering with Liya Aizenberg, Director of Data Engineering at Away
• Rewatch sessions from RADAR: AI Edition

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Businesses are constantly racing to stay ahead by adopting the latest data tools and AI technologies. But with so many options and buzzwords, it’s easy to get lost in the excitement without knowing whether these tools truly serve your business. How can you ensure that your data stack is not only modern but sustainable and agile enough to adapt to changing needs? What does it take to build data products that deliver real value to your teams while driving innovation?

Adrian Estala is VP, Field Chief Data Officer and the host of Starburst TV. With a background in leading Digital and IT Portfolio Transformations, he understands the value of creating executive frameworks that focus on material business outcomes. Skilled with getting the most out of data-driven investments, Adrian is your trusted adviser to navigating complex data environments and integrating a Data Mesh strategy in your organization.

In the episode, Richie and Adrian explore the modern data stack, agility in data, collaboration between business and data teams, data products and differing ways of building them, data discovery and metadata, data quality, career skills for data practitioners and much more.

Links Mentioned in the Show:

• Starburst
• Connect with Adrian
• Career Track: Data Engineer in Python
• Related Episode: How this Accenture CDO is Navigating the AI Revolution
• Rewatch sessions from RADAR: AI Edition

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AI is becoming a key tool in industries far beyond just tech. From automating tasks in the movie industry to revolutionizing drug development in life sciences, AI is transforming how we work. But with this growth comes important questions: How is AI really impacting jobs? Are we just increasing efficiency, or are we replacing human roles? And how can companies effectively store and leverage the vast amounts of data being generated every day to gain a competitive advantage?

Jamie Lerner is the President and CEO of Quantum, a company specializing in data storage, management, and protection. Since taking the helm in 2018, Lerner has steered Quantum towards innovative solutions for video and unstructured data. His leadership has been marked by strategic acquisitions and product launches that have significantly enhanced the company's market position. Before joining Quantum, Jamie worked at Cisco, Seagate, CITTIO, XUMA, and Platinum Technology. At Quantum, Lerner has been instrumental in shifting the company's focus towards data storage, management, and protection for video and unstructured data, driving innovation and strategic acquisitions to enhance its market position.

In the episode, Richie and jamie explore AI in subtitling, translation, and the movie industry at large, AI in sports, AI in business and scientific research, AI ethics, infrastructure and data management, video and image data in business, challenges of working with AI in video, excitement vs fear in AI and much more. 

Links Mentioned in the Show:

• Quantum
• Connect with Jamie
• Career Track: Data Engineer in Python
• Related Episode: Seeing the Data Layer Through Spatial Computing with Cathy Hackl and Irena Cronin
• Rewatch sessions from RADAR: AI Edition

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The sheer number of tools and technologies that can infiltrate your work processes can be overwhelming. Choosing the right ones to invest in is critical, but how do you know where to start? What steps should you take to build a solid, scalable data infrastructure that can handle the growth of your business? And with AI becoming a central focus for many organizations, how can you ensure that your data strategy is aligned to support these initiatives? It’s no longer just about managing data; it’s about future-proofing your organization.

Taylor Brown is the COO and Co-Founder of Fivetran, the global leader in data movement. With a vision to simplify data connectivity and accessibility, Taylor has been instrumental in transforming the way organizations manage their data infrastructure. Fivetran has grown rapidly, becoming a trusted partner for thousands of companies worldwide. Taylor's expertise in technology and business strategy has positioned Fivetran at the forefront of the data integration industry, driving innovation and empowering businesses to harness the full potential of their data. Prior to Fivetran, Taylor honed his skills in various tech startups, bringing a wealth of experience and a passion for problem-solving to his entrepreneurial ventures.

In the episode, Richie and Taylor explore the biggest challenges in data engineering, how to find the right tools for your data stack, defining the modern data stack, federated data, data fabrics, data meshes, data strategy vs organizational structure, self-service data, data democratization, AI’s impact on data and much more. 

Links Mentioned in the Show:

• Fivetran
• Connect with Taylor
• Career Track: Data Engineer in Python
• Related Episode: Effective Data Engineering with Liya Aizenberg, Director of Data Engineering at Away
• Rewatch sessions from RADAR: AI Edition

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Building and managing AI products comes with its own set of unique challenges. Especially when they are under intense scrutiny like mobile and home assistants have dealt with in recent years. From dealing with the unpredictable nature of machine learning models to ensuring that your product is both ethical and user-friendly, the path to success isn’t always clear. But how do you navigate these complexities and still deliver a product that meets business goals? What key steps can you take to align AI innovation with measurable outcomes and long-term success?

Marily Nika is one of the world's leading thinkers on product management for artificial intelligence. At Google, she manages the generative AI product features for Google Assistant. Marily also founded AI Product Academy, where she runs a BootCamp on AI product management, and she teaches the subject on Maven. Previously, Marily was an AI Product Lead in Meta's Reality Labs, and the AI Product Lead for Google Glass. She is also an Executive Fellow at Harvard Business School.

In the episode, Richie and Marily explore the unique challenges of AI product management, experimentation, ethical considerations in AI product management, collaboration, skills needed to succeed in AI product development, the career path to work in AI as a Product Manager, key metrics for AI products and much more. 

Links Mentioned in the Show:

• Komo AI
• Connect with Marily
• Marily’s Course: AI Product Management Bootcamp with Certification
• Skill Track: AI Business Fundamentals
• Related Episode: Building Human-Centered AI Experiences with Haris Butt, Head of Product Design at ClickUp
• Rewatch sessions from RADAR: AI Edition

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Every organization today is exploring generative AI to drive value and push their business forward. But a common pitfall is that AI strategies often don’t align with business objectives, leading companies to chase flashy tools rather than focusing on what truly matters. How can you avoid these traps and ensure your AI efforts are not only innovative but also aligned with real business value? 

Leon Gordon, is a leader in data analytics and AI. A current Microsoft Data Platform MVP based in the UK, founder of Onyx Data. During the last decade, he has helped organizations improve their business performance, use data more intelligently, and understand the implications of new technologies such as artificial intelligence and big data. Leon is an Executive Contributor to Brainz Magazine, a Thought Leader in Data Science for the Global AI Hub, chair for the Microsoft Power BI – UK community group and the DataDNA data visualization community as well as an international speaker and advisor.

In the episode, Adel and Leon explore aligning AI with business strategy, building AI use-cases, enterprise AI-agents, AI and data governance, data-driven decision making, key skills for cross-functional teams, AI for automation and augmentation, privacy and AI and much more. 

Links Mentioned in the Show:

• Onyx Data
• Connect with Leon
• Leon’s Linkedin Course - How to Build and Execute a Successful Data Strategy
• Skill Track: AI Business Fundamentals
• Related Episode: Generative AI in the Enterprise with Steve Holden, Senior Vice President and Head of Single-Family Analytics at Fannie Mae
• Rewatch sessions from RADAR: AI Edition

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AI has rapidly emerged as an incredibly transformative technology, and nowhere has its impact been felt more unexpectedly than in the creative arts. Just a decade ago, few would have predicted that AI would evolve from automating routine tasks to generating paintings, music, and even poetry. Yet today, the role of AI in the arts has entered mainstream conversations, even contributing to the debates seen in last year’s Hollywood strikes. 

Kent Kersey is a creative technologist who has served as a Product and Business leader in startups across B2B, B2C, and Enterprise SaaS. He is the founder and CEO of Invoke, an open-source Enterprise platform built to empower creatives to co-create with custom/fine-tuned AI products.

In the episode, Adel and Kent explore intellectual property and AI, the legal landscape surrounding AI models, open vs closed-source models, the future of creative teams and GenAI, innovations in GenAI, the role of artists in an AI-world, GenAI’s impact on the future of entertainment and much more. 

Links Mentioned in the Show:

• Invoke
• How to Use Midjourney: A Comprehensive Guide to AI-Generated Artwork Creation
• Course: Generative AI Concepts
• Related Episode: Seeing the Data Layer Through Spatial Computing with Cathy Hackl and Irena Cronin
• Rewatch sessions from RADAR: AI Edition

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With AI tools constantly evolving, the potential for innovation seems limitless. But with great potential comes significant costs, and the question of efficiency and scalability becomes crucial. How can you ensure that your AI models are not only pushing boundaries but also delivering results in a cost-effective way? What strategies can help reduce the financial burden of training and deploying models, while still driving meaningful business outcomes? 

Natalia Vassilieva is the VP & Field CTO of ML at Cerebras Systems. Natalia has a wealth of experience in research and development in natural language processing, computer vision, machine learning, and information retrieval. As Field CTO, she helps drive product adoption and customer engagement for Cerebras Systems' wafer-scale AI chips. Previously, Natalia was a Senior Research Manager at Hewlett Packard Labs, leading the Software and AI group. She also served as the head of HP Labs Russia leading research teams focused on developing algorithms and applications for text, image, and time-series analysis and modeling. Natalia has an academic background, having been a part-time Associate Professor at St. Petersburg State University and a lecturer at the Computer Science Center in St. Petersburg, Russia. She holds a PhD in Computer Science from St. Petersburg State University.

Andy Hock is the Senior VP, Product & Strategy at Cerebras Systems. Andy runs the product strategy and roadmap for Cerebras Systems, focusing on integrating AI research, hardware, and software to accelerate the development and deployment of AI models. He has 15 years of experience in product management, technical program management, and enterprise business development; over 20 years of experience in research, algorithm development, and data analysis for image processing; and  9 years of experience in applied machine learning and AI. Previously he was Product Management lead for Data and Analytics for Terra Bella at Google, where he led the development of machine learning-powered data products from satellite imagery. Earlier, he was Senior Director for Advanced Technology Programs at Skybox Imaging (which became Terra Bella following its acquisition by Google in 2014), and before that was a Senior Program Manager and Senior Scientist at Arete Associates. He has a Ph.D. in Geophysics and Space Physics from the University of California, Los Angeles.

In the episode, Richie, Natalia and Andy explore the dramatic recent progress in generative AI, cost and infrastructure challenges in AI, Cerebras’ custom AI chips and other hardware innovations, quantization in AI models, mixture of experts, RLHF, relevant AI use-cases, centralized vs decentralized AI compute, the future of AI and much more. 

Links Mentioned in the Show:

• Cerebras
• Cerebras Launches the World’s Fastest AI Inference
• Connect with Natalia and Andy
• Course: Implementing AI Solutions in Business
• Rewatch sessions from RADAR: AI Edition

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In healthcare, data is becoming one of the most valuable tools for improving patient care and reducing costs. But with massive amounts of information and complex systems, how do organizations turn that data into actionable insights? How can AI and machine learning be used to create more transparency and help patients make better decisions? And more importantly, how can we ensure that these technologies make healthcare more efficient and affordable for everyone involved? 

Travis Dalton is the President and CEO at Multiplan overseeing the execution of the company's mission and growth strategy. He has 20 years of leadership experience, with a focus on reducing the cost of healthcare, and enabling better outcomes for patients and healthcare providers. Previously, he was a General Manager and Executive VP at Oracle Health.

Jocelyn Jiang is the Vice President of Data & Decision Science at MultiPlan, a role she has held since 2023. In her position, she is responsible for leading the data and analytics initiatives that drive the company’s strategic growth and enhance its service offerings in the healthcare sector. Jocelyn brings extensive experience from her previous roles in healthcare and data science, including her time at EPIC Insurance Brokers & Consultants and Aon, where she worked in various capacities focusing on health and welfare consulting and actuarial analysis.

In the episode, Richie, Travis and Jocelyn explore the US healthcare system and the industry-specific challenges professionals face, the role of data in healthcare, ML and data science in healthcare, the future potential of healthcare tech, the global application of healthcare data solutions and much more. 

Links Mentioned in the Show:

• Multiplan
• PlanOptix: Providing Innovative Healthcare Price Transparency   
• Using a Data Mining Service on Claims Data Can Reveal Significant Overpayments
• Connect with Travis and Jocelyn
• Course: Intro to Data Privacy
• Related Episode: Data & AI for Improving Patient Outcomes with Terry Myerson, CEO at Truveta
• Rewatch sessions from RADAR: AI Edition

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As AI becomes more accessible, a growing question is: should machine learning experts always be the ones training models, or is there a better way to leverage other subject matter experts in the business who know the use-case best? What if getting started building AI apps required no coding skills? As businesses look to implement AI at scale, what part can no-code AI apps play in getting projects off the ground, and how feasible are smaller, tailored solutions for  department specific use-cases?

Birago Jones is the CEO at Pienso. Pienso is an AI platform that empowers subject matter experts in various enterprises, such as business analysts, to create and fine-tune AI models without coding skills. Prior to Pienso, Birago was a Venture Partner at Indicator Ventures and a Research Assistant at MIT Media Lab where he also founded the Media Lab Alumni Association.

Karthik Dinakar is a computer scientist specializing in machine learning, natural language processing, and human-computer interaction. He is the Chief Technology Officer and co-founder at Pienso. Prior to founding Pienso, Karthik held positions at Microsoft and Deutsche Bank. Karthik holds a doctoral degree from MIT in Machine Learning.

In the episode, Richie, Birago and Karthik explore why no-code AI apps are becoming more prominent, uses-cases of no-code AI apps, the steps involved in creating an LLM, the benefits of small tailored models, how no-code can impact workflows, cost in AI projects, AI interfaces and the rise of the chat interface, privacy and customization, excitement about the future of AI, and much more. 

Links Mentioned in the Show:

• Pienso
• Google Gemini for Business
• Connect with Birago and Karthik
• Andreesen Horowitz Report: Navigating the High Cost of AI Compute
• Course: Artificial Intelligence (AI) Strategy
• Related Episode: Designing AI Applications with Robb Wilson, Co-Founder & CEO at Onereach.ai
• Rewatch sessions from RADAR: AI Edition

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We’ve all met someone with a limiting belief, someone who describes their relationship with data as: “I’m not a data person” or “I can’t tell a data story.” Oftentimes, this mindset starts in childhood. Data storytelling is an incredible vehicle to challenge and reshape these beliefs early on. Imagine if kids could develop the skills to ask the right questions, interpret data, and tell powerful stories with it from a young age. How can we introduce children to data storytelling in a fun and engaging way?

Cole Nussbaumer Knaflic has always had a penchant for turning data into pictures and into stories. She is CEO of Storytelling with Data, the author of the best-selling books, Storytelling with Data: a Data Visualization Guide for Business Professionals, Storytelling with Data: Let’s Practice!, and Storytelling with You: Plan, Create, and Deliver a Stellar Presentation. For more than a decade, Cole and her team have delivered interactive learning sessions sought after by data-minded individuals, companies, and philanthropic organizations all over the world. They also help people create graphs that make sense and weave them into compelling stories through the popular SWD community, blog, podcast, and videos.

In the episode, Adel and Cole explore Cole’s book Daphne Draws Data, challenging limiting beliefs that can develop during childhood, why early exposure to data literacy is important, engaging with children using data, adapting complex topics, data storytelling for adults, data visualization, building a data storytelling culture, the future of data storytelling in the age of AI, and much more. 

Links Mentioned in the Show:

• Cole’s Book: Daphne Draws Data
• Storytelling with Data
• Connect with Cole
• Skill Track: Data Storytelling
• Related Episode: Navigating Parenthood with Data with Emily Oster
• Rewatch sessions from RADAR: AI Edition

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Lot’s of AI use-cases can start with big ideas and exciting possibilities, but turning those ideas into real results is where the challenge lies. How do you take a powerful model and make it work effectively in a specific business context? What steps are necessary to fine-tune and optimize your AI tools to deliver both performance and cost efficiency? And as AI continues to evolve, how do you stay ahead of the curve while ensuring that your solutions are scalable and sustainable? 

Lin Qiao is the CEO and Co-Founder of Fireworks AI. She previously worked at Meta as a Senior Director of Engineering and as head of Meta's PyTorch, served as a Tech Lead at Linkedin, and worked as a Researcher and Software Engineer at IBM. 

In the episode, Richie and Lin explore generative AI use cases, getting AI into products, foundational models, the effort required and benefits of fine-tuning models, trade-offs between models sizes, use cases for smaller models, cost-effective AI deployment, the infrastructure and team required for AI product development, metrics for AI success, open vs closed-source models, excitement for the future of AI development and much more. 

Links Mentioned in the Show:

• Fireworks.ai
• Hugging Face - Preference Tuning LLMs with Direct Preference Optimization Methods
• Connect with Lin
• Course - Artificial Intelligence (AI) Strategy
• Related Episode: Creating Custom LLMs with Vincent Granville, Founder, CEO & Chief Al Scientist at GenAltechLab.com
• Rewatch sessions from RADAR: AI Edition

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The rapid rise of generative AI is changing how businesses operate, but with this change comes new challenges. How do you navigate the balance between innovation and risk, especially in a regulated industry? As organizations race to adopt AI, it’s crucial to ensure that these technologies are not only transformative but also responsible. What steps can you take to harness AI’s potential while maintaining control and transparency? And how can you build excitement and trust around AI within your organization, ensuring that everyone is ready to embrace this new era?

Steve Holden is the Senior Vice President and Head of Single-Family Analytics at Fannie Mae, leading a team of data science professionals, supporting loan underwriting, pricing and acquisition, securitization, loss mitigation, and loan liquidation for the company’s multi-trillion-dollar Single-Family mortgage portfolio. He is also responsible for all Generative AI initiatives across the enterprise. His team provides real-time analytic solutions that guide thousands of daily business decisions necessary to manage this extensive mortgage portfolio. The team comprises experts in econometric models, machine learning, data engineering, data visualization, software engineering, and analytic infrastructure design. Holden previously served as Vice President of Credit Portfolio Management Analytics at Fannie Mae. Before joining Fannie Mae in 1999, he held several analytic leadership roles and worked on economic issues at the Economic Strategy Institute and the U.S. Bureau of Labor Statistics.

In the episode Adel and Steve explore opportunities in generative AI, building a GenAI program, use-case prioritization, driving excitement and engagement for an AI-first culture, skills transformation, governance as a competitive advantage, challenges of scaling AI, future trends in AI, and much more. 

Links Mentioned in the Show:

• Fannie Mae
• Steve’s recent DataCamp Webinar: Bringing Generative AI to the Enterprise
• Video: Andrej Karpathy - [1hr Talk] Intro to Large Language Models
• Skill Track - AI Business Fundamentals
• Related Episode: Generative AI at EY with John Thompson, Head of AI at EY
• Rewatch sessions from RADAR: AI Edition

Join the DataFramed team!

• Data Evangelist 
• Data & AI Video Creator

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The pressure to innovate with AI is immense. There is seemingly a race against the clock for organizations to incorporate AI into their product offering, aside from continual digital transformation. As the speed of AI development accelerates, many organizations struggle to keep up, facing challenges from data readiness to changing traditional business processes. How can businesses ensure that their AI initiatives not only align with strategic goals but also foster real, tangible progress? What steps can leaders take to build AI fluency across their teams and turn potential into actionable outcomes?

Alison McCauley is a Best-Selling Author, Keynote Speaker, AI Strategist. She is Chief Advocacy Officer at Think with AI and Founder of Unblocked Future, a consultancy that leads the way in adopting emerging technologies, and has been collaborating with AI pioneers since 2010. With nearly 30 years of experience at the intersection of enterprise and disruptive innovation, Alison specializes in unlocking business value from cutting-edge technologies by focusing on the human aspects of change. She has been recognized as a Top Voice in AI, authored the book Unblocked, is a keynote speaker at global conferences, and her writings have appeared in Harvard Business Review, Forbes, and Venture Beat. Additionally, over 90,000 students have taken her LinkedIn course.

In the episode, Richie and Alison explore digital transformation and AI’s role in it, strategic alignment and shifting mindsets, AI fluency, challenges in data readiness, organizational resistance fuelled by fear, the role of management in AI transformation, practical steps to avoid AI risks, the long term impact of AI in the future and much more. 

Links Mentioned in the Show:

• Think with AI
• Unlocked Future
• Unblocked: How Blockchains Will Change Your Business (and What to Do About It)
• Connect with Alison
• Course - Artificial Intelligence (AI) Strategy
• Related Episode: How are Businesses Really Using AI? With Tathagat Varma, Global TechOps Leader at Walmart Global Tech
• Rewatch sessions from RADAR: AI Edition

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One of the prerequisites for being able to do great data analyses is that the data is well structured and clean and high quality. For individual projects, this is often annoying to get right. On a corporate level, it’s often a huge blocker to productivity. And then there’s healthcare data. When you consider all the healthcare records across the USA, or any other country for that matter, there are so many data formats created by so many different organizations, it’s frankly a horrendous mess. This is a big problem because there’s a treasure trove of data that researchers and analysts can’t make use of to answer questions about which medical interventions work or not. Bad data is holding back progress on improving everyone’s health.

Terry Myerson is the CEO and Co-Founder of Truveta. Truveta enables scientifically rigorous research on more than 18% of the clinical care in the U.S. from a growing collective of more than 30 health systems. Previously, Terry enjoyed a 21-year career at Microsoft. As Executive Vice President, he led the development of Windows, Surface, Xbox, and the early days of Office 365, while serving on the Senior Leadership Team of the company. Prior to Microsoft, he co-founded Intersé, one of the earliest Internet companies, which Microsoft acquired in 1997.​

In the episode, Richie and Terry explore the current state of health records, challenges when working with health records, data challenges including privacy and accessibility, data silos and fragmentation, AI and NLP for fragmented data, regulatory grade AI, ongoing data integration efforts in healthcare, the future of healthcare and much more. 

Links Mentioned in the Show:

• Truveta
• Connect with Terry
• HIPAA
• Course - Introduction to Data Privacy
• Related Episode: Using AI to Improve Data Quality in Healthcare
• Rewatch sessions from RADAR: AI Edition

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Empower your business with world-class data and AI skills with DataCamp for business

Guardrails are not something we actively use in our day-to-day lives, they’re in place to keep us safe when we lack the control needed to keep us on course, and for that, they are essential. Navigating the complexities of decision-making in AI and data can be challenging, especially on a global scale when many are searching for any sort of competitive advantage. Every choice you make can have significant impacts, and having the right frameworks, ethics and guardrails in place are crucial. But how do you create systems that guide decisions without stifling creativity or flexibility? What practices can you employ to ensure your team consistently make better choices and flourish in the age of AI?

Viktor Mayer-Schönberger is a distinguished Professor of Internet Governance and Regulation at the Oxford Internet Institute, University of Oxford. With a career spanning over decades, his research focuses on the role of information in a networked economy. He previously served on the faculty of Harvard’s Kennedy School of Government for ten years and has authored several influential books, including the award-winning “Delete: The Virtue of Forgetting in the Digital Age” and the international bestseller “Big Data.” Viktor founded Ikarus Software in 1986, where he developed Virus Utilities, Austria’s best-selling software product. He has been recognized as a Top-5 Software Entrepreneur in Austria and has served as a personal adviser to the Austrian Finance Minister on innovation policy. His work has garnered global attention, featuring in major outlets like the New York Times, BBC, and The Economist. Viktor is also a frequent public speaker and an advisor to governments, corporations, and NGOs on issues related to the information economy.

In the episode, Richie and Viktor explore the definition of guardrails, characteristics of good guardrails, guardrails in business contexts, life-or-death decision-making, principles of effective guardrails, decision-making and cognitive bias, uncertainty in decision-making, designing guardrails, AI and the implementation of guardrails, and much more.

Links Mentioned in the Show:

• Guardrails: Guiding Human Decisions in the Age of AI by Urs Gasser and Viktor Mayer-Schönberger
• Book - The Checklist Manifesto by Atul Gawande
• Connect with Viktor
• Course - AI Ethics
• Related Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision Scientist
• Rewatch sessions from RADAR: AI Edition

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Doing sales better is perhaps the most direct route to making more revenue, so it should be a priority for every business. B2B sales is often very complex, with a mix of emails and video calls and prospects interacting with your website and social content. And you often have multiple people making decisions about a purchase. All this generates a massive data—or, more accurately, a mess of data—which very few sales teams manage to harness effectively. How can sales teams can make use of data, software, and AI to clean up this mess, work more effectively, and most of all, crush those quarterly targets? 

Ellie Fields is the Chief Product and Engineering Officer at Salesloft leading Product Management, Engineering, and Design. Ellie previously led development teams at Tableau responsible for product strategy and engineering for collaboration and mobile portfolio. Ellie also launched and led Tableau Public.

In the episode Richie and Ellie explore the digital transformation of sales, how sales technology helps buyers and sellers, metrics for sales success, activity vs outcome metrics, predictive forecasting, AI, customizing sales processes, revenue orchestration, how data impacts sales and management, future trends in sales, and much more. 

Links Mentioned in the Show:

• Salesloft
• Connect with Ellie
• Forrester Research
• Course - Understanding the EU AI Act
• Related Episode: Data & AI at Tesco with Venkat Raghavan, Director of Analytics and Science at Tesco
• Rewatch sessions from RADAR: AI Edition

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One of the big use cases of generative AI is having small applications to solve specific tasks. These are known as AI agents or AI assistants. Since they’re small and narrow in scope, you probably want to create and use lots of them, which means you need to be able to create them cheaply and easily. I’m curious as to how you go about doing this from an organizational point of view. Who needs to be involved? What’s the workflow and what technology do you need?

Dmitry Shapiro is the CEO of MindStudio. He was previously the CTO at MySpace and a product manager at Google. Dmitry is also a serial entrepreneur, having founded the web-app development platform Koji, acquired by Linktree, and Veoh Networks, an early YouTube competitor. He has extensive experience in building and managing engineering, product, and AI teams.

In the episode, Richie and Dmitry explore generative AI applications, AI in SaaS, approaches to AI implementation, selecting processes for automation, changes in sales and marketing roles, MindStudio, AI governance and privacy concerns, cost management, the limitations and future of AI assistants, and much more.

Links Mentioned in the Show:

• MindStudio
• Connect with Dmitry
• [Webinar] Dmitry at RADAR: From Learning to Earning: Navigating the AI Job Landscape
• Related Episode: Designing AI Applications with Robb Wilson, Co-Founder & CEO at Onereach.ai
• Rewatch sessions from RADAR: AI Edition

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Perhaps the biggest complaint about generative AI is hallucination. If the text you want to generate involves facts, for example, a chatbot that answers questions, then hallucination is a problem. The solution to this is to make use of a technique called retrieval augmented generation, where you store facts in a vector database and retrieve the most appropriate ones to send to the large language model to help it give accurate responses. So, what goes into building vector databases and how do they improve LLM performance so much?

Ram Sriharsha is currently the CTO at Pinecone. Before this role, he was the Director of Engineering at Pinecone and previously served as Vice President of Engineering at Splunk. He also worked as a Product Manager at Databricks. With a long history in the software development industry, Ram has held positions as an architect, lead product developer, and senior software engineer at various companies. Ram is also a long time contributor to Apache Spark. 

In the episode, Richie and Ram explore common use-cases for vector databases, RAG in chatbots, steps to create a chatbot, static vs dynamic data, testing chatbot success, handling dynamic data, choosing language models, knowledge graphs, implementing vector databases, innovations in vector data bases, the future of LLMs and much more. 

Links Mentioned in the Show:

• Pinecone
• Webinar - Charting the Path: What the Future Holds for Generative AI
• Course - Vector Databases for Embeddings with Pinecone
• Related Episode: The Power of Vector Databases and Semantic Search with Elan Dekel, VP of Product at Pinecone
• Rewatch sessions from RADAR: AI Edition

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By now, many of us are convinced that generative AI chatbots like ChatGPT are useful at work. However, many executives are rightfully worried about the risks from having business and customer conversations recorded by AI chatbot platforms. Some privacy and security-conscious organizations are going so far as to block these AI platforms completely. For organizations such as EY, a company that derives value from its intellectual property, leaders need to strike a balance between privacy and productivity. 

John Thompson runs the department for the ideation, design, development, implementation, & use of innovative Generative AI, Traditional AI, & Causal AI solutions, across all of EY's service lines, operating functions, geographies, & for EY's clients. His team has built the world's largest, secure, private LLM-based chat environment. John also runs the Marketing Sciences consultancy, advising clients on monetization strategies for data. He is the author of four books on data, including "Data for All' and "Causal Artificial Intelligence". Previously, he was the Global Head of AI at CSL Behring, an Adjunct Professor at Lake Forest Graduate School of Management, and an Executive Partner at Gartner.

In the episode, Richie and John explore the adoption of GenAI at EY, data privacy and security, GenAI use cases and productivity improvements, GenAI for decision making, causal AI and synthetic data, industry trends and predictions and much more. 

Links Mentioned in the Show:

• Azure OpenAI
• Causality by Judea Pearl
• [Course] AI Ethics
• Related Episode: Data & AI at Tesco with Venkat Raghavan, Director of Analytics and Science at Tesco
• Catch John talking about AI Maturity this September
• Rewatch sessions from RADAR: AI Edition

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There’s been a lot of pressure to add AI to almost every digital tool and service recently, and two years into the AI hype cycle, we’re seeing two types of problems. The first is organizations that haven’t done much yet with AI because they don’t know where to start. The second is organizations that rushed into AI and failed because they didn’t know what they were doing. Both are symptoms of the same problem: not having an AI strategy and not understanding how to tactically implement AI. There’s a lot to consider around choosing the right project and putting processes and skilled talent in place, not to mention worrying about costs and return on investment.

Tathagat Varma is the Global TechOps Leader at Walmart Global Tech. Tathagat is responsible for leading strategic business initiatives, enterprise agile transformation, technical learning and enablement, strategic technical initiatives, startup ecosystem engagement, and internal events across Walmart Global Tech. He also provides support to horizontal technical and internal innovation programs in the company. Starting as a Computer Scientist with DRDO, and with an overall experience of 27 years, Tathagat has played significant technical and leadership roles in establishing and growing organizations like NerdWallet, ChinaSoft International, McAfee, Huawei, Network General, NetScout System, [24]7 Innovations Labs and Yahoo!, and played key engineering roles at Siemens and Philips.

In the episode, Richie and Tathagat explore failures in AI adoption, the role of leadership in AI adoption, AI strategy and business objective alignment, investment and timeline for AI projects, identifying starter AI projects, skills for AI success, building a culture of AI adoption, the potential of AI and much more. 

Links Mentioned in the Show:

• Walmart Global Tech
• Connect with Tathagat
• [Course] Data Governance Concepts
• Related Episode: How Walmart Leverages Data & AI with Swati Kirti, Sr Director of Data Science at Walmart
• Rewatch sessions from RADAR: AI Edition

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One of the most annoying conversations about data that happens far too often is: “Can you do an analysis and answer this business problem for me?” “Sure, where’s the data?” “I don’t know. Probably in one of our databases.” At this point more time is spent hunting for data than actually analyzing it. Rather than grumbling about it, it would obviously be more productive to learn how to solve data discoverability issues. What’s the best way to properly document data sets? How can you avoid spending all your time maintaining dashboards that no one actually uses? 

Shinji Kim is the Founder & CEO of Select Star, an automated data discovery platform that helps you understand your data. Previously, she was the CEO of Concord Systems (concord.io), a NYC-based data infrastructure startup acquired by Akamai Technologies in 2016. She led building Akamai’s new IoT data platform for real-time messaging, log processing, and edge computing. Prior to Concord, Shinji was the first Product Manager hired at Yieldmo, where she led the Ad Format Lab, A/B testing, and yield optimization. Before Yieldmo, she was analyzing data and building enterprise applications at Deloitte Consulting, Facebook, Sun Microsystems, and Barclays Capital. Shinji studied Software Engineering at University of Waterloo and General Management at Stanford GSB. She advises early stage startups on product strategy, customer development, and company building.

In the episode, Richie and Shinji explore the importance of data governance, the utilization of data, data quality, challenges in data usage, why documentation matters, metadata and data lineage, improving collaboration between data and business teams, data governance trends to look forward to, and much more. 

Links Mentioned in the Show:

• Select Star
• Connect with Shinji
• [Course] Data Governance Concepts
• Related Episode: Making Data Governance Fun with Tiankai Feng, Data Strategy & Data Governance Lead at ThoughtWorks
• Rewatch sessions from RADAR: AI Edition

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One of the best applications of data science is that it allows experimentation within any organization at scale. The ability to test a new checkout feature, the color of a button, and analyze whether that improves customer experiences can be truly magical when done correctly. However, doing this at scale means that the entire organization needs to be bought into the experimentation agenda. So how do you do this and how do you make sure this becomes part of your organization’s culture?

Amit Mondal is the VP & Head of Digital Analytics & Experimentation at American Express. Throughout his career Amit has been a financial services leader in digital, analytics/data science and risk management, driving digital strategies and investments, while creating a data driven & experimentation first culture for Amex. Amit currently leads a global team of 200+ Data Scientists, Statisticians, Experimenters, Analysts, and Data experts.

In the episode, Adel and Amit explore the importance of experimentation at American Express, key components of experimentation strategies, ownership and coordination in experimentation processes, the pillars that feed into a culture of experimentation, frameworks for building successful experiments, robust experiment design, challenges and trends across industries and much more. 

Links Mentioned in the Show:

• American Express
• Decoding Marketing Mix Modeling
• [Course] A/B Testing in Python
• Related Episode: Data & AI at Tesco with Venkat Raghavan, Director of Analytics and Science at Tesco
• Rewatch sessions from RADAR: AI Edition

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Meta has been at the absolute edge of the open-source AI ecosystem, and with the recent release of Llama 3.1, they have officially created the largest open-source model to date. So, what's the secret behind the performance gains of Llama 3.1? What will the future of open-source AI look like?

Thomas Scialom is a Senior Staff Research Scientist (LLMs) at Meta AI, and is one of the co-creators of the Llama family of models. Prior to joining Meta, Thomas worked as a Teacher, Lecturer, Speaker and Quant Trading Researcher. 

In the episode, Adel and Thomas explore Llama 405B it’s new features and improved performance, the challenges in training LLMs, best practices for training LLMs, pre and post-training processes, the future of LLMs and AI, open vs closed-sources models, the GenAI landscape, scalability of AI models, current research and future trends and much more. 

Links Mentioned in the Show:

• Meta - Introducing Llama 3.1: Our most capable models to date
• Download the Llama Models
• [Course] Working with Llama 3
• [Skill Track] Developing AI Applications
• Related Episode: Creating Custom LLMs with Vincent Granville, Founder, CEO & Chief Al Scientist at GenAltechLab.com
• Rewatch sessions from RADAR: AI Edition

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Excel often gets unfair criticism from data practitioners, many of us will remember a time when Excel was looked down upon—why would anyone use Excel when we have powerful tools like Python, R, SQL, or BI tools? However,  like it or not, Excel is here to stay, and there’s a meme, bordering on reality, that Excel is carrying a large chunk of the world’s GDP. But when it really comes down to it, can you do data science in Excel?

Jordan Goldmeier is an entrepreneur, a consultant, a best-selling author of four books on data, and a digital nomad. He started his career as a data scientist in the defense industry for Booz Allen Hamilton and The Perduco Group, before moving into consultancy with EY, and then teaching people how to use data at Excel TV, Wake Forest University, and now Anarchy Data. He also has a newsletter called The Money Making Machine, and he's on a mission to create 100 entrepreneurs. 

In the episode, Adel and Jordan explore excel in data science, excel’s popularity, use cases for Excel in data science, the impact of GenAI on Excel, Power Query and data transformation, advanced Excel features, Excel for prototyping and generating buy-in, the limitations of Excel and what other tools might emerge in its place, and much more. 

Links Mentioned in the Show:

• Data Smart: Using Data Science to Transform Information Into Insight by Jordan Goldmeier
• [Webinar] Developing a Data Mindset: How to Think, Speak, and Understand Data
• [Course] Data Analysis in Excel
• Related Episode: Do Spreadsheets Need a Rethink? With Hjalmar Gislason, CEO of GRID
• Rewatch sessions from RADAR: AI Edition

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This special episode of DataFramed was made in collaboration with Analytics on Fire! Nowadays, the hype around generative AI is only the tip of the iceberg. There are so many ideas being touted as the next big thing that it’s difficult to keep up. More importantly, it’s challenging to discern which ideas will become the next ChatGPT and which will end up like the next NFT. How do we cut through the noise?

Mico Yuk is the Community Manager at Acryl Data and Co-Founder at Data Storytelling Academy. Mico is also an SAP Mentor Alumni, and the Founder of the popular weblog, Everything Xcelsius and the 'Xcelsius Gurus’ Network. She was named one of the Top 50 Analytics Bloggers to follow, as-well-as a high-regarded BI influencer and sought after global keynote speaker in the Analytics ecosystem. 

In the episode, Richie and Mico explore AI and productivity at work, the future of work and AI, GenAI and data roles, AI for training and learning, training at scale, decision intelligence, soft skills for data professionals, genAI hype and much more. 

Links Mentioned in the Show:

• Analytics on Fire Podcast
• Data Visualization for Dummies by Mico Yuk and Stephanie Diamond
• Connect with Miko
• [Skill Track] AI Fundamentals
• Related Episode: What to Expect from AI in 2024 with Craig S. Smith, Host of the Eye on A.I Podcast
• Rewatch sessions from RADAR: AI Edition

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Despite GPT, Claude, Gemini, LLama and the other host of LLMs that we have access to, a variety of organizations are still exploring their options when it comes to custom LLMs. Logging in to ChatGPT is easy enough, and so is creating a 'custom' openAI GPT, but what does it take to create a truly custom LLM? When and why might this be useful, and will it be worth the effort?

Vincent Granville is a pioneer in the AI and machine learning space, he is Co-Founder of Data Science Central, Founder of MLTechniques.com, former VC-funded executive, author, and patent owner. Vincent’s corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, and CNET. He is also a former post-doc at Cambridge University and the National Institute of Statistical Sciences. Vincent has published in the Journal of Number Theory, Journal of the Royal Statistical Society, and IEEE Transactions on Pattern Analysis and Machine Intelligence. He is the author of multiple books, including “Synthetic Data and Generative AI”.

In the episode, Richie and Vincent explore why you might want to create a custom LLM including issues with standard LLMs and benefits of custom LLMs, the development and features of custom LLMs, architecture and technical details, corporate use cases, technical innovations, ethics and legal considerations, and much more. 

Links Mentioned in the Show:

• Read Articles by Vincent
• Synthetic Data and Generative AI by Vincent Granville
• Connect with Vincent on Linkedin
• [Course] Developing LLM Applications with LangChain
• Related Episode: The Power of Vector Databases and Semantic Search with Elan Dekel, VP of Product at Pinecone
• Rewatch sessions from RADAR: AI Edition

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The role of the data scientist is changing. Some organizations are splitting the role into more narrowly focused jobs, while others are broadening it. The latter approach, known as the Full Stack Data Scientist, is derived from the concept of a full stack software engineer, with this role often including software engineering tasks. In particular, one of the key functions of a full stack data scientist is to take machine learning models and get them into production inside software. So, what separates projects from production?

Savin Goyal is the Co-Founder & CTO at Outerbounds. In addition to his work at Outerbounds, Savin is the creator of the open source machine learning management platform Metaflow. Previously Savin has worked as a Software Engineer at Netflix and LinkedIn.

In the episode, Richie and Savin explore the definition of production in data science, steps to move from internal projects to production, the lifecycle of a machine learning project, success stories in data science, challenges in quality control, Metaflow, scalability and robustness in production, AI and MLOps, advice for organizations and much more. 

Links Mentioned in the Show:

• Outerbounds
• Metaflow
• Connect with Savin on Linkedin
• [Course] Developing Machine Learning Models for Production
• Related Episode: Why ML Projects Fail, and How to Ensure Success with Eric Siegel, Founder of Machine Learning Week, Former Columbia Professor, and Bestselling Author
• Rewatch sessions from RADAR: AI Edition

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Conversations about the future of AI tend to be rather divisive, with opinions ranging from artificial superintelligence arriving to save the world, or to eradicate humanity. There's a sense that the latter is undesirable and that something ought to be done to prevent it. In order to get from that vague feeling to having steps that are practical in order to shape the future of AI, we can draw lessons from history. Looking back, to look ahead. 

Verity Harding is a globally recognised leader at the intersection of technology, politics and public policy. She is Founder of Formation Advisory Ltd, a bespoke technology consultancy firm, and Director of the AI & Geopolitics Project at Cambridge University's Bennett Institute for Public Policy. Her debut book ‘AI Needs You’ was published by Princeton University Press in March 2024.

In the episode, Richie and Verity explore why history is important for the future of AI, the space race, the role of AI in society, historical analogies including comparisons of AI to the cold war, the evolution of the internet, IVF, the role of government and regulation, multi-stakeholder models and much more. 

Links Mentioned in the Show:

• Verity’s Book: AI Needs You
• Connect with Verity on Linkedin
• The Warnock Committee 
• Outer Space Treaty
• [Skill Track] Developing AI Applications
• Related Episode: The History of Data and AI, and Where It's Headed with Cristina Alaimo, Assistant Professor at Luiss Guido Carli University
• Rewatch sessions from RADAR: AI Edition

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Generative AI is here to stay, fundamentally altering our relationship with technology. But what does its future hold? In this session, Tom Tunguz, General Partner at Theory Ventures, Edo Liberty, CEO at Pinecone, and Nick Elprin, CEO at Domino Data Lab, explore how generative AI tools & technologies will evolve in the months and years to come. They navigate through emerging trends, potential breakthrough applications, and the strategic implications for businesses poised to capitalize on this technological wave. 

Links Mentioned in the Show:

• Rewatch Session from RADAR: AI Edition

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Generative AI's transformative power underscores the critical need for high-quality data. In this session, Barr Moses, CEO of Monte Carlo Data, Prukalpa Sankar, Cofounder at Atlan, and George Fraser, CEO at Fivetran, discuss the nuances of scaling data quality for generative AI applications, highlighting the unique challenges and considerations that come into play. Throughout the session, they share best practices for data and AI leaders to navigate these challenges, ensuring that governance remains a focal point even amid the AI hype cycle.

Links Mentioned in the Show:

• Rewatch Session from RADAR: AI Edition

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From data science to software engineering, Large Language Models (LLMs) have emerged as pivotal tools in shaping the future of programming. In this session, Michele Catasta, VP of AI at Replit, Jordan Tigani, CEO at Motherduck, and Ryan J. Salva, VP of Product at GitHub, will explore practical applications of LLMs in coding workflows, how to best approach integrating AI into the workflows of data teams, what the future holds for AI-assisted coding, and a lot more.

Links Mentioned in the Show:

• Rewatch Session from RADAR: AI Edition

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As AI continues to be a critical driver of innovation and competitive advantage, the imperative for organizations to upskill their workforce in this domain has never been more pressing. In this session, Mike Baylor, Vice President & CDAO at Lockheed Martin, Carolann Diskin, Senior Technical Program Manager at Dropbox, and Giorleny Altamirano Rayo, Chief Data Scientist at U.S. Department of State, outline the critical steps to creating a successful AI upskilling program within your organization. They focus on best practices for building internal AI academies, from curriculum development to engagement strategies and measuring impact. This session covers everything you need to launch and sustain an effective AI learning ecosystem that drives innovation and enhances organizational capabilities. 

Links Mentioned in the Show:

• Rewatch Session from RADAR: AI Edition

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Whether big or small, one of the biggest challenges organizations face when they want to work with data effectively is often lack of access to it. This is where building a data platform comes in. But building a data platform is no easy feat. It's not just about centralizing data in the data warehouse, it’s also about making sure that data is actionable, trustable and usable. So, how do you make sure your data platform is up to par?

Shuang Li is Group Product Manager at Box. With experience of building data, analytics, ML, and observability platform products for both external and internal customers, Shuang is always passionate about the insights, optimizations, and predictions that big data and AI/ML make possible. Throughout her career, she transitioned from academia to engineering, from engineering to product management, and then from an individual contributor to an emerging product executive.

In the episode, Adel and Shuang explore her career journey, including transitioning from academia to engineering and helping to work on Google Fiber, how to build a data platform, ingestion pipelines, processing pipelines, challenges and milestones in building a data platform, data observability and quality, developer experience, data democratization, future trends and a lot more. 

Links Mentioned in the Show:

• Box
• Connect with Shuang on Linkedin
• [Course] Understanding Modern Data Architecture
• Related Episode: Scaling Enterprise Analytics with Libby Duane Adams, Chief Advocacy Officer and Co-Founder of Alteryx

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All the hype around generative AI means that every software maker seems to be stuffing chat interfaces into their products whenever they can. For the most part, the jury is still out on whether this is a good idea or not. However, design goes deeper than just the user interface, so it’s also useful to know about how the designs interact with the rest of the software. Once you move beyond chatbots into things like agents, there are also thorny questions around which bits of your workflow should still be done by a human, and which bits can be completely automated. True insight in this context lies in a gray area, across software, UX and AI.

Robb is an AI researcher, technologist, designer, innovator, serial entrepreneur, and author. He is a contributor to Harvard Business Review and the visionary behind, OneReach.ai, the award winning conversational artificial intelligence platform that ranked highest in Gartner's Critical Capabilities Report for Enterprise Conversational AI Platforms. He earned an Academy Award nomination for technical achievement as well as over 130 innovation, design, technology, and artificial intelligence awards, with five in 2019 including AI Company of the Year and Hot AI Technology of the Year. Robb is a pioneer in the user research and technology spaces. He founded EffectiveUI, a user experience and technology research consultancy for the Fortune 500, which was acquired by WPP and integrated into the core of Ogilvy’s digital experience practice. He also created UX Magazine, one of the first and largest XD (experience design) thought leadership communities. 

In the episode, Richie and Robb explore chat interfaces in software, the advantages of chat interfaces over other methods of interaction with data & AI products, geospatial vs language memory, good vs bad chat interfaces, the importance of a human in the loop, personality in chatbots, handling hallucinations and bad responses, scaling chatbots, agents vs chatbots, ethical considerations for AI and chatbots and much more. 

Links Mentioned in the Show:

• Onereach.ai
• Invisible Machines Podcast
• Gartner: The Executive Guide to Hyperautomation
• [Skill Track] Developing AI Applications
• Related Episode: Building Human-Centered AI Experiences with Haris Butt, Head of Product Design at ClickUp
• Sign up to RADAR: AI Edition

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Loyalty schemes are a hallmark of established retailers—not only do they build consumer trust, they are intelligent and constantly evolving, and Tesco’s Clubcard is the UK’s favorite retail loyalty program. The effects of these discounts are far-reaching, especially for families who rely on getting the best deals to make the most of their money. As Tesco’s tagline goes, every little helps. In turn, the identification and specific details of discounted products can have a profound impact on how consumers view the largest supermarket retailer in the United Kingdom, as well as the operational costs and profits that shareholders are concerned with. How do data and AI inform these offers, what goes into the enterprise-scale analytics that keeps Tesco’s Clubcard the UK’s favorite?

Venkat Raghavan is Director of Analytics and Science at Tesco. Venkat’s area of expertise is customer analytics, having been very heavily involved with the Tesco Clubcard loyalty program. Venkat also set up an analytics center of excellence to help break down data silos between teams. Previously, he was a Director of Analytics at Boston Consulting Group and Senior Director for Advanced Analytics & AI for Manthan and a Cross Industry Delivery Leader at Mu Sigma.

In the episode, Richie and Venkat explore Tesco’s use of data, the introduction of the clubcard scheme, Tesco’s data-driven innovations in online food retail, understanding customer behavior through loyalty programs and in-app interactions, improving customer experience at Tesco, operating a cohesive data intelligence platform that leverages multiple data sources, communication between data and business teams, pricing and cost management, the challenges of data science at scale, the future of data and much more. 

Links Mentioned in the Show:

• Tesco Clubcard
• McKinsey: State of Grocery Europe 2024
• [Course] Data Science for Business
• Related Episode: Scaling Enterprise Analytics with Libby Duane Adams, Chief Advocacy Officer and Co-Founder of Alteryx
• Sign up to RADAR: AI Edition

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Arguably one of the verticals that is both at the same time most ripe for disruption by AI and the hardest to disrupt is search. We've seen many attempts at reimagining search using AI, and many are trying to usurp Google from its throne as the top search engine on the planet, but I think no one is laying the case better for AI assisted search than perplexity. AI. Perplexity doesn't need an introduction. It is an AI powered search engine that lets you get the information you need as fast as possible.

Denis Yarats is the Co-Founder and Chief Technology Officer of Perplexity AI. He previously worked at Facebook as an AI Research Scientist. Denis Yarats attended New York University. His previous research interests broadly involved Reinforcement Learning, Deep Learning, NLP, robotics and investigating ways of semi-supervising Hierarchical Reinforcement Learning using natural language.

In the episode, Adel and Denis explore Denis’ role at Perplexity.ai, key differentiators of Perplexity.ai when compared to other chatbot-powered tools, culture at perplexity, competition in the AI space, building genAI products, the future of AI and search, open-source vs closed-source AI and much more. 

Links Mentioned in the Show:

• Perplexity.ai
• NeurIPS Conference
• [Course] Artificial Intelligence (AI) Strategy
• Related Episode: The Power of Vector Databases and Semantic Search with Elan Dekel, VP of Product at Pinecone
• Sign up to RADAR: AI Edition

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Spatial computing is revolutionizing the way we interact with digital and physical worlds, but its adoption comes with questions about practicality and return on investment. As businesses explore this cutting-edge technology, they must consider how it can enhance productivity and streamline operations. What are the best strategies to integrate spatial computing into your current systems? How can you ensure that it not only boosts efficiency but also delivers measurable benefits to your bottom line? 

Cathy Hackl is a web3 and metaverse strategist, tech futurist, speaker and author. She's worked with metaverse-related companies such as HTC VIVE, Magic Leap, and AWS, and currently consults with some of the world's leading brands, including P&G, Clinique, Ralph Lauren, Orlando Economic Partnership and more. Hackl is one of the world's first Chief Metaverse Officers and the co-founder of Journey, where she works with luxury, fashion, and beauty brands to create successful metaverse and web3 strategies and helps them build worlds in platforms like Roblox, Fortnite, Decentraland, The Sandbox, and beyond. She is widely regarded as one of the leading thinkers on the Metaverse.

Irena Cronin is SVP of Product for DADOS Technology, which is making an Apple Vision Pro data analytics and visualization app. She is also the CEO of Infinite Retina, which helps companies develop and implement AI, AR, and other new technologies for their businesses. Before this, she worked as an equity research analyst and gained extensive experience in evaluating both public and private companies.

In the episode, Richie, Cathy and Irina explore spatial computing, the current viability of spacial computing and it's prominence alongside the release of Apple's Vision Pro, expected effects of spatial computing on gaming and entertainment, industrial applications as well as data visualization and AI integration opportunities of spatial computing, how businesses can leverage spatial computing, future developments in the space and much more. 

Links Mentioned in the Show:

• Cathy’s Book
• Irena’s Books
• Apple Vision Pro
• Marvel Studios and ILM Immersive Announce 'What If...? - An Immersive Story'
• [Course] Artificial Intelligence (AI) Strategy
• Related Episode: Why the Future of AI in Data will be Weird with Benn Stancil, CTO at Mode & Field CTO at ThoughtSpot
• Sign up to RADAR: AI Edition

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Memory, the foundation of human intelligence, is still one of the most complex and mysterious aspects of the brain. Despite decades of research, we've only scratched the surface of understanding how our memories are formed, stored, and retrieved. But what if AI could help us crack the code on memory? How might AI be the key to unlocking problems that have evaded human cognition for so long?

Kim Stachenfeld is a Senior Research Scientist at Google DeepMind in NYC and Affiliate Faculty at the Center for Theoretical Neuroscience at Columbia University.  Her research covers topics in Neuroscience and AI. On the Neuroscience side, she study how animals build and use models of their world that support memory and prediction. On the Machine Learning side, she works on implementing these cognitive functions in deep learning models. Kim’s work has been featured in The Atlantic, Quanta Magazine, Nautilus, and MIT Technology Review. In 2019, she was named one of MIT Tech Review’s Innovators under 35 for her work on predictive representations in hippocampus. 

In the episode, Richie and Kim explore her work on Google Gemini, the importance of customizability in AI models, the need for flexibility and adaptability in AI models, retrieval databases and how they improve AI response accuracy, AI-driven science, the importance of augmenting human capabilities with AI and the challenges associated with this goal, the intersection of AI, neuroscience and memory and much more. 

Links Mentioned in the Show:

• DeepMind
• AlphaFold
• Dr James Whittington - A unifying framework for frontal and temporal representation of memory
• Paper - Language models show human-like content effects on
• reasoning tasks
• Kim’s Website
• [Course] Artificial Intelligence (AI) Strategy
• Related Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision Scientist
• Sign up to RADAR: AI Edition

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In the fast-paced work environments we are used to, the ability to quickly find and understand data is essential. Data professionals can often spend more time searching for data than analyzing it, which can hinder business progress. Innovations like data catalogs and automated lineage systems are transforming data management, making it easier to ensure data quality, trust, and compliance. By creating a strong metadata foundation and integrating these tools into existing workflows, organizations can enhance decision-making and operational efficiency. But how did this all come to be, who is driving better access and collaboration through data?

Prukalpa Sankar is the Co-founder of Atlan. Atlan is a modern data collaboration workspace (like GitHub for engineering or Figma for design). By acting as a virtual hub for data assets ranging from tables and dashboards to models & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Slack, BI tools, data science tools and more. A pioneer in the space, Atlan was recognized by Gartner as a Cool Vendor in DataOps, as one of the top 3 companies globally. Prukalpa previously co-founded SocialCops, world leading data for good company (New York Times Global Visionary, World Economic Forum Tech Pioneer). SocialCops is behind landmark data projects including India’s National Data Platform and SDGs global monitoring in collaboration with the United Nations. She was awarded Economic Times Emerging Entrepreneur for the Year, Forbes 30u30, Fortune 40u40, Top 10 CNBC Young Business Women 2016, and a TED Speaker.

In the episode, Richie and Prukalpa explore challenges within data discoverability, the inception of Atlan, the importance of a data catalog, personalization in data catalogs, data lineage, building data lineage, implementing data governance, human collaboration in data governance, skills for effective data governance, product design for diverse audiences, regulatory compliance, the future of data management and much more. 

Links Mentioned in the Show:

• Atlan
• Connect with Prukalpa
• [Course] Artificial Intelligence (AI) Strategy
• Related Episode: Adding AI to the Data Warehouse with Sridhar Ramaswamy, CEO at Snowflake
• Sign up to RADAR: AI Edition

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One thing we like to do on DataFramed is cover the current state of data & AI, and how it will change in the future. But sometimes to really understand the present and the future, we need to look into the past. We need to understand just exactly how data became so foundational to modern society and organizations, how previous paradigm shifts can help inform us about future ones, and how data & AI became powerful social forces within our lives.

Cristina Alaimo is Assistant Professor (Research) of Digital Economy and Society at LUISS University, Rome. She co-wrote the book Data Rules, Reinventing the Market Economy with Jannis Kallinikos, Professor of Organization Studies and the CISCO Chair in Digital Transformation and Data Driven Innovation at LUISS University. The book offers a fascinating examination of the history and sociology of data. 

In the episode, Adel and Cristina explore the many of the themes covered in the book, from the first instance of where data was used, to how it became central for how organizations operate, to how usage of data introduced paradigm shifts in organizational structure, and much more.

Links Mentioned in the Show:

• Data Rules, Reinventing the Market Economy
• The Age of Surveillance Capitalism by Shoshana Zuboff
• Connect with Cristina
• [Course] Artificial Intelligence (AI) Strategy
• Related Episode: What to Expect from AI in 2024 with Craig S. Smith, Host of the Eye on A.I Podcast
• Sign up to RADAR: AI Edition

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In today's fast-paced digital world, managing IT operations is more complex than ever. With the rise of cloud services, microservices, and constant software deployments, the pressure on IT teams to keep everything running smoothly is immense. But how do you keep up with the ever-growing flood of data and ensure your systems are always available? AIOps is the use of artificial intelligence to automate and scale IT operations. But what exactly is AIOps, and how can it transform your IT operations?

Assaf Resnick is the CEO and Co-Founder of BigPanda. Before founding BigPanda, Assaf was an investor at Sequoia Capital, where he focused on early and growth-stage investing in software, internet, and mobile sectors. Assaf’s time at Sequoia gave him a front-row seat to the challenges of IT scale, complexity, and velocity faced by Operations teams in rapidly scaling and accelerating organizations. This is the problem that Assaf founded BigPanda to solve.

In the episode, Richie and Assaf explore AIOps, how AIOps helps manage increasingly complex IT operations, how AIOps differs from DevOps and MLOps, examples of AIOps projects, a real world application of AIOps, the key benefits of AIOps, how to implement AIOps, excitement in the space, how GenAI is improving AIOps and much more. 

Links Mentioned in the Show:

• BigPanda
• Gartner: Market Guide for AIOps Platforms
• [Course] Implementing AI Solutions in Business
• Related Episode: Adding AI to the Data Warehouse with Sridhar Ramaswamy, CEO at Snowflake
• Sign up to RADAR: AI Edition

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Trust is the foundation of any relationship, whether it's between friends or in business. But what happens when the entity you're asked to trust isn't human, but AI? How do you ensure that the AI systems you're developing are not only effective but also trustworthy? In a world where AI is increasingly making decisions that impact our lives, how can we distinguish between systems that genuinely serve our interests and those that might exploit our data? 

Bruce Schneier is an internationally renowned security technologist, called a “security guru” by The Economist. He is the author of over one dozen books—including his latest, A Hacker’s Mind—as well as hundreds of articles, essays, and academic papers. His influential newsletter “Crypto-Gram” and his blog “Schneier on Security” are read by over 250,000 people. He has testified before Congress, is a frequent guest on television and radio, has served on several government committees, and is regularly quoted in the press. Schneier is a fellow at the Berkman Klein Center for Internet & Society at Harvard University; a Lecturer in Public Policy at the Harvard Kennedy School; a board member of the Electronic Frontier Foundation and AccessNow; and an Advisory Board Member of the Electronic Privacy Information Center and VerifiedVoting.org. He is the Chief of Security Architecture at Inrupt, Inc.

In the episode, Richie and Bruce explore the definition of trust, the difference between trust and trustworthiness, how AI mimics social trust, AI and deception, the need for public non-profit AI to counterbalance corporate AI, monopolies in tech, understanding the application and potential consequences of AI misuse, AI regulation, the positive potential of AI, why AI is a political issue and much more.

Links Mentioned in the Show:

• Schneier on Security
• Books by Bruce
• [Course] AI Ethics
• Related Episode: Building Trustworthy AI with Alexandra Ebert, Chief Trust Officer at MOSTLY AI
• Sign up to RADAR: AI Edition

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Building a successful data engineering team involves more than just hiring skilled individuals—it requires fostering a culture of trust, collaboration, and continuous learning. But how do you start from scratch and create a team that not only meets technical demands but also drives business value? What key traits should you look for in your early hires, and how do you ensure your team’s projects align with the company’s goals?

Liya Aizenberg is Director of Data Engineering at Away and a seasoned data leader with over 22 years of experience spearheading innovation in scalable data engineering pipelines and distribution solutions. She has built successful data teams that integrate seamlessly with various business functions, serving as invaluable organizational partners. She focuses on promoting data-driven approaches to empower organizations to make proactive decisions based on timely and organized data, shifting from reactive to proactive business strategies. Additionally, as a passionate advocate for Women in Tech, she actively contributes to fostering diversity and inclusion in the technology industry.

In the episode, Adel and Liya explore the key attributes that forge an effective data engineering team, traits to look for in new hires, what technical skill sets set people up for success in a data engineering team, leveraging knowledge transfer between external experts and internal stakeholders, upskilling and career growth, aligning data engineering initiatives with business goals, measuring the ROI of data projects, working agile in data engineering, balancing innovation and practicality, future trends and much more. 

Links Mentioned in the Show:

• Away Travel
• Connect with Liya on Linkedin
• [Career Track] Data Engineer with Python
• Related Episode: Scaling Data Engineering in Retail with Mo Sabah, SVP of Engineering & Data at Thrive Market
• Sign up to RADAR: AI Edition

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Everything in the world has a price, including improving and scaling your data and AI functions. That means that at some point someone will question the ROI of your projects, and often, these projects will be looked at under the lens of monetization. But how do you ensure that what you’re working on is not only providing value to the business but also creating financial gain? What conditions need to be met to prove your project's success and turn value into cash?

Vin Vashishta is the author of ‘From Data to Profit’ (Wiley), the playbook for monetizing data and AI. He built V-Squared from client 1 to one of the oldest data and AI consulting firms. For the last eight years, he has been recognized as a data and AI thought leader. Vin is a LinkedIn Top Voice and Gartner Ambassador. His background spans over 25 years in strategy, leadership, software engineering, and applied machine learning.

Dr. Tiffany Perkins-Munn is on a mission to bring research, analytics, and data science to life. She earned her Ph.D. in Social-Personality Psychology with an interdisciplinary focus on Advanced Quantitative Methods. Her insights are the subject of countless lectures on psychology, statistics, and their real-world applications.

As the Head of Data and Analytics for the innovative CDAO organization at J.P. Morgan Chase, her knack involves unraveling complex business problems through operational enhancements, augmented financials, and intuitive recruiting. After over two decades in the industry, she consistently forges robust relationships across the corporate spectrum, becoming one of the Top 10 Finalists in the Merrill Lynch Global Markets Innovation Program.

In the episode, Richie, Vin, and Tiffany explore the challenges of monetizing data and AI projects, including how technical, organizational, and strategic factors affect your input, the importance of aligning technical and business objectives to keep outputs focused on core business goals, how to assess your organization's data and AI maturity, examples of high data maturity businesses, data security and compliance, quick wins in data transformation and infrastructure, why long-term vision and strategy matter, and much more.

Links Mentioned in the Show:

• Connect with Tiffany on Linkedin
• Connect with Vin on Linkedin
• Vin’s Website
• [Course] Data Governance Concepts 
• Related Episode: Scaling Enterprise Analytics with Libby Duane Adams, Chief Advocacy Officer and Co-Founder of Alteryx

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As we close out our focus on how the venture capital industry identifies and decides which future companies to fund, it might be easy to fall into the trap of thinking that the latest methods for discovering future unicorns are ubiquitous among all VCs. However, many VCs still work ‘the old way,’ using data to back up human assumptions. But what happens when a data engineer pivots to VC? What does a data-driven, data-first approach look like, and how does it compare to the incumbent processes?

Dr. Andre Retterath is a Partner in Earlybird’s Munich Office, focussing on enterprise software with a particular interest in developer, data and productivity tools, alongside AI-centric products and robotics. Before transitioning into VC in 2017, he gained more than 5 years of experience as a process automation and predictive maintenance engineer at ThyssenKrupp and further insights as a management consultant at GE North America. Andre also has his own VC, AI & data newsletter, Data-Driven VC.

In the episode, Richie and Andre explore the concept of data-driven venture capital, the challenges of traditional VC and why digitization has had a huge impact on the industry, the data-driven VC process, the use of modern data and AI technologies in identifying potentially successful projects, the human element in VC, the challenges and opportunities of early-stage investments, the importance of early identification of these ventures, cultural and organizational indicators and much more. 

Links Mentioned in the Show:

• Data-Driven VC
• Earlybird VC
• Aleph Alpha
• Pareto Principle
• Related Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision Scientist
• Sign up to RADAR: AI Edition

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In almost every industry, the rate of innovation is increasing, and this is great for consumers around the globe. However, with constant innovation and continual disruption of the status quo, where to innovate next becomes much harder to identify. If your industry hasn’t been disrupted yet, it’s next on the list. So, in order to deal with uncertainty, a new culture is needed, and there’s a clear group of companies that constantly deal with uncertainty and innovation—VC’s. 

Ilya A. Strebulaev is the David S. Lobel Professor of Private Equity and Professor of Finance at the Stanford Graduate School of Business, and a Research Associate at the National Bureau of Economic Research. He is an expert in corporate finance, venture capital, innovation financing, and financial decision-making. He is the founder and director of the Stanford GSB Venture Capital Initiative.

In the episode, Richie and Ilya explore the venture mindset, the importance of embracing unknowns, how VC’s deal with unpredictability, how our education affects our decision-making ability, practical examples from Ilya’s teaching experiences at Stanford, adapting to market changes and continual innovation, venture mindset principles and much more. 

Links Mentioned in the Show:

• Ilya’s Website
• Sequoia Capital
• Stanford University 
• Related Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision Scientist
• Sign up to RADAR: AI Edition

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Speedily adopting new technologies can give your business a competitive advantage, but with so much happening in the world of generative AI, it's difficult to know what to adopt. In this episode, Richie chats to two venture capitalists to get their view on the global AI landscape, where we are in the AI hype cycle, and how to adopt AI tech. Beyond this, we explore Rocketship.vc's use of data and algorithms to make investment decisions in early-stage startups. If our previous episode’s deep dive into 2024’s data & AI trends with VC Tom Tunguz got you excited about how investors are looking at the market at the moment, then this episode is sure to do the same. This time, we have twice the insight, thanks to our two guests.

Madhu Shalini Iyer is a Managing Partner at Rocketship.vc, a Silicon Valley based fund investing globally. She was the Chief Data Officer of Gojek and helped grow the business into a $10 billion unicorn. In addition to being a board member, she started the Singapore office and played an active role in the strategy, new business development, and ‘data as a competitive advantage’. Prior to Gojek, Madhu was part of the founding team of Intuit’s Quickbooks Lending Platform. As the data science leader at Intuit, Madhu helped grow the platform to $300 million and holds 2 patents in the areas of user data augmented algorithms for financial inclusion. Madhu was also the Chief Data Officer for Ethoslending. There she built the underwriting platform and was responsible for all b2c revenue, resulting in $65 million gross market value per month. Madhu was further responsible for building and running the marketing team. Prior, Madhu was a partner at a $150m private equity fund, Stem Financial, in Hong Kong. She started her career as a senior data scientist with a leading think tank in Menlo Park, CA.

Sailesh Ramakrishnan is also a Managing Partner at Rocketship.vc. Prior to Rocketship.vc, Sailesh was CTO and co-founder of LocBox (acquired by Square), a startup focussed on marketing for local businesses. Sailesh worked with Anand and Venky at their previous startup Kosmix, and continued on to Walmart as a Director of Engineering at @WalmartLabs. Before jumping into the startup world, Sailesh worked as a Computer Scientist at NASA Ames Research Center. Sailesh earned his Bachelors degree in Civil Engineering from IIT Madras, his Masters degree in Construction Management from Virginia Tech and another Master degree in Intelligent Systems from University of Pittsburgh. He was a Ph.D. candidate in Artificial Intelligence at the University of Michigan.

In the episode, Richie, Madhu and Sailesh explore the generative AI revolution, categorizing generative AI tools, the impact of genAI across industries, investment philosophy and data-driven decision-making, the challenges and opportunities when investing in AI, future trends and predictions, regulatory and ethical considerations of AI, and much more. 

Links Mentioned in the Show:

• Rocketship.vc
• [Course] Implementing AI Solutions in Business
• Related Episode: Inside Algorithmic Trading with Anthony Markham, Vice President, Quantitative Developer at Deutsche Bank
• Sign up to RADAR: AI Edition

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Rapid change seems to be the new norm within the data and AI space, and due to the ecosystem constantly changing, it can be tricky to keep up. Fortunately, any self-respecting venture capitalist looking into data and AI will stay on top of what’s changing and where the next big breakthroughs are likely to come from. We all want to know which important trends are emerging and how we can take advantage of them, so why not learn from a leading VC. 

Tomasz Tunguz is a General Partner at Theory Ventures, a $235m early-stage venture capital firm. He blogs sat tomtunguz.com & co-authored Winning with Data. He has worked or works with Looker, Kustomer, Monte Carlo, Dremio, Omni, Hex, Spot, Arbitrum, Sui & many others.

He was previously the product manager for Google's social media monetization team, including the Google-MySpace partnership, and managed the launches of AdSense into six new markets in Europe and Asia. Before Google, Tunguz developed systems for the Department of Homeland Security at Appian Corporation. 

In the episode, Richie and Tom explore trends in generative AI, the impact of AI on professional fields, cloud+local hybrid workflows, data security, and changes in data warehousing through the use of integrated AI tools, the future of business intelligence and data analytics, the challenges and opportunities surrounding AI in the corporate sector. You'll also get to discover Tom's picks for the hottest new data startups.

Links Mentioned in the Show:

• Tom’s Blog
• Theory Ventures
• Article: What Air Canada Lost In ‘Remarkable’ Lying AI Chatbot Case
• [Course] Implementing AI Solutions in Business
• Related Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision Scientist
• Sign up to RADAR: AI Edition

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With seemingly every organization wanting to enhance their AI capabilities, questions arise about who should be in charge of these initiatives. At the moment, it’s likely a CTO, CIO, or CDO, or a mixture of the three. The gold standard is to have someone in the C-suite whose sole focus is their AI projects: the Chief AI Officer. This role is so new that it's not yet widely understood. In this episode, we explore what the CAIO job entails.

Philipp Herzig is the Chief AI Officer at SAP. He’s held a variety of roles within SAP, most recently SVP Head of Cross Product Engineering & Experience, however his experience covers intelligent enterprise & cross-architecture, head of engineering for cloud-native apps, a software development manager, and product owner. 

In the full episode, Richie and Philipp explore what his day-to-day responsibilities are as a CAIO, the holistic approach to cross-team collaboration, non-technical interdepartmental work, AI strategy and implementation, challenges and success metrics, how to approach high-value AI use cases, insights into current AI developments and the importance of continuous learning, the exciting future of AI and much more. 

Links Mentioned in the Show:

• SAP’s AI CoPilot Joule
• SAP
• [Course] Implementing AI Solutions in Business
• Related Episode: How Walmart Leverages Data & AI with Swati Kirti, Sr Director of Data Science at Walmart
• Rewatch sessions from RADAR: The Analytics Edition

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Countless companies invest in their data quality, but often, the effort from their investment is not fully realized in the output. It seems like, despite the critical importance of data quality, data governance might be suffering from a branding issue. Data governance is sometimes looked at as the data police, but this is far from the truth. So, how can we change perspectives and introduce fun into data governance?

Tiankai Feng is a Principal Data Consultant and Data Strategy & Data Governance Lead at Thoughtworks, He also works part-time as the Head of Marketing at DAMA Germany. Tiankai has had many data hats in his career—marketing data analyst, data product owner, analytics capability lead, and data governance leader for the last few years. He has found a passion for the human side of data—how to collaborate, coordinate, and communicate around data. TIankai often uses his music and humor to make data more approachable and fun.

In the episode, Adel and Tiankai explore the importance of data governance in data-driven organizations, the challenges of data governance, how to define success criteria and measure the ROI of governance initiatives, non-invasive and creative approaches to data governance, the implications of generative AI on data governance, regulatory considerations, organizational culture and much more. 

Links Mentioned in the Show:

• Tiankai’s YouTube Channel
• Data Governance Fundamentals Cheat Sheet
• [Webinar] Unpacking the Fun in Data Governance: The Key to Scaling Data Quality
• [Course] Data Governance Concepts
• Rewatch sessions from RADAR: The Analytics Edition

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Databases are ubiquitous, and you don’t need to be a data practitioner to know that all data everywhere is stored in a database—or is it? While the majority of data around the world lives in a database, the data that helps run the heart of our operating systems—the core functions of our computers— is not stored in the same place as everywhere else. This is due to database storage sitting ‘above’ the operating system, requiring the OS to run before the databases can be used. But what if the OS was built ‘on top’ of a database? What difference could this fundamental change make to how we use computers?

Mike Stonebraker is a distinguished computer scientist known for his foundational work in database systems, he is also currently CTO & Co-Founder At DBOS. His extensive career includes significant contributions through academic prototypes and commercial startups, leading to the creation of several pivotal relational database companies such as Ingres Corporation, Illustra, Paradigm4, StreamBase Systems, Tamr, Vertica, and VoltDB. Stonebraker's role as chief technical officer at Informix and his influential research earned him the prestigious 2014 Turing Award.

Stonebraker's professional journey spans two major phases: initially at the University of California, Berkeley, focusing on relational database management systems like Ingres and Postgres, and later, from 2001 at the Massachusetts Institute of Technology (MIT), where he pioneered advanced data management techniques including C-Store, H-Store, SciDB, and DBOS. He remains a professor emeritus at UC Berkeley and continues to influence as an adjunct professor at MIT’s Computer Science and Artificial Intelligence Laboratory. Stonebraker is also recognized for his editorial work on the book "Readings in Database Systems."

In the episode, Richie and Mike explore the the success of PostgreSQL, the evolution of SQL databases, the shift towards cloud computing and what that means in practice when migrating to the cloud, the impact of disaggregated storage, software and serverless trends, the role of databases in facilitating new data and AI trends, DBOS and it’s advantages for security, and much more. 

Links Mentioned in the Show:

• DBOS
• Paper: What Goes Around Comes Around
• [Course] Understanding Cloud Computing
• Related Episode: Scaling Enterprise Analytics with Libby Duane Adams, Chief Advocacy Officer and Co-Founder of Alteryx
• Rewatch sessions from RADAR: The Analytics Edition

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Over the past 199 episodes of DataFramed, we’ve heard from people at the forefront of data and AI, and over the past year we’ve constantly looked ahead to the future AI might bring. But all of the technologies and ways of working we’ve witnessed have been built on foundations that were laid decades ago. For our 200th episode, we’re bringing you a special guest and taking a walk down memory lane—to the creation and development of one of the most popular programming languages in the world.

Don Chamberlin is renowned as the co-inventor of SQL (Structured Query Language), the predominant database language globally, which he developed with Raymond Boyce in the mid-1970s. Chamberlin's professional career began at IBM Research in Yorktown Heights, New York, following a summer internship there during his academic years. His work on IBM's System R project led to the first SQL implementation and significantly advanced IBM’s relational database technology. His contributions were recognized when he was made an IBM Fellow in 2003 and later a Fellow of the Computer History Museum in 2009 for his pioneering work on SQL and database architectures. Chamberlin also contributed to the development of XQuery, an XML query language, as part of the W3C, which became a W3C Recommendation in January 2007. Additionally, he holds fellowships with ACM and IEEE and is a member of the National Academy of Engineering.

In the episode, Richie and Don explore his early career at IBM and the development of his interest in databases alongside Ray Boyce, the database task group (DBTG), the transition to relational databases and the early development of SQL, the commercialization and adoption of SQL, how it became standardized, how it evolved and spread via open source, the future of SQL through NoSQL and SQL++ and much more. 

Links Mentioned in the Show:

• The first-ever journal paper on SQL. SEQUEL: A Structured English Query Language
• Don’s Book: SQL++ for SQL Users: A Tutorial
• System R: Relational approach to database management
• SQL Courses
• SQL Articles, Tutorials and Code-Alongs
• Related Episode: Scaling Enterprise Analytics with Libby Duane Adams, Chief Advocacy Officer and Co-Founder of Alteryx
• Rewatch sessions from RADAR: The Analytics Edition

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Last year saw the proliferation of countless AI tools and initiatives, many companies looked to find ways where AI could be leveraged to reduce operational costs and pressure wherever possible. 2023 was a year of experimentation for anyone trying to harness AI, but we can’t walk forever. To keep up with the rapidly changing landscape in business, last year’s experiments with AI need to find their feet and allow us to run. But how do we know which initiatives are worth fully investing in? Will your company culture impede the change management that is necessary to fully adopt AI?

Sanjay Srivastava is the Chief Digital Strategist at Genpact. He works exclusively with Genpact’s senior client executives and ecosystem technology leaders to mobilize digital transformation at the intersection of cutting-edge technology, data strategy, operating models, and process design. In his previous role as Chief Digital Officer at Genpact, Sanjay built out the company’s offerings in artificial intelligence, data and analytics, automation, and digital technology services. He leads Genpact’s artificial-intelligence-enabled platform that delivers industry-leading governance, integration, and orchestration capabilities across digital transformations. Before joining Genpact, Sanjay was a Silicon Valley serial entrepreneur and built four high-tech startups, each of which was successfully acquired by Akamai, BMC, FIS, and Genpact, respectively. Sanjay also held operating leadership roles at Hewlett Packard, Akamai, and SunGard (now FIS), where he oversaw product management, global sales, engineering, and services businesses.

In the episode, Sanjay and Richie cover the shift from experimentation to production seen in the AI space over the past 12 months, the importance of corporate culture in the adoption of AI in a business environment, how AI automation is revolutionizing business processes at GENPACT, how change management contributes to how we leverage AI tools at work, adapting skill development pathways to make the most out of AI, how AI implementation changes depending on the size of your organization, future opportunities for AI to change industries and much more. 

Links Mentioned in the Show:

• Genpact
• [Course] Implementing AI Solutions in Business
• Article: AI adoption accelerates as enterprise PoCs show productivity gains
• Related Episode: How Generative AI is Changing Business and Society with Bernard Marr, AI Advisor, Best-Selling Author, and Futurist
• Rewatch sessions from RADAR: The Analytics Edition

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There aren’t many retail giants like Walmart. In fact, there are none. The multinational generates 650bn in revenue, (including 50bn in eCommerce)—the highest revenue of any retailer globally. With over 10,000 stores worldwide and a constantly evolving product line, Walmart’s data & AI function has a lot to contend with when it comes to customer experience, demand forecasting, supply chain optimization and where to use AI effectively. So how do they do it? What can we learn from one of the most successful and well-known organizations on the planet?

Swati Kirti is a Senior Director of Data Science, leading the AI/ML charter for Walmart Global Tech’s international business in Canada, Mexico, Central America, Chile, China, and South Africa. She is responsible for building AI/ML models and products to enable automation and data-driven decisions, powering superior customer experience and realizing value for omnichannel international businesses across e-commerce, stores, supply chain, and merchandising.

In the episode, Swati and Richie explore the role of data and AI at Walmart, how the data and AI teams operate under Swati’s supervision, how Walmart improves customer experience through the use of data, supply chain optimization, demand forecasting, retail-specific data challenges, scaling AI solutions, innovation in retail through AI and much more. 

Links Mentioned in the Show:

• Article - Walmart’s Generative AI search puts more time back in customers' hands
• Walmart Global Tech
• [Course] Implementing AI Solutions in Business
• Related Episode: How Generative AI is Changing Business and Society with Bernard Marr, AI Advisor, Best-Selling Author, and Futurist
• Rewatch sessions from RADAR: The Analytics Edition

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Generative AI has had a wide range of uses, but some of its strongest use cases are in coding and programming. One of the companies that has been leading the way in AI-assisted programming has been GitHub with GitHub CoPilot. Many software engineering teams now have tools like CoPilot embedded into their workflows, but what does this mean for the future of programming?

Kyle Daigle is the COO of GitHub, leading the strategic initiatives, operations, and innovation of the world's largest platform for software development and collaboration. With over 10 years of experience at GitHub, Kyle has a deep understanding of the needs and challenges of developers and the ecosystem they work in.

In the episode, Adel and Kyle explore Kyle’s journey into development and AI, how he became the COO at GitHub, GitHub’s approach to AI, the impact of CoPilot on software development, how AI tools are adopted by software developers, the future of programming and AI’s role within it, the risks and challenges associated with the adoption of AI coding tools, the broader implications tools like CoPilot might have and much more. 

Links Mentioned in the Show:

• GitHub CoPilot
• Kyle on GitHub
• [Code Along] Pair Programming with GitHub Copilot
• [Course] GitHub Concepts
• Related Episode: What to Expect from AI in 2024 with Craig S. Smith, Host of the Eye on A.I Podcast
• Rewatch sessions from RADAR: The Analytics Edition

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Since the launch of ChatGPT, one of the trending terms outside of ChatGPT itself has been prompt engineering. This act of carefully crafting your instructions is treated as alchemy by some and science by others. So what makes an effective prompt?

Alex Banks has been building and scaling AI products since 2021. He writes Sunday Signal, a newsletter offering a blend of AI advancements and broader thought-provoking insights. His expertise extends to social media platforms on X/Twitter and LinkedIn, where he educates a diverse audience on leveraging AI to enhance productivity and transform daily life.

In the episode, Alex and Adel cover Alex’s journey into AI and what led him to create Sunday Signal, the potential of AI, prompt engineering at its most basic level, strategies for better prompting, chain of thought prompting, prompt engineering as a skill and career path, building your own AI tools rather than using consumer AI products, AI literacy, the future of LLMs and much more. 

Links Mentioned in the Show:

• [Alex’s Free Course on DataCamp] Understanding Prompt Engineering
• Sunday Signal
• Principles by Ray Dalio: Life and Work
• Related Episode: [DataFramed AI Series #1] ChatGPT and the OpenAI Developer Ecosystem
• Rewatch sessions from RADAR: The Analytics Edition

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Driving impact with analytics goes beyond numbers and graphs; it's about telling a story that resonates. In this session, Brent Dykes, author of "Effective Data Storytelling" & the Founder & Chief Data Storyteller at AnalyticsHero, Lea Pica, author of "Present Beyond Measure" & the Founder at Story-driven by Data, and Andy Cotgreave, co-author of "The Big Book of Dashboards" and Senior Data Evangelist at Tableau, will unveil how to transform data into compelling narratives. 

They shed light on the art of blending analytics with storytelling, a key to making data-driven insights both understandable and influential within any organization.

You've just invested in licenses for your favorite analytics tool, but now what? In this session, Laura Gent Felker, GTM Analytics Lead at MongoDB, Tiffany Perkins-Munn, Managing Director & Head of Data & Analytics at JPMC and Omar Khawaja, CDAO & Global Head Data & Analytics at Givaudan will explore best practices when it comes to scaling analytics adoption within the wider organization. They will discuss how to approach change management when it comes to driving analytics adoption, the role of data leaders in driving a culture change around analytics tooling, and a lot more. 

Driving trust with data is essential to succeeding with analytics. However, time and time again, data quality remains an issue for most organizations today. In this session, Esther Munyi, Chief Data Officer at Sasfin, Amy Grace, Director, Military Engines Digital Strategy at Pratt & Whitney, Stefaan Verhulst, Chief Research & Development Officer, Director of Data Program at NYU Governance Lab, and Malarvizhi Veerappan, Program Manager and Senior Data Scientist at the World Bank will focus on strategies for improving data quality, fostering a culture of trust around data, and balancing robust governance with the need for accessible, high-quality data.

Creating a culture of continuous learning within analytics functions isn't just beneficial; it's essential. In the session, Russell Johnson, Chief Data Scientist at Marks & Spencer, Denisse Groenendaal-Lopez, Learning & Development Business Partner at Booking Group, and Mark Stern, VP of Business Intelligence & Analytics at BetMGM will address the importance of fostering a learning environment for driving success with analytics. They will provide insights on developing a culture where continuous learning, experimentation, and curiosity are the norms—and strategies leaders can adopt today to drive up excitement around analytics adoption & upskilling. 

Everyone has seen the reach and impact of generative AI, and with countless use-cases across a variety of fields, the question is often not "can we do things with AI?", but rather "what should we do with AI?". What are the key areas where generative AI has had a profound impact already? Which economies, industries, and businesses have taken full advantage of the abilities of GenAI already? It takes a lot of wisdom and experience within the data & AI space to distill high-level insights from such a rapidly changing world, but, luckily we have one of the best people in the world to quiz on the current landscape and future of AI. 

Bernard Marr is an internationally best-selling business author, keynote speaker and strategic advisor to companies and governments. He advises many of the world’s best-known organizations such as Amazon, Google, Microsoft, IBM, Toyota, and more.

LinkedIn has recently ranked Bernard as one of the top 5 business influencers in the world. He has authored 19 best-selling books, including his new book Generative AI in Practice: 100+ Amazing Ways Generative Artificial Intelligence is Changing Business and Society. Every day Bernard actively engages his over 4 million social media followers. He is one of the world’s most highly respected experts when it comes to future trends, strategy, business performance, digital transformation and the intelligent use of data and AI in business.

In the episode, Richie and Bernard explore how AI will impact society through the augmentation of jobs, the importance of developing skills that won’t be easily replaced by AI, how generative AI is revolutionizing creative fields already, how AI will impact education, AI’s role in coding and software development, use cases of generative AI in business, how personalization is set to improve through AI, concerns and ethical considerations surrounding AI, why we should be optimistic about the future of AI, and much more. 

Links Mentioned in the Show:

• Bernard’s book: Generative AI in Practice
• Bernard’s Website, Twitter and Linkedin
• [Skill Track] AI Business Fundamentals
• Related Episode: Adapting to the AI Era with Jason Feifer, Editor in Chief of Entrepreneur Magazine

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There is a concept in software engineering which is called ‘shifting left’, this focuses on testing software a lot earlier in the development lifecycle than you would normally expect it to. This helps teams building the software create better rituals and processes, while also ensuring quality and usability are key aspects to evaluate as the software is being built. We know this works in software development, but what happens when these practices are used when building AI tools?

Saurabh Gupta is a seasoned technology executive and is currently Chief Strategy & Revenue Officer The Modern Data Company. With over 25 years of experience in tech, data and strategy, he has led many strategy and modernization initiatives across industries and disciplines. Through his career, he has worked with various Internation Organizations and NGOs, Public sector and Private sector organizations. Before joining TMDC, he was the Head of Data Strategy & Governance at ThoughtWorks & CDO/Director for Washington DC Gov., where he developed the digital/data modernization strategy for education data. Prior to DCGov he played leadership and strategic roles at organizations including IMF and World Bank where he was responsible for their Data strategy and led the OpenData initiatives. He has also closely worked with African Development Bank, OECD, EuroStat, ECB, UN and FAO as a part of inter-organization working groups on data and development goals. As a part of the taskforce for international data cooperation under the G20 Data Gaps initiative, he chaired the technical working group on data standards and exchange. He also played an advisor role to the African Development Bank on their data democratization efforts under the Africa Information Highway.

In the episode, Adel & Saurabh explore the importance of data quality and how ‘shifting left’ can improve data quality practices, the role of data governance, the emergence of data product managers, operationalizing ‘shift left’ strategies through collaboration and data governance, the challenges faced when implementing data governance, future trends in data quality and governance, and much more. 

Links Mentioned in the Show:

• The Modern Data Company
• Monte Carlo: The Annual State of Data Quality Survey
• [Course] Data Governance Concepts
• [Webinar] Crafting a Lean and Effective Data Governance Strategy 
• Related Episode: Building Trust in Data with Data Governance

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Generative AI has made a mark everywhere, including BI platforms, but how can you combine AI and BI together? What effects can this have across organizations? With constituent aspects such as data quality, your AI strategy, and the specific use-case you’re trying to solve, it’s important to get the full picture and tread with intent. What are the subtleties that we need to get right in order for this marriage to work to its full potential?

Nick Magnuson is the Head of AI at Qlik, executing the organization’s AI strategy, solution development, and innovation. Prior to Qlik, Nick was the CEO of Big Squid, which was acquired by Qlik in 2021. Nick has previously held executive roles in customer success, product, and engineering in the field of machine learning and predictive analytics. As a practitioner in this field for over 20 years, Nick has published original research in these areas, as well as cognitive bias and other quantitative topics. He has also served as an advisor to other analytics platforms and start-ups. A long-time investment professional, Nick continues to hold his Chartered Financial Analyst designation and is a past member of the Chicago Quantitative Alliance and Society of Quantitative Analysts. 

In the episode, Richie and Nick explore what Qlik offers, including products like Sense and Staige, how Staige uses AI to enhance customer capabilities, use cases of generative AI, advice on data privacy and security when using AI, data quality and its effect on the success of AI tools, AI strategy and leadership, how data roles are changing and the emergence of new positions, and much more. 

Links Mentioned in the Show:

• Qlik
• Qlik Staige
• Qlik Sense
• [Skill Track] AI Fundamentals
• Related Episode: Adapting to the AI Era with Jason Feifer, Editor in Chief of Entrepreneur Magazine
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Despite the critical role of analytics in guiding business decisions, organizations continue to face significant challenges in harnessing its full potential. As data sets expand and deadlines shrink, the urgency to scale analytics processes becomes paramount. What data leaders now need to focus on are essential strategies for analytics at scale, including fostering a culture of continuous learning, prioritizing data governance, and leveraging generative AI.

Libby Duane Adams is the Chief Advocacy Officer and co-founder of Alteryx. She is responsible for strengthening upskilling and reskilling efforts for Alteryx customers to enable a culture of analytics, scaling the presence of the Alteryx SparkED education program and furthering diversity and inclusion in the workplace. As the former Chief Customer Officer, Libby has helped many Fortune 100 executives to identify and seize market opportunities, outsmart their competitors, and drive more revenue from their current businesses using analytics. 

In the episode, Richie and Libby explore the differences between analytics and business intelligence, analytics as a team sport, the importance of speed in analytics, generative AI and its implications in analytics, the role of data quality and governance, Alteryx’s AI platform, data skills as a workplace necessity, using AI to automate documentation and insights, success stories and mistakes within analytics, and much more. 

Links Mentioned in the Show:

• Alteryx
• Alteryx SparkED Program
• [Course] Introduction to Alteryx
• Related Episode: From Data Literacy to AI Literacy with Cindi Howson, Chief Data Strategy Officer at ThoughtSpot
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Generative AI is fantastic but has a major problem: sometimes it "hallucinates", meaning it makes things up. In a business product like a chatbot, this can be disastrous. Vector databases like Pinecone are one of the solutions to mitigating the problem.

Vector databases are a key component to any AI application, as well as things like enterprise search and document search. They have become an essential tool for every business, and with the rise in interest in AI in the last couple of years, the space is moving quickly. In this episode, you'll find out how to make use of vector databases, and find out about the latest developments at Pinecone.

Elan Dekel is the VP of Product at Pinecone, where he oversees the development of the Pinecone vector database. He was previously Product Lead for Core Data Serving at Google, where he led teams working on the indexing systems to serve data for Google search, YouTube search, and Google Maps. Before that, he was Founder and CEO of Medico, which was acquired by Everyday Health.

In the episode, RIchie and Elan explore LLMs, hallucination in generative models, vector databases and the best use-cases for them, semantic search, business applications of vector databases and semantic search, the tech stack for AI applications, cost considerations when investing in AI projects, emerging roles within the AI space, the future of vector databases and AI, and much more.  

Links Mentioned in the Show:

• Pinecone Canopy
• Pinecone Serverless
• LlamaIndex
• Langchain
• [Code Along] Semantic Search with Pinecone
• Related Episode: Expanding the Scope of Generative AI in the Enterprise with Bal Heroor, CEO and Principal at Mactores
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One of the most immediate needs to come out of the generative AI boom has been the need for guardrails and governmental regulation of AI technologies. Most of the work already completed in the AI space has been industry-led, with large organizations pushing AI forward to improve their efficiency as businesses and to create new avenues for revenue. This focus on industry and revenue can potentially create more inequality in the world, with companies not interested in the negative effects of AI being driven by profit, towards profit. To combat this, the UN has set up an AI Advisory Board, with members from different nationalities, backgrounds and expertises to ensure that AI is for all, and not just for profit. In this episode, we speak to two members of the board. 

Ian Bremmer is a political scientist who helps business leaders, policy makers, and the general public make sense of the world around them. He is president and founder of Eurasia Group, the world's leading political risk research and consulting firm, and GZERO Media, a company dedicated to providing intelligent and engaging coverage of international affairs.

Ian is credited with bringing the craft of political risk to financial markets, creating Wall Street's first global political risk index (GPRI), and for establishing political risk as an academic discipline. His definition of emerging markets— "those countries where politics matters at least as much as economics for market outcomes”—has become an industry standard. “G-Zero,” his term for a global power vacuum in which no country is willing and able to set the international agenda, is widely used by policymakers and thought leaders.

A prolific writer, Ian is the author of eleven books, including two New York Times bestsellers, “Us vs Them: The Failure of Globalism” which examines the rise of populism across the world, and his latest book “The Power of Crisis: How Three Threats—and Our Response—Will Change the World” which details a trio of looming global crises (health emergencies, climate change, and technological revolution) and outlines how governments, corporations, and concerned citizens can use these crises to create global prosperity and opportunity.

Jimena Viveros currently serves as the Chief of Staff and Head Legal Advisor to Justice Loretta Ortiz at the Mexican Supreme Court. Her prior roles include national leadership positions at the Federal Judicial Council, the Ministry of Security, and the Ministry of Finance, where she held the position of Director General. Jimena is a lawyer and AI expert, and possesses a broad and diverse international background. She is in the final stages of completing her Doctoral thesis, which focuses on the impact of AI and autonomous weapons on international peace and security law and policy, providing concrete propositions to achieve global governance from diverse legal perspectives. Her extensive work in AI and other legal domains has been widely published and recognized.

In the episode, Richie, Ian and Jimena cover what the UN's AI Advisory Body was set up for, the opportunities and risks of AI, how AI impacts global inequality, key principles of AI governance, the implementation of that governance, the future of AI in politics and global society, and much more. 

Links Mentioned in the Show:

• UN Interim Report: Governing AI for Humanity
• AI for Sustainable Development Goals
• The Power of Crisis: How Three Threats – and Our Response – Will Change the World by Ian Bremmer
• WEF Davos 2024: Sam Altman on the future of AI
• [Course] Artificial Intelligence (AI) Strategy
• Related Episode: What to Expect from AI in 2024 with Craig S. Smith, Host of the Eye on A.I Podcast
• Sign up to RADAR: The Analytics Edition

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Remarkable people walk among us. Some of us may be remarkable ourselves. But none of us start out remarkable. The journey to becoming a person that makes a difference in the world is never easy, as with any story that includes a hero, there are struggles, tests and moments of self-doubt. Remarkable people overcome these feats, and when they are in a position to, they give back. But what kind of mindset do these people have, how do they make decisions? What keeps them on their path towards becoming remarkable. 

Guy Kawasaki is the chief evangelist of Canva and the creator of Guy Kawasaki’s Remarkable People podcast. He is an executive fellow of the Haas School of Business (UC Berkeley), and adjunct professor of the University of New South Wales. He was the chief evangelist of Apple and a trustee of the Wikimedia Foundation. He has written Wise Guy, The Art of the Start 2.0, The Art of Social Media, Enchantment, and eleven other books. Kawasaki has a BA from Stanford University, an MBA from UCLA, and an honorary doctorate from Babson College.

In the episode, Richie and Guy explore the concept of being remarkable, growth, grit and grace, the importance of experiential learning, imposter syndrome, finding your passion, how to network and find remarkable people, dealing with failure, management and encouraging growth, work-life balance, measuring success through benevolent impact and much more. 

Links Mentioned in the Show:

• Think Remarkable by Guy Kawasaki
• Guy Kawasaki’s Remarkable People
• Connect with Guy on Linkedin
• Canva
• The Four Agreements: A Practical Guide to Personal Freedom by Don Miguel Ruiz
• How to Change: The Science of Getting from Where You Are to Where You Want to Be by Katy Milkman
• Related Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision Scientist
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We’ve heard so much about the value and capabilities of generative AI over the past year, and we’ve all become accustomed to the chat interfaces of our preferred models. One of the main concerns many of us have had has been privacy. Is OpenAI keeping the data and information I give to ChatGPT secure? One of the touted solutions to this problem is running LLMs locally on your own machine, but with the hardware cost that comes with it, running LLMs locally has not been possible for many of us. That might now be starting to change.

Nuri Canyaka is VP of AI Marketing at Intel. Prior to Intel, Nuri spent 16 years at Microsoft, starting out as a Technical Evangelist, and leaving the organization as the Senior Director of Product Marketing. He ran the GTM team that helped generate adoption of GPT in Microsoft Azure products.

La Tiffaney Santucci is Intel’s AI Marketing Director, specializing in their Edge and Client products. La Tiffaney has spent over a decade at Intel, focussing on partnerships with Dell, Google Amazon and Microsoft. 

In the episode, Richie, Nuri and La Tiffaney explore AI’s impact on marketing analytics, the adoptions of AI in the enterprise, how AI is being integrated into existing products, the workflow for implementing AI into business processes and the challenges that come with it, the importance of edge AI for instant decision-making in uses-cases like self-driving cars, the emergence of AI engineering as a distinct field of work, the democratization of AI, what the state of AGI might look like in the near future and much more. 

About the AI and the Modern Data Stack DataFramed Series

This week we’re releasing 4 episodes focused on how AI is changing the modern data stack and the analytics profession at large. The modern data stack is often an ambiguous and all-encompassing term, so we intentionally wanted to cover the impact of AI on the modern data stack from different angles. Here’s what you can expect:

• Why the Future of AI in Data will be Weird with Benn Stancil, CTO at Mode & Field CTO at ThoughtSpot — Covering how AI will change analytics workflows and tools 
• How Databricks is Transforming Data Warehousing and AI with Ari Kaplan, Head Evangelist & Robin Sutara, Field CTO at Databricks — Covering Databricks, data intelligence and how AI tools are changing data democratization
• Adding AI to the Data Warehouse with Sridhar Ramaswamy, CEO at Snowflake — Covering Snowflake and its uses, how generative AI is changing the attitudes of leaders towards data, and how to improve your data management
• Accelerating AI Workflows with Nuri Cankaya, VP of AI Marketing & La Tiffaney Santucci, AI Marketing Director at Intel — Covering AI’s impact on marketing analytics, how AI is being integrated into existing products, and the democratization of AI

Links Mentioned in the Show:

• Intel OpenVINO™ toolkit
• Intel Developer Clouds for Accelerated Computing
• AWS Re:Invent
• [Course] Implementing AI Solutions in Business
• Related Episode: Intel CTO Steve Orrin on How Governments Can Navigate the Data & AI Revolution
• Sign up to RADAR: The Analytics Edition

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Snowflake has been foundational in the data space for years. In the mid-2010s, the platform was a major driver of moving data to the cloud. More recently, it's become apparent that combining data and AI in the cloud is key to accelerating innovation. Snowflake has been rapidly adding AI features to provide value to the modern data stack, but what’s really been going on under the hood?

At the time of recording, Sridhar Ramaswamy was the SVP of AI at Snowflake, being appointed CEO at Snowflake in February 2024. Sridhar was formerly Co-Founder of Neeva, acquired in 2023 by Snowflake. Before founding Neeva, Ramaswamy oversaw Google's advertising products, including search, display, video advertising, analytics, shopping, payments, and travel. He joined Google in 2003 and was part of the growth of AdWords and Google's overall advertising business. He spent more than 15 years at Google, where he started as a software engineer and rose to SVP of Ads & Commerce. 

In the episode, Richie and Sridhar explore Snowflake and its uses, how generative AI is changing the attitudes of leaders towards data, how NLP and AI have impacted enterprise business operations as well as new applications of AI in an enterprise environment, the challenges of enterprise search, the importance of data quality, management and the role of semantic layers in the effective use of AI, a look into Snowflakes products including Snowpilot and Cortex, the collaboration required for successful data and AI projects, advice for organizations looking to improve their data management and much more.    

About the AI and the Modern Data Stack DataFramed Series

This week we’re releasing 4 episodes focused on how AI is changing the modern data stack and the analytics profession at large. The modern data stack is often an ambiguous and all-encompassing term, so we intentionally wanted to cover the impact of AI on the modern data stack from different angles. Here’s what you can expect:

• Why the Future of AI in Data will be Weird with Benn Stancil, CTO at Mode & Field CTO at ThoughtSpot — Covering how AI will change analytics workflows and tools 
• How Databricks is Transforming Data Warehousing and AI with Ari Kaplan, Head Evangelist & Robin Sutara, Field CTO at Databricks — Covering Databricks, data intelligence and how AI tools are changing data democratization
• Adding AI to the Data Warehouse with Sridhar Ramaswamy, CEO at Snowflake — Covering Snowflake and its uses, how generative AI is changing the attitudes of leaders towards data, and how to improve your data management
• Accelerating AI Workflows with Nuri Cankaya, VP of AI Marketing & La Tiffaney Santucci, AI Marketing Director at Intel — Covering AI’s impact on marketing analytics, how AI is being integrated into existing products, and the democratization of AI

Links Mentioned in the Show:

• Snowflake
• Snowflake acquires Neeva to accelerate search in the Data Cloud through generative AI
• Use AI in Seconds with Snowflake Cortex
• [Course] Introduction to Snowflake
• Related Episode: Why AI will Change Everything—with Former Snowflake CEO, Bob Muglia
• Sign up to RADAR: The Analytics Edition

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Databricks started out as a platform for using Spark, a big data analytics engine, but it's grown a lot since then. Databricks now allows users to leverage their data and AI projects in the same place, ensuring ease of use and consistency across operations. The Databricks platform is converging on the idea of data intelligence, but what does this mean, how will it help data teams and organizations, and where does AI fit in the picture?

Ari is Databricks’ Head of Evangelism and "The Real Moneyball Guy" - the popular movie was partly based on his analytical innovations in Major League Baseball. He is a leading influencer in analytics, artificial intelligence, data science, and high-growth business innovation. Ari was previously the Global AI Evangelist at DataRobot, Nielsen’s regional VP of Analytics, Caltech Alumni of the Decade, President Emeritus of the worldwide Independent Oracle Users Group, on Intel’s AI Board of Advisors, Sports Illustrated Top Ten GM Candidate, an IBM Watson Celebrity Data Scientist, and on the Crain’s Chicago 40 Under 40. He's also written 5 books on analytics, databases, and baseball.

Robin is the Field CTO at Databricks. She has consulted with hundreds of organizations on data strategy, data culture, and building diverse data teams. Robin has had an eclectic career path in technical and business functions with more than two decades in tech companies, including Microsoft and Databricks. She also has achieved multiple academic accomplishments from her juris doctorate to a masters in law to engineering leadership. From her first technical role as an entry-level consumer support engineer to her current role in the C-Suite, Robin supports creating an inclusive workplace and is the current co-chair of Women in Data Safety Committee. She was also recognized in 2023 as a Top 20 Women in Data and Tech, as well as DataIQ 100 Most Influential People in Data.

In the episode, Richie, Ari, and Robin explore Databricks, the application of generative AI in improving services operations and providing data insights, data intelligence, and lakehouse technology, the wide-ranging applications of generative AI, how AI tools are changing data democratization, the challenges of data governance and management and how tools like Databricks can help, how jobs in data and AI are changing and much more. 

About the AI and the Modern Data Stack DataFramed Series

This week we’re releasing 4 episodes focused on how AI is changing the modern data stack and the analytics profession at large. The modern data stack is often an ambiguous and all-encompassing term, so we intentionally wanted to cover the impact of AI on the modern data stack from different angles. Here’s what you can expect:

• Why the Future of AI in Data will be Weird with Benn Stancil, CTO at Mode & Field CTO at ThoughtSpot — Covering how AI will change analytics workflows and tools 
• How Databricks is Transforming Data Warehousing and AI with Ari Kaplan, Head Evangelist & Robin Sutara, Field CTO at Databricks — Covering Databricks, data intelligence and how AI tools are changing data democratization
• Adding AI to the Data Warehouse with Sridhar Ramaswamy, CEO at Snowflake — Covering Snowflake and its uses, how generative AI is changing the attitudes of leaders towards data, and how to improve your data management
• Accelerating AI Workflows with Nuri Cankaya, VP of AI Marketing & La Tiffaney Santucci, AI Marketing Director at Intel — Covering AI’s impact on marketing analytics, how AI is being integrated into existing products, and the democratization of AI

Links Mentioned in the Show:

• Databricks
• Delta Lake
• MLflow
• Data Lakehouse Architecture by Databricks
• Building and Customizing GenAI with Databricks: LLMs and Beyond
• [Course] Understanding Modern Data Architecture
• [Course] Introduction to Databricks
• [Course] Introduction to MLflow
• Related Episode: Optimizing Cloud Data Warehouses with Salim Syed, VP, Head of Engineering at Capital One Software
• Sign up to RADAR: The Analytics Edition

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One of the biggest surprises of the generative AI revolution over the past 2 years lies in the counter-intuitiveness of its most successful use cases. Counter to most predictions made about AI years ago, AI-assisted coding, specifically AI-assisted data work, has been surprisingly one of the biggest killer apps of generative AI tools and copilots. However, what happens when we take this notion even further? How will analytics workflows look like when generative AI tools can also assist us in problem-solving? What type of analytics use cases can we expect to operationalize, and what tools can we expect to work with when AI systems can provide scalable qualitative data instead of relying on imperfect quantitative proxies? Today’s guest calls this future “weird”. 

Benn Stancil is the Field CTO at ThoughtSpot. He joined ThoughtSpot in 2023 as part of its acquisition of Mode, where he was a Co-Founder and CTO. While at Mode, Benn held roles leading Mode’s data, product, marketing, and executive teams. He regularly writes about data and technology at benn.substack.com. Prior to founding Mode, Benn worked on analytics teams at Microsoft and Yammer.

Throughout the episode, Benn and Adel talk about the nature of AI-assisted analytics workflows, the potential for generative AI in assisting problem-solving, how he imagines analytics workflows to look in the future, and a lot more. 

About the AI and the Modern Data Stack DataFramed Series

This week we’re releasing 4 episodes focused on how AI is changing the modern data stack and the analytics profession at large. The modern data stack is often an ambiguous and all-encompassing term, so we intentionally wanted to cover the impact of AI on the modern data stack from different angles. Here’s what you can expect:

• Why the Future of AI in Data will be Weird with Benn Stancil, CTO at Mode & Field CTO at ThoughtSpot — Covering how AI will change analytics workflows and tools 
• How Databricks is Transforming Data Warehousing and AI with Ari Kaplan, Head Evangelist & Robin Sutara, Field CTO at Databricks — Covering Databricks, data intelligence and how AI tools are changing data democratization
• Adding AI to the Data Warehouse with Sridhar Ramaswamy, CEO at Snowflake — Covering Snowflake and its uses, how generative AI is changing the attitudes of leaders towards data, and how to improve your data management
• Accelerating AI Workflows with Nuri Cankaya, VP of AI Marketing & La Tiffaney Santucci, AI Marketing Director at Intel — Covering AI’s impact on marketing analytics, how AI is being integrated into existing products, and the democratization of AI

Links Mentioned in the Show:

• Mode Analytics
• ThoughtSpot acquires Mode: Empowering data teams to bring Generative AI to BI
• Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are
• [Course] Generative AI for Business
• [Skill Track] SQL Fundamentals
• Related Episode: The Future of Marketing Analytics with Cory Munchbach, CEO at BlueConic
• Sign up to RADAR: The Analytics Edition

New to DataCamp?

• Learn on the go using the DataCamp mobile app
• Empower your business with world-class data and AI skills with DataCamp for business

Just as many of us have been using generative AI tools to make us more productive at work, so have bad actors. Generative AI makes it much easier to create fake yet convincing text and images that can be used to deceive and harm. We’ve already seen lots of high-profile attempts to leverage AI in phishing campaigns, and this is putting more pressure on cybersecurity teams to get ahead of the curve and combat these new forms of threats. However, AI is also helping those that work in cybersec to be more productive and better equip themselves to create new forms of defense and offense. 

Brian Murphy is a founder, CEO, entrepreneur and investor. He founded and leads ReliaQuest, the force multiplier of security operations and one of the largest and fastest-growing companies in the global cybersecurity market. ReliaQuest increases visibility, reduces complexity, and manages risk with its cloud-native security operations platform, GreyMatter. Murphy grew ReliaQuest from a boot-strapped startup to a high-growth unicorn with a valuation of over $1 billion, more than 1,000 team members, and more than $350 million in growth equity with firms such as FTV Capital and KKR Growth. 

In the full episode, Adel and Brian cover the evolution of cybersecurity tools, the challenges faced by cybersecurity teams, types of cyber threats, how generative AI can be used both defensively and offensively in cybersecurity, how generative AI tools are making cybersecurity professionals more productive, the evolving role of cybersecurity professionals, the security implications of deploying AI models, the regulatory landscape for AI in cybersecurity and much more. 

Links Mentioned in the Show:

• ReliaQuest
• ReliaQuest Blog
• IBM finds that ChatGPT can generate phishing emails nearly as convincing as a human
• Information Sharing and Analysis Centers (ISACs)
• [Course] Introduction to Data Security
• Related episode: Data Security in the Age of AI with Bart Vandekerckhove, Co-founder at Raito

New to DataCamp?

• Learn on the go using the DataCamp mobile app

Empower your business with world-class data and AI skills with DataCamp for business

We are in a Generative AI hype cycle. Every executive looking at the potential generative AI today is probably thinking about how they can allocate their department's budget to building some AI use cases. However, many of these use cases won't make it into production.

In a similar vein, the hype around machine learning in the early 2010s led to lots of hype around the technology, but a lot of the value did not pan out. Four years ago, VentureBeat showed that 87% of data science projects did not make it into production. And in a lot of ways, things haven’t gotten much better. And if we don't learn why that is the case, generative AI could be destined to a similar fate. 

Eric Siegel, Ph.D., is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series and its new sister, Generative AI World, the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery,” executive editor of The Machine Learning Times, and a frequent keynote speaker. He wrote the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, as well as The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. Eric’s interdisciplinary work bridges the stubborn technology/business gap. At Columbia, he won the Distinguished Faculty award when teaching graduate computer science courses in ML and AI. Later, he served as a business school professor at UVA Darden. Eric also publishes op-eds on analytics and social justice.

In the episode, Adel and Eric explore the reasons why machine learning projects don't make it into production, the BizML Framework or how to bring business stakeholders into the room when building machine learning use cases, the skill gap between business stakeholders and data practitioners, use cases of organizations have leveraged machine learning for operational improvements, what the previous machine learning hype cycle can teach us about generative AI and a lot more. 

Links Mentioned in the Show:

• The AI Playbook: Mastering the Rare Art of Machine Learning Deployment by Eric Siegel
• Generating ROI with AI
• BizML Cheat Sheet
• Gooder
• Survey: Machine Learning Projects Still Routinely Fail to Deploy
• [Skill Track] MLOps Fundamentals

We don’t think about every decision we make. Some decisions are easy and intuitive, others can be riddled with doubt. In a business setting, decision-making is often crucial, and with that comes pressure to ensure we’re making the right decisions in the best way possible. We can often accompany decision-making with context, providing a narrative for how we might approach a decision, citing what data and insights have had significant input into our choices. But how do we approach storytelling and decision-making to breed success? There’s probably no better person to guide us through the ins and outs of decision-making than the co-author of Business Storytelling For Dummies.

Lori L. Silverman is the owner of Partners for Progress, a management consulting firm. As a business strategist, she has consulted with organizations in fifteen industries including financial services, insurance, manufacturing and petroleum companies, government entities, and professional associations. As a keynote speaker, Lori has positively impacted the lives of thousands of people. She has appeared on over fifty radio and television shows to speak about using stories in the workplace and is the co-author of Critical SHIFT and Stories Trainers Tell. 

She’s a pioneer in the business storytelling field, author of five books, and is known worldwide for her work in collaborative data-informed decision-making.

In the episode, Richie and Lori cover common problems in business decision-making, connecting decision-making to business processes, analytics and decision-making, integrating data practitioners and decision-makers, the role of data visualization and narrative storytelling, the SMARTER decision-making methodology, the importance of intuition, challenges faced when applying decision-making methodologies and much more. 

Links Mentioned in the Show

• Business Storytelling For Dummies by Karen Dietz and Lori Silverman
• Connect with Lori on Linkedin
• Level Up with Lori
• Books by Lori
• The SMARTER Framework for Data-Informed Decision Making
• Monetizing Data Through Informed, Collaborative Decision Making
• The Increasingly Vital Role of Business Storytelling in Leadership
• Pre-Suasion: A Revolutionary Way to Influence and Persuade by Robert Cialdini
• [Skill Track] Data Storytelling

Arianna Huffington, co-founder of The Huffington Post, woke up in a pool of blood nursing a broken cheekbone after collapsing at her desk in 2007. Various stresses and pressures in her life had manifested themself into an episode of extreme mental exhaustion. This event was the catalyst for her to write a book on well-being as well as start the behavioral-change company Thrive Global. Many of us have, or will, experience burnout at some point. The build-up of stress, negative emotions, and internal tension may not result in the same shocking scene Huffington found herself in, but its effects are serious and permeate not just through our profession but into our home life as well. Stress and burnout are especially prevalent in working environments where there is an emphasis on urgency, and with the constant advancements we’ve seen in the data & AI sphere in the past year, leaders and practitioners working in the data space will need to know how to recognize the symptoms of burnout and create workplace cultures that prevent burnout in the first place.

Jen Fisher is Deloitte’s human sustainability leader. Previously, Fisher served as Deloitte’s first-ever chief well-being officer. She’s also a TEDx speaker, coauthor of the book, Work Better Together: How to Cultivate Strong Relationships to Maximize Well-Being and Boost Bottom Lines, editor-at-large for Thrive Global, and host of the “WorkWell” podcast series.

In the episode, Jen and Adel cover Jen’s own personal experience with burnout, the role of a Chief Wellbeing Officer, the impact of work on our overall well-being, the patterns that lead to burnout, defining well-being in the workplace, technology’s impact on our well-being, psychological safety in the workplace, how managers and leaders can looking after themselves and their teams, the future of human sustainability in the workplace and much more. 

Links Mentioned in the Show:

• Work Better Together: How to Cultivate Strong Relationships to Maximize Well-Being and Boost Bottom Lines
• Jen’s TED Talk: The Future of Work
• Brené Brown: Clear Is Kind. Unclear Is Unkind.
• What Is Psychological Safety?

2023 was a huge year for data and AI. Everyone who didn't live under a rock started using generative AI, and much was teased by companies like OpenAI, Microsoft, Google and Meta. We saw the millions of different use cases generative AI could be applied to, as well as the iterations we could expect from the AI space, such as connected multi-modal models, LLMs in mobile devices and formal legislation. But what has this meant for DataCamp? What will we do to facilitate learners and organizations around the world in staying ahead of the curve?

In this special episode of DataFramed, we sit down with DataCamp Co-Founders Jo Cornelissen, Chief Executive Officer, and Martijn Theuwissen, Chief Operating Officer, to discuss their expectations for data & AI in 2024.

In the episode, Richie, Jo and Martijn discuss generative AI's mainstream impact in 2023, the broad use cases of generative AI and skills required to utilize it effectively, trends in AI and software development, how the programming languages for data are evolving, new roles in data & AI, the job market and skill development in data science and their predictions for 2024.

Links Mentioned in the Show:

• Free course - Become an AI Developer
• Webinar - Data & AI Trends & Predictions 2024

Courses:

• Artificial Intelligence (AI) Strategy
• Generative AI for Business
• Implementing AI Solutions in Business
• AI Ethics

In January 2024, six activists were identified by British Police in London, suspected of planning to disrupt the London Stock Exchange through a lock-in. In an attempt to prevent the building from opening for trading. Despite the foiled attempt, the strategy for this protest was inherently flawed. Trading no longer requires a busy exchange with raucous shouting and phone calls to facilitate the flow of investment around the world. Nowadays, machines can trade at a fraction of a second, ingesting huge amounts of real-time data to execute finely tuned-trading strategies. But who programs these trading machines, how do we assess risk when trading at such a high volume and in such short periods of time?

Anthony Markham is Vice President, Quantitative Developer at Deutsche Bank. With a background in Aerospace and Software Engineering, Anthony has experience in Data Science, facial recognition research, tertiary education, and Quantitative Finance, developing mostly in Python, Julia, and C++. When not working, Anthony enjoys working on personal projects, flying aircraft, and playing sports.

In the episode, Richie and Anthony cover what algorithmic trading is, the use of machine learning techniques in trading strategies, the challenges of handling large datasets with low latency, risk management in algorithmic trading, data analysis techniques for handling time series data, the challenges of deep neural networks in trading, the diverse roles and skills of those who work in algorithmic trading and much more. 

Links Mentioned in the Show:

• Flash crash of 2010
• KDB+
• Q Query Language
• [Course] Quantitative Risk Management in Python
• Understanding Value at Risk (VaR)

Cookies were invented to help online shoppers, simply as an identifier so that online carts weren’t lost to the ether. Marketers quickly saw the power of using cookies for more than just maintaining session states, and moved to use them as part of their targeted advertising. Before we knew it, our online habits were being tracked, without our clear consent. The unregulated cookie-boom lasted until 2018 with the advent of GDPR and the CCPA. Since then marketers have been evolving their practices, looking for alternatives to cookie-tracking that will perform comparatively, and with the cookie being phased out in 2024, technologies like fingerprinting and new privacy-centric marketing strategies will play a huge role in how products meet users in the future. 

Cory Munchbach has spent her career on the cutting edge of marketing technology and brings years working with Fortune 500 clients from various industries to BlueConic. Prior to BluConic, she was an analyst at Forrester Research where she covered business and consumer technology trends and the fast-moving marketing tech landscape. A sought-after speaker and industry voice, Cory’s work has been featured in Financial Times, Forbes, Raconteur, AdExchanger, The Drum, Venture Beat, Wired, AdAge, and Adweek. A life-long Bostonian, Cory has a bachelor’s degree in political science from Boston College and spends a considerable amount of her non-work hours on various volunteer and philanthropic initiatives in the greater Boston community. 

In the episode, Richie and Cory cover successful marketing strategies and their use of data, the types of data used in marketing, how data is leveraged during different stages of the customer life cycle, the impact of privacy laws on data collection and marketing strategies, tips on how to use customer data while protecting privacy and adhering to regulations, the importance of data skills in marketing, the future of marketing analytics and much more.

Links Mentioned in the Show:

• BlueConic
• Mattel Creations
• Google: Prepare for third-party cookie restrictions
• Data Clean Rooms
• [Course] Marketing Analytics for Business

We’ve never been more aware of the word ‘hallucinate’ in a professional setting. Generative AI has taught us that we need to work in tandem with personal AI tools when we want accurate and reliable information. We’ve also seen the impacts of bias in AI systems, and why trusting outputs at face value can be a dangerous game, even for the largest tech organizations in the world. It seems we could be both very close and very far away from being able to fully trust AI in a work setting. To really find out what trustworthy AI is, and what causes us to lose trust in an AI system, we need to hear from someone who’s been at the forefront of the policy and tech around the issue. 

Alexandra Ebert is an expert in data privacy and responsible AI. She works on public policy issues in the emerging field of synthetic data and ethical AI. Alexandra is on Forbes ‘30 Under 30’ list and has an upcoming course on DataCamp! In addition to her role as Chief Trust Officer at MOSTLY AI, Alexandra is the chair of the IEEE Synthetic Data IC expert group and the host of the Data Democratization podcast.

In the episode, Richie and Alexandra explore the importance of trust in AI, what causes us to lose trust in AI systems and the impacts of a lack of trust, AI regulation and adoption, AI decision accuracy and fairness, privacy concerns in AI, handling sensitive data in AI systems, the benefits of synthetic data, explainability and transparency in AI, skills for using AI in a trustworthy fashion and much more. 

Links Mentioned in the Show:

• MOSTLY.AI
• Microsoft Research on AI Fairness
• Using Synthetic Data for Machine Learning & AI in Python
• [Course] AI Ethics

Your data project doesn't end once you have results. In order to have impact, you need to communicate those results to others. Presentations filled with endless tables and technical jargon can easily become tedious, leading your audience to lose interest or misunderstand your point.

Data storytelling provides a solution to this: by creating a narrative around your results you can increase engagement and understanding from your audience. This is an art, and there are so many factors that contribute to visualizing data and creating a compelling story, it can be overwhelming. However, with the right approach, creating data stories can become second nature. In this special episode of DataFramed, we join forces with the Present Beyond Measure podcast to glean the best data presentation practices from one of the leading voices in the space.

Lea Pica host of the Founder and Host of the Present Beyond Measure podcast and is a seasoned digital analytics practitioner, social media marketer and blogger with over 11 years of experience building search marketing and digital analytics practices for companies like Scholastic, Victoria’s Secret and Prudential.

Present Beyond Measure’s mission is to bring their teachings to the digital marketing and web analytics communities, and empower anyone responsible for presenting data to an audience.

In the full episode, Richie and Lea cover the full picture of data presentation, how to understand your audience, leverage hollywood storytelling, data storyboarding and visualization, the use of imagery in presentations, cognitive load management, the use of throughlines in presentations, how to improve your speaking and engagement skills, data visualization techniques in business setting and much more. 

Links Mentioned in the Show:

• Present Beyond Measure
• Lea’s Book
• Connect with Lea on Linkedin
• Hollywood Storytelling
• [Course] Data Storytelling Concepts

Data used to be the exhaust of our work activities, until we started seeing the value it can provide. Today, data is a strategic asset, used to gain a competitive advantage and well guarded from those that might use it to harm others. With this change in attitude, how we access and safeguard our data has improved massively. However, data breaches are not a thing of the past, and with the advent of AI, many new techniques for maliciously accessing data are being created. With the extra importance of data security, it is always pertinent to iterate on how we keep our data safe, and how we manage who has access to it. 

Bart Vandekerckhove is the co-founder and CEO at Raito. Raito is on a mission to bring back balance in data democratization and data security. Bart helps data teams save time on data access management, so they can focus on innovation. As the former PM Privacy at Collibra, Bart has seen first hand how slow data access management processes can harm progress. 

In the full episode, Richie and Bart explore the importance of data access management, the roles involved in data access including senior management’s role in data access, data security and privacy tools, the impact of AI on data security, how culture feeds into data security, the challenges of a creating a good data access management culture, common mistakes organizations make, advice for improving data security and much more. 

Links Mentioned in the Show:

• Raito
• Capital One Data Breach
• Optus Data Breach
• IAM
• Course: Introduction to Data Privacy

We learned so much about generative AI and its impact for people and organizations in 2023, we must anticipate many more innovations in the data and AI space 2024. One of the best places to look for this information is through the wisdom of those that spend their time with the Fortune 1000 leaders that are helping shape data and AI practices. Wavestone’s annual Data and AI Executive Leadership Survey is a great way to gain insight into thoughts in current practices, as well as understand what to expect from business leaders and organizations in the near future. In this episode, we speak to the author of the survey. 

Randy Bean is a start-up business founder, CEO, industry thought leader, author, and speaker in the field of data-driven business leadership.  He serves as Innovation Fellow, Data Strategy for Paris-based consultancy Wavestone. Randy is the creator of the Data and AI Leadership Executive Survey discussed in today's episode. He is the author of the bestselling "Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI", and a current contributor to Forbes, Harvard Business Review, and MIT Sloan Management Review.  

In the episode, Richie and Randy explore the 2024 Data and AI Leadership Executive Survey, the impact of generative AI in 2023 and what to expect from it in 2024, the state of generative AI implementation in organizations, healthcare and AI, including examples of generative AI outperforming human doctors, the evolving responsibilities of CDOs, the increasing importance of data-driven decision-making in organizations, the barriers to becoming data-driven, insights on data skills and the generational shift towards more data-savvy business leaders, as well as much more. 

Links Mentioned in the Show:

• Data and AI Leadership Executive Survey
• Randy’s Articles in Forbes
• Ally Financial
• Responsible AI Institute
• Course: Implementing AI Solutions in Business

Regardless of profession, the work we do leaves behind a trace of actions that help us achieve our goals. This is especially true for those that work with data. For large enterprises where there are seemingly countless processes happening at any one time, keeping track of these processes is crucial. Given the scale of these processes, one small efficiency gain can leads to a staggering amount of time and money saved. Process mining is a data-driven approach to process analysis that uses event logs to extract process-related information. It can separate inferred facts, from exact truths, and uncover what really happens in a variety of operations. 

Wil van der Aalst is a full professor at RWTH Aachen University, leading the Process and Data Science (PADS) group. He is also the Chief Scientist at Celonis, part-time affiliated with the Fraunhofer FIT, and a member of the Board of Governors of Tilburg University. 

His research interests include process mining, Petri nets, business process management, workflow management, process modeling, and process analysis. Wil van der Aalst has published over 275 journal papers, 35 books (as author or editor), 630 refereed conference/workshop publications, and 85 book chapters.

Cong Yu leads the CeloAI group at Celonis focusing on bringing advanced AI technologies to EMS products, building up capabilities for their knowledge platform, and ultimately helping enterprises in reducing process inefficiencies and achieving operational excellence.

Previously, Cong was Principal (Research) Scientist / Research Director at Google Research NYC from September 2010 to July 2022, leading the NYSD/Beacon Research Group, and also taught at NYU Courant Institute of Mathematical Sciences. 

In the episode, Wil, Cong, and Richie explore process mining and its development over the past 25 years, the differences between process mining and ML, AI, and data mining, popular use cases of process mining, adoption from large enterprises like BMW, HP, and Dell, the requirements for an effective process mining system, the role of predictive analytics and data engineering in process mining, how to scale process mining systems, prospects within the field and much more.

Links Mentioned in the Show:

• Celonis
• Gartner’s Magic Quadrant for Process Mining
• PM4Py
• Process Query Language (PQL)
• [Couse] Business Process Analytics in R

There are a few caveats to using generative AI tools, those caveats have led to a few tips that have quickly become second nature to those that use LLMs like ChatGPT. The main one being: have the domain knowledge to validate the output in order to avoid hallucinations. Hallucinations are one of the weak spots for LLMs due to the nature of the way they are built, as they are trained to correlate data in order to predict what might come next in an incomplete sequence. Does this mean that we’ll always have to be wary of the output of AI products, with the expectation that there is no intelligent decision-making going on under the hood? Far from it. Causal AI is bound by reason—rather than looking at correlation, these exciting systems are able to focus on the underlying causal mechanisms and relationships. As the AI field rapidly evolves, Causal AI is an area of research that is likely to have a huge impact on a huge number of industries and problems. 

Paul Hünermund is an Assistant Professor of Strategy and Innovation at Copenhagen Business School. In his research, Dr. Hünermund studies how firms can leverage new technologies in the space of machine learning and artificial intelligence such as Causal AI for value creation and competitive advantage. His work explores the potential for biases in organizational decision-making and ways for managers to counter them. It thereby sheds light on the origins of effective business strategies in markets characterized by a high degree of technological competition and the resulting implications for economic growth and environmental sustainability. 

His work has been published in The Journal of Management Studies, the Econometrics Journal, Research Policy, Journal of Product Innovation Management, International Journal of Industrial Organization, MIT Sloan Management Review, and Harvard Business Review, among others. 

In the full episode, Richie and Paul explore Causal AI, its differences when compared to other forms of AI, use cases of Causal AI in fields like drug development, marketing, manufacturing, and defense. They also discuss how Causal AI contributes to better decision-making, the role of domain experts in getting accurate results, what happens in the early stages of Causal AI adoption, exciting new developments within the Causal AI space and much more. 

Links Mentioned in the Show:

• Causal Data Science in Business
• Causal AI by causaLens
• Intro to Causal AI Using the DoWhy Library in Python
• Lesson: Inference (causal) models

Over the past year, we’ve seen a full hype cycle of hysteria and discourse surrounding generative AI. It almost seems difficult to think back to a time when no one had used ChatGPT. We are in the midst of the fourth industrial revolution, and technology is moving rapidly. Better performing and more capable models are being released at a stunning rate, and with the growing presence of multimodal AI, can we expect another whirlwind year that vastly changes the state of play within AI again? Who might be able to provide insight into what is to come in 2024?

Craig S. Smith is an American journalist, former executive of The New York Times, and host of the podcast Eye on AI. Until January 2000, he wrote for The Wall Street Journal, most notably covering the rise of the religious movement Falun Gong in China. He has reported for the Times from more than 40 countries and has covered several conflicts, including the 2001 invasion of Afghanistan, the 2003 war in Iraq, and the 2006 Israeli-Lebanese war. He retired from the Times in 2018 and now writes about artificial intelligence for the Times and other publications. He was a special Government employee for the National Security Commission on Artificial Intelligence until the commission's end in October 2021. 

In the episode, Richie and Craig explore the 2023 advancements in generative AI, such as GPT-4, and the evolving roles of companies like Anthropic and Meta, practical AI applications for research and image generation, challenges in large language models, the promising future of world models and AI agents, the societal impacts of AI, the issue of misinformation, computational constraints, and the importance of AI literacy in the job market, the transformative potential of AI in various sectors and much more. 

Links Mentioned in the Show:

• Eye on AI
• Wayve
• Anthropic
• Cohere
• Midjourney
• Yann Lecun

Effective data management has become a cornerstone of success in our digital era. It involves not just collecting and storing information but also organizing, securing, and leveraging data to drive progress and innovation. Many organizations turn to tools like Snowflake for advanced data warehousing capabilities. However, while Snowflake enhances data storage and access, it's not a complete solution for all data management challenges. To address this, tools like Capital One’s Slingshot can be used alongside Snowflake, helping to optimize costs and refine data management strategies.

Salim Syed is a VP, Head of engineering for Capital One Slingshot product. He led Capital One’s data warehouse migration to AWS and is a specialist in deploying Snowflake to a large enterprise. Salim’s expertise lies in developing Big Data (Lake) and Data Warehouse strategy on the public cloud. He leads an organization of more than 100 data engineers, support engineers, DBAs and full stack developers in driving enterprise data lake, data warehouse, data management and visualization platform services.

Salim has more than 25 years of experience in the data ecosystem. His career started in data engineering where he built data pipelines and then moved into maintenance and administration of large database servers using multi-tier replication architecture in various remote locations. He then worked at CodeRye as a database architect and at 3M Health Information Systems as an enterprise data architect. Salim has been at Capital One for the past six years.

In this episode, Adel and Salim explore cloud data management and the evolution of Slingshot into a major multi-tenant SaaS platform, the shift from on-premise to cloud-based data governance, the role of centralized tooling, strategies for effective cloud data management, including data governance, cost optimization, and waste reduction as well as insights into navigating the complexities of data infrastructure, security, and scalability in the modern digital era.

Links Mentioned in the Show:

• Capital One Slingshot
• Snowflake
• Course: Introduction to Data Warehousing
• Course: Introduction to Snowflake

There's often a debate in technology ethics on whether technology is neutral or not. On one hand, critics have rightfully pointed out examples of technology exacerbating the climate crisis, amplifying bias as we've seen in our recent episode with Dr. Joy Buolamwini, or contributing to the spread of misinformation and disinformation. Conversely, we cannot deny the many wonderful things technology has given us, from better healthcare outcomes, to the ability to communicate wherever we are in the world, or to elevate the quality of life of everyone on the planet.

It is this duality, that today's guest, Marga Hoek, points to as to why technology is neutral, and why it is in our hands to use it for good.

Marga Hoek is a true visionary on sustainable business, capital, and technology and a successful business leader. As a three-time CEO, Board Member, Chair, and Founder of Business for Good, she applies her vision on how business can be a true force for good in practice. As a bestselling and multi-award-winning author, member of Thinkers50, and one of the most in-demand speakers on sustainable business and ESG investment, Marga Hoek has inspired many companies and leaders worldwide. She is also appreciated as a global voice for G20 and G7 Intergovernmental forums, international climate meetings and COPs, and many other prestigious global conferences. 

In the episode, Adel and Marga explore the fourth industrial revolution and the eight technologies that combine to build it, the ethical application of technology and how it can be the biggest lever to combating climate change and building a sustainable society, how data and AI enable real-time information sharing leading to better early warning systems related to the environment, use cases of tech for good initiatives, how collaboration can bridge the gap in investment for sustainable business ventures and a lot more. 

Links Mentioned In the Show:

• Tech for Good
• Azure FarmBeats
• Capgemini in the Mojave Desert
• ReDeTec 3D Printing
• Framlab 3D Printed Homes for the Unsheltered

The consequences of data not being easily accessible within an organization are profound. Good decision-making often relies on good information, and with crucial insights locked behind closed doors, decision-makers may have to rely on incomplete information, stifling their ability to innovate through a lack of comprehensive data access or an inability to leverage data to its full potential. The ramifications of this are not merely operational – they extend to the core of an organization's ability to thrive in the data-driven era. However, democratizing access to data is only the first hurdle in driving a data led organization, employees need to feel confident in their ability to use data, try new tools and adopt new processes. But who is best to show us the benefits of accessing and utilizing data currently, and the cultural benefits it can bring. 

Lilac Schoenbeck is the Vice President of Strategic Initiatives at Rocket Software. Lilac has two decades of experience in enterprise software, data center technology and cloud, with wider experience in product marketing, pricing and packaging, corporate strategy, M&A integrations and product management. Lilac is passionate about delivering exceptional technology to IT teams that helps them drive value for their businesses. 

In the episode, Richie and Lilac explore data democratization and the importance of having widespread data capabilities across an organization, common data problems that data democratization can solve, tooling to facilitate better access and use of data, tool and process adoption, confidence with data, good data culture, processes to encourage good data usage and much more. 

Links mentioned in the show

• Rocket Software
• What Does Democratizing Data Mean? Unlocking the Power of Data Cultures
• Democratizing Data in Large Enterprises
• [Course] Introduction to Data Culture

We are all guilty of getting excited about shiny new toys in whatever guise they present themselves to us. For many of us, lots of the recent shiny new toys have been ways of utilizing AI to update and iterate on the ways that we work. Leadership teams have been looking for ways that their organizations can incorporate AI solutions into their products, regardless of whether they might be the most valuable use of the company's time. A company that fails to incorporate new tools and technology will stagnate and fail altogether right? A failure to adapt to the new state of play will surely stop the company from becoming a high performer? Or will it? What sets apart high-performing organizations from their non high-performing counterparts?

It’s not shiny new toys. It’s culture. Counter to conventional wisdom, the norms and beliefs of an organization, and not the technology and tools it uses, is what drives its performance.

Andrew McAfee is a Principal Research Scientist at the MIT Sloan School of Management, co-founder and co-director of MIT’s Initiative on the Digital Economy, and the inaugural Visiting Fellow at the Technology and Society organization at Google. He studies how technological progress changes the world. His book, The Geek Way, reveals a new way to get big things done. His previous books include More from Less and, with Erik Brynjolfsson, The Second Machine Age.

McAfee has written for publications including Foreign Affairs, Harvard Business Review, The Economist, The Wall Street Journal, and The New York Times. He's talked about his work on CNN and 60 Minutes, at the World Economic Forum, TED, and the Aspen Ideas Festival, with Tom Friedman and Fareed Zakaria, and in front of many international and domestic audiences. He’s also advised many of the world’s largest corporations and organizations ranging from the IMF to the Boston Red Sox to the US Intelligence Community.

Throughout the episode, Adel and Andrew explore the four cultural norms of the Geek way, the evolutionary biological underpinnings of the traits high performing organizations exhibit, case studies in adapting organizational culture, the role of data in driving high performance teams, useful frameworks leaders can adopt to build high performing organizations, and a lot more.

Link mentioned in the show:

• The Geek Way: The Radical Mindset That Drives Extraordinary Results by Andrew McAfee
• The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Andrew McAfee and Erik Brynjolfsson
• The Planning Fallacy
• Annie Duke
• Steven Pinker
• Adam Grant

Poor data engineering is like building a shaky foundation for a house—it leads to unreliable information, wasted time and money, and even legal problems, making everything less dependable and more troublesome in our digital world. In the retail industry specifically, data engineering is particularly important for managing and analyzing large volumes of sales, inventory, and customer data, enabling better demand forecasting, inventory optimization, and personalized customer experiences. It helps retailers make informed decisions, streamline operations, and remain competitive in a rapidly evolving market. Insight and frameworks learned from data engineering practices can be applied to a multitude of people and problems, and in turn, learning from someone who has been at the forefront of data engineering is invaluable.  

Mohammad Sabah is SVP of Engineering and Data at Thrive Market, and was appointed to this role in 2018. He joined the company from The Honest Company where he served as VP of Engineering & Chief Data Scientist. Sabah joined The Honest Company following its acquisition of Insnap, which he co-founded in 2015. Over the course of his career, Sabah has held various data science and engineering roles at companies including Facebook, Workday, Netflix, and Yahoo!

In the episode, Richie and Mo explore the importance of using AI to identify patterns and proactively address common errors, the use of tools like dbt and SODA for data pipeline abstraction and stakeholder involvement in data quality, data governance and data quality as foundations for strong data engineering, validation layers at each step of the data pipeline to ensure data quality, collaboration between data analysts and data engineers for holistic problem-solving and reusability of patterns, ownership mentality in data engineering and much more. 

Links from the show:

• PagerDuty
• Domo
• OpsGene
• Career Track: Data Engineer

In 2015 an MIT Researcher set out to build a mirror that would augment their face to look like those of their idols. The execution of this went well, until it came to testing. When the researcher came to use the mirror, no face was detected. The researcher was not detected in the mirror, until that is, she put on a white mask, at which point, the mirror worked as expected. 

Three years later, a paper named ‘Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification’ was published by the same researcher. Its release started a wider conversation about bias within AI-based facial recognition systems, and about bias within AI in general. Work to fight against algorithmic bias, or ‘The Coded-Gaze’, has been ongoing since. But who spearheaded this work and highlighted these issues to the AI and tech community? 

Dr. Joy Buolamwini is an AI researcher, artist, and advocate. In 2023, she is one of Time’s top 100 most influential people in AI. Joy founded the Algorithmic Justice League to create a world with more equitable and accountable technology. Her TED Featured Talk on algorithmic bias has over 1.5 million views and in 2020 Netflix released the documentary ‘Coded Bias’ following Joy’s research into the flaws of facial recognition systems. Her MIT thesis methodology uncovered large racial and gender bias in AI services from companies like Microsoft, IBM, and Amazon. Her research has been covered in over 40 countries, and as a renowned international speaker she has championed the need for algorithmic justice at the World Economic Forum and the United Nations. She serves on the Global Tech Panel convened by the vice president of European Commission to advise world leaders and technology executives on ways to reduce the harms of A.I.

As a creative science communicator, she has written op-eds on the impact of artificial intelligence for publications like TIME Magazine and New York Times. Her spoken word visual audit "AI, Ain't I A Woman?" which shows AI failures on the faces of iconic women like Oprah Winfrey, Michelle Obama, and Serena Williams as well as the Coded Gaze short have been part of exhibitions ranging from the Museum of Fine Arts, Boston to the Barbican Centre, UK. A Rhodes Scholar and Fulbright Fellow, Joy has been named to notable lists including Bloomberg 50, Tech Review 35 under 35, , Forbes Top 50 Women in Tech (youngest), and Forbes 30 under 30. She holds two masters degrees from Oxford University and MIT; and a bachelor's degree in Computer Science from the Georgia Institute of Technology. Fortune Magazine named her to their 2019 list of world's greatest leaders describing her as "the conscience of the A.I. Revolution."

In the episode, Richie and Joy discuss her journey into AI, the ethics of AI, the inception of Joy’s interest in AI bias, the Aspire Mirror and Gender Shades projects, The Algorithmic Justice League, consequences of biased facial recognition systems, highlights from Joy’s book (Unmasking AI), challenges in AI research such as misleading datasets and the importance of context, balancing working in AI and data while being an artist, and much more. 

Links mentioned in the show:

• Unmasking AI by Joy Buolamwini
• Algorithmic Justice League
• Gender Shades Project
• The Coded Gaze

I think it's safe to say that we are in the peak of the hype cycle with generative AI. Almost every week now, we see new startups with exciting new GenAI use-cases and products. However, exciting doesn't necessarily translate to useful. And now more than ever, it's important for leaders, whether at incumbents or startups, to adapt and drive value with generative AI and focus on useful use-cases. So how can they adapt well to these tectonic changes?

Jason Feifer is the editor in chief of Entrepreneur magazine and host of the podcast Problem Solvers. Outside of Entrepreneur, he is the author of the book Build For Tomorrow, which helps readers find new opportunities in times of change, and co-hosts the podcast Help Wanted, where he helps solve listeners' work problems. He also writes a newsletter called One Thing Better, which each week gives you one better way to build a career or company you love.

In the episode, Jason and Adel explore AI’s role in entrepreneurship, use cases and applications of AI, the effectiveness of certain AI tools, AI’s impact on established business models, frameworks for navigating change, advice for leaders and individuals on using AI in their work and much more. 

Links Mentioned in the Show:

• Build for Tomorrow by Jason Feifer
• One Thing Better Newsletter
• HeyGen
• Burger King Accepting Credit Cards in the 90s
• [COURSE] Implementing AI Solutions in Business

Throughout the past year, we've seen AI go from a nice-to-have, to a must-have in almost every large organization’s boardroom. There’s been more and more focus deploy AI  by leadership teams, and as a result, there's never been more pressure on the data team to deliver with AI. However, as the pressure to deliver with AI grows, the need to build safe and trustworthy experiences has also never been more important. But how do we balance between innovation and building these trustworthy experiences? How do you make responsible AI practical? Who should we get into the room when scoping safe AI use-cases? 

Beena Ammanath is an award- winning senior technology executive with extensive experience in AI and digital transformation. Her career has spanned leadership roles in e-commerce, finance, marketing, telecom, retail, software products, service, and industrial domains. She is also the author of the ground breaking book, Trustworthy AI.

Beena currently leads the Global Deloitte AI Institute and Trustworthy AI/ Ethical Technology at Deloitte. Prior to this, she was the CTO-AI at Hewlett Packard Enterprise. A champion for women and multicultural inclusion in technology and business, Beena founded Humans for AI, a 501c3b non-profit promoting diversity and inclusion in AI. Her work and contributions have been acknowledged with numerous awards and recognition such as 2016 Women Super Achiever Award from World Women’s Leadership Congress and induction into WITI’s 2017 Women in Technology Hall of Fame.

Beena was honored by UC Berkeley as 2018 Woman of the Year for Business Analytics, by the San Francisco Business Times as one of the 2017 Most Influential Women in Bay Area and by the National Diversity Council as one of the Top 50 Multicultural Leaders in Tech.

In the episode, Beena and Adel delve into the core principles of trustworthy AI, the interplay of ethics and AI in various industries, how to make trustworthy AI practical, who are the primary stakeholders for ensuring trustworthy AI, the importance of AI literacy when promoting responsible and trustworthy AI, and a lot more.

Links mentioned in the Show

• Trustworthy AI by Beena Ammanath
• Deloitte AI Institute
• Humans for AI
• Data Literacy by Design, with Valerie Logan, CEO of the Data Lodge
• [Course] Implementing AI Solutions in Business
• [Webinar - October 19th 2023] Building a Capability Roadmap for AI

In today's AI landscape, organizations are actively exploring how to seamlessly embed AI into their products, systems, processes, and workflows. The success of ChatGPT stands as a testament to this. Its success is not solely due to the performance of the underlying model; a significant part of its appeal lies in its human-centered user experience, particularly its chat interface. Beyond the foundational skills, infrastructure, and tools, it's clear that great design is a crucial ingredient in building memorable AI experiences.

How do you build human-centered AI experiences? What is the role of design in driving successful AI implementations? How can data leaders and practitioners adopt a design lens when building with AI?

Here to answer these questions is Haris Butt, Head of Product Design at ClickUp. ClickUp is a project management tool that's been making a big bet on AI, and Haris plays a key role in shaping how AI is embedded within the platform.

Throughout the episode, Adel & Haris spoke about the role of design in driving human-centered AI experiences, the iterative process of designing with large language models, how to design AI experiences that promote trust, how designing for AI differs from traditional software, whether good design will ultimately end up killing prompt engineering, and a lot more.

It's been almost a year since ChatGPT was released, mainstreaming AI into the collective consciousness in the process. Since that moment, we've seen a really spirited debate emerge within the data & AI communities, and really public discourse at large. The focal point of this debate is whether AI is or will lead to existential risk for the human species at large.

We've seen thinkers such as Elizier Yudkowski, Yuval Noah Harari, and others sound the alarm bell on how AI is as dangerous, if not more dangerous than nuclear weapons. We've also seen AI researchers and business leaders sign petitions and lobby government for strict regulation on AI. 

On the flip side, we've also seen luminaries within the field such as Andrew Ng and Yan Lecun, calling for, and not against, the proliferation of open-source AI. So how do we maneuver this debate, and where does the risk spectrum actually lie with AI? More importantly, how can we contextualize the risk of AI with other systemic risks humankind faces? Such as climate change, risk of nuclear war, and so on and so forth? How can we regulate AI without falling into the trap of regulatory capture—where a select and mighty few benefit from regulation, drowning out the competition in the meantime?

Trond Arne Undheim is a Research scholar in Global Systemic Risk, Innovation, and Policy at Stanford University, Venture Partner at Antler, and CEO and co-founder of Yegii, an insight network with experts and knowledge assets on disruption. He is a nonresident Fellow at the Atlantic Council with a portfolio in artificial intelligence, future of work, data ethics, emerging technologies, and entrepreneurship. He is a former director of MIT Startup Exchange and has helped launch over 50 startups. In a previous life, he was an MIT Sloan School of Management Senior Lecturer, WPP Oracle Executive, and EU National Expert.

In this episode, Trond and Adel explore the multifaceted risks associated with AI, the cascading risks lens and the debate over the likelihood of runaway AI. Trond shares the role of governments and organizations in shaping AI's future, the need for both global and regional regulatory frameworks, as well as the importance of educating decision-makers on AI's complexities. Trond also shares his opinion on the contrasting philosophies behind open and closed-source AI technologies, the risk of regulatory capture, and more. 

Links mentioned in the show:

• Augmented Lean: A Human-Centric Framework for Managing Frontline Operations by Trond Arne Undheim & Natan Linder
• Future Tech: How to Capture Value from Disruptive Industry Trends Trond Arne Undheim
• Futurized Podcast
• Stanford Cascading Risk Study
• Course: AI Ethics

From the dawn of humanity, decisions, both big and small, have shaped our trajectory. Decisions have built civilizations, forged alliances, and even charted the course of our very evolution. And now, as data & AI become more widespread, the potential upside for better decision making is massive. Yet, like any technology, the true value of data & AI is realized by how we wield it. 

We're often drawn to the allure of the latest tools and techniques, but it's crucial to remember that these tools are only as effective as the decisions we make with them. ChatGPT is only as good as the prompt you decide to feed it and what you decide to do with the output. A dashboard is only as good as the decisions that it influences. Even a data science team is only as effective as the value they deliver to the organization. 

So in this vast landscape of data and AI, how can we master the art of better decision making? How can we bridge data & AI with better decision intelligence?

​​Cassie Kozyrkov founded the field of Decision Intelligence at Google where, until recently, she served as Chief Decision Scientist, advising leadership on decision process, AI strategy, and building data-driven organizations. Upon leaving Google, Cassie started her own company of which she is the CEO, Data Scientific. In almost 10 years at the company, Cassie personally trained over 20,000 Googlers in data-driven decision-making and AI and has helped over 500 projects implement decision intelligence best practices. Cassie also previously served in Google's Office of the CTO as Chief Data Scientist, and the rest of her 20 years of experience was split between consulting, data science, lecturing, and academia. 

Cassie is a top keynote speaker and a beloved personality in the data leadership community, followed by over half a million tech professionals. If you've ever went on a reading spree about AI, statistics, or decision-making, chances are you've encountered her writing, which has reached millions of readers. 

In the episode Cassie and Richie explore misconceptions around data science, stereotypes associated with being a data scientist, what the reality of working in data science is, advice for those starting their career in data science, and the challenges of being a data ‘jack-of-all-trades’. 

Cassie also shares what decision-science and decision intelligence are, what questions to ask future employers in any data science interview, the importance of collaboration between decision-makers and domain experts, the differences between data science models and their real-world implementations, the pros and cons of generative AI in data science, and much more. 

Links mentioned in the Show:

• Data scientist: The sexiest job of the 22nd century
• The Netflix Prize
• AI Products: Kitchen Analogy
• Type one, Two & Three Errors in Statistics
• Course: Data-Driven Decision Making for Business
• Radar: Data & AI Literacy Edition

In data science, the push for unbiased machine learning models is evident. So much effort is made into ensuring the products we create are done thoughtfully and correctly, but are we investing the same effort in ensuring our teams, the very architects of these models, are diverse and inclusive? Bias in data can lead to skewed results, and similarly, a lack of diversity in teams can result in narrow perspectives. As we prioritize building diversity and inclusion into our data, it's equally crucial to embed these principles within our teams. So, who is best equipped to guide us in integrating DEI from a data perspective?

Tracy Daniels is the Chief Data Officer for Truist Financial Corporation. She leads the team responsible for Truist’s enterprise data capabilities, including strategy, governance, data platform delivery, client, master & reference data, and the centers of excellence for business intelligence visualization and artificial intelligence & machine learning. She is also

the executive sponsor for Truist’s Enterprise Technology & Operations Diversity Council. Daniels joined Truist in 2018. She has more than 25 years of banking and technology experience leading high performing technology portfolio, development, infrastructure and global operations organizations. Tracy enjoys participating in civic and philanthropic endeavors including serving on the Georgia State University Foundation Board of Trustees. She has been recognized as a National 2013 WOC STEM Rising Star award recipient, the 2017 Working Mother magazine Mother of the Year recipient, and a 2021 Women In Technology (WIT) Women of the Year in STEAM finalist.

In the episode Tracy and Richie discuss Truist's approach to Diversity, Equity, and Inclusion (DEI) and its alignment with the company's purpose and values, the distinction between diversity and inclusion, the positive outcomes of implementing DEI correctly, the importance of not missing opportunities both externally with customers and internally with talent, the significance of aligning diversity programs with business metrics and hiring to promote DEI, considerations for job advertisements that appeal to a diverse audience, and much more. 

Links mentioned in the show:

• McKinsey on Diversity and Inclusion
• Brookings Piece on Mitigating Bias in Data
• Algorithmic Justice League
• European Legislation on Data and Diversity
• Course: AI Ethics
• Radar: Data & AI Literacy Edition

It's been a year since ChatGPT burst onto the scene. It has given many of us a sense of the power and potential that LLMs hold in revolutionizing the global economy. But the power that generative AI brings also comes with inherent risks that need to be mitigated. 

For those working in AI, the task at hand is monumental: to chart a safe and ethical course for the deployment and use of artificial intelligence. This isn't just a challenge; it's potentially one of the most important collective efforts of this decade. The stakes are high, involving not just technical and business considerations, but ethical and societal ones as well.

How do we ensure that AI systems are designed responsibly? How do we mitigate risks such as bias, privacy violations, and the potential for misuse? How do we assemble the right multidisciplinary mindset and expertise for addressing AI safety? 

Reid Blackman, Ph.D., is the author of “Ethical Machines” (Harvard Business Review Press), creator and host of the podcast “Ethical Machines,” and Founder and CEO of Virtue, a digital ethical risk consultancy. He is also an advisor to the Canadian government on their federal AI regulations, was a founding member of EY’s AI Advisory Board, and a Senior Advisor to the Deloitte AI Institute. His work, which includes advising and speaking to organizations including AWS, US Bank, the FBI, NASA, and the World Economic Forum, has been profiled by The Wall Street Journal, the BBC, and Forbes. His written work appears in The Harvard Business Review and The New York Times. Prior to founding Virtue, Reid was a professor of philosophy at Colgate University and UNC-Chapel Hill.

In the episode, Reid and Richie discuss the dominant concerns in AI ethics, from biased AI and privacy violations to the challenges introduced by generative AI, such as manipulative agents and IP issues. They delve into the existential threats posed by AI, including shifts in the job market and disinformation. Reid also shares examples where unethical AI has led to AI projects being scrapped, the difficulty in mitigating bias, preemptive measures for ethical AI and much more. 

Links mentioned in the show:

• Ethical Machines by Reid Blackman
• Virtue Ethics Consultancy
• Amazon’s Scrapped AI Recruiting Tool
• NIST AI Risk Management Framework
• Course: AI Ethics
• DataCamp Radar: Data & AI Literacy

For the past few years, we've seen the importance of data literacy and why organizations must invest in a data-driven culture, mindset, and skillset. However, as generative AI tools like ChatGPT have risen to prominence in the past year, AI literacy has never been more important. But how do we begin to approach AI literacy? Is it an extension of data literacy, a complement, or a new paradigm altogether? How should you get started on your AI literacy ambitions? 

Cindi Howson is the Chief Data Strategy Officer at ThoughtSpot and host of The Data Chief podcast. Cindi is a data analytics, AI, and BI thought leader and an expert with a flair for bridging business needs with technology. As Chief Data Strategy Officer at ThoughtSpot, she advises top clients on data strategy and best practices to become data-driven, speaks internationally on top trends such as AI ethics, and influences ThoughtSpot’s product strategy.

Cindi was previously a Gartner Research Vice President, the lead author for the data and analytics maturity model and analytics and BI Magic Quadrant, and a popular keynote speaker. She introduced new research in data and AI for good, NLP/BI Search, and augmented analytics, bringing both BI bake-offs and innovation panels to Gartner globally. She’s frequently quoted in MIT, Harvard Business Review, and Information Week. She is rated a top 12 influencer in big data and analytics by Analytics Insight, Onalytca, Solutions Review, and Humans of Data.

In the episode, Cindi and Adel discuss how generative AI accelerates an organization’s data literacy, how leaders can think beyond data literacy and start to think about AI literacy, the importance of responsible use of AI, how to best communicate the value of AI within your organization, what generative AI means for data teams, AI use-cases in the data space, the psychological barriers blocking AI adoption, and much more. 

Links Mentioned in the Show:

The Data Chief Podcast 

ThoughtSpot Sage 

BloombergGPT 

Radar: Data & AI Literacy

Course: AI Ethics 

Course: Generative AI Concepts

Course: Implementing AI Solutions in Business 

With September and International Literacy Day (September 8th) upon us, we’re dedicating the entire month to cover the ins and outs of data & AI literacy. Make sure to sign up for the events we have in store, and to tune in for this month’s episodes.

• Data & AI Literacy Month
• DataCamp Radar: Data & AI Literacy Edition

The mainstreaming of data & AI is fundamentally altering the way we work and operate. But with rising innovation, comes rising ambiguity and complexity. How can leaders effectively navigate the path ahead? How can leaders adopt data-driven decision-making and learn from their mistakes? How can leaders use data to look inward, and become what today’s guest describes as “meta-leaders”? 

Constance Dierickx is an internationally recognized expert in high-stakes decision-making who has advised leaders and delivered speeches in more than 20 countries. Founder and president of CD Consulting Group, her clients include Fortune 20 companies, private equity firms, and large not-for-profits around the globe. She is a contributor to Harvard Business Review, Forbes, Chief Executive, and others, and has taught strategic decision-making at Skolkovo Institute of Science and Technology in Moscow, Russia. 

In the episode, Richie and Constance delve into what meta-leadership is, the nuances of meta-leadership, the pivotal role of data in leadership, the importance of recognizing subtle behavioral cues, the implications of cognitive biases (particularly overconfidence), and the essence of wisdom in decision-making. Constance also shares insights from her clinical psychology background, highlighting the application of biofeedback mechanisms in managing chronic pain and much more. 

Links From the Show:

Meta-Leadership by Constance Dierickx

High-Stakes Leadership by Constance Dierickx

The Merger Mindset by Constance Dierickx

Design the Life You Love: A Step-by-Step Guide to Building a Meaningful Future

Book by Ayse Birsel

Introducing The State of Data Literacy Report 2023

Data-Driven Decision Making for Business

Throughout history, small businesses have consistently played a pivotal role in the global economy, serving as its foundational backbone. As we navigate the digital age, the emergence of large corporations and rapid technological advancements present new challenges. Now, more than ever, it's imperative for small businesses to adapt, embracing a data-driven approach to remain competitive and sustainable. In this evolving landscape, we need champions dedicated to guiding these businesses, ensuring they harness the full potential of modern tools and insights to ensure a fair and varied marketplace of goods and services for all. 

Dr Kendra Vant, Executive General Manager of Data & AI Products at Xero, is an industry leader in building data-driven products that harness AI and machine learning to solve complex problems for the small-business economy. Working across Australia, Asia and the US, Kendra has led data and technology teams at companies such as Seek, Telstra, Deloitte and now Xero where she leads the company's global efforts using emerging practices and technologies to help small businesses and their advisors benefit from the power of data and insights. Starting with doctoral research in experimental quantum physics at MIT and a stint building quantum computers at Los Alamos National Laboratory, Kendra has made a career of solving hard problems and pushing the boundaries of what's possible.

In the episode, Kendra and Richie delve into the transformative impact of data science on small businesses, use-cases of data science for small businesses, how Xero has supported numerous small businesses with data science. They also cover the integration of AI in product development, the unexpected depth of data in seemingly low-tech sectors, the pivotal role of software platforms in data analysis and much more. 

Links Mentioned in The Show:

Xero

Analyzing Business Data in SQL

Financial Modeling in Spreadsheets

Implementing AI Solutions in Business

Generative AI Concepts

As companies scale and become more successful, new horizons open, but with them come unexpected challenges. The influx of revenue and expansion of operations often reveal hidden complexities that can hinder efficiency and inflate costs. In this tricky situation, data teams can find themselves entangled in a web of obstacles that slow down their ability to innovate and respond to ever-changing business needs. Enter cloud analytics—a transformative solution that promises to break down barriers and unleash potential. By migrating analytics to the cloud, organizations can navigate the growing pains of success, cutting costs, enhancing flexibility, and empowering data teams to work with agility and precision.

John Knieriemen is the Regional Business Lead for North America at Exasol, the market-leading high-performance analytics database. Prior to joining Exasol, he served as Vice President and General Manager at Teradata during an 11-year tenure with the company. John is responsible for strategically scaling Exasol’s North America business presence across industries and expanding the organization’s partner network. 

Solongo Erdenekhuyag is the former Customer Success and Data Strategy Leader at Exasol. Solongo is skilled in strategy, business development, program management, leadership, strategic partnerships, and management.

In the episode, Richie, Solongo, and John cover the motivation for moving analytics to the cloud, economic triggers for migration, success stories from organizations who have migrated to the cloud, the challenges and potential roadblocks in migration, the importance of flexibility and open-mindedness and much more. 

Links from the Show

• Exasol
• Amazon S3
• Azure Blob Storage
• Google Cloud Storage
• BigQuery
• Amazon Redshift
• Snowflake
• [Course] Understanding Cloud Computing
• [Course] AWS Cloud Concepts

Generative AI is here to stay—even in the 8 months since the public release of ChatGPT, there are an abundance of AI tools to help make us more productive at work and ease the stress of planning and execution of our daily lives among other things. 

Already, many of us are wondering what is to come in the next 8 months, the next year, and the next decade of AI’s evolution. In the grand scheme of things, this really is just the beginning. But what should we expect in this Cambrian explosion of technology? What are the use cases being developed behind the scenes? What do we need to be mindful of when training the next generations of AI? Can we combine multiple LLMs to get better results?

Bal Heroor is CEO and Principal at Mactores and has led over 150 business transformations driven by analytics and cutting-edge technology. His team at Mactores are researching and building AI, AR/VR, and Quantum computing solutions for business to gain a competitive advantage. Bal is also the Co-Founder of Aedeon—the first hyper-scale Marketplace for Data Analytics and AI talent.

In the episode, Richie and Bal explore common use cases for generative AI, how it's evolving to solve enterprise problems, challenges of data governance and the importance of explainable AI, the challenges of tracking the lineage of AI and data in large organizations. Bal also touches on the shift from general-purpose generative AI models to more specialized models, fascinating use cases in the manufacturing industry, what to consider when adopting AI solutions in business, and much more.

Links mentioned in the show:

• Pulsar
• Trifacta
• AWS Clarify
• [Course] Introduction to ChatGPT
• [Course] Implementing AI Solutions in Business
• [Course] Generative AI Concepts

'Software is eating the world’ is a truism coined by Mark Andreesen, General Partner at Andreesen Horowitz. This was especially evident during the shift from analog mediums to digital at the turn of the century. Software companies have essentially usurped and replaced their non-digital predecessors. Amazon was the largest bookseller, Netflix was the largest movie "rental" service, Spotify or Apple were the largest music providers.

Today, AI is starting to eat the world. However, we are still at the early start of the AI revolution, with AI set to become embedded in almost every piece of software we interact with. An AI ecosystem that touches every aspect of our lives is what today’s guest describes as ‘Ambient AI’. But what can we expect from this ramp up to Ambient AI? How will it change the way we work? What do we need to be mindful of as we develop this technology?

Daniel Jeffries is the Managing Director of the AI Infrastructure Alliance and former CIO at Stability AI, the company responsible for Stable Diffusion, the popular open-source image generation model. He’s also an author, engineer, futurist, pro blogger and he’s given talks all over the world on AI and cryptographic platforms.

In the episode, Adel and Daniel discuss how to define ambient AI, how our relationship with work will evolve as we become more reliant on AI, what the AI ecosystem is missing to rapidly scale adoption, why we need to accelerate the maturity of the open source AI ecosystem, how AI existential risk discourse takes away focus from real AI risk, and a lot lot more.

Links Mentioned in the Show

• Daniel’s Writing on Medium
• Daniel’s Substack
• AI Infrastructure Alliance
• Stability AI
• Francois Chollet
• Red Pajama Dataset
• Run AI
• Will Superintelligent AI End the World? By Eliezer Yudkowsky 
• Nick Bostrom’s Paper Clip Maximizer
• The pessimist archive 
• [Course] Introduction to ChatGPT
• [Course] Implementing AI Solutions in Business

In a time when AI is evolving at breakneck speeds, taking a step back and gaining a bird's-eye view of the evolving AI ecosystem is paramount to understanding where the field is headed.

With this bird's-eye view come a series of questions. Which trends will dominate generative AI in the foreseeable future? What are the truly transformative use-cases that will reshape our business landscape? What does the skills economy look like in an age of hyper intelligence?

Enter Joanne Chen, General Partner at Foundation Capital. Joanne invests in early-stage AI-first B2B applications and data platforms that are the building blocks of the automated enterprise. She has shared her learnings as a featured speaker at conferences, including CES, SXSW, WebSummit, and has spoken about the impact of AI on society in her TED talk titled "Confessions of an AI Investor." Joanne began her career as an engineer at Cisco Systems and later co-founded a mobile gaming company. She also spent many years working on Wall Street at Jefferies & Company, helping tech companies go through the IPO and M&A processes, and at Probitas Partners, advising venture firms on their fundraising process.

Throughout the episode, Richie and Joanne cover emerging trends in generative AI, business use cases that have emerged in the past year since the advent of tools like ChatGPT, the role of AI in augmenting work, the ever-changing job market and AI's impact on it, as well as actionable insights for individuals and organizations wanting to adopt AI.

Links mentioned in the show:

• JasperAI
• AnyScale
• Cerebras
• [Course] Introduction to ChatGPT
• [Course] Implementing AI Solutions in Business
• [Course] Generative AI Concepts

Spreadsheets have been the unsung heroes of the data world for many decades now. Yet, despite their ubiquity and importance, they've seen little disruption or evolution. The grid of cells we interact with today isn't far removed from the ones our predecessors used in the 1980s.

However, the winds of change have started to blow. As we stand on the cusp of a new era in data and AI, the humble spreadsheet is poised for transformation. The coming changes could redefine how we interact with data, derive insights, and how we make decisions. The implications are vast given the popularity and dependence we have on spreadsheets, and the potential impacts could ripple through every corner of the professional world. 

Hjalmar Gislason is the founder and CEO of GRID, with their main product being a smart spreadsheet with an interactive data visualization layer and integrated AI assistance. Hjalmar previously served as VP of Product Management at Qlik. He was the founder and CEO of DataMarket, founded in 2008 and sold to Qlik in 2014. A career data nerd and entrepreneur, GRID is Hjalmar’s fifth software startup as a founder. 

In the episode, Richie and Hjalmar explore the integral role of spreadsheets in today's data-driven world, the limitations of traditional Business Intelligence tools, and the transformative potential of generative AI in the realm of spreadsheets.

Data and AI are advancing at an unprecedented rate—and while the jury is still out on achieving superintelligent AI systems, the idea of artificial intelligence that can understand and learn anything—an “artificial general intelligence”—is becoming more likely. What does the rise of AI mean for the future of software and work as we know it? How will AI help reinvent most of the ways we interact with the digital and physical world?

Bob Muglia is a data technology investor and business executive, former CEO of Snowflake, and past president of Microsoft's Server and Tools Division. As a leader in data & AI, Bob focuses on how innovation and ethical values can merge to shape the data economy's future in the era of AI. He serves as a board director for emerging companies that seek to maximize the power of data to help solve some of the world's most challenging problems.

In the episode, Richie and Bob explore the current era of AI and what it means for the future of software. Throughout the episode, they discuss how to approach driving value with large language models, the main challenges organizations face when deploying AI systems, the risks, and rewards of fine-tuning LLMs for specific use cases, what the next 12 to 18 months hold for the burgeoning AI ecosystem, the likelihood of superintelligence within our lifetimes, and more.

Links from the show:

• The Datapreneurs by Bob Muglia and Steve Hamm
• The Singularity is Near by Ray Kurzweil
• Isaac Asimov
• Snowflake
• Pinecone
• Docugami
• OpenAI/GPT-4
• The Modern Data Stack

Today's government agencies face unprecedented complexities, and when thinking about the role of government in driving positive change for society at large, data & AI stand out as key levers to empower government agencies to do more with less. However, the road to government data & AI transformation is fraught with risk, and is full with opportunity. So how can government data leaders succeed in their transformation endeavors? 

Steve Orrin is Intel’s Federal Chief Technology Officer. He leads Public Sector Solution Architecture, Strategy, and Technology Engagements and has held technology leadership positions at Intel where he has led cybersecurity programs, products, and strategy. Steve was previously CSO for Sarvega, CTO of Sanctum, CTO and co-founder of LockStar, and CTO at SynData Technologies. He was named one of InfoWorld's Top 25 CTO's, received Executive Mosaic’s Top CTO Executives Award, is a Washington Exec Top Chief Technology Officers to Watch in 2023, was the Vice-Chair of the NSITC/IDESG Security Committee and was a Guest Researcher at NIST’s National Cybersecurity Center of Excellence (NCCoE). He is a fellow at the Center for Advanced Defense Studies and the chair of the INSA Cyber Committee.

Throughout the episode, we talked about the unique challenges government face when driving value with data & AI, how agencies need to align their data ambitions with their actual mission, the nuances between data privacy laws between the united states, Europe, and China, how to best approach launching pilot projects if you are in government, and a lot more.

Every year we become increasingly aware of the urgency of the climate crisis, and with that, the need to usher in renewable energies and scale their adoption has never been more important. However, as we look at the ways to scale the adoption of renewable energy, data stands out as a key lever to accelerate a greener future. 

Today’s guest is Jean-Pierre Pélicier, CDO at ENGIE. ENGIE is one of the largest energy producers in the world and definitely one of the largest in Europe. They operate in more than 48 countries and have committed to becoming carbon neutral by 2045. Data plays a crucial part in these plans.

In the episode, Jean-Pierre shares his unique perspective on how data is not just transforming the renewable energy industry but also redefining the way we approach the climate crisis. From harnessing the power of data to optimize energy production and distribution to leveraging advanced analytics to predict and mitigate environmental impacts, Jean-Pierre highlights the ways data continues to be an invaluable tool in our quest for a sustainable future.

Also discussed in the episode are the challenges of data collection and quality in the energy sector, the importance of fostering a data culture within an organization, and aligning data strategy with a company's strategic objectives.

About 10 years ago, Thomas Davenport & DJ Patil published the article "Data Scientist: The Sexiest Job of the 21st Century" in the Harvard Business Review. In this piece, they described the bourgeoning role of the data scientist and what it will mean for organizations and individuals in the coming decade.

As time has passed, data science has become increasingly institutionalized. Once seen as a luxury, it is now deemed a necessity in every modern boardroom. Moreover as technologies like AI and systems like ChatGPT keep astonishing us with their capabilities in handling data science tasks, it raises a pertinent question: Is Data Science Still the Sexiest Job of the 21st Century?

In this episode, we invited Thomas Davenport on the show to share his perspective on where data science & AI are at today, and where they are headed. Thomas Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, the co-founder of the International Institute for Analytics, a Fellow of the MIT Initiative for the Digital Economy, and a Senior Advisor to Deloitte Analytics. He has written or edited twenty books and over 250 print or digital articles for Harvard Business Review (HBR), Sloan Management Review, the Financial Times, and many other publications. One of HBR’s most frequently published authors, Thomas has been at the forefront of the Process Innovation, Knowledge Management, and Analytics and Big Data movements. He pioneered the concept of “competing on analytics” with his 2006 Harvard Business Review article and his 2007 book by the same name. Since then, he has continued to provide cutting-edge insights on how companies can use analytics and big data to their advantage, and then on artificial intelligence.

Throughout the episode, we discuss how data science has changed since he first published his article, how it has become more institutionalized, how data leaders can drive value with data science, the importance of data culture, his views on AI and where he thinks its going, and a lot more.

Links from the Show:

Working with AI by Thomas Davenport

The AI Advantage: How to Put the Artificial Intelligence Revolution to Work by Thomas Davenport

Harvard Business Review

New Vantage Partners

CCC Intelligent Solutions

Radar AI

Historically in elite team sports, there has often been a dynamic between players and their inherent abilities, and the vision of the coach. In many sports, we’ve seen coaching strategies influence the future of how the game is played.

As the era of professionalism swept across many elite sports in the 90s, we saw the highest-level sports teams achieve a competitive edge by looking at the data, with sports fans often noticing a difference in the ‘feel’ of the way their team plays. In Basketball specifically, we have recently seen the rise of the 3-pointer, a riskier and much more difficult shot to accurately hit, even for professional players. But what has driven the rise of the 3-pointer? Is it another trend among coaches, or does the answer lie with data-based insights and the analysts producing these insights?

Seth Partnow is the Director of North American Sports at StatsBomb, where he previously served as their Director of Basketball Analytics. Prior to joining StatsBomb in 2021, Seth was the Director of Basketball Research for the Milwaukee Bucks basketball team. Seth is also an accomplished Analyst and Author, having worked as an NBA Analyst for The Athletic since 2019 and having published his own book on basketball analytics, The Midrange Theory. Seth’s knowledge and insight bridges the gap between data analytics and elite US sport. 

In the episode, Seth and Richie look into the intricate dynamics of elite basketball. Seth explores the challenges of attributing individual contributions in a sport where the outcome is significantly influenced by the complex interplay between players.

Drawing from his extensive experience in the field, Seth discusses the complexities of analyzing player performance, the nuances of determining why certain players get easier or harder shots, and the difficulty of attributing credit for defensive achievements to individual players.

Seth provides a comprehensive overview of the various roles within sports analytics, from data engineers to analysts, and highlights the importance of finding one's niche within these roles, particularly in the context of elite basketball.

Seth also shares his personal journey into basketball analytics, offering valuable insights and advice for those interested in pursuing a career in this field, stressing the importance of introspection and understanding the unique lifestyle associated with working for a sports team, while also offering industry-agnostic advice on how to approach analyzing and using data in any context.

In the realm of Applied Intelligence, Accenture leads the way in harnessing the power of data and AI to transform industries. From consumer products to life sciences, retail, and aerospace, Accenture's influence is far-reaching. But what drives the organization? How does it navigate the complex landscape of data modernization and transformation? And more importantly, how does it leverage technology not just as an enabler, but as a catalyst for innovation? 

Tracy Ring leads Accenture’s Applied Intelligence Products Category Group, in this role she has leadership across Consumer and Industrial Products, Automotive, Life Sciences, Retail and Aerospace and Defense. As the CDO and Global Generative AI lead for Life Sciences, she personally anchors the NA Applied Intelligence Life Sciences practice of more than 500 practitioners. Tracy has created solutions for Generative AI, Data led transformation, Artificial Intelligence, Data and Cloud Modernization, Analytics, and the organization and operating model strategies for next-generation adoption and AI fluency. 

In the episode, Tracy initially clarifies the difference between data modernization and data transformation, highlighting their distinct meanings and why the terms aren’t interchangeable. 

Tracy also emphasizes the importance of involving business end-users from the outset of data projects as well as advocating for a product-oriented approach to data.

The discussion also covers the topic of team diversity and inclusivity. Tracy shares practical advice on how to build diverse teams and create an environment that encourages curiosity and open dialogue. Tracy also shares her perspective on the future of work and the importance of fostering meaningful conversations in the workplace. She advocates for an attitude of infinite curiosity within teams.

In the context of life sciences, Tracy highlights the high stakes involved and underscores the need for responsible AI, data sharing, and data privacy. She also points out that the challenges in this field are more similar than dissimilar to those in other industries.

Tune in for a wealth of insights from a seasoned leader in the field of Applied Intelligence.

A lot of the times when we walk into a supermarket, we don't necessarily think about the impact data science had in getting these products on shelves. However, as you’ll learn in today's episode, it's safe to say there's a myriad of applications for data science in the FMCG industry. Whether be that supply chain use-cases that leverage time-series forecasting techniques, to computer vision use-cases for on-shelf optimization—the use-cases are endless here. So how can data scientists and data leaders maximize value in this space?

Enter Anastasia Zygmantovich. Anastasia is a Global Data Science Director at Reckitt, which is most known for products like Airwick, Lysol, Detol, and Durex. Throughout the episode, we discuss how data science can be used in the FMCG industry, how data leaders can hire impactful data teams in this space, why FMCG is a great place to work in for data scientists, some awesome use-cases she's worked on, how data scientists can best maximize their value in this space, what generative AI means for organizations, and a lot more.

When we think about video games like Call of Duty, Fifa, or Fortnite, our minds often turn to creative artists, software developers, designers, and producers. These are the people who make our favorite games a reality. But behind the scenes, data & AI actively shape our experience with our favorite video games. From the quality of video games, the accessibility of maps and worlds, even the go to market, data & AI play an impactul role in making or breaking the success of a video game.

Marie de Léséleuc is an accomplished game industry professional with over a decade of experience. Marie started her career as a data analyst, and has since risen through the ranks to a data leader in the gaming industry. She's worked at companies such as Ubisoft, Warner Brothers, and most recently at Eidos, the company most well known for games such as Guardians of the Galaxy and Tomb Raider.

Throughout the episode, we discuss how data science can be used in gaming, the unique challenges data teams face in gaming from really low data volumes to massive changes to production schedules and game vision. We also spoke about the difference between "AI" as we know it in data science, and AI in gaming, which informs how NPCs behave in a video game world—and a lot more.

Imagine making parenting choices not just based on instinct and through the lived experiences of others, but instead using data-driven techniques garnered through a career in data and economics. 

Emily Fair Oster is a Professor of Economics and International and Public Affairs at Brown University. Her work is unique, blending economics, health, and research in new ways. In her books "Expecting Better," "The Family Firm," and "Cribsheet," she's shown how data can help guide us through pregnancy and parenting.

In the episode, Emily shows how she used her knowledge of data and economics when she was pregnant, and how this way of thinking can change how we make decisions.

We look at the tension between what we feel and what the data tells us when we're making parenting choices, and why many of us lean on personal experiences. Emily tells us why it's important to use quality data when making decisions and how to make sense of all the information out there.

Emily talks about the ins and outs of using data to make parenting decisions, discussing the big milestones in a child's life, the role of sleep, and how these can impact a person's future as well as the nuance in applying data-driven decision-making to your parenting. 

Emily also touches on how having two working parents and traditional gender roles can shape how we parent.

Finally, Emily gives some helpful tips on finding and understanding good-quality data. This will help you make better decisions as a parent. Tune in for a thought-provoking look at parenting, data, and economics.

Although many have been cognizant of AI’s value in recent months, the further back we look, the more exclusive this group of people becomes. In our latest AI-series episodes of DataFramed, we gain insight from an expert who has been part of the industry for 40 years.

Joaquin Marques, Founder and Principal Data Scientist at Kanayma LLC has been working in AI since 1983. With experience at major tech companies like IBM, Verizon, and Oracle, Joaquin's knowledge of AI is vast. Today, he leads an AI consultancy, Kanayma, where he creates innovative AI products.

Throughout the episode, Joaquin shares his insights on AI's development over the years, its current state, and future possibilities. Joaquin also shares the exciting projects they've worked on at Kanayma as well as what to consider when building AI products, and how ChatGPT is making chatbots better.

Joaquin goes beyond providing insight into the space, encouraging listeners to think about the practical consequences of implementing AI, with Joaquin sharing the finer technical details of many of the solutions he’s helped build. Joaquin also shares many of the thought processes that have helped him move forward when building AI products, providing context on many practical applications of AI, both from his past and the bleeding edge of today.  

The discussion examines the complexities of artificial intelligence, from the perspective of someone that has been focused on this technology for more than most. Tune in for guidance on how to build AI into your own company's products.

With the advances in AI products and the explosion of ChatGPT in recent months, it is becoming easier to imagine a world where AI and humans work seamlessly together—revolutionizing how we solve complex problems and transform our daily lives. This is especially the case for data professionals.

In this episode of our AI series, we speak to Sarah Schlobohm, Head of AI at Kubrick Group. Dr. Schlobohm leads the training of the next generation of machine learning engineers. With a background in finance and consulting, Sarah has a deep understanding of the intersection between business strategy, data science, and AI. Prior to her work in finance, Sarah became a chartered accountant, where she honed her skills in financial analysis and strategy. Sarah worked for one of the world's largest banks, where she used data science to fight financial crime, making significant contributions to the industry's efforts to combat money laundering and other illicit activities. Sarah shares her extensive knowledge on incorporating AI within data teams for maximum impact, covering a wide array of AI-related topics, including upskilling, productivity, and communication, to help data professionals understand how to integrate generative AI effectively in their daily work.

Throughout the episode, Sarah explores the challenges and risks of AI integration, touching on the balance between privacy and utility. She highlights the risks data teams can avoid when using AI products and how to approach using AI products the right way. She also covers how different roles within a data team might make use of generative AI, as well as how it might effect coding ability going forward.

Sarah also shares use cases for those in non-data teams, such as marketing, while also highlighting what to consider when using outputs from GPT models. Sarah shares the impact chatbots might have on education calling attention to the power of AI tutors in schools.

Sarah encourages people to start using AI now, considering the barrier to entry is so low, and how that might not be the case going forward. From automating mundane tasks to enabling human-AI collaboration that makes work more enjoyable, Sarah underscores the transformative power of AI in shaping the future of humanity.

Whether you're an AI enthusiast, data professional, or someoone with an interest in either this episode will provide you with a deeper understanding of the practical aspects of AI implementation.

With the advent of any new technology that promises to make humans lives easier, replacing concious actions with automation, there is always backlash. People are often aware of the displacement of jobs, and often, it is viewed in a negative light. But how do we try to change the collective understanding to one of hope and excitement? What use cases can be shared that will change the opinion of those that are weary of AI? 

Noelle Silver Russell is the Global AI Solutions & Generative AI & LLM Industry Lead at Accenture, responsible for enterprise-scale industry playbooks for generative AI and LLMs. In this episode of our AI series, Noelle discusses how to prioritize ChatGPT use cases by focusing on the different aspects of value creation that GPT models can bring to individuals and organizations. She addresses common misconceptions surrounding ChatGPT and AI in general, emphasizing the importance of understanding their potential benefits and selecting use cases that maximize positive impact, foster innovation, and contribute to job creation.

Noelle draws parallels between the fast-moving AI projects today and the launch of Amazon Alexa, which she worked on, and points out that many of the discussions being raised today were also talked about 10 years ago. She discusses how companies can now use AI to focus both on business efficiencies and customer experience, no longer having to settle for a trade-off between the two.

Noelle explains the best way for companies to approach adding GPT tools into their processes, which focusses on taking a holistic view to implementation. She also recommends use-cases for companies that are just beginning to use AI, as well as the challenges they might face when deploying models into production, and how they can mitigate them. 

On the topic of the displacement of jobs, Noelle draws parallels from when Alexa was launched, and how it faced similar criticisms, digging into the fear that people have around new technology, which could be transformed into enthusiasm. Noelle suggests that there is a burden on leadership within organizations to create a culture where people are excited to use AI tools, rather than feeling threatened by them.

ChatGPT has leaped into the forefront of our lives—everyone from students to multinational organizations are seeing value in adding a chat interface to an LLM. But OpenAI has been concentrating on this for years, steadily developing one of the most viral digital products this century. In this episode of our AI series, we sit down with Logan Kilpatrick. Logan currently leads developer relations at OpenAI, supporting developers building with DALL-E, the OpenAI API, and ChatGPT. Logan takes us through OpenAI’s products, API, and models, and provides insights into the many use cases of ChatGPT. 

Logan provides fascinating information on ChatGPT’s plugins and how they can be used to build agents that help us in a variety of contexts. He also discusses the future integration of LLMs into our daily lives and how it will add structure to the unstructured nature and difficult-to-leverage data we generate and interact with on a daily basis. Logan also touches on the powerful image input features in GPT4, how it can help those with partial sight to improve their quality of life, and how it can be used for various other use cases.

Throughout the episode, we unpack the need for collaboration and innovation, due to ChatGPT becoming more powerful when integrated with other pieces of software. Covering key discussion points with regard to AI tools currently, in particular, what could be built in-house by OpenAI and what could be built in the public domain. Logan also discusses the ecosystem forming around ChatGPT and how it will all become connected going forward. Finally, Logan shares tips for getting better responses from ChatGPT and the things to consider when integrating it into your organization’s product. 

This episode provides a deep dive into the world of GPT models from within the eye of the storm, providing valuable insights to those interested in AI and its practical applications in our daily lives.

From May 8-11, discover expert insights from four industry leaders from OpenAI, Accenture, Kubrick Group, and Kanayma LLC on how to navigate the era of AI.

Ten years ago, Salesforce was trying to generate $1Bn of revenue in a quarter. Today, they create over $30Bn of revenue in year. Simultaneously, over the last decade we have seen huge advances in the world of data and data science.

In this episode, Laura Gent Felker, Director of Data Insights and Scalability at Salesforce, talks about her experience in building and leading data teams within the organization over the last ten years. Laura shares her insights on how to create a learning culture within a team, how to prioritize projects while accounting for long-term strategy, and the importance of setting aside time for innovation.

Laura also discusses how to ensure that the projects the team works on genuinely provide business value. She suggests creating a two-way street with executive leadership and understanding the collective value across a variety of stakeholders also citing that some of the best innovation she has seen come from her team is when they have had to solve high-priority short-term business problems. 

In addition, Laura shares a multi-layered approach to building a learning community within a data team. She explains that a culture of collaboration and trust is important in the direct data team, and the wider community within organizations. 

Laura also talks about the frameworks and mental models that can help develop business acumen. She highlights the importance of dedicating time to this area and being able to communicate insights effectively.

Throughout the episode, Laura's insights provide valuable guidance for both junior and experienced data professionals, consumers and leaders in creating a learning culture, prioritizing projects, and building a strong data community within organizations.

Data literacy is becoming increasingly recognized as a valuable skill in today's workforce. We all interact with data on a daily basis, and organizations are now realizing the tremendous benefits of having a workforce that is well-versed in data, from interacting with dashboards to data analysis and data science. But, it all starts with data literacy. 

In this episode, we speak with Valerie Logan, CEO and Founder of The Data Lodge. Valerie is committed to data literacy, she believes that in today's digital society, data literacy is a life skill. With advisory services, bootcamps, a resource library and community services at The Data Lodge, Valerie is certifying the world’s first Data Literacy Program Leads and pioneering the path forward in cracking the data culture code. Valerie is also known for helping popularize the term "Data Literacy." In this episode, she shares insights on what a successful data literacy journey looks like, best practices for evangelizing data literacy programs, how to avoid siloed efforts between departments and much more.

Valerie sheds light on the difficulties organizations face when trying to prioritize data literacy and data culture. She suggests that this is because humans are still at the center of organizations, and changing their behaviour is a challenge. She also talks about what data literacy means, and how the definition adapts to use cases. 

Valerie offers guidance on how to secure executive buy-in for data upskilling programs, explaining that finding a sponsor for the program is the first step. She also talks about the importance of extending buy-in to people who are less directly involved with data and upskilling, emphasizing how the program will help strategic objectives.

Valerie also provides insights on the hallmarks of an effective pilot program for data literacy, suggesting that organizations go where there's already interest and that a good pilot is one where before and after effects can be measured. She also shares tips on how organizations can ensure that their data literacy program helps them achieve their strategic business goals.

Throughout the episode, Valerie outlines the benefit and scope data literacy can have on an organization, with one of the most pertinent pieces of wisdom being a warning to organisations that risk ignoring upskilling and investing in data.

Links mentioned in the show:

• RADAR 2023: Building an Enterprise Data Strategy that Puts People First
• The Data Lodge
• The State of Data Literacy in 2023
• What is Data Maturity and Why Does it Matter?

Building machine learning systems with high predictive accuracy is inherently hard, and embedding these systems into great product experiences is doubly so. To build truly great machine learning products that reach millions of users, organizations need to marry great data science expertise, with strong attention to user experience, design thinking, and a deep consideration for the impacts of your prediction on users and stakeholders. So how do you do that?

Today’s guest is Sam Stone, Director of Product Management, Pricing & Data at Opendoor, a real-estate technology company that leverages machine learning to streamline the home buying and selling process. Sam played an integral part in developing AI/ML products related to home pricing including the Opendoor Valuation Model (OVM), market liquidity forecasting, portfolio optimization, and resale decision tooling. Prior to Opendoor, he was a co-founder and product manager at Ansaro, a SaaS startup using data science and machine learning to help companies improve hiring decisions. Sam holds degrees in Math and International Relations from Stanford and an MBA from Harvard.

Throughout the episode, we spoke about his principles for great ML product design, how to think about data collection for these types of products, how to package outputs from a model within a slick user interface, what interpretability means in the eyes of customers, how to be proactive about monitoring failure points, and much more.

Ofcom is the government-approved regulatory and competition authority for the broadcasting, telecommunications and postal industries of the United Kingdom. It plays a vital role in ensuring TV, radio and telecoms work as they should. With vast swathes of information from a wide range of sources, data plays a huge role in the way Ofcom operates - in this episode, we learn the key drivers of Ofcom’s data strategy. 

Richard Davis is the Chief Data Officer at Ofcom, responsible for enabling data and analytics capabilities across the organisation. Prior to Ofcom, Richard worked as a Quantitative Analyst as well as being the former Head of Analytics and Innovation at LLoyds Bank, proving he has a wealth of experience across a variety of data roles. 

After joining Ofcom in 2022, Richard describes his experience of joining Ofcom, his ambition to bring in new processes, and how he leverages the community of data professionals. Richard also shares his advice for a new data leader, which includes understanding the pain points of the team, making insights more efficient, and keeping data teams aligned with the business's needs. He also elaborates on the key components of the data strategy at Ofcom, including aligning to good data, good people, and good decisions.

Also discussed is the importance of cultural change in an organization and how to upskill data experts and train non-data specialists in data literacy, the difference between technical experts and people managers, and how organizations can enable people to grow to become technical leaders.

Finally, Richard emphasizes the importance of evidence-based regulation, and how data literacy supports effective output. Richard provides excellent insight into the world of regulatory data, the challenges faced by Ofcom, and the solutions they can implement to overcome them.

The concept of literate programming, or the idea of programming in a document, was first introduced in 1984 by Donald Knuth. And as of today, notebooks are now the defacto tool for doing data science work. So as the data tooling space continues to evolve at breakneck speed, what are the possible directions the data science notebook can take? 

In this episode of DataFramed, we talk with Dr. Jodie Burchell, Data Science Developer Advocate at JetBrains, to find out how data science notebooks evolved into what they are today, what her predictions are for the future of notebooks and data science, and how generative AI will impact data teams going forward. 

Jodie completed a Ph.D. in clinical psychology and a postdoc in biostatistics before transitioning into data science. She has since worked for 7 years as a data scientist, developing products ranging from recommendation systems to audience profiling. She is also a prolific content creator in the data science community.

Throughout the episode, Jodie discusses the evolution of data science notebooks over the last few years, noting how the move to remote-based notebooks has allowed for the seamless development of more complex models straight from the notebook environment.

Jodie and Adel’s conversation also covers tooling challenges that have led to modern IDEs and notebooks, with Jodie highlighting the importance of good database tooling and visibility. She shares how data science notebooks have evolved to help democratize data for the wider organization, the tradeoffs between engineering-led approaches to tooling compared to data science approaches, what generative AI means for the data profession, her predictions for data science, and more.

Tune in to this episode to learn more about the evolution of data science notebooks and the challenges and opportunities facing the data science community today.

Links to mentioned in the show:

• DataCamp Workspace: An-in Browser Notebook IDE
• JetBrains' Datalore
• Nick Cave on ChatGPT song lyrics imitating his style
• GitHub Copilot 

More on the topic:

• The Past, Present, And Future of The Data Science Notebook
• How to Use Jupyter Notebooks: The Ultimate Guide

In 2023, businesses are relying more heavily on data science and analytics teams than ever before. However, simply having a team of talented individuals is not enough to guarantee success. 

In the last of our RADAR 2023 sessions, Vijay Yadav and Vanessa Gonzalez will outline the keys to building high-impact data teams in 2023. They will discuss what are the hallmarks of a high-performing data team, the importance of diversity of background and skillset needed to build impactful data teams, setting up career pathways for data scientists, and more.

Vijay Yadav is a highly respected data and analytics thought leader with over 20 years of experience in data product development, data engineering, and advanced analytics. As Director of Quantitative Sciences - Digital, Data, and Analytics at Merck, he leads data & analytics teams in creating AI/ML-driven data products to drive digital transformation. Vijay has held numerous leadership positions at various companies and is known for his ability to lead global teams to achieve high-impact results. 

Vanessa Gonzalez is the Sr. Director of Data Science and Innovation at Businessolver where she leads the Computational Linguistics, Machine Learning Engineering, Data Science, BI Analytics, and BI Engineering teams. She is experienced in leading data transformations, performing analytical and management functions that contribute to the goals and growth objectives of organizations and divisions. 

Listen in as Vanessa and Vijay share how to enable data teams to flourish in an ever-evolving data landscape. 

An effective data strategy is one that combines a variety of levers such as infrastructure, tools, organization, processes, and more. Arguably however, the most important aspect of a vibrant data strategy is culture and people.

In the third of our four RADAR 2023 sessions, Cindi Howson and Valerie Logan discuss how data leaders can create a data strategy that puts their people at the center. Learn key insights into how to drive effective change management for data culture, how to drive adoption of data within the organization, common pitfalls when executing on a data strategy, and more. 

Cindi Howson is the Chief Data Strategy Officer at ThoughtSpot and host of The Data Chief podcast. Cindi is an analytics and BI thought leader and expert with a flair for bridging business needs with technology.  As Chief Data Strategy Officer at ThoughtSpot, she advises top clients on data strategy and best practices to become data-driven, speaks internationally on top trends such as AI ethics, and influences ThoughtSpot’s product strategy. 

Valerie Logan is the Founder and CEO of The Data Lodge. Valerie is committed to data literacy, she believes that in today's digital society, data literacy is a life skill. With advisory services, bootcamps, a resource library and community services at The Data Lodge, Valerie is certifying the world’s first Data Literacy Program Leads and pioneering the path forward in cracking the data culture code. In 2018, she was awarded Gartner’s Top Thought Leadership Award for her leadership in the area of Data Literacy.

Listen in as Cindi and Valerie share how to build a data strategy that puts people first in an enterprise organization.

As organizations and the economy at large look to weather the challenges of 2023, data literacy is one of the keys to empowering organizations to navigate the decade's most significant challenges with confidence. 

In the second of our four RADAR 2023 sessions, Jordan Morrow shares how to navigate the future with data literacy, and how organizations can thrive as data becomes ever more prominent.

Jordan is known as the "Godfather of Data Literacy", having helped pioneer the field by building one of the world's first data literacy programs and driving thought leadership on the subject. Jordan is Vice President and Head of Data And Analytics at BrainStorm, Inc., and a global trailblazer in the world of data literacy, building the world's first full scale data literacy program. He served as the Chair of the Advisory Board for The Data Literacy Project, has spoken at numerous conferences around the world and is an active voice in the data and analytics community. He has also helped companies and organizations around the world, including the United Nations, build and understand data literacy.

Listen in as Jordan outlines how and why data literacy can help build individual and organizational resilience, how to scale data literacy within your organization, and more.

As organizations of all sizes continuously look to drive value out of data, the modern data stack has emerged as a clear solution for getting insights into the hands of the organization. With the rapid pace of innovation not slowing down, the tools within the modern data stack have enabled data teams to drive faster insights, collaborate at scale, and democratize data knowledge. However, are tools just enough to drive business value with data? 

In the first of our four RADAR 2023 sessions, we look at the key drivers of value within the modern data stack through the minds of Yali Sassoon and Barr Moses. 

Yali Sassoon is the Co-Founder and Chief Strategy Officer at Snowplow Analytics, a behavioral data platform that empowers data teams to solve complex data challenges. At Snowplow, Yali gets to combine his love of building things with his fascination of the ways in which people use data to reason.

Barr Moses is CEO & Co-Founder of Monte Carlo. Previously, she was VP Customer Operations at customer success company Gainsight, where she helped scale the company 10x in revenue and, among other functions, built the data/analytics team. 

Listen in as Yali and Barr outline how data leaders can drive value creation with data in 2023.

Data leaders play a critical role in driving innovation and growth in various industries, and this is particularly true in highly regulated industries such as aviation. In such industries, data leaders face unique challenges and opportunities, working to balance the need for innovation with strict regulatory requirements. This week’s guest is Derek Cedillo, who has 27 years of experience working in Data and Analytics at GE Aerospace. Derek currently works as a Senior Manager for GE Aerospace’s Remote Monitoring and Diagnostics division, having previously worked as the Senior Director for Data Science and Analytics.

In the episode, Derek shares the key components to successfully managing a Data Science program within a large and highly regulated organization. He also shares his insights on how to standardize data science planning across various projects and how to get a Data Scientists to think and work in an agile manner. We hear about ideal data team structures, how to approach hiring, and what skills to look for in new hires. 

The conversation also touches on what responsibility Data Leaders have within organizations, championing data-driven decisions and strategy, as well as the complexity Data Leaders face in highly regulated industries. When it comes to solving problems that provide value for the business, engagement and transparency are key aspects. Derek shares how to ensure that expectations are met through clear and frank conversations with executives that try to align expectations between management and Data Science teams. 

Finally, you'll learn about validation frameworks, best practices for teams in less regulated industries, what trends to look out for in 2023 and how ChatGPT is changing how executives define their expectations from Data Science teams. 

Links to mentioned in the show:

The Checklist Manifesto by Atul Gawande

Team of Teams by General Stanley McChrystal

The Harvard Data Science Review Podcast

Relevant Links from DataCamp:

Article: Storytelling for More Impactful Data Science

Course: Data Communication Concepts

Course: Data-Driven Decision-Making for Business

Oftentimes, Kaggle competitions are looked at as an excellent way for data scientists to sharpen their machine learning skills and become technically excellent. This begs the question, what are the hallmarks of high-performing Kaggle competitors? What makes a Kaggle Grand Master?

Today’s guest, Jean-Francois Puget PhD, distinguished engineer at NVIDIA, has achieved this impressive feat three times. 

Throughout the episode, Richie and Jean-Francois discuss his background and how he became a Kaggle Grandmaster. He shares his scientific approach to machine learning and how he uses this to consistently achieve high results in Kaggle competitions.

Jean-Francois also discusses how NVIDIA employs nine Kaggle Grandmasters and how they use Kaggle experiments to breed innovation in solving their machine learning challenges. He expands on the toolkit he employs in solving Kaggle competitions, and how he has achieved 50X improvements in efficiencies using tools like RAPIDS. 

Richie and Jean-Francois also delve into the difference between competitive data science on Kaggle and machine learning work in a real-world setting. They deep dive into the challenges of real-world machine learning, and how to resolve the ambiguities of using machine learning in production that data scientists don’t encounter in Kaggle competitions.

Studies have shown that companies lacking in racial diversity also have a corresponding lack in their ability to innovate as a whole, which makes it important for any organization to prioritize an inclusive workplace culture and welcome more women and underrepresented groups in data.

This is why Nikiska Alcindor's work is so vital to the future of the data science industry. Nikisha is the President and Founder of the STEM Educational Institute (SEI), a nonprofit corporation that equips underrepresented high school students with the technological skills needed to build generational wealth and be effective in the workforce. 

Nikisha is a strategic management leader with expertise in organizational change, investing, and fundraising. She is a  recipient of the 2021 Dean Huss Teaching Award, a board member of the Upper Manhatten Empowerment Zone, and has taught a master class at Columbia Business School as well as several guest lectures at Columbia University.

Throughout the episode, we discuss SEI’s three-pillar approach to education,  the rising importance of STEM-based careers, why financial literacy is crucial to a student’s success, SEI’s partnership with DataCamp, contextualizing educational and upskilling programs to your organization’s specific population, how data leaders can positively communicate upskilling initiatives, and much more.

In order for any data team to move from reactive to proactive and drive revenue for the business, they must make sure the basics are in place and that the team and data culture is mature enough to allow for scalable return on investment. 

Without these elements, data teams find themselves unable to make meaningful progress because they are stuck reacting to problems and responding to rudimentary questions from stakeholders across the organization. This quickly takes up bandwidth and keeps them from achieving meaningful ROI.

In today’s episode, we have invited Shane Murray to break down how to effectively structure a data team, how data leaders can lead efficient decentralization, and how teams can scale their ROI in 2023.

Shane is the Field CTO at Monte Carlo, a data reliability company that created the industry's first end-to-end Data Observability platform. Shane’s career has taken him through a successful 9-year tenure at The New York Times, where he grew the data analytics team from 12 to 150 people and managed all core data products. Shane is an expert when it comes to data observability, enabling effective ROI for data initiatives, scaling high-impact data teams, and more.

Throughout the episode we discuss how to structure a data team for maximum efficiency, how data leaders can balance long-term and short-term data initiatives, how data maturity correlates to a team’s forward-thinking ability, data democratization with data insights and reporting ROI, best practices for change management, and much more.

The most common application for data science is to solve problems within your own organization, and as professionals become more data literate, they rely less and less on others to solve their problems and unlock professional growth and career advancement.

But in the world of consulting, data science is used to solve other people’s problems, which adds an additional layer of complexity since consultants aren’t always given all of the tools they need to do the job right.

Enter Pratik Agrawal, a Partner at Kearney Analytics leading the automotive and industrial transportation sector. In this episode, we are taking a look at how data science is applied in the consulting industry and what skills are critical to be a successful data science consultant. 

As a software engineer and data scientist with over a decade of experience in the consulting world at companies like Boston Consulting Group and IRI, Pratik has a deep understanding of how to navigate the industry and how data science can be leveraged in it, as well as expertise in digital transformation projects and strategy.

Throughout the episode, we discuss common problems that consultants encounter, the skills needed to be successful as a consultant, the different approaches to analytics in consulting versus in an organization, how to handle context switching when juggling multiple projects, what makes consulting feel exciting and challenging, and much more.

One of the toughest parts of any data project is experimentation, not just because you need to choose the right testing method to confirm the project’s effectiveness, but because you also need to make sure you are testing the right hypothesis and measuring the right KPIs to ensure you receive accurate results.

One of the most effective methods for data experimentation is A/B testing, and Anjali Mehra, Senior Director of Product Analytics, Data Science, Experimentation, and Instrumentation at DocuSign, is no stranger to how A/B testing can impact multiple parts of any organization. Throughout her career, she has also worked in marketing analytics and customer analytics at companies like Shutterfly, Wayfair, and Constant Contact.

Throughout the episode, we discuss DocuSign’s analytics goals, how A/B testing works, how to gamify data experimentation, how A/B testing helps with new initiative validation, examples of A/B testing with data projects, how organizations can get started with data experimentation, and much more.

Perhaps the biggest obstacle to establishing a data culture is building trust in the data itself, making it vital for organizations to have a robust approach to data governance to ensure data quality is as high as possible.

Enter Laurent Dresse, Data Governance Evangelist and Director of Professional Services at DataGalaxy. Throughout his career, Laurent has served as a bridge between IT and the rest of the business as an expert in data governance, quality, data management, and more.

Throughout the episode, we discuss the state of data governance today, how data leaders and organizations can start their data governance journey, how to evangelize for data governance and gain buy-in across your organization, data governance tooling, and much more.

A special announcement from the DataFramed team. Join us for RADAR, a free two-day digital event curated to equip businesses and individuals with the insights to thrive in the era data, coming to you March 22-23, 2023! 

Register here to secure your spot!

Data quality can make or break any data initiative or product. If you aren’t able to collect data that is accurate, or you have data sets that have varying structures, or are filled with typos and other issues caused by human error, then the chances drop drastically that your data models will be accurate, or even helpful.

When it comes to healthcare, data quality can be an absolute nightmare. With so many different data sources, high data churn rates, and a lack of standardization in many different healthcare categories, it can seem impossible to make quality healthcare more easily accessible to people when they need it.

Ribbon Health seeks to change that by using AI to improve the quality of healthcare data and create a data platform with actionable provider information including insurance coverage, prices, and performance.

Today’s guests are Nate Fox, the CTO, Co-Founder, and President of Ribbon Health, and Sunna Jo, a former pediatrician who is now a data scientist at Ribbon Health.

Throughout the episode, we talk about why data quality in healthcare is messy, why having context around data is necessary to interpret and utilize it properly, how healthcare providers are improving their services because of platforms like Ribbon Health, how to tackle common data cleaning problems, and much more

When working with data, it’s easy for us to think about it as a mechanistic process, where data comes in and products come out. But as we’ve explored throughout the show, succeeding in data, whether you’re a data leader looking to build a data culture, a data scientist ascending the ranks, or even a policy maker looking to have an impact with data, the human side is crucial.

At the heart of the “human side” is empathy— whether it’s for your stakeholders if you’re a data scientist developing a dashboard for them, empathy for your workforce if you’re a data or learning leader, or empathy for the planet and your citizens if you’re a policy maker. 

So how can we all practice better empathy? Specifically, can we all practice better data empathy? Luckily, empathy is a muscle that can be built. It’s not a “you have it, or you don’t” type of skill. So how can individuals and organizations utilize data empathy to improve how they work with data and the success rate of their projects?  

Enter Phil Harvey, an Industrial Metaverse Architect in the Industrial Metaverse Core group at Microsoft. He is an expert in Data & AI Technical and Business Strategy & Philosophy. Harvey is also co-author of the book Data: A Guide to Humans, which explores the concept of Data Empathy, and how it can power better use of data through better communication and understanding of stakeholders in the value chain of data. 

Something we talk about alot on DataFramed is the importance of data literacy and data skills — and how they help both individuals and organizations succeed with data. Oftentimes, when organizations engage in upskilling programs on data literacy, one of the common pushbacks people have is, “I am not a numbers person”. 

So how do you move past that? How can leaders help their people bridge the data literacy gap, and in turn create a data culture?

That’s where Dr. Selena Fisk comes in. Fisk is a data storyteller, coach, and thought leader in the data industry. She works in both the corporate sector and in education to develop data-led strategies that can help organizations grow. Fisk mainly specializes in the areas of data literacy, data visualization, and data storytelling, and is the author of three books, “Using and Analysing Data in Australian Schools,” “Leading Data-Informed Change in Schools,” and “I’m Not a Numbers Person: How to Make Good Decisions in a Data-Rich World.”

Throughout our conversation, we discuss the difference between being data-informed and data-driven, the different levels of data literacy, why change management is crucial to the success of any data literacy program, how to democratize data skills, how to approach data upskilling as a leader, and much more.

Throughout 2022, there was an explosion in generative AI for images and text. GPT-3, DALLE-2, pointed us towards an AI-driven future. Recently, ChatGPT has taken the (data) world by storm — prompting many questions over how generative AI can be used in day to day activities. With the incredible amount of hype surrounding these new tools, we wanted to have a discussion grounded in how these tools are being operationalized today.

Enter Scott Downes. Scott is the CTO of Invisible Technologies, a process automation platform that uses GPT-3 and other generative text technologies. Scott joins the show to talk about how organizations and data professionals can maximize the potential of these tools and how AI and humans can work together in a complementary fashion to optimize workflows, reduce time-intensive, tedious tasks, and do higher quality work.

Scott has a decade of experience in technology, product engineering, and technical leadership, making a veteran in training and mentoring employees across the organization, whether their roles are more creative or more technical.

Throughout the conversation, we talk about what Invisible Technologies uses GPT-3 to optimize workflows, a brief overview of GPT-3 and its use cases for working with text, how GPT-3 helps companies scale their operations, the promises of tools ChatGPT, how AI analysis and human review can work together to save lives, and much more.

In 2022, we saw significant developments in the field of data. From the emergence of generative AI to the growth of low-code data tools and AI assistants—these advancements signal an upcoming paradigm shift, where data-powered tools and machine learning systems will radically transform workflows across various professions.

2022 also saw digital transformation remain a major theme for organizations across industries as they sought to embrace new ways of working, reaching customers, and providing value. As 2023’s looming economic uncertainty puts pressure on organizations to maximize ROI from their investments, digital and data transformation will continue to be one of the key levers by which organizations can cut costs and scale value for their stakeholders.

So we’ve invited DataCamp’s co-founders, CEO Jonathan Cornelissen and COO Martijn Theuwissen to break down the top data trends they are seeing in the data space today, as well as their predictions for the future of the data industry.

Jonathan Cornelissen is the CEO and co-founder of DataCamp. As the CEO of DataCamp, he helped grow DataCamp to upskill over 10M+ learners and 2800+ teams and enterprise clients. He is interested in everything related to data science, education and entrepreneurship. He holds a PhD in financial econometrics, and was the original author of an R package for quantitative finance.

Martijn Theuwissen is the COO and co-founder of DataCamp. As the COO of DataCamp, he helps DataCamp’s enterprise clients on their data and digital transformation strategies, enabling them to make the most of DataCamp for Business’s offering, and helping them transform how their workforce uses data. 

Just as data is used to help businesses determine new directions, set new goals, and measure progress, data can be used in everyday life to help people do the same as they seek to improve themselves.

As the new year arrives, many people are thinking about new goals and new ways to improve their lives, so we have invited Gary Wolf to the show to explore how you can use data-driven thinking to drive meaningful changes in yourself.

Gary Wolf is the Co-Founder of The Quantified Self, an international community of makers and users of self-tracking tools. Prior to co-founding The Quantified Self, Wolf was a contributing editor for Wired Magazine, where he spent two decades covering the intersection of technology and culture, and his cover story in the New York Times is what introduced the general public to self-tracking as an emerging trend.

In this episode, we talk about what The Quantified Self is, why self-tracking projects can be life-changing, how to get started with self-tracking, how to connect with others in the self-tracking community, and much more.

In programming, collaboration and experimentation can be very stressful, since sharing code and making it visible to others can be tedious, time-consuming, and nerve-wracking.Tools like Power BI are changing that entirely, by opening up new ways to collaborate between team members, add layers of customized and complex security to the data teams are working with, and making data much more accessible across organizations.

Ginger Grant joins the show to talk about how organizations can utilize Power BI, Dax, and M to their fullest potential and create new opportunities for experimentation, innovation, and collaboration.

Ginger is the Principal Consultant at the Desert Isle Group, working as an expert in advanced analytic solutions, including machine learning, data warehousing, ETL, reporting and cube development, Power BI, Excel Automation, Data Visualization and training. In addition to her consultant work, she is also a blogger at and global keynote speaker on developments and trends in data. Microsoft has also recognized her technical contributions by awarding her a MVP in Data Platform.

In this episode, we talk about what Power BI is, the common mistakes organizations make when implementing Power BI, advanced use cases, and much more.

The insurance industry thrives on data from utilizing data and analytics to determine policy rates for customers to working with relevant partners in the industry to improve their products and services, data is embedded in everything that insurance companies do.

But insurance companies also have a number of hurdles to overcome, whether it’s transitioning legacy data into new processes and technology, balancing new projects and models with ever-changing regulatory standards, and balancing the ethical considerations of how to best utilize data without resulting in unintended consequences for the end user.

That’s why we’ve brought Rob Reynolds onto the show. Rob is the VP and Chief Data & Analytics Officer at W. R. Berkley, a multinational insurance holding company specializing in property and casualty insurance. Rob brings over two decades of experience in Data Science, IT, and technology leadership, with a particular expertise in building departments and establishing highly functioning teams, especially in highly dynamic environments.

In this episode, we talk in-depth about how insurance companies utilize data, the most important skills for anyone looking for data science jobs in the insurance industry, why the need for thoughtful criticism is growing in data science, and how an expertise in communication will put you ahead of the pack.

With the increasing rate at which new data tools and platforms are being created, the modern data stack risks becoming just another buzzword data leaders use when talking about how they solve problems.

Alongside the arrival of new data tools is the need for leaders to see beyond just the modern data stack and think deeply about how their data work can align with business outcomes, otherwise, they risk falling behind trying to create value from innovative, but irrelevant technology.

In this episode, Yali Sassoon joins the show to explore what the modern data stack really means, how to rethink the modern data stack in terms of value creation, data collection versus data creation, and the right way businesses should approach data ingestion, and much more.

Yali is the Co-Founder and Chief Strategy Officer at Snowplow Analytics, a behavioral data platform that empowers data teams to solve complex data challenges. Yali is an expert in data with a background in both strategy and operations consulting teaching companies how to use data properly to evolve their operations and improve their results.

2022 was an incredible year for Generative AI. From text generation models like GPT-3 to the rising popularity of AI image generation tools, generative AI has rapidly evolved over the last few years in both its popularity and its use cases.

Martin Musiol joins the show this week to explore the business use cases of generative AI, and how it will continue to impact the way the society interacts with data. Martin is a Data Science Manager at IBM, as well as Co-Founder and an instructor at Generative AI, teaching people to develop their own AI that generates images, videos, music, text and other data. Martin has also been a keynote speaker at various events, such as Codemotion Milan. Having discovered his passion for AI in 2012, Martin has turned that passion into his expertise, becoming a thought leader in AI and machine learning space.

In this episode, we talk about the state of generative AI today, privacy and intellectual property concerns, the strongest use cases for generative AI, what the future holds, and much more.

Data Analytics has played a major role in Chelsea’s journey to becoming the seventh most valuable football club in the world, Chelsea has won six league titles, eight FA Cups, five League Cups, and two Champions League titles.

Today, we are going behind the scenes at Chelsea FC to see how they use data analytics to analyze matches, inform tactical decision-making, and drive matchday success in one of the world’s top football leagues, just in time for the 2022 FIFA World Cup in Qatar!

Federico Bettuzzi is a Data Scientist at Chelsea FC. As a specialist in match analytics, Federico works with Chelsea’s first team to inform tactical decision making during matches. Federico joins the show to break down how he gathers and synthesizes data, how they develop match analyses for tactical reviews, how managers prioritize data analytics differently, how to balance long-term and short-term projects, and much more.

To become a data-driven organization, it takes a major shift in mindset and culture, investments in technology and infrastructure, skills transformation, and clearly evangelizing the usefulness of using data to drive better decision-making.

With all of these levers to scale, many organizations get stuck early in their data transformation journey, not knowing what to prioritize and how. In this episode, Ganes Kesari joins the show to share the frameworks and processes that organizations can follow to become data-driven, measure their data maturity, and win stakeholder support across the organization.

Ganes is Co-Founder and Chief Decision Scientist at Gramener, which helps companies make data-driven decisions through powerful data stories and analytics. He is an expert in data, analytics, organizational strategy, and hands-on execution. Throughout his 20-year career, Ganes has become an internationally-renowned speaker and has been published in Forbes, Entrepreneur, and has become a thought leader in Data Science.

Throughout the episode, we talk about how organizations can scale their data maturity, how to build an effective data science roadmap, how to successfully navigate the skills and people components of data maturity, and much more.

During Data Literacy Month, we shared how data journalists curate and distill data stories to the wider public. Since 2020, Data Journalism has risen both in significance and visibility. Throughout the COVID-19 pandemic, data journalists have been instrumental in keeping the public informed by investigating, challenging, interpreting, and explaining complex datasets.

In this episode, Betsy Ladyzhets joins the show to talk about the state of Data Journalism today, and shares from her experience as a data journalist

Betsy is an independent science, health, and data journalist focused on COVID-19 and Founder of the COVID-19 Data Dispatch, an independent publication providing updates and resources on public COVID-19 data. She is also currently working as a Senior Journalism Fellow with the Documenting COVID-19 project at the Brown Institute for Media Innovation and MuckRock. Her work has been featured in Science News, FiveThirtyEight, MIT Tech Review, and the Covid Tracking Project.

Throughout the show, we discuss the importance of letting data shape a narrative, what characteristics of traditional journalism are needed for data journalists, the best practices for delivering effective data stories, how the rise of AI and data visualization are impacting data journalism, and much more.

Links shared during the episode:

• Data Sonification
• The COVID-19 Data Dispatch
• The Data Visualization Society

Learning on DataCamp? Take part in this week’s XP-challenge: http://www.datacamp.com/promo/free-week-xp-challenge-2022

Python has dominated data science programming for the last few years, but there’s another rising star programming language seeing increased adoption and popularity—Julia.

As the fourth most popular programming language, many data teams and practitioners are turning their attention toward understanding Julia and seeing how it could benefit individual careers, business operations, and drive increased value across organizations.

Zacharias Voulgaris, PhD joins the show to talk about his experience with the Julia programming language and his perspective on the future of Julia’s widespread adoption. Zacharias is the author of Julia for Data Science. As a Data Science consultant and mentor with 10 years of international experience that includes the role of Chief Science Officer at three startups, Zacharias is an expert in data science, analytics, artificial intelligence, and information systems.

In this episode, we discuss the strengths of Julia, how data scientists can get started using Julia, how team members and leaders alike can transition to Julia, why companies are secretive about adopting Julia, the interoperability of Julia with Python and other popular programming languages, and much more.

Check out this month’s events: https://www.datacamp.com/data-driven-organizations-2022

Take the Introduction to Julia course for free!

https://www.datacamp.com/courses/introduction-to-julia

While securing the support of senior executives is a major hurdle of implementing a data transformation program, it’s often one of the earliest and easiest hurdles to overcome in comparison to the overall program itself. Leading a data transformation program requires thorough planning, organization-wide collaboration, careful execution, robust testing, and so much more.

Vanessa Gonzalez is the Senior Director of Data and Analytics for ML & AI at Transamerica. Vanessa has experience in data transformation, leadership, and strategic direction for Data Science and Data Governance teams, and is an experienced senior data manager.

Vanessa joins the show to share how she is helping to lead Transamerica’s Data Transformation program. In this episode, we discuss the biggest challenges Transamerica has faced throughout the process, the most important factors to making any large-scale transformation successful, how to collaborate with other departments, how Vanessa structures her team, the key skills data scientists need to be successful, and much more.

Check out this month’s events: https://www.datacamp.com/data-driven-organizations-2022

As data leaders continue to fill their talent gap, how should they approach sourcing, retaining, and upskilling their talent? What strategies should data leaders adopt in order to accomplish their talent goals and become data-driven?

Kyle Winterbottom joins the show to talk about the key differentiators between data teams that build talent-dense teams and those that do not. Kyle is the host of Driven by Data: The Podcast, the Founder & CEO of Orbition, a talent solutions provider, for scaling Data, Analytics, & Artificial Intelligence teams across the UK, Europe and the USA. As an accomplished expert and thought leader in talent acquisition, attraction, and retention, as well as scaling data teams, Kyle was named one of Data IQ’s 100 Most Influential People in Data for 2022.

In this episode, we talk about how data teams can position themselves to attract top talent, how to properly articulate how data team members are adding value to the business, how organizations can accidentally set data leaders up to fail, how to approach upskilling, and how data leaders can create an employer branding narrative to attract top talent.

Check out this month’s events: https://www.datacamp.com/data-driven-organizations-2022

To improve Data Literacy, organizations need high-quality data training programs that give their employees the most valuable and relevant data skills they need. Many companies fall into the trap of implementing training programs that are poorly designed or not relevant for the needs of their learners.

Sharon Castillo is the VP of Global Education at DataRobot, where she developed the DataRobot University, a self-service education portal that features both free and paid courses on AI and machine learning that are available to the public. With over 30 years of experience, Sharon is a leading expert in data training and employee upskilling programs, from development through execution.

Sharon joins the show to talk about what makes an effective data training program, how to ensure employees retain the information, how to properly incentivize training participation, why organizations should prioritize training, and much more. This is essential listening for anyone developing a training program for their team or organization.

We have had many guests on the show to discuss how different industries leverage data science to transform the way they do business, but arguably one of the most important applications of data science is in space research and technology.

Justin Fletcher joins the show to talk about how the US Space Force is using deep learning with telescope data to monitor satellites, potentially lethal space debris, and identify and prevent catastrophic collisions. Justin is responsible for artificial intelligence and autonomy technology development within the Space Domain Awareness Delta of the United States Space Force Space Systems Command. With over a decade of experience spanning space domain awareness, high performance computing, and air combat effectiveness, Justin is a recognized leader in defense applications of artificial intelligence and autonomy.

In this episode, we talk about how the US Space Force utilizes deep learning, how the US Space Force publishes its research and data to find high-quality peer review, the must-have skills aspiring practitioners need in order to pursue a career in Defense, and much more.

Throughout data literacy month, we’ve shined a light on the importance of data literacy skills and how it impacts individuals and organizations. Equally as important is how to actually approach transformational data literacy programs and ensure they are successful.

In this final episode of Data Literacy Month, we are unpacking how CBRE is upskilling over 3,000 of its employees on data literacy skills through a relevant, high-value learning program.

Emily Hayward is the Data and Digital Change Manager at CBRE, a global leader in commercial real estate services and investment. Emily is a transformational leader with a track record of leading successful high-profile technology, data, and cultural transformations across both the public and private sectors through an ardent belief that change cannot be achieved without first winning people over.

Throughout the episode, we talk about Emily’s approach to building CBRE’s learning program, effective change management, why it’s critical to secure executive sponsorship, and much more.

Looking to build a data literacy program of your own? Check out DataCamp for Business: https://bit.ly/3r7BgsF

Understanding and interpreting data visualizations are one of the most important aspects of data literacy. When done well, data visualization ensures that stakeholders can quickly take away critical insights from data. Moreover, data visualization is often the best place to start when increasing organizational data literacy, as it’s often titled the “gateway drug” to more advanced data skills.

Andy Cotgreave, Senior Data Evangelist at Tableau Software and co-author of The Big Book of Dashboards, joins the show to break down data visualization and storytelling, drawing from his 15-year career in the data space. Andy has spoken for events like SXSW, Visualized, and Tableau’s conferences and has inspired thousands of people to develop their data skills.

In this episode, we discuss why data visualization skills are so essential, how data visualization increases organizational data literacy, the best practices for visual storytelling, and much more.

This episode of DataFramed is a part of DataCamp’s Data Literacy Month, where we raise awareness about Data Literacy throughout September through webinars, workshops, and resources featuring thought leaders and subject matter experts that can help you build your data literacy, as well as your organization’s. For more information, visit: https://www.datacamp.com/data-literacy-month/for-teams

Data Literacy may be an important skill for everyone to have, but the level of need is always unique to each individual. Some may need advanced technical skills in machine learning algorithms, while others may just need to be able to understand the basics. Regardless of where anyone sits on the skills spectrum, the data community can help accelerate their careers.

There’s no one who knows that better than Kate Strachnyi. Kate is the Founder and Community Manager at DATAcated, a company that is focused on bringing data professionals together and helping data companies reach their target audience through effective content strategies.

Kate has created courses on data storytelling, dashboard and visualization best practices, and she is also the author of several books on data science, including a children’s book about data literacy. Through her professional accomplishments and her content efforts online, Kate has not only built a massive online following, she has also established herself as a leader in the data space.

In this episode, we talk about best practices in data visualization, the importance of technical skills and soft skills for data professionals, how to build a personal brand and overcome Imposter Syndrome, how data literacy can make or break organizations, and much more.

This episode of DataFramed is a part of DataCamp’s Data Literacy Month, where we raise awareness for Data Literacy throughout the month of September through webinars, workshops, and resources featuring thought leaders and subject matter experts that can help you build your data literacy, as well as your organization’s. For more information, visit: https://www.datacamp.com/data-literacy-month/for-teams

Data Literacy is increasingly becoming a skill that every role needs to have, regardless of whether their role a data-oriented or not. No one knows this better than Jordan Morrow, who is known as the Godfather of Data Literacy.

Jordan is the VP and Head of Data Analytics at Brainstorm, Inc., and is the author of Be Data Literate: The Skills Everyone Needs to Succeed.Jordan has been a fierce advocate for data literacy throughout his career, including helping the United Nations understand and utilize data literacy effectively.

Throughout the episode, we define data literacy, why organizations need data literacy in order to use data properly and drive business impact, how to increase organizational data literacy, and more.

This episode of DataFramed is a part of DataCamp’s Data Literacy Month, where we raise awareness for Data Literacy throughout the month of September through webinars, workshops, and resources featuring thought leaders and subject matter experts that can help you build your data literacy, as well as your organization’s. For more information, visit: https://www.datacamp.com/data-literacy-month/for-teams

Taking inspiration from International Literacy Day on September 8, DataCamp is dedicating the whole month of September to raising awareness about Data Literacy.

Throughout the month, we are featuring thought leaders and subject matter experts in order to get you Data Literacy, and we can’t wait for you to hear the exceptional guests we have lined up for you right here on DataFramed.

Check out the full lineup of events.

Many times, data scientists can fall into the trap of resume-driven development. As in, learning the shiniest, most advanced technique available to them in an attempt to solve a business problem. However, this is not what a learning mindset should look like for data teams.

As it turns out, taking a step back and focusing on the fundamentals and step-by-step iteration can be the key to growing as a data scientist, because when data teams develop a strong understanding of the problems and solutions lying underneath the surface, they will be able to wield their tools with complete mastery.

Ella Hilal joins the show to share why operating from an always-learning mindset will open up the path to a true mastery and innovation for data teams. Ella is the VP of Data Science and Engineering for Commercial and Service Lines at Shopify, a global commerce leader that helps businesses of all size grow, market, and manage their retail operations. Recognized as a leading woman in Data science, Internet of things and Machine Learning, Ella has over 15 years of experience spanning multiple countries, and is an advocate for responsible innovation, women in tech, and STEM.

In this episode, we talk about the biggest mistakes data scientists make when solving business problems, how to create cohesion between data teams and the broader organization, how to be an effective data leader that prioritizes their team’s growth, and how developing an always-learning mindset based on iteration, experimentation, and deep understanding of the problems needing to be solved can accelerate the growth of data teams.

Most companies experience the same pain point when working with data: it takes too long to get the right data to the right people. This creates a huge opportunity for data scientists to find innovative solutions to accelerate that process.

One very effective method is to implement real-time data solutions that can increase business revenue and make it easier for anyone relying on the data to access the data they need, understand it, and make accurate decisions with it.

George Trujillo joins the show to share how he believes real-time data has the potential to completely transform the way companies work with data. George is the Principal Data Strategist at DataStax, a tech company that helps businesses scale by mobilizing real-time data on a single, unified stack. With a career spanning 30 years and companies like Charles Schwab, Fidelity Investments, and Overstock.com, George is an expert in data-driven executive decision-making and tying data initiatives to tangible business value outcomes.

In this episode, we talk about the real-world use cases of real-time analytics, why reducing data complexity is key to improving the customer experience, the common problems that slow data-driven decision-making, and how data practitioners can start implementing real-time data through small high-value analytical assets.