Catalog & Cocktails: The Honest, No-BS Data Podcast

As Season 8 of Catalog & Cocktails comes to a close, Tim and Juan reflect on the conversations, breakthroughs, and trends that shaped the past episodes. From unraveling the complexities of data quality to exploring the future of AI, this recap dives into the standout moments, key lessons, and recurring themes from our incredible guests. Tune in for an honest, no-BS discussion on what we’ve learned this season—and a sneak peek at what’s coming next!

Tim and Juan are live from DGIQ + AIGov with their Honest, No-BS takeaways on the latest topics. Is it the same old story, or is there something new to pay attention to? What’s actually working? What’s worth keeping an eye on? And what are the standout success stories? Tune in this week to find out!

What’s the next big concept in the data world? Meet the Meta Grid—a decentralized architecture inspired by microservices and data mesh, introduced by Ole Olesen-Bagneux, O’Reilly author of The Enterprise Data Catalog. Spoiler: the Meta Grid already exists within your organization, and it’s not a technology! In this episode, Ole joins us to unpack what the Meta Grid really is (and isn’t) and why it’s poised to transform the way we think about enterprise data.

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What’s the next big concept in the data world? Meet the Meta Grid—a decentralized architecture inspired by microservices and data mesh, introduced by Ole Olesen-Bagneux, O’Reilly author of The Enterprise Data Catalog. Spoiler: the Meta Grid already exists within your organization, and it’s not a technology! In this episode, Ole joins us to unpack what the Meta Grid really is (and isn’t) and why it’s poised to transform the way we think about enterprise data.

Why does data governance seem like all talk and no action? In this episode, Nicola Askham, The Data Governance Coach, joins us to break down the barriers to effective data governance. Learn why it’s essential for AI readiness, how to make the case for it in your organization, and practical steps to get started—even with just a spreadsheet. Tune in for honest, actionable advice on turning talk into tangible results.

Enhance your listening experience with C&C Chat at data.world/podcasts

Why does data governance seem like all talk and no action? In this episode, Nicola Askham, The Data Governance Coach, joins us to break down the barriers to effective data governance. Learn why it’s essential for AI readiness, how to make the case for it in your organization, and practical steps to get started—even with just a spreadsheet. Tune in for honest, actionable advice on turning talk into tangible results.

Enhance your listening experience with C&C Chat at data.world/podcasts

Ethan Mollick, Associate Professor at the Wharton School of the University of Pennsylvania, dives into how we can shape the future of AI. Ethan explores why organizations need to rethink their approach to AI adoption, the importance of disciplined experimentation, and how imagination—not just technology—will unlock AI’s true potential. From understanding the psychology of LLMs to embracing R&D as an everyday practice, Ethan shares practical advice for making AI work for you, not the other way around.

Enhance your listening experience with C&C Chat at data.world/podcasts

Ethan Mollick, Associate Professor at the Wharton School of the University of Pennsylvania, dives into how we can shape the future of AI. Ethan explores why organizations need to rethink their approach to AI adoption, the importance of disciplined experimentation, and how imagination—not just technology—will unlock AI’s true potential. From understanding the psychology of LLMs to embracing R&D as an everyday practice, Ethan shares practical advice for making AI work for you, not the other way around.

Enhance your listening experience with C&C Chat at data.world/podcasts

The data world is buzzing about open table formats and lakehouses, but what's the real story? Vinoth Chandar, founder & CEO of Onehouse, creator of Apache Hudi, unpacks the challenges of data siloes and vendor lock-in, and explains how open table formats are the key to unlocking true data flexibility and collaboration.

Enhance your listening experience with C&C Chat at data.world/podcasts

The data world is buzzing about open table formats and lakehouses, but what's the real story? Vinoth Chandar, founder & CEO of Onehouse, creator of Apache Hudi, unpacks the challenges of data siloes and vendor lock-in, and explains how open table formats are the key to unlocking true data flexibility and collaboration.

Enhance your listening experience with C&C Chat at data.world/podcasts

Malcolm Hawker, Chief Data Officer at Profisee and host of CDO Matters Podcast, recently sparked a heated discussion on LinkedIn about where data catalogs are heading. His view? Data catalogs today are being commoditized and we need to pivot from data management to knowledge management. Join Malcolm, Tim, and Juan as they break down this debate and explore what other data professionals had to say about this shift in thinking.

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Andrea Gioia, CTO at Quantyca and Co-founder of Blindata, shares his insights on the real issues holding back data management. Spoiler: It's not the technology. Andrea discusses how the biggest challenges stem from people, collaboration, and the ways we handle knowledge. As AI continues to evolve, poor data management becomes an even bigger obstacle, making it clear that prioritizing knowledge management is more urgent than ever.

Enhance your listening experience with C&C Chat at data.world/podcasts

Andrea Gioia, CTO at Quantyca and Co-founder of Blindata, shares his insights on the real issues holding back data management. Spoiler: It's not the technology. Andrea discusses how the biggest challenges stem from people, collaboration, and the ways we handle knowledge. As AI continues to evolve, poor data management becomes an even bigger obstacle, making it clear that prioritizing knowledge management is more urgent than ever.

Enhance your listening experience with C&C Chat at data.world/podcasts

Can data alone solve your business problems? Colleen Tartow, Ph.D., Field CTO and Head of Strategy at VAST Data, doesn’t think so. In this episode, we explore why it’s crucial to focus on business questions and deliver reliable, high-quality data products. We’ll also break down how to avoid the tech hype and ensure your data strategy is aligned with real business needs. Join us for a candid discussion on data, AI maturity, and finding the true value behind your data initiatives.

Enhance your listening experience with C&C Chat at data.world/podcasts

Can data alone solve your business problems? Colleen Tartow, Ph.D., Field CTO and Head of Strategy at VAST Data, doesn’t think so. In this episode, we explore why it’s crucial to focus on business questions and deliver reliable, high-quality data products. We’ll also break down how to avoid the tech hype and ensure your data strategy is aligned with real business needs. Join us for a candid discussion on data, AI maturity, and finding the true value behind your data initiatives.

Enhance your listening experience at data.world/podcasts

How can active metadata transform the way organizations manage and utilize data? In this episode, Mike Evans, Chief Innovation Officer at Amplifi, explores the often untapped potential of metadata to reveal insights from data usage patterns, optimize data ecosystems, and focus initiatives on what truly drives value.

Enhance your listening experience with C&C Chat at data.world/podcasts

How can active metadata transform the way organizations manage and utilize data? In this episode, Mike Evans, Chief Innovation Officer at Amplifi, explores the often untapped potential of metadata to reveal insights from data usage patterns, optimize data ecosystems, and focus initiatives on what truly drives value.

Enhance your listening experience with C&C Chat at data.world/podcasts

What is the vital role of data quality in the world of GenAI? With data trust at an all-time high, Kevin Hu, CEO & Co-Founder of Metaplane, shares how businesses can prevent data mishaps and maintain the reliability needed for AI success. Is the hype around GenAI just a continuation of data trends from BI to ML, or does it demand a new approach? Find out in this week’s episode.

Enhance your listening experience with C&C Chat at data.world/podcasts

What is the vital role of data quality in the world of GenAI? With data trust at an all-time high, Kevin Hu, CEO & Co-Founder of Metaplane, shares how businesses can prevent data mishaps and maintain the reliability needed for AI success. Is the hype around GenAI just a continuation of data trends from BI to ML, or does it demand a new approach? Find out in this week’s episode.

Enhance your listening experience with C&C Chat at data.world/podcasts

Dael Williamson, EMEA CTO at Databricks, joins us to reveal how interoperability is the key to unlocking expertise, exchanges, and ecosystems. Tune in for an honest, no-BS conversation on navigating the complexities of data ecosystems—and how standardization, context, and interoperability can be leveraged to tackle organizational challenges.

Enhance your listening experience with C&C Chat at data.world/podcasts

Dael Williamson, EMEA CTO at Databricks, joins us to reveal how interoperability is the key to unlocking expertise, exchanges, and ecosystems. Tune in for an honest, no-BS conversation on navigating the complexities of data ecosystems—and how standardization, context, and interoperability can be leveraged to tackle organizational challenges.

Enhance your listening experience with C&C Chat at data.world/podcasts

Responsible AI, what is it and who is responsible when it comes to AI? Mariam Halfhide, Responsible AI Expert at Xebia and former C&C guest, is here to discuss the answers with Tim and Juan on this week's episode.

Enhance your listening experience with C&C Chat at data.world/podcasts

Responsible AI, what is it and who is responsible when it comes to AI? Mariam Halfhide, Responsible AI Expert at Xebia and former C&C guest, is here to discuss the answers with Tim and Juan on this week's episode.

Amalia Child, Data Manager and Librarian, joins us to explore how librarians have evolved in the digital age—trading shelves of books for data repositories. We’ll discuss her post “The Five Laws of Data Enablement: How the father of library science would make his data team indispensable” and why library science is essential in data organizations.

Amalia Child, Data Manager and Librarian, joins us to explore how librarians have evolved in the digital age—trading shelves of books for data repositories. We’ll discuss her post “The Five Laws of Data Enablement: How the father of library science would make his data team indispensable” and why library science is essential in data organizations.

How can we create a more inclusive workplace and data infrastructure? Dawn Carter, Enterprise Data Architect at BAE Systems, joins us this week to explore the intersection of diversity, inclusion, and data management. Together, we'll discuss actionable strategies for integrating these principles into change management and driving meaningful transformation in the data world.

How can we create a more inclusive workplace and data infrastructure? Dawn Carter, Enterprise Data Architect at BAE Systems, joins us this week to explore the intersection of diversity, inclusion, and data management. Together, we'll discuss actionable strategies for integrating these principles into change management and driving meaningful transformation in the data world.

What does it mean to humanize data strategy? Tiankai Feng, Data Strategy & Governance Lead at Thoughtworks Europe and author of Humanizing Data Strategy, stops by to discuss the often-overlooked human side of technology and why it's crucial for data strategy & governance. While many companies struggle to prioritize the people aspect in their tech initiatives, Tiankai has a solution with the 5 C's - Competency, Collaboration, Creativity, Communication, and Conscience.

What does it mean to humanize data strategy? Tiankai Feng, Data Strategy & Governance Lead at Thoughtworks Europe and author of Humanizing Data Strategy, stops by to discuss the often-overlooked human side of technology and why it's crucial for data strategy & governance. While many companies struggle to prioritize the people aspect in their tech initiatives, Tiankai has a solution with the 5 C's - Competency, Collaboration, Creativity, Communication, and Conscience.

Anna Bergevin, Sr. Product Manager at ResMed, brings her honest no-bs takes about data product management work and who should be doing it. We will discuss her path into data product management and how she is using AI to be productive and try new things!

Anna Bergevin, Sr. Product Manager at ResMed, brings her honest no-bs takes about data product management work and who should be doing it. We will discuss her path into data product management and how she is using AI to be productive and try new things!

Thanks to all Catalog & Cocktails #honestnobs data podcast listeners who have been following us for 4 years. Tim and Juan are going to take a break for a few weeks to brainstorm what’s next, and we would appreciate your honest no-bs feedback! We would love your input:

- What new topics should we cover?

- What topics should we dive into?

- Should we have a new format (e.g panel, debates)?

- Who would you like to see as a guest?

Reach out to us on LinkedIn or email [email protected] to share your feedback!

AI Ops is a new and upcoming role focused on understanding and improving the business process by using AI. In this episode, Brandon Gadoci, VP of AI Ops at data.world shares his experience on how to get started and what are the opportunities for the business.

AI Ops is a new and upcoming role focused on understanding and improving the business process by using AI. In this episode, Brandon Gadoci, VP of AI Ops at data.world shares his experience on how to get started and what are the opportunities for the business.

We can’t believe it’s been 4 years since the start of Catalog & Cocktails! In this episode, Tim and Juan will reminisce on 4 years of the podcast, reflect on developments in the data world, and share takeaways from Gartner Data & Analytics Summit in London. We'll also close with tips for those looking to start their own podcast.

Knowledge Graphs are gaining more and more attention due to their role of structuring data and knowledge and providing accuracy for LLMs through GraphRAG. In this episode, we are joined by Ora Lassila, one of the “fathers” of RDF graphs and semantic web, which are the foundations for modern knowledge graphs, where we will dive into the questions you’ve always wanted to ask but haven’t.

Knowledge Graphs are gaining more and more attention due to their role of structuring data and knowledge and providing accuracy for LLMs through GraphRAG. In this episode, we are joined by Ora Lassila, one of the “fathers” of RDF graphs and semantic web, which are the foundations for modern knowledge graphs, where we will dive into the questions you’ve always wanted to ask but haven’t.

CDO, CDAO, CAIO, CIO, CTO! Oh my, it's a cluster! Sol Rashidi joins Tim and Juan to help navigate this cluster, sharing honest no bs advice from her vast experience in the Data and AI world. If you are a leader, or a practioner aspiring to go to leadership, this is the must listen episode!

CDO, CDAO, CAIO, CIO, CTO! Oh my, it's a cluster! Sol Rashidi joins Tim and Juan to help navigate this cluster, sharing honest no bs advice from her vast experience in the Data and AI world. If you are a leader, or a practioner aspiring to go to leadership, this is the must listen episode!

When you think about Gen AI applications, one of the first things that comes to mind is Vector Databases as part of the stack. In this episode, Tim and Juan chat with Bob van Luijt, co-founder of Weaviate, to demystify Vector Databases.

When you think about Gen AI applications, one of the first things that comes to mind is Vector Databases as part of the stack. In this episode, Tim and Juan chat with Bob van Luijt, co-founder of Weaviate, to demystify Vector Databases.

Everyone wants to be doing AI and we know that data is at the center. So, how do we become AI Ready? Tim and Juan chat with Peter Kromhout and Mariam Halfhide from Xebia to discuss strategies and techniques to become AI Ready.

Everyone wants to be doing AI and we know that data is at the center. So, how do we become AI Ready? Tim and Juan chat with Peter Kromhout and Mariam Halfhide from Xebia to discuss strategies and techniques to become AI Ready.

Neil Burge has been researching who’s been buying data catalogs and why. His LinkedIn posts are a gold mine for data practitioners, leaders and vendors. In this episode, Tim and Juan chat with Neil to go over his research, learnings and recommendations for the data industry.

Neil Burge has been researching who’s been buying data catalogs and why. His LinkedIn posts are a gold mine for data practitioners, leaders and vendors. In this episode, Tim and Juan chat with Neil to go over his research, learnings and recommendations for the data industry.

Data governance and security may be scary and hard, but with the right solutions in place, data teams can enable the right balance between security and usability to drive their businesses forward!

Data governance and security may be scary and hard, but with the right solutions in place, data teams can enable the right balance between security and usability to drive their businesses forward!

Tim and Juan will be attending Data Council Austin and will be live interviewing anyone who wants to be interviewed! One question: what’s your honest no-bs take of the data world?

Shhh...do you hear that? It's the Data Whisperer. And he's here to tell a story: A data story. He'll disclose some hard-won truths. He'll philosophically approach the idea of truth before meaning. There may even be...data puppets.

Shhh...do you hear that? It's the Data Whisperer. And he's here to tell a story: A data story. He'll disclose some hard-won truths. He'll philosophically approach the idea of truth before meaning. There may even be...data puppets.

What are we hearing at talks and in the hallways at Gartner Data and Analytics 2024? AI is not only the cool kid, Metadata is everywhere. Join us to learn about the latest takeaways from the conference.

Amy works at the cross-section of arguably the most important thing to a business: aligning data strategy with corporate vision. How do you ACTUALLY use data to achieve business goals, and how do you know if you're going in the right direction? Amy stops by to discuss.

You can’t lift and shift your traditional data governance practices to AI governance. AI's unique quirks bring unique governance requirements: understanding its limitations, ensuring fairness, protecting personal and intellectual property rights, and tailoring accuracy to specific use cases.

If you really want to win, you need to take some risks and be the first with something new. But how do you minimize risk? Eva Nahari, Principal at DNX ventures, talks to us about the current genAI investment environment.

Paco Nathan has a career on bridging innovation and open source project on Data and AI to the real world. This episode will be his honest, no-bs take on the impact of open source in the real world. And of course, we will also talk about Knowledge Graphs and LLMs.

A world without AI? Unimaginable. And that's why - so is a world without information architecture, according to Jessica Talisman, information architect at Amazon. Information architecture extends beyond browse menus and UX/UI. We talk about why.

Santona Tuli is the head of data at Upsolver. She's a voice we like to listen to about how data teams assemble: data roles, data culture, the function of data at a company...She stops by Catalog & Cocktails to talk workflows, architecture, and looking at data teams with a 10,000-foot view.

Samia Rahman is the Director of Enterprise Data Strategy and Governance at Seagen. She's a data mesh expert, and she's here to tell us why data mesh often gets mesh-understood. Also - we discuss knowledge graphs, prompt engineering, and whether AI and data governance should be separate.

Jeremiah Owyang is a general partner at Blitzscaling Ventures. His career arc has spanned web, sharing economy, and autonomous/AI technologies. He believes that AI is going to help humanity accomplish many of the big challenges we have for society, from health to learning to work and more. But the way we communicate and measure work will change - radically. What does our AI-future look like? He stops by to discuss.

2023 is coming to a close and so is our season 6 with 20 episodes. Join Tim and Juan where they will provide the takeaway of takeaways for all the episodes of season 6. We will be back in January 2024 and kick off Season 7.

Data contracts are all the rage and it’s about shifting responsibility to the left. What does that actually mean? How do you do that? Do we actually need more technology for it? Who needs to be involved? So many honest no-bs questions and who best to answer them than Andrew Jones, inventor of data contracts.

In this episode we will chat with Doris Lee on how data science started from an exclusive, hi-tech branch, how it's become more accessible over time, and what's still missing. We will also go over the data science market and the role of open source.

Investing in Knowledge Graph provides higher accuracy for LLM-powered question-answering systems. That's the conclusion of the latest research that Juan Sequeda, Dean Allemang and Bryon Jacob have recently presented. In this episode, we will dive into the details of this research and understand why to succeed in this AI world, enterprises must treat the business context and semantics as a first-class citizen.

Data Storytelling is a topic that constantly comes up, but do we really know what it is? And what it isn’t? Who best to have this #honestnobs discussion than Kat Greenbrook, author of the upcoming book The Data Storyteller’s Handbook: How to create business impact using data storytelling.

Data Storytelling is a topic that constantly comes up, but do we really know what it is? And what it isn’t? Who best to have this #honestnobs discussion than Kat Greenbrook, author of the upcoming book The Data Storyteller’s Handbook: How to create business impact using data storytelling.

RAG, Retrieval Augemented Generation, is the term you now constantly hear in conjunction with LLM that provides context. But how does it actually work? And what's the relationship with Vector Databases and Knowledge Graphs? This will be a geeky AI episode with Mike Dillinger.

Technical folks miss the boat and are boring when they talk about the features of data catalog such as glossaries and data lineage to business people. In this episode Krystin Kim will share how a data catalog should be presented to the business: the ultimate place to share ideas across big companies, a treasure trove of use cases for others to discover and make even better, recipes to make amazing things that returns millions in business value.

What's the state of data stewardship today and where is it going? Will data stewards continue to exist? How is this evolving with respect to data products? And what is the impact of AI? All of these questions and more is what Tim and Juan ranted about in this episode.

With the hype of Generative AI, how do we keep focused on the goal of delivering the right valuable and business facing analytics and data use-cases with as low friction and maximum agility as possible? Jon Cooke, founder of Dataception has a lot of honest no-bs thoughts to share!

Takeaways from With the hype of Generative AI, how do we keep focused on

the goal of delivering the right valuable and business facing analytics

and data use-cases with as low friction and maximum agility as

possible? Jon Cooke, founder of Dataception has a lot of honest no-bs

thoughts to share!

Tim and Juan provide an honest no-bs summary of what they observed and learned at Big Data London and the AI Conference in San Francisco. Tune in to get the latest on Data and AI and short interviews from Peter Norvig (Google), Nazneen Rajani (Hugging Face), Fabiana Clemente (YData), Danny Bickson (Visual Layer), Gev Sogomonian (AimStack), Yujian Tang (Zilliz), Patrick McFadin (Datasax) and Ofer Mendelevitch (Vectara)

Live from #meandatastreets of Big Data London, Tim and Juan will be chatting with Chris Tabb on how data teams need to be driving the message on providing the ability to increase the performance of the business across all domains with the use if data.

Tim and Juan will share their latest thoughts about where the budget for data projects are heading.

If data isn't driving results is useless. In this episode, Alexa Westlake will share from her experience on how to define and scale a council, create a culture of joint ownership of metrics, and discuss the relationship between outputs and outcomes.

Sports analytics requires video, scouting textual reports, streaming data, numerical results of games. How can these types of analytics be accomplished? Where does Generative AI fit? Enter the data lakehouse. Ari Kaplan, the real money ball guy, will share his experience with Tim and Juan.

Are you a data leader with a technical data strategy and struggling to connect it with the business? Are you trying to get a “seat at the table” and not succeeding? This episode is for you! Aaron Wilkerson will share his thoughts and learnings with Tim and Juan

Are you a data leader with a technical data strategy and struggling to connect it with the business? Are you trying to get a “seat at the table” and not succeeding? This episode is for you! Aaron Wilkerson will share his thoughts and learnings with Tim and Juan

While Juan is out on PTO he took this time to reflect on all the chatter around AI and Generative AI and it's impact on being able to "talk to your data." Tim also offers his own thoughts on the importance of how this impacts the business.

Have you asked yourself what is the relationship between the exponential growth of data and the environmental and business sustainability goals? Simone Steel, an experienced CDAO, has been asking these questions. This episode is a follow up to her thought provoking presentation at 2023 CDOIQ Conference.

Have you asked yourself what is the relationship between the exponential growth of data and the environmental and business sustainability goals? Simone Steel, an experienced CDAO, has been asking these questions. This episode is a follow up to her thought provoking presentation at 2023 CDOIQ Conference.

Everyone says they want to be a data-driven driven company. To do so, data must be a first class citizen, at the heart of all strategy and reporting to the CEO. Is that really happening at your company? Wendy Turner-Williams will discuss how data is at the center of AI, Ethics, Privacy, Security and more.

Everyone says they want to be a data-driven driven company. To do so, data must be a first class citizen, at the heart of all strategy and reporting to the CEO. Is that really happening at your company? Wendy Turner-Williams will discuss how data is at the center of AI, Ethics, Privacy, Security and more.

How is your data team driving revenue and reducing costs? How is the impact of your data team being measured? All of these business value topics and more will be discussed with Ethan Aaron, CEO of Portable.

How is your data team driving revenue and reducing costs? How is the impact of your data team being measured? All of these business value topics and more will be discussed with Ethan Aaron, CEO of Portable.

Live from CDOIQ, we will joined by Tom Redman, The Data Doc, to discuss what's broken, why we go in circles, and how we can objectively evaluate the data world. Oh, we will also chat about his upcoming book: People and Data

Live from CDOIQ, we will joined by Tom Redman, The Data Doc, to discuss what's broken, why we go in circles, and how we can objectively evaluate the data world. Oh, we will also chat about his upcoming book: People and Data

In this episode, Joe Reis, best-selling author of Fundamentals of Data Engineering explains why data modeling and semantics need to take the center stage for AI to truly succeed.

In this episode, Joe Reis, best-selling author of Fundamentals of Data Engineering explains why data modeling and semantics need to take the center stage for AI to truly succeed.

Tim Gasper and Juan Sequeda debrief after 4 days on-site at Snowflake Summit 2023 in Las Vegas.

Key Takeaways:

• [00:00 - 00:40] Intro, Tim and Juan are at the Snowflake Summit in Las Vegas
• [00:43 - 01:27] AI
• [01:27 - 04:04] Large language models (LLMs), and associated costs
• [04:04 - 04:46] Costs of maintaining data infrastructure for larger models
• [04:50 - 06:58] AI and LM use cases for businesses
• [07:07 - 09:52] New capabilities added to Snowflake: Snow Park and Snowflake Container Services, and Streamlit
• [09:52 - 11:15] Juan predicts that Snowflake will build their own data centers
• [11:18 - 12:47] Databricks conference announcements
• [13:29 - 19:11] Introducing Ethan Aaron from Portable, and his take on trends with Snowflake

In this episode, we delve into the world of change management and its crucial role in the nonprofit sector, particularly concerning data governance.

Our special guest, Aakriti Agrawal from American Express, shares her expertise and insights on the change management process, emphasizing the need for leadership rather than mere management.

Join hosts Juan and Tim for this insightful episode as we explore change management in the nonprofit sector, uncover the power of data governance, and learn strategies to effectively lead change within organizations.

We all need a break sometimes.

Juan is on vacation but he wasn't skipping this week's episode of Catalog & Cocktails.

Join Tim Gasper and Juan Sequeda for this weeks mini-sode.

Are you criminally under leveraging your data?

This week Juan Sequeda and Tim Gasper will be joined by Maddy Want, VP of Data at Fanatics and author of “Precisely: Working with Precision Systems in a World of Data” to discuss digital transformations stories from her book.

Join this weeks episode of Catalog & Cocktails LIVE from the Knowledge Graph Conference.

Hosts Tim Gasper and Juan Sequeda will be joined by Katariina Kari, Lead Ontologist at IKEA to chat about all things Knowledge Graph.

With the hype of AI, one of the important topics to discuss is Responsible AI.

In this episode, we will be unpacking Responsible AI, what is the problem, what are solutions such as principles, governance, auditing, regulation with renowned computer scientist researcher, Ricardo Baeza-Yates.

There’s a high turnover of CDO/CDAO positions. Data teams are not directly contributing to ROI. What’s the root cause? It’s all in the DNA of the organization.

Understanding this will be critical in addressing this issue in organizations.

Hosts Juan and Tim will be joined by special guest Alex Vayner, from KPMG US to also discuss the data roles in the different types organizations and also dive into the buy vs. build decision-making process.

Knowledge graphs and large language models. A match made in heaven. This pair will drive centralization and eventually work with AI to further consolidate and connect your data.

So why aren’t people prepared for it and what’s going to happen when AI steps in?

Join us on this weeks episode of Catalog & Cocktails with hosts Tim, Juan, and special guest Tony Seale, Knowledge Graph Engineer.

Survival guides, rulebooks, blueprints and more. The best way to prepare yourself as a next generation data leader is this episode of Catalog & Cocktails with Tim Gasper, Juan Sequeda, and special guest Veronika Durgin, VP of Data at Saks 5th Avenue.

Get deep into some preparation-curriculum with topics like:-Do’s and Don’ts of Data Leaders

-Are you skeptical enough?

-How curious are you?

-Joining communities that educate and inspire

-and MORE

Is business the problem or is it the data? Business science hasn’t changed since Peter Druker. But, it needs to change. It was designed in an era of certain technologies and data limitations. We had to work in isolation.

Those days are long gone.

So who’s to blame for inefficiencies, unsolved problems, mismanaged processes and other barriers? Join this weeks episode of Catalog & Cocktails with hosts Tim, Juan and special guest Benny Benford to find out where the buck stops.

Metrics are one of the most important and durable primitives in a company. And if data teams are looking to create value FAST… they have to learn how their businesses work.

To figure out how you business works, you have to find the right starting point; back to the METRICS.

Join Tim, Juan and special guest Abhi Sivasailam from Levers Lab on this week’s episode of Catalog & Cocktails to hear more about the most valuable use cases.

Episode highlights:

• [00:09 - 02:45] Introductions & Cheers
• [02:49 - 03:42] Warmup: What are things in life that you wish happened faster?
• [03:49 - 04:57] Abhi's talk about data teams and company growth models
• [04:59 - 07:21] There's too many data people
• [07:24 - 08:57] What are people wasting their time on: variance and standardization
• [09:01 - 11:07] Challenges to understanding the business and growth models
• [11:12 - 13:03] Metrics form the core of well-defined end states
• [13:05 - 15:03] What Abhi thinks of as a metric, and how it ties into the knowledge graph
• [15:04 - 16:37] A plan of attack improves semantic knowledge and avoiding building as you go
• [16:47 - 17:51] The accretive development problem
• [17:53 - 22:48] A core structure of standards with uniqueness layered on top
• [22:53 - 25:30] Focus on specificities instead of repeated work
• [25:46 - 27:24] Not a bridge too far to say the semantic layer doesn't need to be that creative, but more standardized
• [27:32 - 29:09] Abhi thinks automated integration is impossible
• [29:19 - 32:29] Arbitrary uniqueness vs being coupled to an open standard
• [32:31 - 33:40] SOMA
• [33:42 - 36:22] How does SOMA affect a data person's life, and how does a company adopt the standard?
• [36:23 - 40:09] Building a web of metrics for growth
• [40:12 - 42:06] The relationship between metrics, SOMA, and OKRs
• [42:31 - 49:56] Lightning round
• [50:00 - 55:27] Takeaways
• [55:29 - 57:59] Three final questions

Juan and Tim give their ultimate takeaways from Gartner in Orlando with a TON of guests.

Guests like:

Cindi Howson, Chief Data Strategy Officer at Thoughtspot, and Host of the Data Chief Podcast

Wayne Eckerson, President at Eckerson Group

Sean Anderson, Host of Sources & Destinations Podcast, and Head of Product Marketing at SelectStar

Benn Stancil, CTO + Founder @ Mode

Malcolm Hawker, Head of Data Strategy, Profisee

Malcolm Chisholm, President, Data Millennium

Kuldeep Sharma, Sr Manager Data Governance, Enterprise Data

Vassil Momtchev, CTO at Ontotext

Anthony Colichio, Senior Value Engineer, Reltio

In this episode of Catalog & Cocktails, Kristin Schooley from Learning Care Group sits down with Juan Sequeda and Tim Gasper to discuss the importance of teamwork and building relationships when it comes to scaling an analytics team within a large organization. The conversation covers a lot of ground, including the need to understand what reporting is needed and what story is being told, how to ensure scalability and compliance, as well as the importance of measuring usage and interpreting why certain data may not be utilized.

Key Takeaways:

• [00:00 - 01:10] Introduction & Cheers
• [01:15 - 03:11] What was your favorite class in school and why?
• [03:16 - 06:34] A data team of five
• [06:36 - 11:29] Partnering with business units and thinking about what is being learned
• [11:43 - 14:07] Data as a product and lifecycle management
• [14:22 - 15:13] Data literacy isn't necessarily the phrase we should be using
• [15:15 - 18:13] Business analytics and understanding how to present data
• [18:16 - 22:51] How organizations organize their data teams with checks and balances
• [22:52 - 26:18] Business literacy, centralized teams, and scaling beyond a bottleneck
• [26:20 - 30:31] Incentivizing for having documentation up to date
• [30:40 - 34:57] Lightning round
• [35:00 - 39:46] Tim & Juan's Takeaways
• [39:46 - 43:06] Three questions

Are we doing data and analytics correctly? Self service, centralization vs decentralization, analytics vs operations… so many aspects that data teams need to consider.

Join this week’s episode of Catalog & Cocktails with hosts Juan and Tim as they speak with special guest Bethany Lyons to discuss how we should have a separation between data operations an data analytics

Key Takeaways:

• [00:10 - 02:36] Meet Bethany Lyons, Chief Proctor Officer at KAWA Analytics
• [02:40 - 04:23] What's your favorite board game?
• [04:24 - 05:29] Encouraging self-service analytics is why Bethany thinks we are doing data analytics wrong
• [05:30 - 08:04] How the self-service analytics problem started
• [08:10 - 11:12] How business operators utilize data, and examples with Tableau deployment
• [11:13 - 14:09] Tying business logic to an analytics tools and what could go wrong
• [14:10 - 15:55] "Is this an analytics request or an operational request?"
• [15:55 - 16:35] Changing parameters and asking the right system
• [16:35 - 19:35] Evolving from self-service analytics to self-service governance programs
• [19:39 - 21:14] Spreadsheet data, security, and incentives
• [21:22 - 24:25] Self-service governance, shared accountability paired with great security
• [24:26 - 25:50] Data stewards and ownership over business areas
• [25:51 - 27:04] Sharing, auditing, and tracking
• [27:12 - 28:50] Decentralizing accountability and mindsets when dealing with data
• [28:50 - 32:40] How to make the switch to prioritizing decentralizing accountability over who writes SQL queries, and KAWA's role
• [32:41 - 35:15] Centralized policy frameworks and definitions
• [35:22 - 39:05] Thoughts on self-service operations and the centralization of analytics
• [39:06 - 40:52] Critical business logic and Excel spreadsheets
• [40:53 - 42:06] Centralizing business logic vs decentralizing business logic
• [42:06 - 44:33] Avoiding business logic being owned by one individual, and looking at decision models
• [44:33 - 45:45] Critical documents, and the idea of a Google Drive for data
• [45:49 - 52:41] Lightning round
• [52:42 - 57:26] Takeaways
• [57:44 - 01:00:16] Three questions

This week, we are having a throwback elixir with some very special surprise guests.

Tune in with hosts Juan and Tim to find out who.

Key Takeaways:

• [00:26 - 01:51] Episode intro: Data Day and What keeps you up at night in data?
• [02:04 - 02:54] Joe Reis, the economy and showing value
• [02:59 - 03:30] Omar Khawaja: Anything that ends with board, data dashboards
• [03:54 - 04:05] Vip Parmar: Jet lag & underutilized data
• [04:16 - 04:51] Laura Ellis: Data governance and change management
• [04:59 - 06:18] Mohammed Syed: Change management and data solutions
• [06:22 - 06:49] Tim: Determining focus in a sea of technology opportunities
• [06:50 - 07:58] Juan: Disconnect between executives and how data teams work with the business

Increasing adoption of data products, by design

Look no further than Brian T. O’Neill’s bio to tell you that’s what he does best.

Our special guest this week knows that low adoption of data products are enterprises biggest enemy. Not just in terms of quantity, but also quality of these investments. Why are teams so often creating technically right, effectively wrong data products? Why do people fail to adopt when it’s them post crucial part of becoming data driven organizations?

These burning questions have answers.

Join hosts Juan, Tim, and guest Brian T. O’Neill on this weeks episode of Catalog & Cocktails.

Key takeaways:

• [00:06 - 03:30] Intro & Cheers
• [03:33 - 04:54] What is your instrument of choice and what would your band name be?
• [04:59 - 07:55] Honest no BS definition of a data product
• [07:59 - 11:17] Alternative definitions of data as products
• [11:18 - 14:11] Data products can be many things, and definitions are broad
• [14:11 - 19:39] How human centered design interplays with defining data products
• [19:47 - 25:17] Data therapists, knowledge scientists and engineers
• [25:21 - 33:48] Where does the burden lay, and with whom
• [33:49 - 38:35] Brian's perspective on data product management versus software product management
• [38:38 - 43:59] How do you achieve good adoption?
• [44:05 - 46:39] What is the best way people can start learning about human-centered design?
• [46:42 - 47:20] Can dashboards be data products, and can machine learning be data products?
• [47:32 - 49:45] Should companies be investing in data managers
• [49:45 - 52:45] Value and adoption, should companies track data ROI
• [52:58 - 58:20] Takeaways
• [58:23 - 01:02:09] Three questions

Metadata management has been a topic for a while now. Lately, the industry is pushing that metadata is a knowledge graph problem. What does metadata in a pre and post graph world look like?

Join Juan Sequeda and Tim Gasper with special guest Mohammad Syed, Head of Data Architecture & Engineering at Capco to chat about metadata and knowledge graphs.

Key Takeaways:

• [00:06 - 02:40] Introduction & Cheers
• [02:43 - 03:55] What's your go-to karaoke song?
• [03:57 - 05:38] Metadata is a graph problem, yes or no?
• [05:55 - 08:23] What would we call metadata in a pre-graph world?
• [08:29 - 13:06] The biggest inflection point that has moved us collectively from a pre-graph world to a post-graph world
• [13:06 - 15:10] Use cases for metadata in finance
• [15:14 - 17:43] Different contexts in which you use data,
• [17:46 - 19:33] Metadata historically as a means of documentation
• [23:29 - 24:59] How we should be taking advantage of the graph structure
• [25:01 - 29:18] Applying basic graph techniques and algorithms to identify data use cases
• [28:11 - 30:30] The process of data governance and data protection
• [30:33 - 32:47] Outcomes of metadata graphs
• [33:04 - 35:19] Metadata management
• [35:26 - 39:59] How to get started with metadata mapping
• [40:05 - 43:30] Governance can't be living in an ivory tower
• [43:57 - 49:52] Lightning round
• [50:01 - 55:56] Takeaways
• [55:58 - 59:44] Three Questions

Entity resolution.Is it a single source of truth? Is it multiple sources of truth? Is it multiple data sources that refer to the same real-world thing?

How do we make sense of it? How can we harness the value of data through entity resolution? Talk to Jeff Jonas from Senzing on this week’s episode of Catalog & Cocktails and you’ll understand.

Key Takeaways

• [00:33 - 05:14] Episode intro & cheers
• [05:20 - 06:53] What is something you thought to be true in life that ended up being false?
• [06:58 - 08:15] Why don't we realize entity resolution if it is everywhere?
• [08:18 - 09:55] Tech stacks and tools for entity resolution
• [10:01 - 11:55] Record matching, link detection, fuzzy matching, and duplication
• [12:14 - 14:12] Master Data Management as the old school approach versus modern technology and its limits
• [14:18 - 18:38] Entity resolution is for the elite
• [18:48 - 21:11] Commoditizing entity resolution to the equivalent of a spell checker
• [21:14 - 25:41] Principles and Guidelines
• [25:51 - 28:48] The number of principles should fit on a single screen
• [28:50 - 33:03] Different levels of abstractions, and thinking about what kinds of things can solve similar problems
• [33:09 - 35:25] How does metadata tie into entity resolution?
• [35:32 - 40:45] Ontologies, semantics, and mapping
• [40:54 - 42:10] Thousands of transactions per second
• [42:14 - 46:23] You shouldn't have to tinker with any settings to be effective
• [46:28 - 47:44] ChatGPT
• [51:54 - 56:17] Takeaways
• [56:21 - 59:40] Three Final Questions

If there were a tombstone for this episode it would read “here lies Master Data Management,” But would you agree?

In this episode, join Tim, Juan, and special guest Malcolm Hawker from Profisee, will discuss the history of master data management, the buzzwords surrounding it, and where it’s headed, assuming it’s still alive.

Key Takeaways:

• [00:01 - 00:54] Introduction
• [02:31 - 04:55] Toast to sun on the beach
• [05:21 - 10:09] What's something in the world that is popular or trending that really needs to fade away and RIP?
• [10:36 - 13:33] How do we define MDM today?
• [16:43 - 19:52] How did we get here? Understanding the history of how MDM began
• [20:50 - 24:04] Governance: Where do we start? Approaches to figuring out how to decentralize
• [26:52 - 29:51] With the single version of the truth and for each context, how do we preserve the data integrity across independent or interconnected functions?
• [30:45 - 34:35] How data modeling and MDM apply to new capabilities such as data sharing and DBT
• [39:43 - 43:13] What does data ownership mean?
• [46:27 - 56:08] Lightning round
• [59:20 - 01:01:56] Advice, and important resources

Data modeling isn’t new. So why is it still a problem?

Maybe the problem isn’t data modeling itself, but rather there is no modern solution for companies, or the incentives are not well understood. It’s a learn-as-you-go type of thing, but that’s where the trouble lies.

Do you hire one data engineer and have them do everything? Do we encourage training in data modeling? OR… do we throw in the towel and keep doing things as they’ve always been done?

Join Tim, Juan, and special guest Anna Abramova from SqlDBM to answer these burning questions on this week’s episode of Catalog & Cocktails.

Key Takeaways

• [00:11 - 01:03] Introduction to Anna Abramova
• [01:03 - 04:03] Champagne vending machines & cheers all around
• [04:11 - 05:27] If you could model your home after one famous building or monument what would it be?
• [05:30 - 06:35] What's the deal with data modeling at SqlDBM
• [06:37 - 09:09] The origin story of SqlDBM
• [09:09 - 10:58] A modern approach to data modeling
• [10:58 - 12:04] What modern means in the context of data modeling
• [12:08 - 14:30] Why is data modeling now becoming such a hot topic?
• [14:33 - 17:24] Data modeling trends: startups and small businesses
• [17:25 - 20:41] Data modeling trends in medium-sized companies
• [20:43 - 21:32] Data modeling trends in large organizations
• [21:33 - 26:16] Summarizing data trends and real world applications
• [26:48 - 30:40] Metaphors around data modelings and architecting, problem solving
• [30:51 - 35:37] The biggest value triggers around data modeling
• [35:40 - 37:21] The pearl of the ocean
• [37:25 - 40:39] How are people learning data modeling?
• [40:42 - 43:17] A role or a skill?
• [43:18 - 45:10] Data modeling isn't the sexiest topic
• [45:12 - 46:16] A good foundation and resilience
• [46:26 - 49:39] Lightning round
• [49:55 - 55:19] Juan & Tim's Takeaways
• [55:37 - 58:51] Three questions about data and life

Imagine planting seeds and not watering them. Or putting water on the stove but not turning on the gas. Sounds silly, right?

Now take your organizations data and think about not leveraging it as an asset. It’s like taking the initial step of planting something in the garden and deciding not to provide nourishment.

Join Tim, Juan, and special guest Jane Urban from Takeda to understand the difference between a data asset and a data product and why companies still struggle finding the real value.

Key Takeaways

• [00:06 - 02:14] Introduction & Cheers
• [02:15 - 03:46] What New Year's resolutions have you stuck to so far?
• [03:49 - 05:14] Leveraging data as an asset
• [05:20 - 07:06] Definitions and examples of data as a product
• [07:13 - 09:42] Business needs and data value
• [09:43 - 11:49] The transition from something smaller to something growing, and lessons learned
• [11:50 - 14:58] Metaphors Jane uses to make communication around data value simple and relatable
• [15:00 - 16:42] A manufacturing process metaphor
• [16:46 - 20:46] Data stewards and the idea of finalizing a system versus ongoing system maintenance
• [20:46 - 24:35] Data value in the pharma perspective
• [24:45 - 27:02] Outcomes for data assets to data products, or are they unrelated?
• [27:05 - 28:57] Data as an asset on the balance sheet
• [29:01 - 32:30] How digital technology its in an organization, and potential as a revenue generator
• [32:46 - 38:08] Navigating how to be agile in pharma, which is not often agile
• [38:24 - 40:08] Find the astronauts
• [40:09 - 42:35] Leadership and executive evolution toward digital growth
• [42:45 - 47:01] The good, the bad, and the ugly of starting small and working towards global teams
• [47:15 - 55:47] Lightning round
• [55:51 - 01:01:16] Takeaways
• [01:01:24 - 01:04:08] Three questions

Those who don’t know their history are doomed to repeat it. If there is someone who can speak to data history, it’s Bill Inmon. How did data warehouses start? Why is the computing profession still immature?

Join Tim Gasper, Juan Sequeda and Bill Inmon, the father of the data warehouse and Founder of Forest Rim Technology, to learn about the past and present of data, and where things might be heading in the future.

Key Takeaways

• [00:05 - 01:00] Introduction to the Father of the data warehouse, Bill Inmon
• [01:11 - 02:28] Cocktails & Cheers
• [02:31 - 03:33] What actor would you cast for yourself?
• [03:39 - 06:47] Running in circles in the data world, a result of the profession's maturity
• [06:49 - 10:20] Bill's recent post about loyalty to technology versus loyalty to business
• [10:22 - 12:58] Specialists in computer technology, how programming has changed over time
• [13:06 - 15:08] Continuing education and certifications with programming paths
• [15:10 - 24:40] The history of computing: from army, to mafia, to your pocket
• [24:40 - 32:22] Key moments and milestones Bill has observed throughout his career in networking
• [32:28 - 33:59] Understanding the customer and looking after them
• [33:53 - 38:00] Microsoft Chatbot
• [38:00 - 41:04] A solution in search of a problem, showing business value
• [41:10 - 43:41] The Inmon and Kimball approaches
• [43:58 - 45:50] What's next for Bill?
• [46:11 - 58:46] Lightning round
• [58:53 - 01:04:16] Takeaways

Well, another incredible season of Catalog & Cocktails concludes this week with hosts Tim Gasper and Juan Sequeda.

Join in for the ultimate takeaways of the takeaways as Tim and Juan recap best moments, favorite hot takes, and the most controversial opinions over the last season.

Listeners, please submit your feedback: https://forms.gle/FdjMfarUaVnJ3SzB9

Key Takeaways

• [00:05 - 02:40] Introduction, end of year growth
• [02:43 - 04:54] Themes in culture, team structures, education opportunities
• [04:55 - 06:11] Training and Enablement
• [06:11 - 06:51] Shiny object syndrome, magpie syndrome
• [06:53 - 07:55] Loris Marini and culture, genuine conversations
• [07:56 - 09:09] Data problems with Laura Ellis, celebrating what people do with data
• [09:11 - 10:49] Culture, self-service, and shadow IT
• [10:54 - 11:09] Shared KPIs and alignment
• [11:12 - 12:40] Follow the money, and ask why you're working on what you're working on
• [12:40 - 14:29] Data projects don't fail for technical reasons, aligning business value, ROI is the key
• [14:35 - 15:09] How the business makes money, and where does it flow to organizationally
• [15:26 - 16:59] Who are the right people, and how do you continue to motivate and empower them
• [17:01 - 18:13] Ask for opinions and anecdotes, starting small
• [18:15 - 19:39] Loris on emphasizing connection and relationships, curriculum-driven development
• [19:40 - 21:35] Laura Ellis on data user experiences
• [21:39 - 23:19] The Chief Data Officer and managing data
• [23:24 - 26:17] What does real time streaming mean, and its impact on business, cost, reporting
• [26:45 - 29:59] AI and how it is already affecting data, knowledge, and more instant feedback
• [30:01 - 30:45] Where is AI funding coming from?
• [30:46 - 33:19] Horizontal and Vertical AI, value and use cases
• [33:19 - 34:00] Putting brain power toward ads and clicks instead of something like solving cancer
• [34:02 - 35:24] More education for people with diverse backgrounds, AI teams creating their own feature sets
• [35:27 - 37:16] "What should a catalog do?"
• [37:17 - 38:50] The spectrum of search, a lifecycle of data
• [38:52 - 40:19] Cross-functional collaboration and business expertise, strategies for metadata transformation and integration
• [40:21 - 41:32] Data modeling, cataloging, and semantics
• [41:34 - 43:29] Semantics with Dan Bennett, and data layers
• [43:32 - 45:58] Jumping into the pool with Allison Segraves
• [45:59 - 49:21] DGIQ, data governance, and closing out semantics
• [51:38 - 54:21] Predictions for the next year

What better place to discuss the current state of data governance and what should the industry be focusing on than a live episode at the Data Governance and Information Quality (DGIQ) Conference.

Join Tim Gasper and Juan Sequeda LIVE from DGIQ in Washington, D.C. with special guests Anthony Algmin and Shannon Moore to have an honest no-bs discussion data governance and how it's all about people.

Key Takeaways

• [00:09 - 03:39] Introduction, Cheers & Drinks
• [03:40 - 04:38] Is Die Hard a Christmas movie or not?
• [04:38 - 06:06] The State of Data Governance in 2022, making people care
• [06:06 - 08:28] Making data governance about people and how they measure success
• [08:29 - 10:54] The biggest opportunities for getting people to care more about data governance
• [10:54 - 12:56] Data governance is a means to an end for corporations, a hotel analogy
• [13:02 - 15:03] Think like a marketer, and data as a product
• [15:04 - 17:18] Providing a value proposition for data governance
• [17:18 - 19:37] We need to talk to our marketing friends
• [19:38 - 21:08] Making data governance an ongoing business function, understanding the inherent value
• [21:12 - 23:33] Governance is still very project-based, and not often holistic as a function of business
• [23:41 - 26:20] Data privacy regulation to comply with
• [27:31 - 30:41] Education pathways in data governance
• [30:42 - 34:46] Strategies within organizations, business and leadership goals
• [34:59 - 37:50] The most important priority for governance in 2023
• [37:59 - 40:16] What vendors should be doing differently in 2023
• [40:21 - 45:43] Lightning round
• [45:54 - 50:17] Takeaways
• 50:18 - 53:40[] Three final questions

Think back to when you were first learning to swim. How’d you do it? Chances are, you weren’t thrown into the ocean being circled by sharks. We sure hope not, anyway.

You probably picked it up under the watch of a lifeguard in most cases.But once it became second nature, the lifeguard didn’t hold SO much power. Well, data engineers are the lifeguard, and if there isn’t a checks and balances system between them and the business teams, the engineers will be determining your every move in AND out of the water.

Join Catalog & Cocktails hosts Juan and Tim, with special guest Allison Sagraves as they discuss how you get in the water by yourself without drowning.

Key Takeaways:

• [00:06 - 04:15] Introduction & Cheers
• [04:18 - 05:31] Beach, lake, or swimming pool?
• [05:35 - 07:17] Entering the data pool without drowning
• [07:28 - 09:40] The pool is like the new sandbox
• [09:42 - 13:53] Cutting through confusion and BS
• [14:00 - 17:03] Data literacy and understanding that everyone needs to be trained on all things
• [17:09 - 18:10] A dog paddle towards more technical areas of focus during a transition
• [18:16 - 20:36] The idea of data products
• [20:37 - 23:52] Why Allison cannot stand the phrase "data is the new oil"
• [23:54 - 29:34] Building data products is not an easy thing to do
• [29:37 - 32:33] Culture and a digital mindset
• [32:38 - 36:25] Allison's experience as the CDO at M&T Bank for 30 years
• [37:28 - 41:40] The data pool analogy, and the lifeguard for that data
• [44:14 - 51:08] Lightning round
• [51:12 - 56:00] Takeaways
• [56:06 - 01:02:21] Three questions
• [01:02:28 - 01:03:27] Next week's episode

You have to have a lot of data to get AI to work. But the data folks are not jumping on it as fast as they should.

So what happens when data teams aren’t up to speed, companies are hiring more data scientists than they are engineers, AND current data teams are focusing too much on biz reporting and not supporting AI?

This week on Catalog & Cocktails, join hosts Tim Gasper and Juan Sequeda as they chat with special guest, Theresa Kushner, Head of North America Innovation Center at NTT Data Services to discuss how the AI train is leaving the station and data teams can only run so fast.

Key Takeaways

• [00:10 - 02:25] Introduction & Cheers
• [02:28 - 04:12] What's your favorite way to travel and why?
• [04:15 - 07:01] Are data teams keeping up with AI teams?
• [07:01 - 08:50] Are data teams and AI teams helping each other or avoiding each other?
• [08:54 - 13:09] AI teams become a data set in themselves
• [13:10 - 14:45] Data ownership and control
• [14:51 - 17:20] Thoughts on purchasing data
• [17:20 - 20:53] Data products and observations
• [20:53 - 24:52] CDO versus the CDAO, definitions and comparisons
• [24:53 - 27:05] Should there be a CDO or a CDAO?
• [27:03 - 30:04] Data makes AI work
• [30:02 - 34:58] If you want results you have to collaborate
• [34:59 - 37:29] Creating culture tied to data quality
• [37:27 - 42:01] The skill sets for managing data products
• [42:02 - 46:09] Theresa's message of advice to data teams
• [46:12 - 52:36] Lightning Round
• [52:36 - 58:16] Takeaways
• [58:17 - 01:00:31] Three questions

Machines and people. Why can't we just speak the same language? The truth is we can, and doing so could make life demonstrably better for data scientists. Yet here we are, living in a world of rows and columns that few people outside of the data owner understand.

Join this weeks episode of Catalog & Cocktails as hosts, Juan Sequeda and Tim Gasper with special guest, Dan Bennett, tackle semantics and how to get everyone -- machines and people -- on the same page.

Key Takeaways:

• [00:01 - 02:47] Intro & Cheers
• [02:49 - 04:57] If you were the picture for a word in the dictionary, which word would it be?
• [04:58 - 08:35] The Greatest Sin of Tabular Data
• [08:40 - 11:02] Examples of semantics missing inside of tabular data and their utility
• [11:03 - 12:39] Adding context and profiling data
• [12:43 - 14:55] How are constraints and semantics being defined, and what is a scaleable approach?
• [15:02 - 16:24] Data producers and enriching data
• [16:30 - 17:58] Enrichment that travels with the data
• [18:00 - 20:14] What are the tools we use, the data dictionary, and standardizing
• [20:16 - 22:34] Metadata and the bridge to the semantic world
• [22:36 - 24:57] Innovation and Dan's thoughts on relational model table relationships
• [24:57 - 28:09] Solving the same problems over and over again
• [28:12 - 30:19] Network effect, the marketplace of ideas and social spheres
• [30:21 - 33:56] Diving into the network effect and the semantic world
• [33:58 - 36:48] Why redefine if an option exists that can be used, and thoughts on simple ideas being the best solutions
• [36:52 - 40:11] Figuring out supply and demand curves for S&P Global
• [40:12 - 44:34] The business value of data and data literacy in accurate findings
• [44:44 - 46:19] Advice to data leaders and vendors
• [46:37 - 50:14] Lightning Round
• [50:29 - 56:48] Takeaways
• [56:52 - 59:44] Three final questions

Data and business teams become a convoluted intersection, and when they struggle to communicate, it leads to bigger problems than awkward water-cooler talk.

So what comes first? Translation? Data literacy? Company culture? The chicken? The egg?

Co-founders of Preql, Gabi Steele and Leah Weiss, join hosts Tim and Juan to discuss how to put the business in charge of their own data and how this leads to the answers AND massive alignment between data and biz teams.

Key Takeaways

• [00:05 - 04:15] Introduction and Cheers to Tim
• [04:17 - 06:50] What is the wildest or weirdest thing you've seen while stopped at a red light?
• [06:55 - 10:29] Putting businesses in charge of their own data
• [10:29 - 12:04] Inviting the business into the process and building community
• [12:05 - 15:21] Developing a curriculum for data, teaching SQL and data visualization
• [15:22 - 19:17] Refining the model and curriculum, tailoring the fit
• [19:17 - 21:41] Identifying the people who wanted to be part of data modeling
• [21:43 - 22:55] Teaching interested parties another way to handle data, using an application process to find people
• [22:57 - 24:17] Stewardship in handling data
• [24:17 - 27:04] The process of engaging and completing data modeling: a brain for architecture, python, and sql
• [27:05 - 28:32] Interesting problems to solve, and you have to be creative to get there
• [28:36 - 30:40] Understanding technical debt and how to support engineering teams, skills for good data modeling stewardship
• [30:42 - 33:18] DBT and building robust analytics teams
• [33:30 - 37:35] Tooling solutions for helping business be in charge of their own data
• [37:42 - 38:52] Focus on the technical side, and a no-code semantic layer
• [38:53 - 42:31] Bringing in a technical analytics engineer to the business team
• [42:31 - 45:33] The knowledge gap and bridging it with a framework
• [45:33 - 49:39] Alignment and building the semantic layer
• [49:43 - 56:36] Lightning Round
• [56:38 - 01:01:30] Takeaways
• [01:01:32 - 01:05:50] Three Questions

You can’t buy a predictive model off the shelf. And if you COULD… would you? It’s the old “build versus buy” debate and with data in a constant state of change, building and deploying AI is more challenging than ever.

Join Tim, Juan, and Andrew Eye, CEO at ClosedLoop.ai, to discuss the challenges of AI deployment, AIOps, and maintaining models as data changes.

Key Takeaways

• [00:08 - 01:45] Cheers for AI
• [01:48 - 04:44] Taking advantage of incoming data and considering the data footprint
• [04:45 - 05:55] Building the most accurate and useful model, no best solution
• [06:01 - 09:26] AI strategy and developing prediction models
• [09:33 - 15:17] Should you build or should you buy?
• [15:21 - 20:02] Verticalized AI versus Horizontal data science and AI-oriented solutions
• [20:04 - 23:16] Build a custom predictive model
• [23:42 - 25:24] The concept of "feature drift" in AI
• [25:26 - 30:02] Vertical AI and the opportunity with healthcare
• [30:07 - 33:52] The concept of a click and the idea of semantic contracts
• [33:53 - 38:08] Great minds designing AI for Facebook instead of healthcare
• [38:12 - 39:41] Designing AI for good is addictive
• [39:47 - 41:27] The next generation and the promise of AI that can really impact lives
• [41:41 - 47:57] Lightning Round
• [48:02 - 52:03] Takeaways
• [52:05 - 53:24] Three questions

The most effective way to understand your business is the special balance between things like tribal knowledge and extracted knowledge, technical teams and business teams, confidence and skepticism.

Join Tim, Juan, and Loris Marini, CEO of Discovering Data, as they zoom in to find that balance and the humility it may take.

Key Takeaways

• [00:11 - 03:10] Introduction & Toasts
• [03:13 - 05:32] What's the weirdest place you've ever misplaced your keys?
• [05:44 - 08:01] The most effective way to follow the business is to follow the money
• [08:02 - 10:35] Thinking about business literacy and how to learn that perspective
• [10:37 - 12:35] How do we make change happen?
• [12:37 - 13:39] Having genuine conversations with people
• [13:44 - 18:19] Coaching, psychological safety, and communicating better at work
• [18:20 - 22:10] The definition of creating knowledge within an organization
• [22:11 - 25:34] Knowledge, insight, and levels of knowledge distributed in your team
• [25:35 - 28:52] You've got to spend time and you have to have a platform
• [29:10 - 31:48] We can do better than "just the smartest will survive"
• [31:57 - 35:56] Incentives are about subtle things like a team that listens, not just pay and flexible time
• [35:59 - 39:35] A data therapist and becoming more aware of issues
• [39:43 - 41:32] Advice to engineers who write the data pipeline, engineer the SQL queries
• [42:06 - 45:24] Lightning Round
• [45:28 - 51:10] Takeaways
• [52:06 - 53:57] Three questions

Data needs to be in the hands of the people who really need it. Sounds simple, right? So where is the disconnect? If your teams aren’t partnering to get projects done, it’s often due to a lack of understanding of each others pain points. That’s where empathy is a must.

Join Tim, Juan and special guest Laura Ellis from Rapid7 as they discuss supporting all the cooks in the kitchen when it comes to data projects, why that’s a hill to die on and HOW to actually get that done.

Key Takeaways:

• [00:06 - 02:09] Intros & Toasts
• [02:09 - 03:49] What is the most controversial hill you'll die on?
• [03:52 - 05:30] Getting data into the hands of people who need it
• [05:30 - 07:01] Biggest problems for collaborating and making data easier to work with across the organization
• [07:05 - 09:32] Take a business problem, break it into a data problem
• [09:32 - 11:00] Breaking down problems, making it less intimidating
• [11:00 - 12:34] A user experience problem around data
• [12:30 - 13:52] Who is responsible for figuring out how the pieces of the puzzle fit together
• [13:55 - 17:22] Understanding centralization, decentralization, and embracing ownership
• [17:25 - 20:48] Identifying issues and opportunities research provides for enabling data access
• [20:48 - 22:31] Internal user research and Rapid7's "data therapist"
• [22:31 - 24:33] Business literacy and data literacy, cross-functional learning
• [24:28 - 26:39] More technology and more tools
• [26:37 - 33:14] Putting data into the hands of people who need it, making money and saving money
• [33:20 - 36:36] Gathering community and team content from passionate people
• [36:34 - 38:24] Brainstorming definitions of success
• [38:24 - 41:31] Start talking to people, do surveys, understanding the monetary impact
• [41:39 - 43:31] Dive into the details
• [43:36 - 47:23] Lightning Round
• [47:27 - 54:09] Takeaways
• [54:10 - 57:57] Three questions

AI and ML are both at the center of many applications today from autonomous vehicles to healthcare. But where is this ship heading?

Join Tim, Juan, and Patrick Bangert, VP of AI at Samsung SDS where they will discuss where the focus of AI is today and where it should be tomorrow.

Key Takeaways

• [00:04 - 01:06] Episode introduction/trailer
• [01:18 - 02:22] Drinks & Toasts
• [02:23 - 04:40] What is the most unfocused situation you've ever been in?
• [04:49 - 10:08] Where is the focus of AI today? Business application.
• [10:09 - 13:10] The technical side of AI, thinking about AI behind the wheel
• [13:43 - 15:15] The problems outside autonomous driving that AI can solve
• [15:16 - 20:09] AI and the impact on healthcare
• [20:15 - 21:35] AI will allow doctors to become better caregivers
• [21:45 - 27:08] Open issues and gaps in technology with AI currently
• [27:13 - 30:41] Keeping data clean, and identifying bias and context
• [30:46 - 36:18] Effective strategies companies are taking to label data better
• [36:37 - 38:56] Using AI to help build AI, "AI squared"
• [38:58 - 43:25] Inserting knowledge and representation, a solvable problem with better data
• [43:25 - 46:23] Chat bots, autonomous driving, and complexity of tasks
• [46:43 - 48:36] Doug Lennick, lessons from the 80s, and the state of GPT-3
• [48:47 - 52:16] The role of venture capital in AI development
• [52:31 - 57:34] Lightning round
• [57:40 - 01:05:58] Takeaways and the current state of AI
• [01:06:06 - 01:08:03] Three questions

You have been asked to start the Data Governance program in your organization. Sounds easy, right? How do you start? How do you define success? Who needs to be part of the team?

Join Tim, Juan and Rupal Sumaria, Head of Governance of Penguin Random House UK to discuss the steps for a successful data governance program and what to avoid.

Key Takeaways

• [00:06 - 03:16] Introduction and Toasts
• [03:17 - 04:51] If you could only keep three apps on your phone, what would they be?
• [04:52 - 07:33] Struggles in setting up a data governance program
• [07:40 - 09:42] Rupal's presentation at the data.world summit
• [09:46 - 13:03] Networking and communicating early while creating the data governance program
• [13:07 - 15:06] Who Rupal was collaborating with while setting up the data governance program
• [15:06 - 17:34] When was the last time you spoke to sales and marketing?
• [17:34 - 19:32] A tailored approach to reaching other departments
• [19:32 - 22:56] Tips for when you're struggling with engagement
• [22:56 - 26:42] Change the game, don't make it boring
• [26:48 - 32:14] Companies have to measure ROI
• [34:14 - 36:54] A four step process to data governance
• [36:54 - 39:10] Where to start, departmentally
• [39:13 - 40:28] Advice for those "stuck" in data governance roles
• [40:28 - 41:34] Is there anything Rupal would have done differently, two years down the line?
• [41:51 - 42:58] Data governance technology
• [43:24 - 45:41] Rupals tip's for navigating processing technology tools
• [45:43 - 48:59] Lightning round
• [49:02 - 56:51] Takeaways
• [57:03 - 59:25] Three questions

Answering critical business questions relies on integrating data from a variety of systems. But it takes a lot of work to understand what the disparate data means and how it all fits together. How do we make data as reliable as an electricity?

Join Tim Gasper, Juan Sequeda and Fraser Harris, VP of Product at Fivetran, as they celebrate the 100th live episode of Catalog & Cocktails and discuss how #metadata, #datacatalogs, and #dataintegration act as the power source for your connected enterprise

Key Takeaways:

• [02:02 - 03:49] Cheers to 100th episode, good health, children, and sky miles
• [04:10 - 05:11] Keeping it 100, Millennial and Gen Z slang
• [05:12 - 07:05] What metadata means to Fraser, the data about the data
• [07:07 - 10:00] Fivetran's new metadata API
• [11:32 - 13:33] Action, enforcement, and results in understanding data management
• [13:52 - 17:51] Data contracts and the interface
• [17:53 - 20:07] Upstream notifications and transforming data
• [20:55 - 24:01] Perspectives on having a system and record owner for data contracts
• [24:31 - 30:38] Representing business process change in contract evolutions
• [30:40 - 31:58] Cultures around data at newer companies
• [32:15 - 34:34] The two main use cases of Fivetran's data and the impact analysis
• [34:34 - 36:25] Two dimensions to data proactivity, data maturity and company size
• [36:26 - 41:26] Steering data complexity to simplicity, business value behavior and technology costs
• [41:28 - 43:47] Reliability and data pipeline
• [43:52 - 45:10] What Fraser wants to see happen around metadata
• [45:10 - 47:15] The process of migrating to the cloud and adopting new data policies
• [48:47 - 55:25] Lightning round
• [55:28 - 01:00:43] Tim & Juan's takeaways
• [01:00:57 - 01:02:49] Three questions for Fraser
• [01:04:43 - 01:05:14] Next week's guest, Rupal Sumaria from Penguin Random House

Data Catalogs are at the center of every enterprise’s data strategy. It’s important to explore the current state and how data catalogs are evolving. Who better to talk about Data Catalog than the author of the upcoming O’Reilly book “The Enterprise Data Catalog”, Ole Olesen-Bagneux from GN.

Join Tim, Juan, Ole to discuss the future of data catalogs and why knowledge is at the center.

Conversation highlights:

• [00:07 - 03:06] Intro & Drinks
• [03:10 - 05:11] Warm up, if data catalog were a cocktail what would it be
• [05:19 - 07:06] Where the hell are data catalogs going
• [07:07 - 09:01] What the library and information sciences world is bringing to data catalogs
• [09:03 - 10:43] Don't think that catalogs should move away from a Google Paradigm
• [10:43 - 12:14] Finding more valuable data sets, and Boolean queries
• [12:16 - 16:23] Accessing what is inside a catalog and thinking about metadata
• [16:27 - 23:36] The "shopping experience" and expressive ways of searching
• [23:44 - 26:55] How cataloging intersects and impacts search and metadata
• [26:55 - 29:00] Catalogs will evolve into repositories and a thesaurus
• [29:01 - 32:05] The lifecycles of data and system life cycles
• [32:24 - 35:19] The POSMAD Framework
• [35:26 - 38:56] Data monetization and modern data architecture
• [38:57 - 42:11] The potential for POSMAD and practical relation to data catalogs
• [42:16 - 43:15] Here's the data, what are you going to do with it?
• [43:19 - 44:30] Data to Information to knowledge to wisdom
• [44:40 - 47:06] Cataloging ontologies and knowledge layers
• [47:07 - 50:00] Knowledge graphs and combining with simple search and browse features
• [50:18 - 56:34] Lightning round
• [56:35 - 01:02:12] Tim and Juan talk takeaways
• [01:02:17 - 01:05:55] Three questions about data, life, and the show's next guest

In this episode, Tim, Juan, and Ole Olesen-Bagneux, author of O’Reilly “The Enterprise Data Catalog, discuss the future of data catalogs.

This Takeaway episode will feature the summary of the upcoming show.

What is the state of Data engineering today and where is it going (or should it be going)? Who better to talk about Data Engineering than the authors of the recent O’Reilly book “Fundamentals of Data Engineering”, Joe Reis and Matt Housley from Ternary Data.

Join Tim, Juan, Joe and Matt to discuss the state of data engineering.

Conversation highlights:

• [00:44] Introduction to Joe Reis and Matt Housley
• [03:24] Warm up: what have you engineered or built that has gone terribly wrong?
• [08:28] Data engineering tools and magpie syndrome
• [13:15] How often and where do you see data teams stumbling to get the value out of the technology they have?
• [17:08] Thoughts on diving deep into technologies where there is a skills gap
• [19:39] The curse of familiarity in the context of tools
• [23:55] Learning outside the context of an enterprise, and continously educating yourself on data
• [27:23] Data modeling and where to start learning
• [30:48] Data modeling = business concepts and definitions to relations among data
• [32:05] The human aspect and technical side of data modeling
• [36:19] Business literacy for data engineers
• [38:39] The lifecycle of data
• [43:02] Analytics engineering, the knowledge scientists
• [46:09] Roles within data engineering and bifurcation
• [48:26 The future of data engineering and tabular data
• [52:12] Auto Machine Learning and Tabular Data
• [55:00] Lightning round
• [01:01:01] Tim and Juan's takeaways
• [01:06:18] Three questions about data, life, resources and the show's next guest

Lucid Streaming; how to take full advantage of data streaming

• Data streaming, Stream Processing, Real-time analytics, operational analytics — what is this? What’s the difference?
• Most important use cases for data streaming
• There are lots of misconceptions especially for the MDS crowd (not as much enterprise) between fast batch vs streaming
• Memory-first processing (in-memory) vs disk space batch jobs
• Change data capture (and only capture of change)
• Data warehouses are now tying to support streaming more (like Snowflake)
• This will be a big deal to make it so that more streaming can happen
• Streaming warehouses (Rockset, Materialize) vs data streaming
• Lineage - transformed data - can I trust this data I'm looking at
• How does data streaming and lineage come together? What’s unique about lineage in a streaming context?
• If time: what does it mean to do streaming data products in a data mesh context?

You are listening to great talks, podcasts, webinars on how to improve and take your data and analytics to the next level. You want to take all these ideas and start implementing them in your organization. But how do you start? Join Tim, Juan and Roel Pot, Global Data Manager at Vopak where they discuss how to go from thought leadership to practice.

Conversation highlights:

• [01:09] Today's drinks
• [05:02] Sifting through noise and convincing your organization on the importance of data
• [07:11] Challenging tradition in data governance and modeling
• [09:53] Data as an asset and finding business value
• [14:06] Data and business literacy through the data academy
• [18:16] Bridging the gap in language, business, and data literacy
• [19:48] The academy's purpose
• [21:16] Building Vopak into a resilient company to last another 400 years
• [25:21] How being an asset-rich business has affected Vopak's data strategy
• [27:45] Connecting the dots with data acquisition and distributed systems
• [29:58] Decentralized teams and better collaboration
• [31:51] Real-time data sensors and minimizing time for data analysis
• [35:24] Why real-time for analytics?
• [36:24] To Data Mesh, or Not To Data Mesh
• [38:46] Self-service and understanding ways of gathering and processing data
• [40:19] Different roles and the confusion it can cause within organizations

This Takeaway episode will feature the summary of the upcoming episode with Roel and Valentijn from Vopak.

Catalog & Cocktails is BACK! Tune in for the SEASON FOUR PREMIERE with hosts Juan Sequeda and Tim Gasper!

Bridging the gap between business and IT is an age-old problem. IT doesn’t understand the business. The business doesn’t understand IT. Everyone looks at IT and business as if they were separate and point fingers, when they should actually be together.

Join Tim, Juan and Vipul P., Global Head of Data Management at WPP where they discuss approaches to bridge the gap between business and IT.

Conversation Highlights:

• [01:10] Today's Drinks
• [03:26] Two things that typically wouldn't go together, but do really well
• [05:17] Vip's thoughts on bridging the gap between business and IT
• [08:19] Empathy, curiosity, and measuring business value
• [09:58] Empowering teams to ask questions, and thinking about different company cultures
• [14:18] On defining roadmaps and determining business value
• [16:37] A culture around data and the interplay between business and IT
• [20:57] Data stewards and the idea of embedded roles
• [24:50] An ideal team structure for bridging the gap: data analysts, scientists, and super users
• [26:33 ] The data mesh and future leaders of tomorrow
• [28:30] Future leaders in data, and delivering for both IT and business needs
• [30:10] Empowering data literacy and learning paths
• [35:24] Incorporating IT and data knowledge in employee onboarding
• [41:19] The role of technology in bridging the business and IT gap
• [45:15] Lightning Round
• [51:02] Takeaways
• [55:48] Three questions for VIP
• [01:00:08] Next week: Raoul Pott

Catalog & Cocktails is BACK! Tune in for the SEASON FOUR PREMIERE with hosts Juan Sequeda and Tim Gasper!

Bridging the gap between business and IT is an age-old problem. IT doesn’t understand the business. The business doesn’t understand IT. Everyone looks at IT and business as if they were separate and point fingers, when they should actually be together.

Join Tim, Juan and Vipul P., Global Head of Data Management at WPP where they discuss approaches to bridge the gap between business and IT.

Juan's Trip Takeaways from MIT CDOIQ

Ever wondered how Catalog & Cocktails happens?

How does the show go from an idea to a full-blown episode every single week?

Karli Burghoff, Producer of Catalog & Cocktails AND data.world Demand Generation Specialist, joins this bonus episode to talk about the processes, pains and prizes of creating a podcast. Oh, and she roasts the hosts multiple times in this episode too, don't miss that.

Join Juan and Tim for the Season Three Finale of Catalog & Cocktails. The hosts discuss the takeaways of the takeaways, best guest moments and valuable advice from some of the guests from the last 20 episodes!

We live in a data first world. Give me more and more data. But what about the meaning of that data? How should the data be modeled so it can be easily understood and consumed by data scientist and analyst?

In this episode, Tim, Juan, and Ergest Xheblati, will live discuss why data modeling should be at the core of an enterprise.

This episode will also cover:

• How data modeling is going to change the world moving forward.
• How to start in data modeling
• Why are data models such freakin divas?

In this episode, Tim, Juan, and Ergest Xheblati, will live discuss why data modeling should be at the core of an enterprise.

This Takeaway episode will feature the summary of the upcoming show.

LIVE from the Snowflake Summit in Vegas. The hosts of Catalog & Cocktails sit down with Peter Kapur from WM to explore how we should be getting true value from metadata, data and analytics.

LIVE from the Snowflake Summit in Vegas. The hosts of Catalog & Cocktails sit down with Peter Kapur from WM to explore how we should be getting true value from metadata, data and analytics.

This Takeaway episode will feature the summary of the upcoming show.

So much of the focus of the analytics and vendor community today is on data. We have solutions for data visualization, warehousing, observability, cataloging, transformations, and more. But data is nothing without context. So why does metadata tend to draw the short straw?

In this episode, Tim, Juan, and Sanjeev Mohan, former Gartner analyst and principal at SanjMo, explore how metadata acts as a proverbial travel guide for your data and analytics journey.

This episode will also cover:

• Visions of a data catalog, the ordinary and profound
• Who needs lineage and why
• In the Magic Quadrant of summer foods, what goes in the leaders quadrant, and what’s in niche?

In this episode, Tim, Juan, and Sanjeev Mohan, former Gartner analyst and principal at SanjMo, explore how metadata acts as a proverbial travel guide for your data and analytics journey.

This Takeaway episode will feature the summary of the upcoming show.

Why do some data-driven decisions seem to go so disastrously wrong? Ironically, the answer to this question likely isn’t found in the data at all, but rather our subconscious. In a time when companies have never been more data rich, it’s often our inherent information biases that doom critical analytics and data science work.

Is it possible to take the bias out of data work? That’s the question we ponder in this episode featuring Ciaran Dynes, chief product officer at Matillion.

This episode will also cover:

• What responsibilities companies and people have to curb information bias
• How hypothesis testing and experimentation can improve data work
• What’s the most egregious example of information bias in the wild?

Is it possible to take the bias out of data work? That’s the question Tim and Juan ponder in this episode featuring Ciaran Dynes, chief product officer at Matillion.

This Takeaway episode will feature the summary of the upcoming show.

What does it take to build a successful data & analytics business? An entrepreneurial spirit, a problem-solving mindset, a sense of adventure, and the strength to get started.

We are live from the data.world All Hands and joined by Brett Hurt, CEO and co-founder of data.world, author, and serial entrepreneur. Brett will share what it’s like to fundraise and scale a data company in this challenging economic climate, who inspires him and why, and how he sees the data and analytics landscape evolving.

This episode will also feature:

• How to hire and retain great people
• What we can all learn from failure
• Recommendations for wines under $20

In this episode, Tim, Juan, and Brett discuss what it’s like to fundraise and scale a data company in this challenging economic climate, who inspires him and why, and how he sees the data and analytics landscape evolving.

This Takeaway episode will feature the summary of the upcoming episode.

Some of the greatest breakthroughs throughout human history can be attributed to the biotech field, from life-saving vaccines to advances in supply chains. So it’s no surprise that biotech is also a fast-mover when it comes to innovative socio-technical paradigms like data mesh.

In this episode, Tim, Juan, and Omar Khawaja, Global Head of BI at Roche, break down the good, the bad, and the ugly of the company's data mesh journey.

This episode will also feature:

• How to identify potential use cases and pick the one to prioritize
• How to think about ownership when data and tools are widely distributed
• You’ll be at Snowflake Summit in Las Vegas next month. So will we. What’s the best buffet food?

Some of the greatest breakthroughs throughout human history can be attributed to the biotech field, from life-saving vaccines to advances in supply chains. So it’s no surprise that biotech is also a fast-mover when it comes to innovative socio-technical paradigms like data mesh.

In this episode, Tim, Juan, and Omar Khawaja, Global Head of BI at Roche, break down the good, the bad, and the ugly of the company's data mesh journey.

This episode will also feature:

• How to identify potential use cases and pick the one to prioritize
• How to think about ownership when data and tools are widely distributed
• You’ll be at Snowflake Summit in Las Vegas next month. So will we. What’s the best buffet food?

Everyone has an opinion on the modern data stack. What’s in it? What’s out? What’s the cool new buzzy technology? Unfortunately, it’s often the most important question that people fail to ask: How are your tools delivering value?

In this episode, Tim, Juan, and Sarah Krasnik get back to first principles and remind us why everyone was excited about the modern data stack to begin with.

This episode will also feature:

• How to think about “minimum viable data stack” vs. modern data stack
• Criteria for evaluating the minimum viability of data tools
• You once compared data self-service to shopping for frozen yogurt. Defend your most controversial frozen yogurt take.

In this episode, Tim, Juan, and Sarah Krasnik get back to first principles and remind us why everyone was excited about the modern data stack to begin with.

This Takeaway episode will feature the summary of the upcoming episode.

How do the most successful companies stitch together data governance, understanding, access, and use to create active and engaged data cultures? The answer, as usual, can be found in the knowledge graph.

And what better place to talk nodes and edges than the Knowledge Graph Conference in New York City? In this episode, Tim and Juan are joined by conference chair, Francois Scharffe, to discuss how knowledge graphs are driving the automated enterprise.

This episode will also feature:

• How to apply knowledge graphs to address critical business problems
• What automation looks like when powered by a knowledge graph
• What kind of graph is the New York Subway system?

What better place to talk nodes and edges than the Knowledge Graph Conference in New York City? In this episode, Tim and Juan are joined by conference chair, Francois Scharffe, to discuss how knowledge graphs are driving the automated enterprise.

This Takeaway episode will feature the summary of the upcoming episode.

Data architecture is more layered than an onion. Data, metadata, access, transform, virtualization, visualization. And with the modern data stack, we’re now talking more about semantics and metrics layers.

This week, Tim and Juan are joined by Nick Handel, CEO of Transform, to talk all things semantics and metrics and why enterprises need to be thinking more about more about relationships.

This episode will also feature:

• Clear definitions for semantic layer and metrics layer and the relationships between them
• How to design your architecture to take advantage of semantics and metrics
• Cake, dip, or sandwich. What is the best layered food?

This week, Tim and Juan are joined by Nick Handel, CEO of Transform, to talk all things semantics and metrics and why enterprises need to be thinking more about more about relationships.

This Takeaway episode will feature the summary of the upcoming episode.

It’s not easy being a “data leader.” For one, you have to advocate for good data practices at your company. But you may also feel a strong desire to create your own brand on social media and at conferences. With the constant noise, posturing, and rigorous debate over every facet of data and analytics, this can be daunting. And sometimes, you feel like an imposter.

In this episode, Tim and Juan are joined by Steve Perry, Director of Data and Analytics at Genius Sports, to talk about the challenges that rising data leaders face, and how to block out the noise, and chart your path to success.

This episode will also feature:

• How to get over your fear of saying something stupid in a public square
• Success metrics for data leaders
• What’s an awkward conversation you’ve walked into and quickly realized you don’t belong?

In this episode, Tim and Juan are joined by Steve Perry, Director of Data and Analytics at Genius Sports, to talk about the challenges that rising data leaders face, and how to block out the noise, and chart your path to success.

This Takeaway episode will feature the summary of the upcoming episode.

Imagine sitting down at a restaurant. Your food is already on the table, and the waiter hands you a menu of different dishes and silverware you can use to consume your meal. That’s a completely backwards experience, but it’s kind of how data architecture works today. We have our data in cheap storage, and then we purchase software that helps us consume it. Is this also wrong?

In this special episode, Tim and Juan are joined by Chad Sanderson, Head of Product, Data Platform at Convoy, to discuss whether or not we're incorrectly architecting our modern data stack.

This episode will also feature:

• Lessons learned from Amazon and Facebook on building services before data
• How to align your data team with business goals
• Getting data architecture right often requires paying down legacy debt. What’s something you recently purchased that you maybe shouldn't have?

In this special episode, Tim and Juan are joined by Chad Sanderson, Head of Product, Data Platform at Convoy, to discuss whether we’re incorrectly architecting our modern data stack.

This Takeaway episode will feature the summary of the upcoming episode.

Everyone - including us - wants to talk about how to get value from your data, but the truth is you can’t even get your data program off the ground without policy. Policies guide our decisions, our access controls, and even our data supply chains. But can policy make us more productive?

In this special episode, Tim and Juan are joined by Nong Li, CEO of Okera, to discuss why and how the data world should be thinking about policies.

This episode will also feature:

• How to think about policy as it relates to GDPR, CCPA, and the right to be forgotten
• The impact of the modern data stack on data policy
• One of the lyrics In the 1990 Depeche Mode song, “Policy of Truth,” is “Never again Is what you swore, the time before.” What’s something you swore you’d never do again, but did it anyway?

Join Tim, Juan and special guest Nong Li, CEO of Okera, to discuss why and how the data world should be thinking about policies.

This Takeaway episode will feature the summary of the upcoming episode.

Companies have most of the last decade in pursuit of data. We’ve waded through data lakes, explored data warehouses, routed through data hubs, and migrated our data to the cloud. But the end goal was never about the data. It was the pursuit of knowledge.

In this special episode, Tim and Juan are joined by a legend in our industry. Bob Muglia, the former CEO of Snowflake, will hang out and chat about the modern data stack, new approaches to governance, and the search for the enterprise holy grail… knowledge

This episode will also feature:

• Demystifying the approaches between lakehouses and warehouses
• Interesting developments in data governance technology
• You were the first product manager for SQL Server. What’s your favorite movie SQL?

Join Tim, Juan and special guest Bob Muglia, former CEO of Snowflake, to talk about the future of data; knowledge

This Takeaway episode will feature the summary of the upcoming episode.

Agile processes in software development date back to the beginning of the century. We’re now seeing concepts like scrum and sprints make their way into the world of data. data.world is even applying agile to data governance.

But what exactly does it mean to be agile in data? What’s the end product, and how do you measure and scale the practice in your organization? Join Tim, Juan and special guest Shane Gibson, Founder of AgileData.io to talk agile in the domain of data.

This episode will feature:

• How and why you should apply agile practices to data
• Why your sprints should actually be three weeks, not two
• As a longtime fan of the podcast, who’s your favorite host, and why isn’t it Tim?

Join Tim, Juan and special guest Shane Gibson from AgileData.io, to talk agile in the domain of data.

This Takeaway episode will feature the summary of the upcoming episode.

What does the word, “privacy” mean to you? Is it about being left alone? Being forgotten? In today’s world of ubiquitous digital apps, we think about privacy and utility as a give and get. To get the best experiences from Google, Facebook, and Amazon, I need to give up some measure of privacy. But, is that true?

Join Tim, Juan and special guest Patricia Thaine, CEO from Private AI to discuss the state of data privacy and how enterprises and customers should think about it.

This episode will feature:

• Examples of privacy done right and wrong
• How to balance privacy and customer experience
• Which company’s privacy policy is required reading for insomniacs?

Join Tim, Juan and special guest Patricia Thaine, CEO from Private AI to discuss the state of data privacy and how enterprises and customers should think about it.

This Takeaway episode will feature the summary of the upcoming episode.

Are you on Team Batch or Team Stream? Next-gen data warehouses, lakehouses, ELT, and orchestration have made batch more modern and popular than ever, but more and more use cases - from fraud detection to smart energy grids - rely on continuous streams to make real-time decisions. Is streaming the coming revolution, or just another way to process data?

Join Tim, Juan and special guest Arjun Narayan, CEO of Materialize to discuss all things data streams and how these pipelines fuel the modern data stack.

This episode will feature:

• Reasons why streaming analytics still kind of sucks
• The future of batch processing in the modern data stack
• Brook, creek, or river: Which is the best stream and why?

Join Tim, Juan and special guest Arjun Narayan, CEO of Materialize to discuss all things data streams and how these pipelines fuel the modern data stack.

This Takeaway episode will feature the summary of the upcoming episode.

Data transformations, dbt, data catalogs, data teams, data culture, data mesh. It’s a lot. But pour yourself a drink and enjoy this special edition of Catalog & Cocktails.

Hosts Juan Sequeda and Tim Gasper broadcast live from the 2021 dbt Coalesce conference and were joined by special guests, Meetesh Karia and Claire Look from The Zebra to take a glimpse into the future of analytics engineering.

This episode will feature:

• Why is dbt such a big deal?
• Data teams and the role of an Analytics Engineer
• What have you transformed (you, someone else, a product, …) in your own life?

Have you ever had a tax form sent to the wrong address, a credit card with no suspicious activity get deactivated, or been billed for a service you’ve never used? If so, you may be the victim of poor data modeling.

Join Tim, Juan and Karen Lopez, aka @datachick from InfoAdvisors, to discuss what assumptions people and applications make about the simplest data. We’ll also try and answer the question, “why are phone numbers, and addresses so damn hard to model?!?”

This episode will feature:

• Why modeling simple things is harder than it seems, and what we can do about it
• The best and worst data modeling fails
• If Sports Illustrated had an issue dedicated to data models, what would be on the cover?

Join Tim, Juan and Karen Lopez, aka @datachick from InfoAdvisors, to discuss what assumptions people and applications make about the simplest data. We’ll also try and answer the question, “why are phone numbers, and addresses so damn hard to model?!?”

This Takeaway episode will feature the summary of the upcoming episode.

Analytics are great for answering the “what.” Did we hit our sales targets or not? Did our supply chain hold up under the strain of a prolonged pandemic? But smart data leaders recognize that real transformational insights come from people, processes, and tools that answer the bigger question of “why.”

Join Tim, Juan and special guest Luke Slotwinski, VP of Data & Analytics at Prologis, to discuss how data leaders can empower their teams by asking for more from their data.

This episode will feature:

• Questions everyone should be asking about their data
• Critical tools and skills for diving deeper into data and analysis
• Why did the chicken cross the road? Data-driven answers only, please.

Join Tim, Juan and special guest Luke Slotwinski, VP of Data & Analytics at Prologis, to discuss how data leaders can empower their teams by asking for more from their data.

This Takeaway episode will feature the summary of the upcoming episode.

For all the incredible innovation in the field of data and analytics, one practice unfortunately remains stuck in neutral. Data governance, when applied with an agile approach that aligns with business objectives and focuses people first, can be a boon for data-driven organizations. However, most companies today still think about governance in terms of tools, workflows, or privacy and compliance.

If data governance had a help line, it would likely be staffed by this week’s guest. Join Tim, Juan and Laura Madsen, author of “Disrupting Data Governance,” for a discussion on how we can all be better ambassadors for data.

This episode will feature:

• How to talk to your execs about agile data governance
• How to measure the effectiveness of your governance program
• What’s your favorite story of data governance malpractice?

Juan and Tim sit down for Catalog & Cocktailsv with Laura Madsen, CEO of Moxy Analytics and author of “Disrupting Data Governance,” for a discussion on how we can all be better ambassadors for data.

This Takeaway episode will feature the summary of the upcoming episode.

Data mesh continues to be a hot topic in 2022, and it feels like we’re finally past the definition stage. The big question to resolve this year is: how do you do federated data governance in a decentralized environment? Can you even do it?

Join Tim, Juan and special guest Dora Boussias, Director of Enterprise Architecture and Data Strategy, for a live brainstorming session on how to implement data governance for the data mesh.

This episode will feature:

• The difference between data mesh and virtualization
• Best practices for federated data governance
• How long before we start seeing companies offering a “data mesh” as a product?

Juan and Tim sit down for Catalog & Cocktails with Dora Boussias, Director of Enterprise Architecture and Data Strategy.

This Takeaway episode will feature the summary of the upcoming episode.

We all like to measure things: height, weight, company revenue, active users on our SaaS platform. Measurement, and a common understanding of that measure is critical whether you’re a doctor, an athlete, or a business decision maker. Self-service analytics is much easier today, but without shared definitions for the metrics we’re reporting on, trust in analytics is still relatively low. Is it time for a Metrics Layer?

Join Tim, Juan and special guest Benn Stancil, chief analytics officer and founder of Mode to discuss all things Metrics Layer including what it is and why we should care.

This episode will feature:

• Where does a metrics layer fit within the modern data stack
• What tools and skills are needed to make this a reliable part of your data strategy
• In honor of the Friday Fight post, what is the most controversial post/blog you’ve ever written, and what kind of feedback did you receive?

Juan and Tim sit down for Catalog & Cocktails with Benn Stancil, chief analytics officer and founder of Mode

This Takeaway episode will feature the summary of the upcoming episode.

Sure, it’s fun to chat with data executives and thought leaders, but this season, we are focusing on the people who roll up their sleeves and get the data work done. And there’s no better place for a podcast like ours to start than with a data practitioner at Drizly.

Special guest Emily Hawkins, analytics and data platform lead, joins Tim and Juan to share how the data team and the data stack were established and how the e-commerce platform is evolving following the company’s acquisition by Uber.

This episode will feature:

• The role of analytics engineer on a high-performing data team
• The evolving role of self-service BI
• What’s a delivery service you wish we had today that doesn’t exist yet?

Juan and Tim sit down for Catalog & Cocktails with Emily Hawkins from Drizly.

This Takeaway episode will feature the summary of the upcoming episode.

The new year means it’s time for another trek around the sun. It’s also time to make a few predictions about what might change in data & analytics. Is it time for data practitioners to participate in boardroom conversations? Will we see data teams embedded in business units? What new technologies will become part of the modern data stack?

Join Tim, Juan and Sarah Catanzaro from Amplify Partners,, to discuss what’s in store in 2022.

This episode will feature:

• Suggestions on organizational design that keeps data closer to the business teams.
• Ways to address data gaps in strategic decision making
• What was the best/worst prediction you made at the start of 2021?

Juan and Tim sit down for the Catalog & Cocktails Season Three Premier with Sarah Catanzaro from Amplify Partners to discuss what’s in store in 2022.

This Takeaway episode will feature the summary of the upcoming episode.

Let's wrap Season Two of Catalog & Cocktails with the Takeaways of the Takeaways!

In this wildly diverse yet hyperconnected world, it's hard enough to understand how people communicate with one another. So how do machines do it?

Join Juan, Tim, and special guest, Steve Whitla from Visual Meaning for a discussion about how humans and systems talk to one another, and even have meaningful conversations.

Also in this episode:

• The role of semantics in reaching shared meaning
• What constitutes "understanding" when it comes to machine to machine communication
• Better talking machine: K.I.T.T. or Johnny 5?

In a relatively short time, we’ve gone from collecting and neglecting data to managing, enriching, and learning from data. We are in the age of collective data empowerment, where user-friendly apps and data ubiquity mean almost anyone can answer complex business questions. So why does the “data-driven” enterprise still sound like a pipe dream?

Join Tim, Juan and special guest Kelly Wright, president and COO at Gong and former EVP at Tableau, for a discussion on data and analytics past, present, and future.

This episode will feature:

• Ways organizations can maximize the value of data they are already collecting
• How to address challenges with data and application sprawl
• The Gong Show was a popular TV talent show in the 70s. What was your favorite game show growing up?

The modern data stack is often defined by the type of technologies that exist within it. Cloud-based, open source, low/no code tools, ELT, and reverse ETL. But surely there’s more to it… isn’t there?

What holds the modern data stack together and makes it the architecture of choice for so many data-driven enterprises? Join Tim, Juan and special guest, Nick Schrock, founder of Elemental and creator of Dagster and GraphQL, to chat about all things MDS.

This episode will feature:

• Is modern data stack a methodology or a set of disparate cloud technologies?
• Thoughts on consolidation among MDS tools

Describe your reaction upon glancing at Matt Turck’s latest data landscape diagram

It wasn’t that long ago that enterprise software was dominated by proprietary data architectures from the likes of IBM, Oracle, and SAP. Once you made a selection, you were stuck with it for better or worse, in sickness and in health.

But that’s not the case today. The methodical movement towards resilient, open SaaS applications and best-of-breed tools has ushered in the era of DataOps buoyed by the “modern data stack.” But, is this a good thing?

Join Tim, Juan and special guest Andy Palmer, CEO of Tamr, to talk open vs. closed ecosystems and how to decide what’s right for you.

This episode will feature:

• How DataOps will reshape the next wave of best-of-breed systems
• Will monoliths make a comeback?
• What was a big purchase you made that you almost immediately regretted?

Self-service is great for listening to music, pumping gas, and figuring out what to binge watch on TV. But is self-service really what we want from our analytics? What happens to the business world when everyone is a data analyst?

Tim, Juan, and special guest Cindi Howson, chief data strategy officer at ThoughtSpot and host of The Data Chief Podcast explore the positives and potential downsides of data democratization and how to drive data cultures that bias for good.

This episode will feature:

• How BI in the enterprise evolved from the domain of the few to a tool for the many
• Why change management is critical for improving data cultures
• What’s something that’s not self-service today but probably should be?

New data tools drop daily, but are they worth the hype? Some launch with overinflated expectations, while others solve a problem that doesn’t really exist. On the flip side, there are tools that transform the enterprise and have the potential to change the way we do data forever. The question is, how can you tell the difference?

Special guest Erik Bernhardsson, formerly with Better.com and Spotify, joins Tim and Juan for a conversation on data tools; the good, bad and the ugly.

This episode will feature:

• An overview of data tool types and who they serve
• How to define “good, bad and ugly”
• What’s your favorite spaghetti western movie?

If data, and information more broadly, is a company's most important asset, then why is it so often treated with less respect than office furniture? It's in various states of disarray, often unaccounted for, and employees bring it home against corporate policy.

This week, we're joined by Doug Laney, author of the best-selling book, "Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage.” We'll talk about taking better care of your data, so it can take better care of you.

This episode will feature:

• Tips for how to truly measure the value of all your information assets
• Steps for getting your information house in order
• Is data the new oil, bacon, gold, oxygen, currency, or sand?

“What is a data mesh?” It’s one of the most frequently asked questions on this podcast, and is almost a microcosm for data and analytics as a whole. It may be the next big thing. It may be yet another passing fad in architectural design. We compared and contrasted it with data fabric in Episode 32 and spoke with Zhamak Dehghani, the founder of the data mesh paradigm in Episode 44. We even hosted a special panel discussion dedicated to data mesh.

After all these discussions, we are still trying to figure out what it is. So, we’re settling this the only way we know how: a good old-fashioned debate. Only one side can win.

This episode will feature:

• The definitive(ish) definition of data mesh
• A look at why organizations struggle to understand and implement it
• What is your data “hot take” that everyone else disagrees with?

When we think about good, responsible data stewardship, we often think in terms of compliance and governance. We talk about access, trust, and consumption. But there’s another dimension we must consider if we’re to be truly responsible with data. We simply can’t ignore the role of ethics in data use.

Join Tim, Juan and special guest Partha Srinivasa, award-winning CIO/CTO/CDO and current CDO of Verisk, for a conversation about data responsibility and ethics.

This episode will feature:

• Common misperceptions about data and ethics
• How to build ethics into your data practice
• Can you be both ethical and a fan of the morally dubious, Seinfeld?

Quick, what’s the difference between a data engineer and a data analyst? One preps the data, the other analyzes the data, right? A data scientist, meanwhile, analyzes more complex and disorganized data. The truth is all three of these roles perform overlapping functions leading to an incredible amount of confusion in the job market.

Join Tim, Juan and special guest Danielle Oberdier, founder of DiKayo Data and host of the popular DataFemme podcast, to talk about the state of data jobs. How do we make sense of the roles that are out there today, who should companies be looking for in their hiring process, and what new and exciting data positions are starting to emerge?

This episode will feature:

• Certifications that make you more marketable
• How to stand among your peers in a crowded job market
• What was the very first job you ever had?

Is your data infrastructure designed for the next decade of data work? Probably not, but are you at least heading in the right direction? It’s an existential question that often reveals fundamental flaws in the system.

Join Tim, Juan and special guest Mammad Zadeh, former VP of Engineering at Intuit, for a deep dive on data tools, leadership, and change management.

This episode will feature:

• A look at the rocky road ahead for CDOs
• How to effectively scale data teams
• What's your crisis comfort food?

Fall summit 2021 is right around the corner!!!! Join speakers from Nationwide, ThoughtSpot and MANY MORE to discuss hot topics like Data Mesh, DataOps, and the future of data management on Sep. 29: data.world/events/summit

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With the hype of graph databases and knowledge graphs, a common (mis)practice is to quickly migrate your existing siloed data into a graph database. But be careful! You may just be bringing the complexity of your silos into the graph.

Join Tim, Juan and guest Jans Aasman from Franz Inc, the makers of AllegroGraph, for a conversation on why your graph-based machine learning and 360 projects should start with data modeling.

This episode will feature:

• Data modeling approaches you should consider
• Tips to avoid data modeling pitfalls
• Juan and Tim striking their best model pose

Don't forget to register for the data.world Fall summit 2021: data.world/events

We all strive to be data-driven. And yet we all instinctively know that we’re not very good at it. In fact, if you believe any number of recent surveys, it seems we may actually be getting worse at data. One reason for this is a misalignment on what success looks like. How do you define and measure it? What is the actual value of your data?

This week, Tim and Juan join Lars Albertsson, founder of Scling, to talk about data productivity and balancing a technology vs product-driven approach to data.

This episode will feature:

• Companies that have truly unlocked data value and how they did it
• Common missteps along the data maturity journey
• What have you been practicing... but not actually getting any better at?

ETL (Extract Transform and Load) was the SOP for data integration for 25+ years. A decade ago the introduction of data lakes pushed transformation to the end of the process and into tools like Snowflake, BigQuery, and Redshift. Now the latest chatter in the data management industry is Reverse ETL. Shouldn’t we call this LTE?

Join Tim, Juan and special guest Tejas Manohar, CEO of Hightouch for a conversation about Reverse ETL and why it matters now.

This episode will feature:

• The evolution of data integration pipelines
• Use cases for Reverse ETL
• What other acronyms make you smh?

You can’t get from your home to the grocery simply by owning a car. You have to actually drive the vehicle to get to a place that delivers value. Sounds obvious right? But we don’t instinctively think this way when it comes to data. We focus so much on tools, processes, and architectures, but we don’t talk enough about actually using the data.

This week, Tim and Juan are joined by Mike Ferguson, managing director of Intelligent Business Strategies Limited to look at why we’re not using data nearly as effectively as we think and what can be done about it.

This episode will feature:

• Data usage do’s and don’ts
• How to get your company aligned on effective data use and measurement?
• What’s the most expensive thing you bought that you’ve never used?

We all want to get more value from our data, right? So should we wrangle it, prep it, classify it, mine it, or model it? Then once we’ve got our data squared away, should we do prescriptive or predictive analytics? And does that require real-time or just-in-time data? If we had a nickel for every buzzword we hear, we could afford far better podcasting equipment.

In this episode, Tim and Juan are joined by Kirk Borne, Chief Science Officer at Dataprime AI to demystify some of our most egregious buzzwords and look at what everyone’s going to be talking about next.

This episode will feature:

• What is industry 4.0?
• How can an edge be intelligent?
• What is the buzzword that makes you want to pull your hair out the most?

Open source software is one of the largest and fastest growing segments within the data landscape. And if you’re implementing DataOps practices or considering data mesh, openness and flexibility are key architectural principles.

This week, Juan and Tim are joined by Denise Gosnell, CDO of Datastax, to talk about the business of open source and how community-centric data applications are reshaping the enterprise.

This episode will feature:

• A glimpse into the future of open source data
• The difference between open source and open core
• What open source tool best personifies your personality?

The data landscape has evolved substantially over the last decade. We’ve gone from data lakes to data hubs to lake houses. We see data represented as documents, columns, graphs, and time series. So how might this evolution continue over the next ten years?

To help us ponder this question, Juan and Tim are bringing in Emil Eifrem, CEO of Neo4j. We’ll take a look at how we got to this point, and what new data management challenges and opportunities await.

This episode will feature:

• A look at database categories that may spring up over the next decade
• Positives and negatives of database standardization
• What’s changed the most about the hosts/guest over the past 10 years?

We’re back in the office. Now what?

We roll into season two with a straightforward question: “Did we learn anything new about data work and data people during the pandemic?”

Who better to address that subject than DJ Patil, mathematician, entrepreneur, and the very first U.S. Chief Data Scientist? Join Tim, Juan, and DJ for a wide-ranging conversation on data cultures, architectures, roles, and enduring lessons from the past 16-months of largely remote work.

This episode will feature:

• Hot new job titles and emerging opportunities
• How to establish a new data culture built around hybrid work environments
• Favorite podcast, book, or show discovered during the Pandemic

*Corrected Audio*

Data Architecture is evolving and there are many questions with various perspectives. What is the balance between centralization and decentralization? How do you start treating data as a product? How do you incentivize people? What’s the role of Data Mesh, Data Fabric, Knowledge Graphs?

This special edition of Catalog and Cocktails is the Data Architecture panel from the Knowledge Graph Conference, moderated by Juan Sequeda. Listen and learn from Teresa Tung Chief Technologist of Accenture’s Cloud First group , Zhamak Dehghani director of emerging technologies at Thoughtworks and founder of Data Mesh concept, and Jay Yu, Distinguished Architect at Intuit.

You can watch the panel here and follow Juan’s takeaways.

What’s the old saying? “The journey IS the destination.”

What started as an experiment and a way to kick back with colleagues and peers on a video call, turned into a thriving, honest, no BS podcast about enterprise data management.

Hosts Juan and Tim embarked on this journey that turned into a 50-episode series. The episodes boasted conversation topics spanning from identity graphs, modern data stacks, and building data teams, all the way to data lineage, data trust issues and learning what a CDO actually does. Our audience heard vulnerable and transparent talks from leaders at companies like AirBnB, McKinsey and Company, Wunderman Thompson and more.

This season finale is a look back at season one and a candid talk about the lessons learned.

Business intelligence has come a long way since the early days of IBM’s decision support systems. Today BI is the backbone of many data-driven organizations, and companies often look to BI tools to help them make accurate predictions.

In this episode, we take a look into the future of BI itself. Join Tim, Juan, and Ashley Kramer, the Chief Product Officer from Sisense, as they explore the intersection of BI, analytics, and artificial intelligence.

Other topics include:

• How companies can take advantage of the convergence of BI and AI
  
• Separating AI reality from AI fiction
  
• What one thing would you want to know about your own future?
  

We always talk about managing metadata and data...but what about managing knowledge? It has to be more than keeping a spreadsheet of a business glossary!

Join Tim, Juan, and Joe Hilger from Enterprise Knowledge for a conversation about how the data management and knowledge management communities need to unite in order to best manage your unstructured and structured data.

Other topics include:

• How to best manage unstructured and structured data?
  
• Taxonomies, Knowledge Graphs: how does it fit?
  
• What is one piece of knowledge that you know NOW that you wish you knew when you were younger?
  

Classic Rock, historical paintings, and fine wine; some things are better left untouched. When it comes to your data stack however, that rule doesn’t apply.

Having the right foundation- a mix of tools, people, and processes- is absolutely essential to getting value from your data. Join Juan, Tim, and Brandon Chen from FiveTran for a chat about letting go of your organization’s old tools and processes and welcoming in the new.

Other topics include:

• Obvious signs that it's time to change your data architecture
  
• How to prevent your data stack from becoming another silo
  
• Classic TV shows worth modernizing and rebooting
  

Every maturing company hits a point where the processes and systems that once worked no longer scale. Don’t let this happen to your data organization.

Join Juan, Tim, and this week’s guest, Meetesh Karia from The Zebra, while they live brainstorm about scaling data teams and processes. They’ll look at how the small seeds you plant along the journey can pay huge dividends later on. It all stems from the seeds you plant along the way; the people on your team, the balance of efficiency vs. resilience and the processes...

Other topics include:

• How to strike a balance between efficiency and resilience
  
• Structuring teams for continued success
  
• If you could grow a garden of anything, what would it be? (Not the obvious; money)
  

Accelerating the delivery of data applications is at the top of the to-do list for many enterprises. That requires overcoming friction in your data architecture, managing complex requirements, and handling a variety of semantics challenges.

Join Tim, Juan, and Chris Bergh from Data Kitchen for a conversation about DataOps. The trio will look at how this emerging data management methodology can improve the flow of knowledge within data teams.

Other topics include:

• How to create the right DataOps culture
  
• Measuring the value of your DataOps strategy
  
• Worst reason you’ve been called into work on your day off
  

Mesh is everywhere. It’s in our clothes, our fishing nets, our Wifi networks, and now our data architecture. But what is a data mesh? Do you need one? And if so, how do you start?

Zhamak Dehghani is the director of Emerging Technologies at Thoughtworks and the leading expert on data mesh. We’ll chat about the emergence of the data mesh as a concept, why the approach works for eliminating architectural silos, and how it's producing more data-driven cultures.

Other topics include:

• Key tools, technologies, and skills for adopting a data mesh
  
• What to do if you’re data mesh curious
  
• Inspiring architectural designs outside the data space
  

The data science discipline is in a constant state of evolution with new techniques and applications being introduced almost daily. And this journey often begins at institutions of higher learning around the world. Many universities offer bachelors and masters degrees in data science, but are these programs adequately preparing the data professionals of tomorrow?

In this episode, Juan, Tim, and Prof George Fletcher of Eindhoven University of Technology will discuss the state of data science education and explore how these programs can be extended to satisfy industry needs.

Other topics include:

• What universities get right and wrong about data science education
  
• What skills we should be teaching that we aren’t today
  
• What should be the mascot at Data Science University?
  

Companies spend an obscene amount of money every year on data and analytics initiatives. And almost all of that spend goes toward applications that employ vastly different data models. Normalizing data structures is a painstaking process that most IT teams are used to by now. But should we normalize normalization?

In this episode, Juan, Tim, and Dave McComb, President of Semantic Arts and author of Software Wasteland and The Data-Centric Revolution, discuss what it takes to shift from an application-centric to a data-centric mindset.

Other topics include:

• What it means to be data-centric
  
• How to undo decades of questionable data management practices
  
• Debate: French Revolution vs. American Revolution vs. Beatles Revolution
  

Here’s your mission, should you choose to accept it: Your company is making poor decisions about how to bring its latest product to market. Time is running out, and the company risks missing a unique and lucrative opportunity. You must convince your exec team to stop using gut instinct and start trusting in data.

Step one is building a strong data and analytics team with the right mix of people, process, and technical know-how. In this episode, Juan, Tim, and Patrick Barry, VP of Data and Analytics at SPM Marketing and Communications, discuss what it takes to assemble a team from scratch.

Other topics include:

• What roles and skills to prioritize
  
• Why diversity is critical
  
• What things do we wish would self-destruct
  

Where does this data come from? Who created it? How has it been used? Like the origins of the universe, there can be quite a mystery surrounding the genesis of your company’s datasets. Understanding data provenance is the first step in answering those critical questions.

Join Tim, Juan, and Professor Deborah McGuiness of Rensselaer Polytechnic Institute, renowned AI scientist and pioneer in provenance research to discuss data provenance and why it matters to you.

In this episode, we discuss:

• The origin story and evolution of data provenance
  
• Provenance standards every data person should know
  
• Which fictional character has the best origin story
  

What’s the best way to get to know your customers? For most companies the solution is creating a 360 profile using data integration, data warehouse, master data management, and a slew of marketing tools. But there is another option: the Identity Graph.

Join Tim, Juan, and guests Michael Murray and Bret Harper of Wunderman Thompson Data for a look at how and why Identity Graphs are disrupting the company-customer relationship.

In this episode, we’ll discuss what an identity graph is and why you need one, why graph technology is a game changer for understanding customers, and the true identity of St. Patrick and what he might buy if he were alive today.

Data warehouses have been around for decades, and we’ve relied on data integration processes like ETL (Extract-Transform-Load) to get the data in. While data warehouses evolved to data lakes and data lakehouses, and ETL became ELT, little else has changed.

This week’s special guest is Drew Banin, co-founder of Fishtown Analytics. They’re the team behind the open source tool Data Build Tool (better known as dbt), and for disrupting the data transformation process (the T in ETL).

Discussion topics include how to build a modern tech stack for your data-driven business, what actually started the dbt revolution, and (of course, obviously) if you could transform into any animal on the planet, what would it be and why?

When data is powering your business, you expect that data to be trustworthy in real time, all the time, but that’s easier said than done. That’s where data quality comes in. Join special guest Lior Gavish, co-founder of Monte Carlo Data, for a conversation about data quality, reliability, and trust. Discussion topics include quantity vs. quality in data science and analytics, the downsides of applying band-aid fixes versus repeatable solutions, and whether quality wines are really worth the extra expense.

If data is the new oil, does that mean the Chief Data Officer is the new baron? Not exactly, but it is one of the fastest growing and most critical executive roles in the enterprise. So... what exactly does a CDO do?

In this episode, Tim and Juan welcome special guest Mohammed Aaser, CDO at McKinsey & Company, the world’s largest management consulting firm. We’ll discuss the unique responsibilities and challenges for a CDO in an increasingly data-driven economy. Plus, we’ll learn how the firm builds and maintains its own thriving data culture.

Discussion topics include how a CDO drives critical value for the business, future trends and potential disruptions in the data space, and a clear verdict on whether Tim or Juan would make the better CDO.

We test our food. We test our cars. We test our code. But do we test our data?

Join Tim, Juan, and special guest Sam Bail from Superconductive, the company behind open source data testing tool Great Expectations. We’ll chat about how to incorporate data testing into your workflow and who should be involved. We’ll also discuss why data quality is not just a tool, but a state of mind and a commitment.

This episode will feature tools for test-driven data development, best practices to incorporate data testing, and which of our hosts received the highest SAT score.

The insurance industry thrives on robust, diverse, accurate, and innovative data. In fact, it’s one of the most data rich verticals around, dealing in claims, ratings, coverage, pricing, geographic, and people data and so much more. But let’s be honest, working with the disparate data sources and applications is incredibly messy and inefficient!

In this episode, Juan and Tim will be joined by insurance veteran, John Lucker to find out what we can all learn from the data and analytics challenges in insurance.

This episode will include an honest look at the ugly side of insurance data, opportunities to address insurance data challenges, and an important deep dive: State Farm vs. Progressive. Which has better commercials?

Is data governance making you more productive? Or is it a pain that you just have to deal with for the good of the company? If you’ve got data issues, it may be time for a therapy session.

Join resident psychologists Tim, Juan, and Ashleigh Faith, EBSCO’s Director of Knowledge Graph and Semantic Search, as we explore the people and culture sides of data governance. We may not be able to treat all your symptoms, but we can change the way you feel about data governance.

This episode will feature ways to transform data governance from knowledge barrier to knowledge bridge, suggestions for consultative approaches to governance, and a discussion of who has started playing chess after watching The Queen’s Gambit.

Let’s talk data management frameworks. What is a data fabric and how is it different from a data mesh? No, seriously, we’re trying to figure it out ourselves. Is this an attempt by industry analysts and enterprising vendors to rebrand existing technology or are these fundamentally new data architectures?

In this episode, we’ll explore who and what are driving the conversation and try to demystify these emerging data disciplines.

This episode will feature:

• Clear definitions for data fabric and data mesh
• Where these frameworks overlap, intersect, and chart their own course
• Tim’s excuses following a Cleveland Browns playoff loss

There’s a dashboard for everything in the enterprise now, so it’s easy to answer your complex questions in graphical form. And new features like natural language processing (NLP) make interactions and queries faster and easier than ever. But surely there’s more on the horizon in BI and analytics, right?

Join Tim, Juan and special guest Peter Bailis, CEO of Sisu Data for a conversation on the current state of business intelligence and data analytics. We will also discuss what’s next and the all important question: why?

This episode will feature:

• The state of BI today
• The evolution of BI and why it’s no longer “the new black”
• Where analytics should be heading
• The data-driven culture and why it matters
• Tim and Juan’s New Year’s resolutions

In October, veteran election "race caller" Dwayne Desaulniers shared how The Associated Press has evolved their process to provide neutral, third party analysis of our most critical elections. It's even more interesting to revisit this conversation months later as we look back on what turned out to be a tumultuous election cycle.

As the year winds down, we're revisiting our favorite (and most popular) episodes exploring some of the more unique data roles out there. Next year, we hope to keep bringing guests like Dwayne onto the show, and we want to know: what are you curious about? Whose story would you like to hear? Do you have an interesting perspective to share? Drop us a line at [email protected]. We'd love to hear from you.

Have a happy, healthy new year, everyone. We'll see you again in 2021.

In this episode from early August, Claire Cahill from The Zebra shared what it's like to be a data product manager at a company where data is the key to success.

As the year winds down, we're revisiting our favorite (and most popular) episodes exploring some of the more unique data roles out there. Next year, we hope to keep bringing guests like Claire onto the show, and we want to know: what are you curious about? Whose story would you like to hear? Do you have an interesting perspective to share? Drop us a line at [email protected]. We'd love to hear from you.

In this episode, you'll hear:

* The characteristics of a great data product manager and how you can land the same role
* Who data product managers work with on a daily basis
* Whether a good podcasting mic really makes a difference (spoiler: it does)

Earlier this year, our podcast began with an ambitious episode titled “The Future of Data Management.” We’ve covered a lot of ground since then, from career paths and culture changes to data governance and data policy. In our final Catalog & Cocktails show of 2020, Juan and Tim review some of the hottest topics in the dataverse and look forward to what’s new and exciting next year.

YOU may recognize that your company needs a data catalog, but does your boss get it? Can you justify the investment and determine how quickly and what kind of return you’ll get? These may seem like daunting questions, but do not fear!

In this episode, Juan and Tim will discuss how to calculate your data catalog ROI, showing you how to measure a catalog’s ability to boost revenue and productivity while reducing risk. If you need to make a business case for an enterprise data catalog, don’t miss this episode!

This episode will feature:

• Success metrics for a data catalog initiative
• Different methods for calculating catalog ROI
• What Juan and Tim like to cook
  

What’s the secret behind Airbnb’s famously data-driven culture? It’s the company’s ability to provide all employees - not just the ‘data people’ - the ability to discover, understand, and trust data. Join Juan, Tim, and special guest Jeff Feng, product lead at Airbnb, to learn how the vacation experience juggernaut designed and developed its enterprise data catalog, DataPortal.

Whether you’re still unclear on the business value of a data catalog, or debating whether to build or buy, do not miss this episode.

This episode will feature:

• The good, bad, and ugly of a build-it-yourself data catalog
• How data democratization can promote data literacy and deliver business value
• Juan talking about how much he misses flying

Master data management (MDM) sits at the intersection of metadata and data and has a lot in common with data integration, data modeling, and knowledge building use cases. It’s “old school” technology that even today confuses many data leaders. But... is it still relevant? Should it be part of my data governance strategy?

In this episode, Tim and Juan discuss the merits of MDM, how it fits into metadata and governance concepts, and how it may evolve (or not) in the future.