«Companies are already wanting to position themselves ahead of the legislation, because they see the value of actually adaption best practices early on and not waiting for enforcement.»

Prepare to dive into the risk-based approach of legislation for artificial intelligence with the insights of Laiz Batista Tellefsen from PwC Norway, who brings her expertise in AI from a legal perspective to our latest episode. We tackle the imminent European Union's AI Act with its sophisticated risk-based approach, dissecting how AI systems are categorized by the risks they pose.

Norwegian companies, listen up: the AI Act is on its way, and it's time to strategize. We discuss the necessary steps your business should consider to stay ahead of the curve, from embracing AI literacy to reinforcing data privacy. Laiz and I dissect the balance between innovation and risk management, and we shed light on how cultivating a culture of forward-thinking can ensure safety doesn't come at the cost of progress. This segment is a must for businesses aiming to turn compliance into a competitive edge.

Zooming out to the broader scope of AI governance, we offer advice for maintaining the delicate dance between compliance and cultivating innovation. Discover the vital guardrails for capitalizing on AI's potential while readying for the unknown risks ahead. We peel back the layers of the AI Act's impact on the legal sector, unearthing the nuances of intellectual property rights and data transfer laws that could reshape your organization's approach to AI. Join us for a conversation that promises to leave you not only prepared for the AI Act but poised to thrive in an AI-centric future.

Here are my key takeaways:

• Looking at AI from a risk perspective is the right way to tackle the challenges within.
• Risk based approach makes sure that development is not freezed.
• Our job as experts in the field is to demystify compliance within the use of AI systems.
• Find the right balance between compliance and innovation, by assessing potential risks.
• "The AI Act is part of the European Digital Strategy and is the first comprehensive legal framework for AI in the entire world.»
• CE marking forces you to have constant monitoring and compliance of the system, as well as registration in a register.
• Have a holistic approach to AI: How does it fit in the wider setting of my company, both from a data, business and cultural perspective?
• There are big differences in companies maturity to operationalizing AI for value creation.
• The focus on risk and safety does not correlate to the need for speed in AI adoption.
• It’s not about starting from scratch, but about understanding the actual use-cases and needs.
• The AI Act can foster innovation, because you know what your framework is.
• "Make sure that the date you are using reflects the diversity and the reality of the people and situations that the AI system will encounter."
• Observe and control data quality and distribution continuously.

What to consider now:

• Make sure the company has very good control of known risks, like privacy.
• Make data risk awareness part of your culture.
• Understand roles and responsibilities in our organization towards data risks.
• Have your policies updated.
• Ensure your stakeholders are well trained.

«The journey Software development went through during the last 10 years, working towards DevOps and agile development, is something that we can really benefit from in the data space.»

Uncover the synergy between agile software development and data management as we sit down with Alexandra Diem, head of Cloud Analytics and MLOps at Gjensidige, who bridges the gap between these two dynamic fields. In a narrative that takes you from the structured world of mathematics to the true data-driven insurance data sphere, Alexandra shares her insights on Cloud Analytics, Software Development, Machine Learning and much more. She illustrates how software methodologies can revolutionize data work.

This episode peels back the layers of MLOps, drawing parallels with the established tenets of software engineering. As we dissect the critical role of continuous development, automated testing, and orchestration in data product management, we also navigate the historical shifts in software project strategies that inform today's practices. Our conversation ventures into the realm of domain knowledge, product mindset, and federated governance, providing you with a well-rounded understanding of the complexities at play in modern data management.

Finally, we cast a pragmatic eye over the challenges and solutions within data engineering, advocating for a focus on practical effectiveness over the elusive pursuit of perfection. With Alexandra's expert perspective, we delve into the strategy of time-boxed approaches to data product development and the indispensable role of cross-functional teams. Join us for an episode that promises to enrich your view on the interplay between software and data.

Here are some key takeaways:

• There is a certain push in the insurance industry towards data, AI and autiomation.
• Gjensidige has over 20 decentralized analyst teams.
• Data Mesh is about empowering analyst teams to take control over their data.
• By taking responsibility over their own data, analyst teams take off the load from Data engineering teams, so they can focus on the tricky stuff.
• MLOps, DataOps, or classic DevOps in the Data Space is about using System Development principles in the Data Space.
• The questions that arise within data today, are questions that software engineering went through 10 years ago.
• Software development also went through a maturing, that brought forth a domain driven focus, best practice focus, product thinking, etc.
• Documentation should live, where the code also lives. It should be part of the code.
• Introduce more software development best practices into the data teams.
• Do not think about the solution you want to develop, but the problem you want to solve.
• Time-box exploratory efforts into sprints.

The pitfalls

1. Software Development Lifecycle vs. Data Lifecyle – they overlap, but there are clear differences, especially in the late phases.
2. Feature-driven (or functionality-driven) vs. Data-driven: Is there a problem with software engineering mindset in data?
3. Hypothesis - Data Science vs. Engineering mindset: Explorational vs. structural thinking can cause friction
4. Environmental challenges: How does Test-Dev-Prod split fit with data?

«I took the time to actually go through all of my notes, all of the training courses, all of the things that I looked at over the past 30 years of work. And I thought, I want to give myself a reference book. Wherever I go, I have this single thing that will have enough information to remind me of stuff I need to consider. This is now my book of Patterns."

Get ready to have your perspectives on data management revolutionized! This Holiday special serves up a treasure trove of insights, as we dive deep into the interconnections of data, information, knowledge, and wisdom. We'll be shining light on the importance of quality data and the emerging role of data officers in organizations, challenge conventional thinking about systemic behavior changes and their impact on data management, while also stressing the utmost necessity of experimentation and testing to comprehend the ever-changing data patterns.

I was lucky to pick the brain of the experienced data expert Jonathan Sunderland, whose career has spanned an array of industries and roles. The conversation is a call to arms for organizations to have clear purposes and goals when striving to become "data-driven." Plus, you'll get an exclusive peek into our guest's impressive "book of patterns" project, which promises to be an invaluable reference for future endeavors.

This is a thought-provoking exploration of the fine balance that large organizations need to strike between agility and long-term goals. We'll confront the dangers of resistance to change and the pitfalls of a myopic focus on quick wins, offering insights on how to foster a culture of innovation without falling into the trap of over-optimization or outsourcing purely for cost reduction. Moreover, we'll dive into the world of data governance, discussing its crucial role in fostering trust with data and facilitating informed decision-making. Finally, we distill the essence of personal growth into three potent rules of challenge, enable, and inspire. So, what's your capacity? How can you elevate it to tap into your fullest potential? This episode inspires to ponder these questions and propel your personal and professional growth.

Happy Holidays!

«Sentralt I dette med å skape verdi er tverrfaglighet og involvere hele bedriften, ikke bare et lite Data Science miljø.» / «Central to creating value is multidisciplinarity and involving the entire company, not just a small Data Science environment.»

Prepare for a journey into the landscape of data strategy with seasoned Data Scientist, Heidi Dahl from Posten Bring, one of the largest logistics organizations in Norway. She is not just engaged in strategic discussions about data, AI and ML, but also a passionate advocate for Women in Data Science, took the initiative to create a chapter of WiDS in Oslo, and co-founded Tekna Big Data.

In our chat to understand the  dynamics of data science and IT, we talk about their balance between research and practical development. Heidi articulates the urgency for a dedicated data science environment, exploring the hurdles that organizations often confront in its creation.

We cross into the world of logistics, shedding light on the potential power of data science to revolutionize this industry. We uncover how strategic use of data can streamline processes and boost efficiency. Finally, we underscore the importance of nurturing an environment conducive for data professionals to hone their skills and highlight the role of a data catalog in democratizing data accessibility.

Here are my key takeaways:
Digital Transformation of Posten Bring

• An organization that is 376 year old and has been innovative throughout all of those years.
• The Data Science department was stated in 2020 under Digital Innovation, now a part of Digital technology and security.
• The innovative potential is found through use-case based work closely integrated with the business domains.
• There are several algorithms that made their way into production, and that is a goal to measure against.
• The Data Science teams consist of cross-functional skillsets, bringing together Data Science, Developers, Data Engineering and Business users.
• The exploratory phase is vital, but has to have a deadline.
• IT driven development projects do not always match with the needs of Data Scientists.
• Data and IT need to work together, but for exploratory work, Data Science should be able to set ut needed infrastructure.
• On cloud infrastructure it can be vise to think multi-cloud to ensure availability of a specter of relevant services.
• Posten/Bring is looking to build a digital twin for their biggest package terminal for better insight, control and distribution of packages.

Strategic use of data

• How can we use data to make better decisions, be more effective and smarter?
• The 4 core elements of the Data Strategy:

1. Establish distributed ownership of data and data products
2. Increase the amount of self-service.
3. Build competency tailored to your user groups needs.
4. Strive towards the goal of great services and products based on data for your users and customers.

• Role based self-service capabilities .
• A data catalog is discussed, to gain a better understanding of the data available, security, but also context of origin and data lineage.
• A data catalog needs to be able to serve different user needs.

Competency

• There are three perspectives:

1. How to recruit new and needed competency?
2. How to train and share competency internally?
3. How to retain competency?

• Data Engineer is a newer and more specialist role, that is hard to find on the market.
• You need to give your data professionals the possibility to do purposeful work, bring into production and connect to value creation.
• The entire organization should be aware of how to use data to make work more efficient and smart - think data literacy

«A combination of strong buy in from top-management and strong flow of change agents (…) is a requirement for succeeding.»

Eager to unlock the secrets behind building a trustful relationship with AI systems? I am sitting down with Ieva Martinkenaite, head of Telenor's Research and Innovation department to shed light on the interplay of accountability, ethics and AI technology. Through her role as translator between tech, leadership and business , Ieva brings a refreshing vantage point to the dialogue, providing a unique bridge between the tech and business spheres.

We're taking a deep dive into the creation of responsible AI within an organization. Our conversation explores the firm foundation of clear values and top management's proclamations, to cultivate a bottom-up process for a governance structure. Understand the three-layer structure of AI governance and the imperative of expert support for data professionals. Plus, we'll be scrutinizing how adopting responsible AI as a core principle can fetch a positive social impact.

In the finale of our discussion, we underscore the essence of responsible AI use and the value of investing in data professionals. Discover how individuals and companies can not only fulfill, but surpass compliance standards. Remember, it's not just about employing AI responsibly but about finding a responsible approach that fits you as an individual and your company. 

Here are my key takeaways:

The two scenarios of concern with AI in Telecom:

1. Missing out!
2. Messing  up! 

• Telecom still needs to catch up, but with a string focus on using and scaling AI technology.
• The biggest differentiator in the sector is applying methods and technology to provide the best customer service.
• To scale AI, you need to have very solid data capabilities .
  • Cloud native data platform with various continuously upgrading technologies.
  • Efficient and scalable storage and processing capabilities.
  • Data Governance structures to ensure accessibility and use of data in a secure, privacy friendly, ethical way.
• You need that foundation before you can start building advanced AI capabilities.
• Apart from data you need people who are data literate and technical adverse.
• A strong data culture is important, not just for the data experts in your organization, but for everyone.

Responsible AI

• ResponsibleAI should be build on a solid Data Governance foundation.
• The biggest concern of executives with AI is the lack of traceability with data.
• We need to a) understand what are the risks, b) create responsibleAI by design.
• Executive support and belief in the AI journey is key.
• Data professionals have a responsibility to communicate complexity, translate and apply their knowledge to ensure a more general data literacy.
• You should do anything possible to be able to explain how your models work.
• You need to ensure that it is save to talk about, also not understanding systems.

Steps to building Responsible AI Governance:

1. Decide as an organization on your core principles / value - how may they be challenged by AI?
2. Define your principles / values for AI - these should be AI specific, but adopted to your setting, concerning risk, portfolio, etc.
3. Make these principles / values actionable.
4. Seek endorsement
5. Build a Governance structure
6. Ensure training and awareness

Positive Social Impact

• Companies should feel a social responsibility to go beyond what is required to build better, more ethical systems and use of those.
• Ask yourself, why are you doing responsible AI and Governance? For compliance obligations or do you what to go beyond that to build based on high ethical standards?

«How well are we rigged in Norway to handle this?»

What a fantastic talk -  With so much happening in Norway in autumn 2023, I brought on Alex Moltzau for a chat in AI policy and Norway. Alex Moltzau is a Senior Policy Advisor at the Norwegian Artificial Intelligence Consortium (Nora.ai), and one of the most outspoken experts on AI policy and ethics in Norway.

• Throughout the last years, there has been a significant change in public attention to AI, even though AI has been part of our lives for quite some time.
• There is a great AI community in Norway, with great research that is done.

What is NORA.ai?

• NORA is a Norwegian collaboration between 8 universities, 3 university colleges and 5 research institutes within AI, machine learning and robotics.
• NORA is strengthening Norwegian research, education and innovation within these fields.
• NORA’s ambition is International recognition of Norwegian AI research, education and innovation.
• NORA’s vision is excellence in AI research, education and innovation.
• NORA is active both in the Nordics, but also collaborating broadly on the international stage, like exchange programs for Ph.D. students, collaboration with other national institutes, contribution to eg. OECD, even contributing to shaping bi-lateral agreements, +++

Why AI policy?

• There is a growing concern in society about AI and its impact on our lives, how it affects elections, misinformation, our work
• How can AI help us to handle information on our citizens more effectively?
• How does AI affect our children, their learning?
• There is a misconception, that we don’t have sufficient regulations for AI. Existing laws apply to AI as much as to other methods and technologies.
• What kind of infrastructure do we need to build in society? Is language an important infrastructure for our society?
• What is the public infrastructure, the public good we need to invest in as a nation?

State of AI in Norway

• What Government mechanisms are we going to build to handle artificial intelligence?
• There are three major announcements that have shaped the state of AI in Norway during the last weeks and months:

1. The AI Billion: The Norwegian Prime minister has announced that the Norwegian Government will invest 1 billion NOK in AI over the course of 5 years.
2. The Ministry of Defense has published their AI strategy.
3. A new Ministry of Digitization and Governance has been established in the Norwegian Government, with responsibility of AI.

• Internationally there are two concerns around AI that are predominant:

1. Security - how to ensure cyber security and reliability in models.
2. Bias - how to tackle bias in AI systems, work with fairness and trust.

• We need to ensure that possibilities through AI configure to our Norwegian society.
• We need to think about the values we have build our society on, and how AI can support these values.
• Norway is earlier than most countries on actively working with regulating AI, eg. in relation to privacy.
• AI is about implementation - it is about trying, failing and trying again.
• We need to minimize possibilities for disaster, by taking learning from other countries.
• There need to be mechanisms to ensure that the cost of compliance with regulations is not too high.

The role of Data Professionals

• We would love to see data folks should take a more active role in society in regards to help everyone to understand the challenges within data and AI better.
• Data Management professionals can ensure safety and trust in our society going forward, and should therefore have a more active role in politics.

https://www.nora.ai/

«I think that having a very good framework, where you can put all ML and AI in, makes it much easier, much more clear. (Jeg tror det å ha et veldig bra rammeverk, der du kan putte all ML og AI inn i, det gjør at du får det mye lettere, mye mer oversiktlig.)»

Frende Forsikring, a Norwegian Insurance Company has build up a team of 6 people that work with Machine Learning (ML) and Artificial Intelligence in the company. Their goal is to ensure the companies growth through automation. Anders Dræge is the Head of the Machine Learning and Artificial Intelligence team at Frende Forsikring and he has always had an interest for data and automation.

Anders is not just an award winning Data Scientist, one of the Nordic 100 in 2023, but also a person that is happy to share his knowledge.

The goal for Automation

• Automation is a target that can be measured against
• You can measure both, time saving as well as saved cost
• High-risk items are a good use-case to show the effect of ML: Its not necessarily about replacing work tasks, but to ensure that human focus in on the items that are of highest risk and value
• Automation is a way of scaling and growing your business, without increasing resources.
• The need for automation becomes more clear, and to avoid over-allocation of resources, the need is evident in the business.
• Your goals fro AI and automation have to be aligned with your organizations business goals

The composition of the team

• The Machine Learning team is 6 people string, consisted of
  • 2 ML engineers
  • 2 are 50% actuary (domain knowledge connection)
  • 1 data engineer that prepares data 
  • 1 MLOps developer with interest in ML to build connections with IT department
• Close collaboration with RPA (Robotic Process Automation) team and other departments.

The process

• The trinity of data in ML is paramount for quality results:
  • 1 set to train
  • 1 set to validate
  • 1 set to test
• There are possibilities to automate testing procedures
• Monitoring can and should be automated

The technological framework

• Find a framework that can control your processes, detect deviations and monitor effectively.
• Implementation and setting things in production is much more efficient with a proper framework
• Find a standard way of operating, will also have a positive effect on on-boarding new people

Key factors for success

• «One factor that was decisive for a very good collaboration across teams and departments is that we are very close. (En faktor som var avgjørende for et veldig godt samarbeid på tvers av team og avdelingene, er det at vi sitter veldig nært.)»
• Physical co-location is a success factor
• A lot of key competency is in-house
• Clear and transparent message on automation
• A culture that is actively striving for automation, finding ways to improve
• Culture is really important: People have to be receptive to the ideas of automation
• Find the right time to talk about automation - ideally before the need arises
• Human in the loop
• Monitoring of process output by humans is important for most ion the processes. This is about evaluating output with expectations from human experience
• Human evaluation becomes input for re-training of the model

The use cases

1. Automatic email distribution
2. Processing of physical mail
3. Monitoring of laws

For the work Frende Forsikring has done with Natural Language Processing (NLP) for email distribution, the team won the Dataiku Frontrunner Award 2023.
https://www.frende.no/aktuelt/frende-vant-internasjonal-ai-konkurranse/

«There should be very little reason to say: Hey, I need a human to look these operational things for me. They are all defined as code.»

Lars Albertsson has a long career in Data and Software Engineering, including Google and Spotify. Lars is on a mission to spread the superpowers of working with data, with the vision to: «Enable companies outside of the absolute technical elite to work with data with the same efficiency or effectiveness as the technical elite companies in an industrial manner.»

4 types of companies:

1.  Born digital - Data is the basis of their business model.
2. Born digital in a traditional market - completely natural to use data as a competitive advantage.
3. Traditional industries «born before the internet» - big difference wether they handle information or are in the physical world.
4. Information Handlers - Banks, Media, etc have digitalized their whole activity chain a long time ago.

The differences

• Significant differences in cycle-time in different industries and businesses.
• The only way to beat this cycle is to try out, fail fast, learn, try again.
• «Successful companies have been really good at failing fast.»
• Fast moving cultures are more effective and therefore have a better risk focus, without slowing down.
• To move fast in a slow moving industry, you need to choose your technology and approach wisely, keeping complexity down .
• Cultural slowness - «The challenge to change the way people work and people think is extraordinarily difficult.»
• Risk and Governance are addressed by rituals, rather then tasks.
• The value chain data to client outcome, needs to be anchored in a company. Have a clear picture of what this means.

Getting close

• Success can be measured by how close you are to the end user. The closer you get to a customer, the better the changes of success.
• «There is no substitute in value creation, than talking to the people you actually want to make happy.»

Automation is Innovation

• You need to find ways to ignite people's domain innovation capacity.
• Automation is a gradual process. People don’t loose their work to machines over night.
• Human-oversight is still really important, and there is a long journey with humans as part of the process.
• The focus on automation now is in knowledge workers, yet those have a different stand in society and are able to resist better, compared to the workforce during the Industrial Revolution.
• «If it changes quicker than one generation, there won’t be natural attrition that matches the changes in the need of the workforce.»

Automated Data Management

• Automating and industrializing data management processes is lower risk then software development, but still not as common.
• Great value to gain, from delaying simple automation processes to data management.
• You need to build everything from raw data to end product to find ways to automate.
• The raw data is the soul of the end product and the other way around. You need to keep these two outer points of the pipeline in mind, when think of data quality and data products.
• The limitations in Hadoop forced to work in a certain way. That way can be adopted to data management.
• Hadoop really pushed people in the functional Big-data patterns, that are still the basis of much of the work we are doing today.
• Workflow orchestration can help to know, which data you choose for a certain computation.
• Data Management as code is an area that is underdeveloped and under-appreciated.
• Minimize the technical barriers from Governance, and focus on the social aspects.

Ford CEO on Software: https://www.youtube.com/shorts/HrNN6goQe50

«How do you develop good procedures around testing or how do you drive experimentation in a product or business setting?»

Carl Johan Rising works as Director of Data at Too Good To Go, a marketplace that enables food businesses to sell their surplus food instead of throwing it in the bin.

We talked about how to form a product team, and how to rethink the role of Data Scientists in your team, shape it in an embedded team, close to domains and with expertise and customer focus. We talked about skills, product focus, business partners and much more.

«(A career in) data gives you a bit of everything.»

• Data is a nice intersection between aspects of business, academia, physiological problems, and technical challenges.
• Business - especially understanding and decision making 
• Academia - working with hypothesis

Data at Too Good To Go

• If you look into how you want to use data to really drive decisions, it becomes more of a change management challenge, and not just a technical challenge
• «Start with a proper infrastructure foundation - a good clean data model»
• «Foundation building is invisible, and doesn’t by itself bring business value»
• The business sees the data team as one unite, without distinction between different capabilities in the team - Therefore the expectations are different
• «Make it very explicit what people can do and what their capacity is.» - gain understanding business

Product Analyst:

• «And soon as you have any emphasis on product, its development, its iterations, then it makes sense to have Product Analysts.»
• Too Good To Go works with multiple Product teams, each with their own problem spec
• in a Product team - Product Manager, Designer, Engineering Lead, Engineers, Machine Learning Engineers, and Product Analysts embedded in the team
• Product Analysts in each team to drive good identification of problem spaces and to enable the teams to do rapid experimentation
• The role will ask the "how do we drive good?" and set an experimentation agenda - driven by the Product Analyst
• Emphasis is on statistical knowledge and technical skills.
• There are two main stakeholders for Product Analyst -> Engineering Lead and Product Manager
• Focus on gathering the best resources to tackle a problem
• What skills and experiences do you need in a PA role?
  • statistical knowledge
  • programming
  • understanding of tech. aspects
  • ability to explain results

«I think the role of Data Scientist can mean a lot of different things.»

• Be a bit more explicit about what the work is and what it entails
• Minimize the possible confusion between expectations on Data Scientists In a company

Data Analytics Business Partner

• An embedded role that is part of the business with co-ownership of the outcomes
• Through this partnership it is much easier to gather context if you work with the domain

«We (DAMA) have a role to play, (…) develop the Data Management profession ultimately for the benefit of the society.»

It is good to be back with Season 3 of MetaDAMA, and as always, we start with a DAMA-focused episode.

Nino Letteriello is one of Europes most influential data leaders, president of DAMA Italy and Coordinator for the DAMA EMEA region (Europe, Middle East, Africa). Nino started his carrier in project management, educated in civil engineering, and got involved in Data Management around 2017. Since 2019 he is the regional coordinator for DAMA EMEA.

Here are my key takeaways:

What is so special about DAMA EMEA?

• Lots of passion and commitment by volunteers
• Giving back to society to proff how the society becomes more data literate
• «Whilst we are a geographical region, I see very very different scenarios, very different levels of maturity.»

• Middle East - Saudi Arabia:
  • Government drove a framework, build on DMBoK for public administration 
  • This is cascading down from public agencies to the big corporations working in Saudi Arabia, and subsequently to SME
• African Countries show a scattered approach to Data Management
  • Great appetite for knowledge in data
  • There is an enormous sense of «missing out»
• Mediterranean Countries and Central Europe
  • Initiatives of «data alphabetization» or data literacy at an early stage
  • Teaching data management at an earlier age, eg. Program in Italy to teach DM in middel school and high school (DataHigh)
• Nordics
  • Different level of maturity.
  • Staring early with digital competency development
  • Inspirational is the nordics view on data for social good and ethical handling of data

Data Literacy and awareness

• Nino was involved in a WEF (World Economic Forum) study on how SMEs (Small, medium sized enterprises) are leveraging the power of data
• Collected information form over 200 Small and medium sized enterprises
• «Interesting how many companies still consider data an IT thing, a subset of IT.»
• There is still an over reliance on IT, not seing data as a business problem
• Immature on Data Governance and formalization of roles
• Awareness is not necessary followed by clear action
• SMEs face the same issues as big corporations, but without the means to handle these issues accordingly
• SMEs have the possibility to me very agile in facing these issues
• Still a lot of «reinventing the wheel» - we should use DMBok and other existing frameworks actively as a basis to work from

Is DAMA still relevant?

•  Importance of DAMA and DM is still large, also and especially in times of AI
• «Garbage in - garbage out» is still as valid as every
• New methods, new techniques, how languages, everything is dependent on the quality of the data you put in
• Everything starts with awareness
• The real differentiator is that data is a business asset, not an IT asset

DAMA EMEA Conference

• Organized for the third year in row, first time both digital and physical in Bologna November 29th - December 2nd 2023.
• Conference provides clear, filtered, categorized, relevant information
• Possibilities to share ideas and network
• Closed session for all board members in EMEA region to discuss a declaration of intents for DAMA EMEA
• Nino likes the idea to do Data Management Maturity Assessments across countries tom compare DM maturity
• There is also a possible intent to work closer with European Commission
• Giving DAMA EMEA a vice towards legislation makers

Get more information and sign up here for the conference: https://data-emea.org

«Sometimes, you look at the problem and think it’s valuable, you spend a lot of time on it, and then you find out that this is really a NO!»

Ida Haugland and I talked about the importance of Data in Shipping, about a concrete examples for success through digitalization and mainly about the focus on Product Management.

Ida works as Principle Product Manager at Omny. At the time of recording Ida was part of Klaveness Digital and is presenting from her perspective from her work there. She started here career with data, while working for Customer Satisfaction for Facebook in 2010.

Here are my key takeaways:

Shipping & Logistics

• We are talking about really complex lines, that need to be managed. Data is the basis for everything.
• In Shipping and Logistics it is vital to «Put together a comprehensive plan to see cause and effect.»
• Shipping and Logistics is on a way to detailed insight in to their supply chain, as much as logistics for finished good.
• « A Lot of principles and thinking behind other products can be applied to shipping.»

Carbon Emissions and the role of Data

• Carbon Emissions can be minimized based on data driven insights.
• Concrete actions based on data:
  • Slow down - timing shipping and arrival at port with possibility to embark and birth the shipload.
  • Use bigger vessels - more cargo, less voyages.
  • Use the full tonnage of the vessel - fill it up to capacity.
  • Reduce ballast distance - find vessels close to you, to reduce empty voyages.

Digital Product Management

• There is a lot of hype around product management in data and digital.
• Product thinking means turning your attention to the user/consumer of the product and their needs.
• You need to collect data, ensure quality, display its in a way that makes sense, and display it in the right context - that means products.
• There has been a development in product management to become more data informed and customer focused.
• There are still misinterpretations between product thinking and project thinking.
• Projects have a set scope and an end-date. Projects are defined by their constrains, on scope, time, or quality.
• Product development does not have nan end-date and cannot be done in isolation.
• In Product management you have constrains on features, not on the entire product.
• In Product management you can deliver a lot of value, but you don’t always know when.
• You can apply Project Management methods in product management.
• When you deliver a product you need to have your consumer, customer and end-user needs in focus throughout the entire development of that product.
• Separate your own opinions and preference out of the product during development.
• Don’t just ask what products people want, but rather elicit requirements through a controlled process, to find out what they need.
• A product manager needs to facilitate and get customer, designer, software engineer, etc. on the same page.

Skills for a Digital Product Manager

• User empathy
• Storytelling
• Elicit needs
• Data Analysis
• Cross-departmental communication

And the Number One rule as a Digital Product Manager: «Don’t mistake your own opinion for the right opinion.»

«How can we consolidate data and describe it in a standardized way?»

Scientific Data management has some unique challenges, but also provides multiple learnings for other sectors. We focused on Data Storage and Operations as a knowledge area in DMBok. A topic that is often viewed as basic, often not in focus, but is a fundamental part of data operations.

I talked to Nicolai Jørgensen at NMBU - Norwegian University of Life Sciences. Nicolai has a really diverse background. His journey in data started in 1983! In his free time, Nicolai spends time with photography and AI for text to image generation

Here are my key take aways:

Scientific Data Management

• To describe data in a unified way, we need standards, like Dublin Core or Darwin Core for scientific data.
• Data is an embedded part of Science and Research - you can’t have those without data.
• You need to make sure you collect the right data, the right amount of data, valid data, +++
• You need to optimize your amount of time, energy and expenses when collecting and validating data.
• You need to standardize the way you collect data, to ensure that it can be verified.
• There needs to be an audit trail (lineage) between the data you have collected and the result presented in a publication.
• Data needs to be freely available for research and testing hypothesis.
• Data needs to be findable, accessible and interoperable, but a also reusable.
• ML algorithms can help extract and find changes to scientific data, that is internationally available.
• Describing data is key to tap into knowledge - for that you need metadata.
• In times of AI and ML, Metadata is still the key to uncover data.
• The development of AI models is a race - maybe we need to pause and get a better picture of cause and effect, and most of all risk.

Standardizing Infrastructure

• How can were standardize on the infrastructure for research projects
  • Minimize or get rid of volatile data storage and infrastructure
  • Standardize data storage solutions
  • Secure what needs to be secured
  • Splitt out sensitive or classified data and store separate (eg. Personal data)
  • Train your end users and educate data stewards
• Have good guidelines for researchers on how to store, use and manipulate data.
• There is a direct correlation between disc-space use and sustainability.
• Storage is cheap, is a correct saying, if you look at its in isolation - but in the bigger picture the cost is just moved.
• Just adding more storage doesn’t solve your problems, it might just yet increase them.

Long-term Preservation & Integrity

• To preserve data for long-term you need to
   
  • Encapsulate data at a certain level
  • Standardize the way you describe the data
  • Upload data package to a common governed platform
  • Enclose if there is a government body that can take responsibility to preserve your data for the time necessary
  • Ensure that metadata is machine-readable
  • Formats like XML provide the possibility to read the data by both machines and humans 
• Research integrity: conducting research in a way which allows others to have trust and confidence in the methods used and the findings in that result.
• Ensure lineage and audit trails for your scientific data.
• Fake data, data fabrication, are serious issues in research - the understanding and methods for keeping data integrity at the highest possible level is not getting easier, but increasingly important.
• Changes to data (change logs, change data capture, etc) can be studied as well; you can build models to build scenarios around data changes.
• You can fetch data from other sources to enrich the quality of your data.

"It's about taking a step back to ask yourself: Should we even have a data-driven system for this?" («Det handler om å ta et steg tilbake for å spørre seg: Skal vi i det hele tatt ha et data-drevent system på dette her?»)

We finish season 2 with a though-provoking episode, to maybe start som debate about data-driven public administration.

Lisa Reutter is PostDoc at the University of Copenhagen connected to a project called: «Datafied Living». We talk about the importance of Social Science in Data, and how data is intertwined with our lives. Lisa is researching in the field of «Critical data and algorithm studies», at the interplay between tech, data and society.

Here are my key takeaways:

Data in Public Administration

• For a modern state to function properly and to ensure citizen rights, services, security, etc is provided the state needs data.
• Data Management by the state for its citizens is not a new concept but has a long historical foundation.
• During the last years we use more, different and new data in administrative processes, and enhance technological development and a tool box to derive value from data
• Public administration has had a monopoly over management and ownership for citizen data. But this has been challenged by private companies.
• Data-driven systems in public sector are not there for profit, but to create value for society. Therefor they need to be build on and with the purpose to enhance our democratic values.

Registers

• Norway and other Scandinavian countries have established national registers to manage and administrate society.
• There is a reason why registers are not unified in Norway, and this is to ensure a balance of powers
• The opposite example, of what can happen if a state collects information on its citizens without boundaries, is to be found in the GDR (Eastern Germany)
• If all data of all aspects of your life are collected one place, it is really easy to misuse this data
• Through data a state could see, predict, and control the behaviors of citizens.

The public debate about data-driven

• Discussions can and should be about what data are we collecting, where do we store data, what are we using data for, who could and should have access to that data, etc.
• Even with public debate about data use in public administration, limits and boundaries can never be defined clearly. Also because this is individual and relative to context.
• Datafication is a political act. The citizens need to be involved in the process of technological advancement and intelligent use of data.
• The debate around «data-driven public administration» in Norway, has not included the public actively.

Customer-centric vs. data-as-an-asset vs. democratizing data

• Is there a rhetorical ambiguity between being customer-centric and data-as-an-asset?
• Data democratization demands that citizens have to use their time, resources and energy to ensure that public administration is working correctly.
• Is making data available leading to commercial parties capitalizing on that data and building solutions, rather than creating transparency for citizens?
• The right education and skills are important, but it needs to be available and attainable for all parts of society.
• Data Literacy is an own subject that is in dispute about what it should contain.

We need to understand, that this has implications on how we...

1. Trust in the state

2. Trade - what do I give my data for? What do I get in return?

3. Build in accepted ways

4.  Weight opportunities against risk

5. Ensure that the responsibility for understanding does not lie with the citizen alone

6. Gain knowledge, and how everyone can get it

7. Should invite for debate

«When technology evolves really fast, also the skills you need to hire for evolve really fast.»

Within a rapidly changing environment, fueled by technology and great ideas, it can be hard to define a stable career path. So I brought in an expert on developing companies and building legacies. Pedram Birounvand has a background in quantum physics, data engineering, experience from Spotify and moved into private Equity 6 years ago. Now Pedram started a new chapter in his career as the CEO of a startup working with Data Monetization.

Here are my key takeaways:

Data Skills for the Future

• As a leader in the data domain, you need to be a storyteller, to tell the story about the necessity of data, like quality or governance.
• The Job titles for Data Scientist, Data Engineer, etc stayed consistent, but what we expect from someone with that job title changed greatly through the last years
• Hard skills in data are not as important as they where for every company
• Make sure you know, what you are optimizing for in your career
  • Are you optimizing for flexibility, self-employed consultant is best
  • Are you optimizing for building a legacy, be an entrepreneur
  • Are you optimizing for leading people and see people grow, become a line manager
• Dont become a manger if your passion is within engineering. You will need to optimize your time for coaching people, not working on problem solving as an engineer.
• «The technology of applying AI and ML becomes more and more simple and becomes more and more commoditized.»
• Dont hire Data Scientist to build models that you can buy out of the box.
• Don’t hire Data Scientist if you need Data Analysts. They work entirely different and the work is not comparable.
• If you hire a Data Scientist before having good Data Engineers, then the Data Scientist cannot create any value
• «In order to be successful as an engineer, you need to have a really transformative mindset.»
• You need to enjoy the learning process, if not focus on something else in the IT-domain
• Adopt an agile mindset. Agile fundamentals are key to todays work life.
• «You need to embrace to be able to incubate things.» Build incubator squads as soon as a good idea pops up.

Recruitment

• In a small company you need to be much more flexible and broader in the way you tackle problems, than in a larger company where you can be more specialized
• As a hiring manager, don’t lean too much on the titles, but make sure you understand what you need in your company. This is key to writing a good add and attracting the right talent
• In a job advertisement be specific: What does it mean to be a Data Engineer in the context of your business?
• What is important for you as a company today, based on the trends coming?
• Building code has become so much simpler. Do you still need developers that need to know all the details about a certain language?
• Maybe a person that can be close to the business, and not so deep in programming can add more value?
• «You have to know what it is you are optimizing for.» If you have an extremely complicated technology stack you need deep knowledge, if not don’t hire it.
• In a recruitment process, focus on soft skills of rapid learners that can adjust to new situations and having an interest in understand your business use cases.
• «My interview secret: Share a whiteboard session with me»
  • Try to figure out how self-sufficient a candidate can be in understanding how the business works and where to get relevant data
  • Test how candidates react in uncomfortable situations, with customers that are not always happy about results and solutions.
  • Look for candidates that show resilience in new and uncomfortable situations
• Career models should be technology agnostic

«Infants with guns!»

Are we mature enough to track, collect and handle data responsibly, according to ethical standards? I talked with Director of Data Innovation at IIH Nordic Steen Rasmussen about the Business impact of Data Ethics.

Here are my key take aways:

• If were track, collect, and keep all data for any random opportunistic purpose, we put your companies at risk.
• This includes a «commercial curse» of budget-heavy tracking and budget-light management and business value creation through data.
• ROT, Norwegian for clutter, is an acronym for Redundant, Obsolete, Trivial - the dat that clutters your way to find valuable data.
• Collection and tracking of data is still too much dependent on people: If there is a change in personal, you get situations where new people «clutter the clutter»

Marketing & Sales

• For many companies it was Marketing & Sales that drove the data-driven agenda.
• The big value of Marketing & Sales is to add the market-dimension to the data.
• You can actually relate your product to the market, ship to where the market is.
• Analyzing market data is «putting a fixed entity on a moving target». The market changes to rapidly to provide good analysis.
• The more you push behavioral forecasting into the future, the bigger your uncertainty.

Business value & Ethics

• Corporate irresponsibility is an issue.
• Sometimes we get involved in a projects for the projects sake.
• Data projects have for a long time been theoretical, so the impact was not visible.
• Chat-GPT is a black box. Should we really give it more firepower, if we don’t know how it works?
• The market determines that there is a business value in being first.
• The speed of innovation doesn’t give time for reactive regulatory bodies to regulate efficiently.
• Companies need data ethical guidelines to say, how they will, shall and can use data.
• Who should define data ethical guidelines in a company? It is still done on a user-level, whilst senior management is looking at market situations and weighting them against ethical guidelines.
• We need regulatory and top-level guidelines that cannot be bent according to market situations.
• «Ideally, but highly unlikely we need a global set of data ethical guidelines.»
• The more trustworthy you are as a company, the more relevant data is shared with you.
• With that trust and data, you can understand the market better than companies that are not trustworthy and basically flying blind.
• Personal Data Literacy is important, and we need basic digital skills in our society.
• There is also a lack of understanding, when it comes to measures set in place for peoples benefit, eg. Cookie-banners.
• We are still lacking good privacy approved alternatives to the tools we are using on an everyday basis.
• Everyone has to follow ethical guidelines. We cannot have a DarkOps department in our company.
• Data Ethics guidelines should be something everyone can refer to.
• Ask yourself: What is the minimum of data we require to collect? Anything else should become an ethical question.

Data Protection Laws:

• There is a difference between the interpretation of regulations in the EU.
• Nordic countries interpret law relatively, is this just, fair, reasonable?
• Southern European countries use a more napoleonic or dogmatic approach, where «the law is the law, and the law must be obeyed.»
• Both Chat-GPT and Google Analytics have been handled differently by data protection authorities.
• Data Protection Authorities generalize to much, and don’t look at differences in technology.
• Is a strict, generalized interpretation creating panic, for users of eg. Google Analytics?

«There is a fundamental conflict between the essence of fashion and Machine Learning.» 

Fashion is always about change, innovation and identity. Whilst ML is good at making predictions based on historical patterns on those things, not change. How do those go together?

I had a fantastic chat with Celine Xu, who is Head Data Scientist at H&M Group, with a mixed background from Applied Mathematics and Business (MBA).

Here are my key takeaways:

Three things Data can achieve:

1. Automation of systems, where we have a clear understanding ion the process.
2. Sourcing, filtering, ranking of data input for decision making.
3. Ad-hoc analysis of all kinds of AB testing or correlation relationship.

Focus for ML in Fashion

• «ML can be embedded across the supply chain from product design to customer.»
• H&M focuses ML on two areas:

1. Future proof for customer experience: search experience, personalization, fashion inspiration, etc.
2. Demand-driven supply chain: reduce cost and be more sustainable through demand forecasting, cruise control, logistic optimization

ML in Product Design (some examples)

• 3D modeling can make the customer more comfortable in a visual way, in choosing the right size.
• Digitalizing fashion sampling is an interesting use case.
• Early detection of format, color, etc. that can generate trends.
• Optimizing logistics and storage through demand forecasting.
• Shop assistance for employees, with sales data, inventory and warehouse information.
• Language to image: use of stable diffusion for design patterns, on the material level.
• H&M is experimenting with trending words on TikTok or Instagram and transferring them to production design.
• Focus is not in understanding a sentence or the sentiment behind it, but instead use hashtag or product description to find out, what are the trends and link those back to a certain garment feature.
• Purchasing records (when and where did you buy what) and web viewing records (devices uses, pages that lead you to your purchase, etc.) are vital pieces to collect for behavioral analysis.

The Challenges of ML on behavioral and cultural data

• ML models are often evaluated on ML metrics, like accuracy, recency, etc. but not really business metrics, like revenue, profitability, etc.
• You fashion taste is influenced by your music taste, your interior design, and so much more.
• GDPR or webside viewing regulation are limiting the data source availability.
• Even harder to get the data accurate on a granular level, eg. Do you buy for yourself or doing you purchased a gift?
• With a lot of different dimensions in the data, you need to balance the accuracy with the cost.
• ML is always learning from something that already happened. If a new situation accrues, there is a lack of information and data.
• The complexity of behavioral data increases bias.
• You could artificially enrich your data set, eg. by drawing conclusions from certain data like income, area of residence, etc.
• Emotional and temperament data (what value do you hold to your fashion) can be important data to provide for your model, but also as an indication how you would react to recommendations.
• A small decision in your model or source data can lead to a butterfly effect.
• In fashion the definition of style is vague. But when you want to quantify certain feature in ML.
• Fashion trends last around 3 months on SOME, but production time is around 6 months. So how can you react in time?
• Images of material can be deceiving. Camera angle, light etc can influence how the color is represented.
• Presentation of textile is hard, because only a limited number of different textiles can be identified by image only.

«MLOps is a set of practices that bring people, process and platform together into a stream-aligned process to manage End-2-End Machine Learning lifecycles.»

MLOps is taken about a lot, so I asked an expert what we are actually talking about. Xiaopeng Li is AI business lead at Microsoft for the Western European market, located in Oslo. Xiaopeng is a passionate influencer in the field of Data and AI/ML, who was nominated as AI influencer of the Year at last years DAIR-awards in Stockholm.

Here are my key takeaways:

Patterns in AI adoption

• AI adoption projects are quite diverse, but with some patterns that are visible across. Here are use-cases that a lot of industries are working with:
  • Business Process Automation as an AI use case 
  • Adopting AI to process documents automatically and extract key-values
  • Natural Language understanding and processing, but also Natural Language generation
  • Chat GPT
  • Knowledge Mining
  • Unstructured data analysis

«Nordic countries are at the forefront when it comes to adopting AI and ML»

• Some of the most advanced search capabilities used in Microsoft are developed in Norway
• Nordic countries are typically quite tach-savvy
• Nordic countries have very good infrastructure

What is MLOps?

• MLOps is about agility, productivity, consistency and quality
• It is about creating scalability for your Data Science work
• MLOPs is a vage concept and you can probably find a variety of different definitions. Is MLOps at the intersection between DevOps, ML and Software Engineering?
• Scale ML development and deployment with constancy, with quality, with speed

The three elements that are most important are people, process and platform

• People:
  • 5 particularly important roles: Stakeholder, Cloud Infrastructure Architect, Data Engineer, Data Scientist, Machine Learning Engineer
  • There are many different roles involved in MLOps, from cleaning data to testing a model an implementing it. These roles need to be orchestrated
  • Domain experts and stakeholders play a critical role in defining the challenge in the first place. They can formulate what to achieve and what is good enough
  • Change Management is important, especially if your ML implementation triggers behavioral change
• Platform:
  • You are in need of a secure, scalable infrastructure to would your models on
  • Mature organizations who do ML at scale, have most an integrated architecture for Data Management, Analytics and Machine Learning
• Process:
  • Data collection,. Data processing and data management are processes you need to focus on in MLOps
  • You need a process and the right competencies to gather use-cases in the first place
  • Build a backlog of initiatives and then go through prioritization based on eg. Data availability, feasibility of solution given current etch-landscape, value for business, cost, time to marked,..

Path to MLOps

• Always start with assessing your current landscape and maturity
• Start by assessing your platform capabilities
• Ensure you have the right competencies and people
• If you want to operationalize MLOps, don’t look at it as a technological problem, but something that includes the entire organization
• Key is to bring key stakeholders as early as possible into the discussion

Oslo AI:
https://www.linkedin.com/company/oslo-ai/

https://www.meetup.com/oslo-ai/

Link to MS learning:

MLOps Maturity Model

«The real value in changing the status quo and getting more women to the forefront, is by having women share about the important work that they do, and not just talk about their gender as a topic.»

This episode was recorded right before Christmas, when I had the pleasure to chat with Alexandra Gunderson and Sheri Shamlou.

Alexandra inspired by a Women in Data dinner in New York, took it upon her to find likeminded people in Norway. That is how she came across Women in Data Science, the conference that was brought to Norway by Heidi Dahl in 2017. First meetup as a Community was June 2018, and this years WiDS event «Crossing the AI Chasm» is coming to Oslo (and digitally) on May 24th, 2023.

Here are my key takeaways:

Women in Data Science (WiDS)

• «Creating a meeting place, a place for people to connect and get inspired»
• Creating a platform and stage for outstanding women.
• Here are some of the events WiDS organizes:
  • «Champagne Coding» - hands on event
  • «Data after Dark» - after work event: 1-2 quick high level presentations
  • «Data for Good» - get together and solve difficult challenges for greater causes
• An important mission is to increase the number of role models in the community to look top to.
• The goal is to provide arenas to learn together, son it is as important to share stories about failure and collaborate around the learnings from those.
• WiDS is looking for sponsors, and one benefit can be, that trough events real-life uses cases can be solved.
• The focus for 2023 is «scalability» how to get unstuck from ML and AI pilots and bring your work to production?

The Quest for Diversity

• Diversity is a complex topic with several perspectives: gender, nationality, background, knowledge, expertise, and experience.
• Why is diversity important?

1. Leads to more innovative and effective solutions
2. Leads to more fair and just outcomes

• The starting point when working with diversity on a daily basis is awareness.

Diversity in the workplace

• Diversity doesn’t magically happen. You have to work for it.
• Awareness is a first step, but you also need to collaborate in broader groups.
• The value is gained when you are able to include everyone in your events and talks.
• For people to work together against biases of any kind, you need an inclusive culture from the beginning.
• Be open in your communication and foster a culture of collaboration.
• The «3rd shift» is an important requisite for women to be able to spend the same amount of time and intellectual capacity at work.
• The work for an inclusive work environment is never over. We have to continuously work on its and talk about it.

Diversity in recruitment

• You have to actively seek out to hire people with different backgrounds.
• In recruitment, be aware of how you write a job announcement.

1. Use gender neutral language (avoid stuff like «Data Science Ninja» or «Data Rock Star»).
2. There are online applications to check if your language is gender neutral and with suggestions for replacements of bias´ words.
3. You need to highlight more possibilities with a job, like growth and learning opportunities.
4. Minimize the list of requirements in a job-posting.
5. Be aware of you own biases and work with diverse teams also in recruiting.
6. When screening CVs, be aware that different people write in different styles.
7. In an interview process, eg. Women don’t like to do coding tests, with someone watching them.

Get involved: LinkedIn group, Meetup or https://www.widsoslo.com/.

«Data Mesh promises so much, so of course everyone is talking about it.»

I had the pleasure to chat with Karin Håkansson about Data Governance in a Mesh. Karin has worked with Data Governance and Data Mesh and is really active in the Data Mesh Community, speaking at podcasts and moderating the LinkedIn Group «Data Governance - Data Mesh»

Here are my key takeaways from our conversation:

Data in Retail

• The culture in retail is about innovation, experimentation, new products. So governance has to adapt to this environment in order to be successful. 
• If retail would do, what we do in data a fashion retailer would sell yarn instead of t-shirts.
• Retail knows what the customer wants, before the customer wants it. What would happen if we in data think like retailers?
• Its more about understanding the business better, then making the business data literate.

Data Governance

• Data Governance best practices in the DMBOK is still relevant, also in a Data Mesh setting.
• Data Governance has been on a journey from compliance driven to business value driven.
• Centralized Data governance creates a bottleneck. Decentralized Governance creates silos. So federated Data Governance is the middle ground.
• Create incentives to create trust.
• If you utilize your platform correctly, you can have high expectations towards computational governance.

Data Mesh

• Data Mesh comes with a cost - you need to invest in Data Mesh.
• But more than anything Data Mesh implementation is an enormous change effort.
• If you don’t know why Data Mesh, you will implement something else
• Implement Data Mesh in an agile way: «start small, fail fast and iterate»
• To start with Data Mesh, work with a business team that is eager to get started and sees the benefits - «You have to have business onboard, otherwise its not going to work.»
• Always check if you get the value that you expected.
• When you do it, make sure you get Governance, business and tech teams to work together and a re aligned on the why.
• Make sure two upskill for Data Mesh - its is fundamentally different: talk about it, have debates, book clubs, ++
• The 4 elements of data mesh: Can you implement those in a sequence or should you look at them as a unite to implement within a limited scope? 
• Start finding ways for people to work together (eg. Common goal, and environment where it is ok to share).
• A good first step is to find an example of data with a certain issue or limitation and talk with the business user about exactly this.
• Data Governance, as much as Data Mesh, is about change management: You need to get close to the business and collaborate actively.
• Your first two steps should be:

1. Start with one business unite, an early adopter.
2. Find their most critical data and talk about actual data.

MDM and Data Mesh

• Are we still hunting for that golden record? How do we work with MDM in a mesh? This is not solved yet.
• You can refer to data, instead of collecting data in a MDM system.
• Maybe the best approach so far are global IDs to track data cross domains, but how you link your data might become the new MDM.
• «You still need to connect the data, but you don’t need to collect the data.» - MDM in a Mesh.

Domains, federation and responsibility

• If you federate responsibility to the domains, they also need the resources and competency to fulfill these responsibilities.
• If the domain data teams are successful in abstracting the complexity, it will become easy to create data products.
• If you scale too fast (faster than your data platform), you might end up having to duplicate teams.

«If we think of Data Mesh as an evolution of data lakes, knowledge graphs are an evolution of Master Data Management.»

Data overload is becoming a real challenge for all types of businesses. With all data that is gathered, both in multiple formats and huge volumes, has created a need for connected, contextualized data. Combined with continuing developments in AI, has resulted in increasing interest in knowledge graphs as a means to generate context-based insights.

I had a fantastic chat with Mozhgan Tavakolifard. Mozhgan describes herself as «incubator and alchemist». She worked with PhD-research on trust and SOME. The techniques Mozhgan used to collect data for her research introduced her to Data Science. On this episode of #MetaDAMA, we talked about Knowledge Graph enabled Data Mesh.

Here are my key takeaways:

Data-driven transformation

• Personal transformation and transformation of businesses are quite similar.
• Very few companies can blame that they are industrializing data and are transformed in a data-driven way.
• For a transformation to be successful, you need to have a holistic view and invest in a practical manner according to your business case.
• Don’t change your business culture to match a data culture, but rather let data be an enabler for the business.

Data Mesh

• Each domain can have a different culture, produce different data products.
• 4 indigents:

1. Give data back to the domains where it is produced
2. Create self-service data infrastructure
3. Federated governance
4. Data as a product

• The centralized data and analytics platform has failed.
• Knowledge Graph can basically be considered as Data Supply Chain for Data Mesh.
• KG can be used to semantically link data products.

Knowledge Graphs

• KG enable a human brain-like approach to derive new knowledge. 
• A KG is quite simply any graph of data that accumulates and conveys knowledge of the real world.
• Every consumer-facing digital brand, such as Google, Amazon, Facebook, Spotify, etc., has invested significantly in building knowledge and the concept of graphs has evolved to underpin everything from critical infrastructure to supply chains and policing. 
• There is a difference between Knowledge graph and graph data store. The semantic layer is what makes data smart.
• KG is when you have a dynamic and rich context around knowledge.
• KG can be used to semantically search for data.
• KG are a very important part of data mesh.
• If you want to start with knowledge graphs: find your business case. What is the purpose for you?

«RIP Semantic Web! The Semantic Web is dead.»

• Maybe part of the problem was that is was academy focused, more than practical, industry-focused.
• If we manage to implement Data Mesh on a society level, we might have taken a big step realizing some parts of the semantic web.

Trust

• Trust and explainability is based on context. Knowledge graphs can provide context and connect between them, this can ultimately generate trust.

«How can you use AI algorithms to make your city more sustainable?»

How can we use AI to work more sustainable and optimize our operations for less pollution and more efficiency? I talked with Umair, Head of Data Science, Data Warehouse and Artificial Intelligence at Ruter AS, the public transport authority in the Oslo region, the Norwegian capital. Umair is also a associate professor at OsloMet, teaching AI to bachelor level student, as well as founder and CTO of Bineric Crowdsourcing and founder of the volunteer organization Offentlig AI.

Public Transportation;:

• «Public transportation is complex, because data is not coming in from many different internal and external sources.
• «A bus can have minimum 20 sensors.» All theses sensors are sending realtime data.
• A huge amount of data is collected through external sources.
• Ruter is going away from centralized Data Management teams to more of a mesh approach.
• Data Mesh will give you a more complex data function, with need for more people and more organization and coordination.
• One team cannot have the full ownership for the entire data-driven prerogative of a company.
• 2 factors that helped Ruter succeed with Data and AI:

1. E2E responsibility for the whole AI algorithm
2. Create in Production and don’t overdue PoCs

Sustainability & AI

1. Capacity prediction

• Predict capacity 3 days in advance, but possible up to 1 month in advance
• This gives passages the possibility to plan their trips better
• An operations team that sees a pick in traffic realtime, send additional busses to ensure enough capacity
• Through the prediction algorithm, fleet capacity can be reduced and it is easy to plan for balancing the load beforehand

1. Fleet management

• The long term vision for Ruter is to work more with Order services. In the future you shouldn’t have to walk to a bus stop, but can order a bus to your home.
• The existing solution is for seniors (67+) and is tested in the Viken area
• But how can you ensure that the busses are close to an eventual future order?
• Ruter is training an algorithm to predict where orders might come from to ensure a bus is parked close by. This results in less driving and less emissions.
• To train the algorithm Ruter uses mainly historical information, but combined with e.g. weather information

1. Analysis of customer feedback

• Sentiment analysis to see how happy/unsatisfied a customer is

Explainable AI

• «AI is just statistics on steroids!»
• Its hard to explain how a probability output is achieved
• That makes an AI algorithm a black box
• Developers create a set of tools and applications which can give insight on different factors how a decision was achieved by an AI algorithm

Quantum Computing

• Traditional computing is expensive and needs more time and resources to reach a specific output
• Volumes of data are constantly increasing
• This was a research project together with OsloMet
• Quantum computing was cheaper to work with then traditional computing
• Quantum Computers are more sustainable and energy efficient

🗓️ 1 Year
🎙️ 2 Podcasts
💡7 Lessons
👨‍💻 50 Data Leaders

Together with Loris Marini, host of the great data podcast Discovering Data, I had a chat live for a retrospect on a fantastic podcast year 2022. Combined, we have had the pleasure to talk to over 50 Data Leaders in 2022 and shared their message with our listeners.

Here are our 7 lessons for 2023:

1. Lead: Data is a shared asset with shared benefits. Data quality is a shared responsibility.

2. Network: Tips to build stronger relationships with your stakeholders.

3. Sell: Map the compelling business need behind your data management initiative.

4. Be memorable: A simple framework to increase your storytelling 10x

5. Support: Enable the business to take lead. Provide guidance.

6. Observe: Don't jump to conclusions, but observe the demographics, ways of working, environment.

7. Have fun: provide a framework to ensure collaborative readiness, physiological safety, and encourage failure and try again.

«Its not just about the use of data, but the use of data in a cross-functional setting.»

What a fantastic conversation with Nina Walberg. Nina has been with Oda since 2019 and has a background in Optimization and SCM from NTNU.

Oda strive  to create a society where people have more space for life. Make life as hassle-free as possible. And to achievetis with help from data, Oda has created its 6 principles for how they create value with data.

Here are my key takeaways:

Business model and use of data

• Odas business model allows for a better and more cautious way of thinking sustainability. The quantum of products can be tailored to the actual need.
• Climate recipe: Oda provides data on the climate footprint to its customers, when ordering products.
• Use data to provide not just inspiration to your customers, but also help them to create a complete basket of groceries for the week, to avoid any additional trips to the super market.
• The use of data needs to be combined with all functions, also business functions. All should be part of the development process

6 Principles

• 6 principles recently updated, but originally created by the entire team in 2019.
• Oda believes in autonomous teams and that trust and responsibility is given to these team.

1. Domain knowledge and discipline expertise
   • 70% of Data and Insight people are embedded in cross-functional teams.
   • Data is connected across the company and across domains. So you can not work exclusively in an embedded model. You need some central functionality.
   • Data Maturity differs between domains. So really the embedded model depends on the circumstances and how Oda applies that.
   • Data Mesh has been an inspiration.
2. Distributed data ownership, shared data governance
   • Processes and parts of the product for Oda are developed by the domain teams. To federate the ownership is a natural move.
   • Domain boundaries need to be explicit. «Every model that we have in dbt is tagged with a team.»
   • Ownership of certain products is harder or sometimes not right to distribute. It can either be core products with no natural domain team to own, and that many are dependent on, or the core data platform itself.
   • Customer data first, but without proper product data you can’t live up to that
3. Data as a product
   • Make sure you don’t just deliver a product, but that it meets the customer need, not just the customer demand.
   • Consistency matters, structure matters, naming matters when it comes to data products.
4. Enablement over handovers
   • Enabling others to do what they should be able to do themselves.
   • Oda has established a segmentation model for five levels of self-service with different expectations to different user groups.
   • Self-service needs to be tailored to different roles, maturity, and needs of the internal users.
   • Data University, Data Hours and many other initiatives help to create a learning culture and improve data literacy.
5. Impact through exploration and experimentation
   • It is important to test and see how a solution actually provides value to the expectations.
   • This provides insights and information you can act on.
6. Proactive attitude towards privacy and data ethics
   • Data ethics needs to be incorporated and can’t be an afterthought.
   • Company values can and should be directly linked to the work with ethics.

This episode was recorded in September 2022. Click here if you what to know more.

«If you do guidance correctly, people will follow it. People want to do the correct thing. Nobody wants to do things wrong.»

From fisherman on Island to Data MVP in Copenhagen! Ásgeir Gunnarsson has been through a fantastic journey and we had a great conversation around PowerBI Governance. Asgeir has his own blog about the topic.

Here are my key takeaways:

• Think of platforms at a translation: You are own a journey and platform is where you stand on, from where you push off, start your journey.
• A lot of organizations are only on maturity level 1 when it comes to Power BI, even though they think they range higher, or even if there are different levels of maturity in different parts of the organization.
• Buzzwords help shape opinions, and to have discussions. With good help, these opinions can be shaped into actions.
• If you have the possibility to do a green field approach to data mesh - consider it: It will not get easier than this.
• The parts of Data Governance, that can be solved with a focus on technology, and process are much more mature and easier to handle than the softer parts, that are concerned with people.
• It is easier to lock out intentional mistakes than unintentional.
• Governance is changing focus, from being compliance driven to providing a framework of how we can get more value from our data, it’s about making people productive.
• By making people more productive and efficient, you can pay off the cost of governance really fast.
• If you use data more proactively, you are moving the power from people to the machine. But there needs to be a balance, its not either or.

PoweBI

• In PowerBI implementations we should have talked Governance from the start.
• PowerBI enables people in your organization to use data on their own. There is always value ion that.
• To use self-service tools correctly, you need to either have people formally trained or/and have guidelines in place BEFORE they start doing things.
• Not everyone in your organization will become a PowerBI expert or is a data person. Don’t expect everyone to go there.
• Governance is about giving people a framework and a good chance to do things correctly from the beginning.
• Most low hanging fruit: Build a report inventory!
• Try to keep the usage of PowerBI to the M365 environment and meet people where they are.
• Know your requirements before you start using tools.

The 5 pillars cover all of what your governance strategy implementation should cover:

• The people pillar is about having the right roles in place and train people to perform in these roles.
• Because PowerBI is a self-service tool the administrator role is often allocated randomly.
• The processes and framework pillar is about what proper documents in place to make it work so users can use Power BI correctly and be compliant. This covers administrative documents as well as end user documents.
• The training and support pillar is about making sure everyone that uses Power BI has gotten the required training. This is also about deciding what kind of support mechanisms you need to have in place.
• The monitoring pillar is about setting up monitoring of Power BI. Usually, it involves extracting data from the Power BI activity log as well as the Power BI REST APIs for information about existing artifacts in your Power BI tenant. 
• The settings and external tools pillar is about making sure Power BI settings are correctly sat as well as how to use other approved tools to support Power BI (extensions to Power BI).

«If you think about how we can work effectively together, you need to look at how you can effectuate your delivery teams. »

I had the pleasure of chatting with Trond Sogn-Lunden from Veidekke, one of the biggest construction contractors in the Nordics. The construction industry is characterized by Merger and Acquisition. This is an interesting setting and reflects the importance to understand the organizations culture when looking at ways of working. The other important element for Veidekkes culture and organization is project-orientation. Some projects can last for years and are located away from the corporate office.

Here are my key takeaways:

The Project setting

• "A project is the smallest unit for profit and loss. If the project is a success, the business succeeds. »
• Even if some projects are more digital than others, even frontrunners need to use the same base systems and be consistent in how problems are tackled.
• There is a balance to strike between project autonomy and application landscape consolidation.
• In the same way there is an important distinction between what is best for each project vs. what is best for the company as a whole. For the insight factory, this is a constant push and pull.

The Insight Factory

• The Insight factory brings a factory mindset to data and ensures that data products are delivered.
• Capacity to deliver is more important than the technology stack.
• Developers (solution responsibles) translate business requirement to solutions, close to the business.
• A developer needs to be a combination of «superman and Jesus».
• A coordinating role, ala business analyst, can help to ensure a unified application portfolio.
• This role can help to access relevant raw data for the insight factory to be used for analysis.
• The Insight factory is providing data sets to the business and help to enable the business to do their data visualization on top of it.
• This helps to build data competency in the business, secures ownership, and added capacity.

Product Management

• Product Management is an important concept on how deliveries are organized.
• Product focus and pipeline structure enables Veidekke to have different change rates for different products and manage them as units and can deliver on demand.
• Every product can be connected to a domain, which is what insight is organized around.
• You need allies in the business to get the reach you need as a central insight factory.
• Business users should be part of your data product groups.
• You can set up inspirational events, to get traction and understanding for your insight & analytics work.

«Think of data availability as online vs. offline.»

What much of the discussions around data products, data catalogs, self-service boil down to is data discoverability, observability and availability.

I talked to Ivan Karlovic, Director of Data Analytics and Master Data at Norwegian about these topics and gained some fantastic insights. Ivan always loved analytics and using data to improve the business and started his dat journey with a course in data mining and with «Pure curiosity on how we can use data!»

Here are my key takeaways:

The Airline sector

• Airline industry is reliant on partner «A Flight is just a subset of an end-2-end journey»
• Data privacy and ethics are important topics for Norwegian and are faced with a systematic approach with an aim for automation.
• Norwegian is building a cloud based analytical platform to ensure a greater visibility of the data analytics setup.
• The first improvement should be on data discoverability, closely connected to data observability.
• «A Data Catalog will raise awareness of what we have of data assets.»
• There is a clear goal to ensure an automated observation of all data assets in Real time.

• A central team needs to be able to deliver cross-domain use cases, also across domains with different data maturity.
• «With this crisis-domain approach we are putting away some of the legacy discussions.» We can engage with each domain.
• It is ok to have specific crawlers on local data, but you need to synchronize it into the central data catalog.
• The organization needs to have a way to stay aware of everything that is produced.

Data Availability

• Except for sensitive data, everyone in a domain should be able to see all domain data. Outside the domain, people should be aware of what kind of data each domain maintains.
• «If you work with analytics or machine learning you always what to talk to the domain people, because you can easily misinterpret if you don’t have that domain experience.»
• Domain data products that are domain spesific without a use case outside the domain, do not have to adhere to central strandards. But if they can have a use cases outside the domain, they need to be fed into the central data catalog.
• Communication and understanding intentions from data producers to data users is really important. You have to continuously work with understanding.
• There is lost out business potential in not having data discoverable, no matter the quality.
• Most effort is wasted in rework of data products that where just not discoverable.

Self-Service

• «When it comes to self-service we need to set up technology in a way that the end-user does not have to think about the data, only the problem to solve»
• Even if only 70% of use cases can be solved by self service, we need to strive for 100% to ensure that we offload the expert data analytics team as much as possible to work on the tough cases.
• «Data Catalog: You can buy a monster that gives you 95% of things you don’t need, or cutting edge super-niche start ups. But you have some interesting players somewhere in the midle.»
• «Can we do data engineering on a meta level, without seeing the underlying data? Eg. For PII?»

Documentation

• How can you retain knowledge in a distributed architecture?

1. Ensure domain knowledge is fostered in the domains. Build a documentation repository
2. Infrastructure as code. Technical knowledge supplied with context

• Domain knowledge is the most tricky and most difficult to replace
• Great documentation can be both motivating and time saving. Motivating to reach a higher standard and time saving for problem finding, onboarding, etc.

"The data lifecycles collides with the system lifecycles. It’s a classic."

Let’s talk about the paradoxes of Data: Data Lifecyle, Search and Data Catalog!

 What a fantastic chat Ole Olesen-Bagneux and I had! Ole is writing his O’Reilly book Enterprise Data Catalog, has newsletter Symphony of Search. He brings in a new perspective from Library and Information Science and is a great advocate for transforming the way we think around data and search.

 Ole has worked as a specialist, as a leader and as an architect, and has an academical background as PhD in Information Science from University of Copenhagen.

 Here are my key takeaways:

• Data Lifecycle was first mentioned as the POSMAD lifecycle
  •  Plan - Plan for creation 
  • Obtain - Acquire data 
  • Store - store it in a system 
  • Share - expose it and make it accessible 
  • Maintain - curate data, keep it accurate 
  • Apply - Use the data 
  • Dispose - Archive or delete
• Store, share and apply is where the business value is derived
• The points where you get value from data are normally not the same, we use to manage data.
• The work e.g. national archives do, in cataloguing, and readying data for research is done at the very last stage of the lifecycle. But the value resides much earlier in the lifecycle.
• Data-driven innovation, data-drive culture… What these terms actually mean is that we need to get better at utilizing the value insight data.
• Intangible assets hold the highest value - data is the key to value creation.
• One of the potentials of a data catalog is to push the high-level DM activities to earlier stages of the lifecycle.
• Catalogs are pushing inventory activities from the dispose phase to the store and share phase of the lifecycle.
• There is a huge difference in the perspective of an IT system lifecycle and data lifecycle.
• Data always resides in a system, and that system has its own lifecycle. These lifecycles do not match.
• If you do not maintain data in your systems, any potential data migration becomes exponentially more difficult. What do we migrate, what do we keep, what do we delete?
• The solution can be a Data Catalog and/or metadata repository with retention policies for data.
• The distinction between searching in and searching for data has become really important due to the rise of data science.
• When you search for data, you are looking at data sources with potential value to search in.
• Metadata is key in searching for data - that means we have to manage the metadata lifecycle as well.
• A data Catalog is basically just a search engine.
• Data Catalogs rely more and more on the same technology components as search engines for the web, e.g. knowledge graphs.
• The key capability of data catalogs is a metadata overview over the data in your company.
• Data catalogs have an untouched potential to ensure data lifecycle management

"Data is mainly used to create value for customers, both inside the company and outside!"

 Customer centric is one of the great mantras in data at the time. I wanted to get to the bottom of what Customer Experience actually means. So, whom better to as then Leif Eric Fredheim, Customer Insights Manager at Elkjøp and one of the top 100 Data, Analytics and AI professionals in the Nordics?

 We talked about the retail data quest, what we can learn from retain in other sectors and naturally the value of customer experience and insight.

  Here are my key takeaways:

• Data has to be fast and easily accessible for the business, so they can use and consume data when they need it to make better decisions. This happens on a self-service basis.
• Make people aware how they can use, combine, and analyze data.
• Analysts and Scientists must be placed at the different business domains.
• Elkjøp is organizing towards a product-oriented organization.
• The data governance structure is organized towards freedom under responsibility. There are rules in place, but the creativity should not be hindered.
• Business critical data products are created and maintained centrally.
•  Data privacy is important, especially when it comes to customer data.
• Privacy is an important element of good Customer experience.
• Are data savvy front runners setting the requirements of how we work with data?
• There are data science & analysis teams embedded in data savvy departments.
• Analysis should happen embedded in the business and involve the central data team in necessary.
• Lasting customer relations and a strong focus on customer experience across all channels are learnings from retail towards other industries.
• Omnichannel players need to find ways to connect the physical customer experience with the digital.
• Retail can learn from other sectors like banking and finance, that there is a need for explainable models - how to be transparent with your customers.
• How do we manage the gap between explain ability and performance?
• CX is about listening to the customer and use tools and data to better their experience. 
• Digital stores make it easier to use data for better customer experience then in physical stores
• Quantitative analysis is about CX without directly involving the costumer.
• Qualitative is the opposite: the customer is actively involved to better his/her experience.
• Only 30-40% of CX is focus on the actual buy, the rest is moved before the point of decision and after a purchase.
• Correct and updated product information is vital before the purchase. You can support the decision process and idea phase through helpful articles and inspirational content at the right time at the right place.
• Natural Language Processing (NLP) for review data is a vital part to ensure good customer experience.
• Many have a way to go when it comes to self-service CX. 
• You need to instrumentalize all your customer touchpoints to gain a reliable variant data foundation to ensure CX success.

“The closer you are to the business, the great the chance to make an impact with data!”

 In this episode I interviewed Marti Colominas, VP Head of Data & Insight at reMarkable. When we had our chat this summer, Marti was still working as Head of Data for Kahoot!.

 Marti combines business with data and works on a daily basis for value creation and balance on the crossroads between business and tech. Marti has experience from big corp but was looking for that high pace and ever-changing environment of a startup.

 Here are my key takeaways: 

The Startup / scaleup setting

• In startup and scaleup roles are not that defined. There is a huge amount of flexibility.
• You can react quickly and explore new options without a lot of bureaucracy.
• It is very dynamic, and data is used by everyone to base their decisions on.
• On the other hand, datasets are not as stable and with less quality.
• There is a significantly shorter distance between C-level executives and Operations.

The Business Value of Data

• You need to find balance in delivering fast (time to insight, speed and accuracy).
• Speed is there no matter what, you need to ensure the right level of accuracy at that speed.
• If you want to have impact, you need good data quality. If not, the numbers will not be trusted or used.
• The goal is data that is trustworthy and easy to use.
• Long term commitment to Data Quality and Data Governance, whilst speedy day-to day operations with little time to insight, have been key to success.
• The role of CDO is to ensure that be business derives value from Data Science and Analytics, that counts for a Startup as much as for a large enterprise.
• The combination of business and engineering becomes more and more important.
• The data stack is moving towards speed and scalability, which makes it easier to handle large volumes of data.
• The key innovation will happen on automated data quality, self-serve analytics, even API-contracts for click-steam data, as well as tracking and lineage.
• Data is not always the goal. It can be a means to create value.
• For Kahoot! And reMarkable, data is used to make a better product and to improve the user experience, not to monetize or even mine that data.
• User should see and feel enhancements in the product through their provided data right away.
• To show the business value of Data Management you have to argue with "What if…?" What if we don't do it? You need to show the consequences of not acting to the C-level executives.
• Data quality problems grow exponentially over time. If you do not act, data-driven decision making will eventually be replaced by gut feeling.
• Not having control and metrics in place is like going to the casino: You can win once or twice3, but in the long run you will lose.
• Data products should have an assigned value - so Data as a product can help us argue for the business value of data.
• Step 1 is to treat data as an API contract.
• Self-service is dependent on a good structure and governance at the data producer side.
• Self-service can cover 60-70%, not everything can be self-service.
• A dataset tells the story of what happened in the business. You need business context to understand it, you need to phrase a business question into a data question, you need to know how to manipulate the data correctly.

Welcome to Season 2 of MetaDAMA!

The first episode is dedicated to DAMA. I talked to Marilu Lopez, Leader of the Presidents Council for DAMA, Peter Aiken, President of DAMA International, and Achillefs Tsitsonis, President of DAMA Norway.

We had a great conversation about the vision and mission of the voluntary, vendor-independent organization DAMA and its value for the knowledge worker community, as well as society as a whole.

We also talked about what Data Literacy is, how we can operationalize the term, and to what means. The best definition of Data Literacy so far is “the ability to read, write and communicate data in context, with an understanding of the data sources and constructs, analytical methods and techniques applied, and the ability to describe the use case application and resulting business value or outcome.”

Here are my key takeaways:

The Data Quest

• Data needs to become a profession.
• People come to data through different means, with brings new and different perspectives. It is not easy to view data as an uniform term.
• Data is as much a part of the business world as it is of the IT world. DAMA wants to bring business and IT world together to collaborate and understand each other better.
• The metaverse is collecting all data about us, and we give it willingly. Is there a lack of understanding of consequences in society as a whole?

The aspect of Change

• A lot of Data Management is about Change Management and reliant on the existing culture. At the same time, culture is something unique and needs to be fostered locally.
• We need to prepare data professionals to become change agents.
• The principles of Data Management are about collaboration, and DAMA is trying to live by this principle.

Data literacy in society

• What can we tell people that they objectively need to have in terms of skills and knowledge in order to become data literate?
• What conversations do you need to have in your family to ensure that you are data literate enough to operate a smartphone without exposing your data?

Knowledge workers

• Anyone that works with her/his brain, uses data and thereby is a knowledge worker.
• The sooner we make our knowledge workers more literate the sooner we will end up with smarter and more effective organizations.
• Knowledge workers need a learning path.
• Professionals need to have an ethical compass, an urge or even duty to call out if one sees unethical behavior.
• We all have a responsibility to share our knowledge.

Is the EU providing the legal framework for data-driven value creation?

Digitalization is a focus area for the European commission, and at its core, the European digital strategy is a data strategy - digitalization is focused on data.

The goal is to utilize the value of data and give better conditions to SMB in the European marked.

I was fortunate to talk to Astrid Solhaug from DigDir, working for the Norwegian resource center for sharing and use of data. Astrid provides both a Norwegian and European perspective on the topic.

We talked about:

Eu digitalization strategy

• What does that mean?
• How does the EU approach digitalization at a strategic level?
• How do regulations and incentives interplay with digitalization in the EU?

Eu regulations

• What are EU Data Act, Open Data Directive, Data Governance Act?
• How can we share and reuse data? What does it mean for value creation with data?
• Why do we need a data innovation board and what should it look like?

Challenges

• Can the market be regulated? Is EU regulation harming innovation in the marked?
• How can ethnical values be regulated across countries? 
• Is the EU trying to take an active role in the development of European society?

Possibilities

• What are the possibilities in these regulations? Can it mean safe and easy data sharing?
• What are the effects of simpler easier data sharing between the public and private sector? Can it create value for us?
• What are the opportunities in the regulations for users in Norway?

What I've learned:

• EU is trying to take an active part in democratize data.
• EU defines data is a non-competitive good.
• A data collector has no exclusive rights to the data collected.
• All open governmental data should be accessible for everyone
• The Data Act arranges for private sector data, to secure sharing and end user rights.
• Private organizations will have a duty to share information with public sector in times of crisis
• These three regulations for sharing data are interesting to see in combination with the AI Act, that ensures use of data that now is available.

Are we living "The Truman Show"? I had a chat with Mads Flensted Hauge, Chairman of DAMA Denmark and DPO and Data Governance Manager at a Danish health care provider.

We looked at Data Privacy from four different perspectives:

The society perspective 

• What is data privacy and why is it important? 
• How transparent are we as citizens?
• What considerations need to take place when we talk about data sharing across public agencies? 

The company perspective 

• Why should we care about privacy in our company? Are we just talking about compliance? 
• Where should the DPO role be places in a company?
• Does your HR system need a feature to show your employees home on google maps?

The data worker perspective 

• What does this mean for us data workers in how we treat data? 
• What does privacy by design mean?
• What is the impact of AI, ML,… on privacy?

The personal perspective

• What can I do to keep my personal data save? 
• How many smart devices do I need in my home? Can I live without a washing machine with Wi-Fi connection?
• Has the corona pandemic made it even more ok to share private heath data?

Here are my key takeaways:

• Convenience drives change and digitalization in public sector - and sometimes privacy becomes the victim for efficiency.
• To apply GDPR you need to apply different knowledge areas of Data Management. That is why these two are closely combined.
• It has always been hard to show the value of a data management journey from the start, but with GDPR and the ominous notion of fines, data management got the ear of the C-level.
• Since GDPR is framed as compliance, it leads companies to do just the bare minimum to be compliant.
• GDPR forces you to get a deeper knowledge about your business.
• There is an ethics dimension to data privacy, and DPOs are on the forefront to instigate this ethics site.
• A DPO does not just write policies and procedures but must navigate company culture to promote ethics and privacy actively.

Everyone who ever played with Legos knows that the bricks don't just fall into place. It takes dedication, finding the right brick at the right time, maybe even sorting your bricks.
The same goes for Data Governance.

So whom better to ask about Data Governance, then the Director of Data Governance at the Lego Group, Michael Bendixen?

Michael was really clear in his message to all of us, by given us his 13 commandments for Data Governance:

1. Drop the data management "lingo".

2. Invest the time to build a strong data governance framework.

3. Align your data governance/data ownership structure with existing organizational structures, terminology being used etc. to the extent possible, as that will also make your implementation less intrusive.

4. Make sure to get the right people in the team that facilitates and supports data governance - people with great collaboration and communication skills, that are good a building strong relationships are vital.

 5. Ensure data quality is a part of your setup and that you are able to report on data quality.

6. There is no "one size fits all" when it comes to data governance.

7. Depending on the organization you work for, compliance can be a good driver for data governance - but have a plan that will take you towards a more value focused data governance with more carrot and less stick.

 8. Data governance is not about technology and tools.

9. Communication is key.

10. Data governance is not a project nor a program - it is a lifestyle change and does not have an end date.

11. Invest in training and onboarding the people that will take on data governance roles. 

12. Be ready to support the people that takes on data governance roles - and make sure they know you are there to help them.

13. Be very aware that until you demonstrate business value - you will often just be the guy with a PowerPoint slide-deck talking about something fairly abstract that not everyone understands.

Can you put a value on quality data? Definitely! But how?

Who better to ask than Kristin Otter Rønnevig and Espen Hjelmeland? They both dedicated their master thesis to explore this topic with really interesting results. Their thesis "An Investment Perspective on Data Quality in Data Usage" asks "How can an organization optimize its investments in data quality?"
To answer the question Kristin and Espen posed three Research Questions:

1. What are the main drivers for willingsness to pay for data quality?
2. What part of data quality aspects should one invest in to maximize opportunities and minimize risk?
3. How can the quality of data be improved, and what are the costs?

Here are some of their key observations, I found particularly interesting:

• Increasing confidence in data in order to enhance company operations is one of the
  motivations for willingness to pay for data quality.
• A greater level of knowledge in data quality gives a higher willingness to pay for data quality.
• Demonstrating to the customer how quality improvements may enhance profit at each step
  of the value chain contributes to the willingness to pay for data quality.
• Prioritizing improvements are based on the time and cost of the particular improvement. A way to reverse-engineer the impact of various improvements
  can help to identify how to improve the quality.
• It is critical to invest in a professional, highly skilled team environment to succeed
  in data quality investments.
• To cope with data quality, it is necessary to invest in security.
• To know whether the organization is investing in data quality optimally, it will need a deep understanding of the business and experience with it.

Espen Langbråten is leading Europris' bet on digital platform, data warehouse, and analytics.

We had a great talk about everything data, but at the core we discussed three questions: 

1. How do you build your team?
2. How to recruit and build competencies in an ever-changing environment whilst keeping an eye on cost?
3. What resources do you need in house? What can you outsource to a consultancy?

Here are my key takeaways:

• It is not only hard to find competency in the marked and recruit, but also to retain valuable knowledge in the company,
• Give your employees the responsibilities they want and need to thrive on the job,
• Mentor-programs give the new employees a change to get integrated fast and at the same time the existing employees a boost of their sense of value and the chance to share their knowledge
• It is important to recruit basic technical skills and a hunger for learning, business acumen and domain knowledge is something you learn on the job
• You need to have core-competencies in house and work in close relation with your consultants to transfer knowledge
• The conceptual and even logical architecture needs to be owned in house
• Most problems the business faces are process based. Tech can aid to solve these, but it is seldom a tech problem by definition

What has been the hot topic in Norway this winter? Energy prices. The prices for energy skyrocketed. But how does this relate to data? Can we save energy with Data Management? The answer might be on the infrastructure side.

And since "the color of Data centers is Green!", I took this question to Espen Bjarnes, who works as VP Sales for Green Mountain, one of the most sustainable data centers in the world according to Data Center Magazine.

 Green Mountain was founded in 2009 and opened their first Data Center in Rennesøy (Stavanger) in 2013 in an old NATO facility.

Norway has an unique standing compared to other countries, because 

1. we use close to 100% renewable energy sources.
2. We have cold climate, which reduces cooling expenses
3. We have relatively low energy costs

 With that, there is a chance to reduce CO2 emissions by storing data in Norway.

An on top of that there are political incentives in place to support the data center industry in Norway, such as reduced taxes. So, for Norway, "Data Center is the new oil" - because this might become the driving force for Norwegian industry going forward.

The amount of data in the world doubles every second year, streaming of live data grows exponentially. All this needs data center to support. That translates to a natural growth of the data center industry. At the same time there is a potential for the Nordic data center industry to take the lead, due to sustainability advantages in the Nordics. Espen calls it the "sustainable edge".

How can you democratize data from Norway’s public services?

What does it mean to have a user centric approach to public services?

What needs to be in place to organize your organization, the public services as a whole, while maintaining focus on good services to your citizens?

I had the pleasure of chatting with Gustav Aagesen, Chief Data Officer at Lånekassen. The Norwegian State Educational Loan Fund, who is celebrating its 75th anniversary in 2022. 

Gustav started as Information Architect in Lånekassen in 2012, became analysis manager before taking the position as CDO at Lånekassen. Today, he sees his responsibility in supporting the entire organization and to institutionalize information management.

 We looked at 3 different Perspectives on “Orden I eget hus” a Data Governance framework for public services and Democratization of data in Norway:

 1.       Lånekassen. An internal view on automation and structures, data citizenship, and culture.

2.       Public Norway. A perspective that includes valuable work on the common data catalogue, “orden i eget hus”, common concepts and datasets.

3.        Citizen perspective. Thoughts about finding ways to make use and consumption of data easier and with less barriers, and provide citizen-centric services.

Here are some of my key takeaways:

-          Information Management is not a goal by itself, but a way to create and gain value

-          Information Management has to start with a purpose!

-          Data and Information has a longer lifecycle then applications.

-          Data Lineage is important, with the objective in mind to create services and gather data based on consumer demands, or the needs of the citizens. 

-          The value for the citizen is an end-to-end- value stream that is traceable and can create trust in the data.

-          If you want to give the citizens access to proactive public services, information has to flow between different institutions.

-          It seems easier to get funding for technology then work-processes. That is also a reason automation is in high demand.

-          Data Sharing needs to be balanced with trust and privacy to ensue good solutions for the consumer and citizens.

How can you set up your services to have technology support your data journey? How do you work with tech procurement? And what is the impact of Data Mesh, especially for data governance? How to evaluate which consumer need to satisfy first? How to prioritize what is important to create value?

I chatted with Bente Busch, who is leading the Service Platform Team at NAV, the Norwegian Labour and Welfare Administration. 

Bente and her team are responsible for platform services for the product teams at NAV, the application platform, design toolbox, as well as good practice around digital product development.

Bente is driven by being a product director, helping the users of data to minimize their cognitive burden and deliver an attractive and easy to use service.

Modernization of digital systems and ways of working has been a priority for NAV. In the last 5 years they fundamentally changed the way they deliver projects and programs, by focusing on ongoing product-development. This was done in combination with breaking down technological one-size-fits-all suits, to a micro-service oriented architecture.

With that, NAV provides technology and systems tailored to the consumer-needs.

Here are some of my key takeaways:

• Good technological services can help foster a data culture.
• The goal is for the consumers and citizens to connect with public services easier, faster, and more user friendly.
• Framework agreements can help to liberate resources, gain agility, and tailor to specialized needs.
• Data analysis can help in procurement processes to ensure consistency and integrity in future needs.
• NAV views the consumer needs as digital products to be developed and design products that can be improved and changed if needed.
• Specific solutions that need to be tailored to provide best possible service to the consumer needs to be built, whilst more generic services can be based on "our-of-the-box" solutions.
• Open-source should be a stable part of the architecture, both usage of open-source libraries as well as solutions
• Data is moving in the same direction as software, to a more decentralized architecture.
• A network of actors and collaborators needs to agree and enforce governance in a distributed architecture. 
• Technology can help to codify and automate Data Governance. "Lett å gjøre rett" - Easy to make the right decision.
• The data domains in NAV are building autonomous architectures. They are independent in their architectural decisions but need to account for their surroundings.
• NAV is inspired by "team topologies" to create "APIs" between teams.

You cannot outsource Data Ethics!

For this episode I talked to Sami Laine, Competence Lead at Siili Solutions and President of DAMA Finland. Sami has both the academic background as well as the in the trenches experience in Data Governance, Data Quality, Master Data Management and working with Data Ethics and Artificial Intelligence (AI).
 
For Sami, everybody working with data needs a basic understanding of data ethics, security, privacy, ect. This applies specifically, but not exclusively, for Data Management professionals.
 
Sami and I talked about the following topics:

1.       To what extent is AI part of our life?

2.       Which philosophical schools of thought build the basis for Data Ethics?

3.       What is trust, what does trustworthiness mean, and how do you measure it?

4.       Can AI have a moral compass, a way of controlling its reasoning?

5.       How important is the purpose of a solution and can it be changed?

6.       Is there a need for a common methodology and ways of working with AI?

7.       Can and should AI be regulated?

8.       Can certifications, metrics and measurements help to built trust? 

Målet for Skatteetaten er å bli datadrevet og bidra til å skape en datadrevet offentlig forvaltning, med målsetning å tilby raskere og bedre tjenster til innbyggerne.

Jeg har snakket med Torstein Hoem, direktør for divisjon for Informasjonsforvaltning i Skatteetaten.

Skatteetaten har satt Information Management på dagsorden for hele organisasjonen. Informasjonsforvaltning har blitt en egen divisjon med fokus på informasjon og innhold, og er ikke en del av IT. Noe IM-avdelinger i mange andre organisasjoner etterstreber.

Nøkkelen for å oppnå målene er å forvalte informasjon som produkt og å skape tverrfunksjonelle team, som kombinerer blant annet kunnskap om informasjon og IT. Dette kombinert med å kartlegge et helhetsbilde av informasjonsflyten gjennom hele verdikjeden, vil på sikt resultere i en brukersentrisk offentlig forvaltning, det vi kaller for «bruker i sentrum».

Torstein og jeg snakket om hvor viktig informasjonsforvaltning er i den norske offentlige forvaltningen. Torstein er overbevist over at datastyring på etterspørselssiden bør være en integrert del av hvordan Folkeregisteret er organisert. Hva forventer konsumenten av dataene? Kan vi fange opp og lage data som kan brukes «nedstrøms»?

I tillegg har vi snakket om:

- Arkitektonisk kontroll og metadatahåndtering

– Datakvalitet, og hvorfor det er viktig å finne feil data

– Verdien av standardisering

- Verdien av Folkeregisteret som en felleskomponent for organisering av den norske forvaltning

- Betydningen av medarbeiderengasjement for datakultur og etterlevelse av denne i arbeidshverdagen.

More and more businesses understand the need to become data-driven in a competitive market.

Earlier, data was looked at as a supplement to the business operations. But using data as a valuable asset, has become part of the core business strategy for many companies

I talked with Rushanth Vathanagopalan, Head of Data at the CoE for Data and Analytics at Storebrand, a Norwegian financial services company, focusing on long-term saving and insurance.

Rushanth has done extensive contributions to a data strategy, to eliminate Tribal language, align architecture, promote Data Literacy and forming the Data organization towards a reliable business partner to the business functions.

We touched on the following topics and questions:

• The way we view data has changed, also the business view on data has changed. This is evident in the way a consumer requests insights from the CoE for Data and Analytics.

• By approaching data as an asset in a structure way, you can move from prescriptive to predictive use of data.

• What is the value of a data strategy?
• Data means different things, depending on your role, experience, and opinions.
• Compliance and security must be accounted for.
• There is too much potential value and risk in data to not be strategic about how it is managed.

• Who is responsible and sponsor for a data strategy?
• Tech and Data has set up the strategy, but is worthless without by-in from the business functions.

• A Data Strategy should be aligned with business strategy.

• Maturity assessments are important to get an idea of where you are and what tasks can help you progress. It also makes progress traceable. But also proves that the work is bearing fruits.

• Data Literacy is a pre-requirement. And it should be a consistent effort, to ensure the competency is developed organically across the entire business.

• Has Covid-19, home office and distributed workforce been an accelerator for data usage and ultimately data strategy?
• Both yes and no:
• NO, because it is easier to ask and collaborate, while being in the same room. When you have to schedule meetings to address a problem, people try to find solutions on their own.
• YES, because it is easier to share information digitally, consumer needs are earlier communicated, and feedback is quicker received.

• Data Strategy has an impact on architecture.
• Architecture is based on technology, knowledge, and trends at the time.
• A data strategy gives the possibility to review this and align it with the data goals and vision.
• A data strategy needs to be translated into an architecture that supports the business problems to be solved, from reporting to analytics.

Demand Side Data Management comes from the notion to provide value to customers as our highest outcome and has been around as a concept for demand side data quality for a while. We let the consumer decide what data quality he needs to create value with the data.

•  But can we widen that approach to a Data Management as a Service model?
• Can we put Data Management into a chain of custody?

Aiko Yamashita, Senior Data Scientist at the CoE at DNB, and Karl-Aksel Festø, Head of Advanced Analytics CoE at DNB, gave their input to these questions, supported by examples form their work at DNB.

 We talked about:

• What is the difference between Data Science and Analytics?
• What skills do we need to build data literacy?
• How do we get from analysis to insight, and how do we get from insight to action?
• Who is demanding Data Management?
• How is Data Management linked to the data value chain?
• What does it mean to manage a non-depletable asset that is a common good for many different stakeholders?
• What is the value of good Data Management?

Data Mesh og produktivisering av data er noe av de mest omtalte tema i dataarkitektur, og mange har blitt fascinert av mulighetene.
Kjetil Rønning, Manager, Enterprise Data, Architecture and Analytics, i Equinor snakker med meg om dette og andre tema i denne episoden.

Kjetil og jeg deler våre tanker rundt følgende:

• Hva legger vi i data mesh?
• Hva betyr domenebasert tilnærming?
• Er tankegangen rundt data som produkt en tilnærming til konsumentbehovene?
• Hvordan definere et data produkt?
• Hvordan finne riktig nivå av føderert styring for å sikre både kontroll og frihet?
• Hvordan sikre datakvalitet i en domenebasert struktur?
• Hva er verdien og utforringer knyttet til Selvservice?
• Hvordan starte utfra et konsumentperspektiv?
• Hvordan modne konsumenter til selv-service?

Another episode in English!

How to get started with Data Management? What is a "Data Dugnad"? And what can we as data professionals do to help companies achieve their sustainability goals?

Geoffrey van IJzendoorn-Joshi, Head of Data Management at Møller Mobility Group, and I talked about these topics and much more.

Geoffrey had some really interesting thoughts on how to approach Data Management in a company the size of Møller Mobility Group. Here are some of his key points:

1. The key stakeholder in DM is you! You have to have faith in what you are doing and figure out how you learn and even more important: How you teach and spread your knowledge and how you can motivate others
2. Don't forget about your Subject Matter Experts, they are key stakeholders that can leverage support for your initiative.
3. The importance of Change Management cannot be mentioned often enough.

Other topics we covered are:

• The value of Data Management Maturity assessments
• Should Data be part of the business or digital?
• Suststainability and the emission numbers of data

Episode #6 er på mange måter en hyllest til det norske oljeeventyret.

Eric Toogood, Manager for DISKOS Databasen i Det Norske Oljedirektoratet, brenner for Data Management faget. Han tar oss med på en reise tilbake i tid, og snakker samtidig også om veldig aktuelle tema som f.eks. OSDU.

Etter samtalen sitter jeg igjen med mye kunnskap. Det følgende er de rådene og erfaringene jeg har lyst å trekke frem:

1. Bruk åpne standarder.
2. Sats på samarbeid - det er lite å vinne ved å spille med skjulte kort.
3. Søk læring på tvers av industrier.
4. Det er mye å lære fra den lange erfaringen DISKOS og Oljenæringen har i håndtering og forvaltning av store datamengder.

This is our first episode in English.

For this episode, I had a conversation with Aidan Millar, EVP Group Digital Insights and Chief Data and Analytics Officer at DNB, about Data & Culture.

We follow the theme of culture across three different levels: 

1. How to do data in different countries with different cultures? How does my guest perceive the Norwegian culture?

 2. How to view organizational culture and different tribes inside an organization?

 3.  How do the different cultures in the field of data, from Data Management to Data Science and Analytics reflect on the organization and the value creation?

During the talk we also touch on these topics:

What is the role of CDO?

How to organize for generating value from Data and market that value proposition inside an organization?

Our three lessons learnt are:

1. Recognize and acknowledge that culture underpins any transformation journey.

 2. Start at the top! Executive level has to drive the transformation and become data literate.

 3. Get together different experts from both business and data to solve a business problem together. And always start with a clearly defined business problem.

Hva er Data Security, Information Security, Cybersecurity?
Chris Dale fra River Security er gjest i dagens episode. Og Chris har lang erfaring med som ethical hacker og underviser innenfor Cybersecurity. Derfor har jeg invitert Chris til å gi oss et innblikk i sikkerhetsverden, forklare noen begreper, snakke om viktigheten av gode rutiner og hvodan man best kan sikre seg.

Hvorfor snakker ikke Data Management, Content Management, Records Management,.. samme språk? Bør vi ikke heller fokusere på informasjonen vi ønsker å forvalte, enn å skille oss i lag med forskjellig stammespråk?
Jeg snakker med Alte Skjekkeland, CEO i Infotection og tidligere President i interesseorganisasjonen AIIM (Association for Intelligent Information Management) om en helhetlig tilnærming til Information Management. Vi snakker om Content og Document Management, Metadata, Retention og ikke minst hvordan vi kan samhandle mer og bedre.

Denne episoden er dedekert til Data Management Body of Knowledge og sertifiseringen CDMP.
DAMA Norge sin egen VP of Education Kjetil Eritzland snakker med meg om hvorfor vi trenger standartisering innenfor Data Management og hva verdien av et sertifiseringsløp er.

DAMA Norge er det stedet du skal komme for å stå trygt i rollen som en data management professional, her kan du bygge nettverk og  utveksle erfaringer samt bygge på og formalisere/dokumenterekompetansen din med kurs og sertifisering.

I denne episoden snakker jeg med Maria Camilla Nørgaard, president i DAMA Norge om reisen av DAMA Norge, hva vi ønsker å oppnå og hvordan vi jobber mot vår visjon.