In the season finale of MetaDAMA, we dive deep into the intersection of philosophy, history, and artificial intelligence with guest Sune Selsbæk-Reitz, tech philosopher with a background in both history and philosophy.

Sune introduces the provocative concept of “Promptism”, which is our era’s version of positivism, where we believe that truth can be extracted from language models simply by phrasing the question correctly. But just as historians have learned through centuries of source criticism, we must ask the critical questions: Who trained this model? On what data? With what biases?

Here are Winfrieds key takeaways:

• Are numbers and data points neutral? Or can they be used to convey a message, or even a certain philosophical view point?
• Philosophy is important in data. Here is an example:
   
  • Data Governance according to Immanuel Kant - best possible governance
  • Data Governance according to Utilitarism - focus on business value
• Lessons from history studies: question the authenticity of your sources. Who wrote it? For what purpose? Why are you reading it? - same lessons apply to data.
• That is why we need principles and values in AI ethics.
• Over-reliance on the objectivity of math - is math binary? Right or wrong? - this has been introduced into algorithmic thinking and AI.
• This is the reason why «algorithmic authority» is an issue - because the algorithm says «right» doesn’t mean it is right.
• Our mindset is constantly evolving. That’s why we cannot predict tomorrow’s bias. We need to ensure that our systems evolve with us.

What is the real purpose of AI systems? Are the core values only efficiency, automation, or is it human dignity or autonomy?

Promptism:

• A new way of «positivism: Just because its written down its true»
• Promptism is my term for a subtle but growing mindset around the globe, that you can extract truths from a language model, just by wording your prompt well.»
• LLMs are very fluent and flattering - they say what people want to hear.
• «That’s what Large Language models are: You are not getting the truth. You are just getting the most common answer.»
• Objectivity is a myth. It is always subjective, so we need to read not only the text but also peoples intentions with the text.
• Responsibility for understanding at the limitations and needed criticism of LLM output is shared.
• Producers have a responsibility to ensure that you can know, when models are hallucination, guardrails in models to ensure that output is not looked at as the truth.
• Readers are responsible to learn how to read and understand machines.
• Consumers need rot push for transparency.
• Without accountability, trust is eroding.
• Politeness is a way for machines to ensure that users keep using them.
• Shouldn’t rather an LLM as a «conversation partner» challenge you? Disagreement is part of learning.
• «Agreeableness is addictive.»
• People are starting to get influenced by how LLMs are writing. It changes written conversation.
• Is language narrowed down to a certain path defined through AI? Is language becoming controllable?
• LLMs affect our lives in the way we read, write, talk, even think.
• There is a worldview baked into the system.
• Literacy means also critical thinking.

In this episode of MetaDAMA, we dive into the data strategy of Statens Vegvesen with Bjørn Broum. With 30 years of experience in the data field, Bjørn shares valuable insights.

Bjørn takes us on the journey from Statens Vegvesen’s first data strategy to today’s revised version and explains why a strategic approach to data is essential for success. The conversation also touches on critical questions around privacy, skills development, and organizational challenges. Bjørn shares his most important lesson: keep technical complexity low in the early stages, focus on demonstrating value, and remember that decisions are made with or without data, so make data available when it’s needed!

Here are some key takeaways:

• We need a mindset shift in organizations. Think about the following:
  • You need to keep focus on operational success, that is what you are measured by. 
  • You need to understand how you can impact your architecture, before the actual impact happens.
  • New services needs to be build around data. Data cannot be an afterthought,
• You need to work actively with your executive management to ensure that data becomes a stable, not something that is hype-driven.
• Your data infrastructure is what you need consistent focus and buy-in on. This is the foundation that can support the «hype» on top of it.

Statens Veivesen

• Long standing tradition dating back to 1864, tasked to build and maintain road infrastructure in Norway.
• Maintaining 10.500 km of road infrastructure, including about 6000 bridges and 600 tunnels.
• Utilization of sensors to predict risks and impact of e.g. rockfall, snowdrift, avalanches that all could impact traffic and road security.
• Research on how Norway can prepare for autonomous vehicles and traffic safety.
• Modern cars can be used as sensors. You can e.g. get information on how many people are in a car.
• There is a focus on ensuring personal data protection while maximizing insights from data.
• Ensure that the organization develops in a direction that can address and handle new challenges (data-driven).
• Remember that decisions are made with or without data. If you want data-informed decision data needs to be available for decision making, when needed.

Data Strategy

• It’s a living strategy, that needs to stay updated.
• Data Strategy is always designed to accompany for a need, and that need can change over time.
• Data Strategy needs to hold a big-picture that people can relate their work to, that is recognizable.
• Digital value chain needs to be understood also towards the cross-functional needs across the organization.
• Conways law needs to be recognized.
• The value of data is realized in business. Therefore Data Strategy is build on business objectives.
• Be deliberate with your strategy: what do you need to focus on? For veivesenet this was e.g. to establish a common language and vocabulary.
• Statens Veivesen also makes data accessible for research. This needs to be reflected.
• AI is a topic as well, including data readiness for AI.
• Competency and skills are an important part of data strategy.
• Don’t focus on dat roles, but rather upskilling in your organization.
• Understanding boundaries, goals, and strategic priorities is key when working with data strategy.
• Scaling and dimensions need to be strategically understood.
• Focus on the value of data and the business value it potentially generates, not technology.

Bjørns' blog

In our latest conversation with Mikkel Dengsøe, co-founder of SYNQ and former Head of Data, Ops and Financial Crimes at Monzo Bank, we explore the secrets behind effective scaling of data teams.

Mikkel reveals surprising statistics based on his analysis of over 10,000 LinkedIn data points and valuable insights from Monzo’s scaling journey, where the data team grew from 30 to over 100 people in just two years.

We discuss the critical balance between central data teams and domain experts, the importance of career paths for individual contributors (not just managers), and how data professionals can succeed by building relationships with stakeholders who involve them early in strategic processes.

Here are our key takeaways:

 Data Teams

• There are some high-level questions you need to ask yourself when building, structuring or scaling a new data team
• This includes how big the team should be, also relatively too your organizations size and other teams, how it should be composed and structured, etc.
• A good idea is to collect data to create a benchmark.
• Benchmarks can be hard to combine and are a moving target, but they are nevertheless valueable. 
• Most importantly, you need to ask yourself: WHY do we need to scale our data team?
• Involve people actively in setting the goals based on your WHY.
• Mikkel collected over 10.000 data points from companies on Linkedin. Here’s what he found:
   
  • Median % of data people in companies out of overall staff is 1-4%.
  • Data team relative to engineering team varies between 1 data person per 10 engineers to 1 in 3.
  • From the benchmark it is evident that data governance roles only appear in lager companies.
  • In marketplace companies the effect of data on the business value is easiest to track. Therefore they seem more willing to invest In data teams.
• Investment in data means investment in your business. The consequences of not investing in data will be tangible in your business.
• Find a risk based approach to data as well. At what level can you balance investment, outcome and risk?
• Be cautious of «pseudo-data teams» - teams in a Business unit that do kind-of data work, but are not aligned with the organization.
• Be clear on the skills and competencies you need. What is a data analyst? What does a data scientist do in your organization?
• It is important to have a clear and consistent internal career ladder. Make it visible and understandable what is expected from each role on your team and don’t change these expectations too often.
• Create pulse checks to understand what people are happy about and what not.

Scaling Data Teams

• «Golden Nugget Awards» to showcase good data work every month. These were added to a database, so every new employee could evaluate them to see what good looks like.
• Write down your progression framework to get clear about your ideas and how people excel in your organization.
• You can show open what work lead to promotions. That can be engaging for people to follow in these tracks.
• Hub-n-Spoke model, where people rotate in and out of the central team and the distributed teams.
• Citizen developer programs are a way for larger organizations to scale data work. But It bears risk related to data literacy.
• Don’t try to enable everyone, but enable those that are motivated.
• «You shouldn’t necessarily force people into management to progress.»
• Senior technical careers can ensure an advanced level of quality. Which is a different way of scaling your data team.
• You need a career ladder for professionals that is independent from management careers.
• Create rituals that make good work stand out.

A deep dive into the complex balance between data, ethics, and commercial operations in a modern media organizations. Robert Børlum-Bach from JP/Politikens Hus takes us on a fascinating journey through the media industry's data landscape, where AI is revolutionizing journalism while simultaneously raising critical questions about democracy and public discourse.

What does data management maturity actually mean in an organization? Robert challenges traditional thinking as he explains why his team avoids using the very term "maturity" and instead focuses on visibility and understanding the real challenges faced by teams. He introduces the innovative "stamp model," which visualizes how different departments require varying levels of support on their data journey.

In a world where media organizations must balance dependence on major tech platforms with the need for editorial integrity, Robert shares practical approaches to data contracts and data products that bridge the gap between technical and business needs. We explore how JP/Politikens navigates between centralization and decentralization with a "demarcation line" that is designed to be broken when teams demonstrate higher maturity.

Whether you work in data, media, or leadership, this conversation offers new perspectives on how organizations can develop a healthy data culture that balances innovation and standardization. The most important advice? Stop telling teams they have "low maturity"—start listening to their challenges and questions instead. Because that’s where true data governance begins.

Here are some key takeaways:

Social Media

• Social media is originally not designed for news consumption. Journalists and media houses need to ensure the same quality of news on SOME.
• There is an unequal dependence between media houses and SOME platforms.
• Think about then ethical implications of driving traffic to your side and platform through click bate, or rage bate, which might nit be the way you want to present your organization.

Maturity Assessments

• If data is a support function to the business it might be strange if you demand to assess the businesses dat maturity,
• Sometimes maturity is reflected in the questions you get back from whom you are assessing.
• There is a natural variety of maturity between different parts of the business.
• Showcasing this variety can be a good step towards learning from each other.
• Start with a common baseline, but try to adjust your approach in how you support the different business units to their level of maturity.
• 4 deliverables of a maturity assessment:

1. As-is
2. To-be
3. How to close the gap between as-is and to-be?
4. Measure your progress 

How to increase maturity?

• Data governance sets up the playing field, but you need to play on the field.
• Set a baseline to get a starting point. This needs to be a common determinator that is not too low and not too high, but relevant for all business units.
• «We are seeing more and more data enabling teams.»

Strategic perspective on maturity

• Maturity assessments are a snapshot in time and the world around you is constantly evolving.
• Assessments should be an organic and living thing.
• Keeping updated information on maturity should be the teams themselves.

Federated and domain oriented

• Stated with data contracts to identify producer and consumers and expectations between them.
• Understand usage, requirements, ans success criteria for data.
• Contract as skeleton and products as the flesh around it.
• Understanding roles and responsibilities is a constant challenge you need to face.

Software development is ahead of the data world in many ways, but what can we learn from its methods? Audun Fauchald Strand, Director of Platform and Infrastructure at NAV, shares insights from building autonomous teams and balancing freedom with governance.

Here are my key learnings:

What is a platform?

• A platform is something you build on top of. Something that eases building applications, because if a common ground layer.
• A platform reduces the need for competency, since it predefines some choices.
• The capacity for change is higher when building based on a common platform, then when buying applications. 
• It’s important to find a balance in how much you should standardize through a platform and how much room for innovation should be beyond that.
• A platform can provide an «easy choice» that is the best practice way, but it should also allow for alternative, innovative ways to get jobs done.

Software Development and Data

• Software development has been a role model for data work, but they are at different stages of maturity.
• What is happening now in data platform is something Audun has experiences in Software 10 years ago.
• Yet, the principles are largely the same.
• Data is not as mature as software. There is still something to understand when it comes to use of tools and open source, etc.
• «Verden har blitt god å lage software. /The world has become real good at making software.»
• There is a certain agreement on what good looks like in software.
• Software has embraced open source in a much larger degree then data.

Data Mesh and the need for scale

• Data Mesh was a description if the data world from the perspective of a software developer.
• Change in architecture of operational environments have led to more possibilities to scale and to speed up operational data processing.
• This changes the need for speedy analytical data environments that have traditionally been built to compensated for the reduced flexibility of operational environments.
• Scaling up in a Data Mesh fashion, requires more data and analytical competency in teams.

Teams

• Working in teams, and having team ownership of products creates a certain sustainability over time.
• In software, with open source utilization, only teams can take technological decisions. Who else could decide what library to use for what purpose, then the team working hands on?
• You have to believe in autonomous teams for them to work. If not you will create dependencies that minimize their authority as a team and thereby capabilities to deliver.
• To create alignment there are some steps to take, from communicating openly, creating a tech radar to ensure alignment between team, to describing deliberate choices, rather then general principles.

5 learnings from article on alignment and autonomy:

1. Insource data expertise to ensure ownership, security, and business alignment.
2. Embed data professionals in cross-functional teams to enhance collaboration and generate actionable insights.
3. Balance team autonomy with governance by allowing flexible tool use within standardized data policies.
4. Foster continuous learning through asynchronous knowledge-sharing tools like wikis and Slack.
5. Provide clear direction with self-service data platforms to support scalability, efficiency, and governance.

In this clarifying conversation, Finnish data expert Säde Haveri shares her 18 years of experience and introduces a practical framework consisting of five key elements that can guide any organization's data governance journey.

Säde, who is a Data Governance Manager at Relax Solutions and co-founder of Helsinki Data Week, first explains the important difference between a framework and a playbook. While many consultants offer ready-made solutions, Säde argues that a truly effective framework functions more like scaffolding, helping organizations uncover their own best path forward.

We dive deep into the five elements: the choice between a top-down or bottom-up approach, the balance between defensive and offensive strategies, how to define the right scope, identifying key stakeholders, and the strategic role of external consultants. Säde illustrates how these decisions affect the structure, implementation, and success of data governance, with practical examples from his own experience.

Here are our hosts key takeaways:

• Data Governance is at the heart of the socio-technical system - it requires a variety of skills.
• The experience for the end user has not change much in the last almost 20 years.
• There is a need for «group support» for data people.

What is a framework?

• There are ambiguous connotations of the word «framework».
• A framework is not a playbook.
• A framework describes the what, not the how. You need two adjust it to your reality.
• Think of a framework as non-prescriptive.
• Frameworks are related to best practices, but they are not the same thing.
• Use it to identify your strengths and build your data governance practices around those.

Top-down or bottom-up

• Can be a management awakening (e.g. GDPR), or a need for better data at a practitioner level that initiates the need for data governance.
• Top-down often materializes in conceptual approaches.
• You start at a conceptual level, you will create data governance roles around these conceptual entities.
• From a bottom-up perspective you are building governance around your tables or datasets.
• As a middle way, you can focus on data products as objects to build governance around.

Aligning strategy defensively or offensively 

• Defensive vs. Offensive strategy - based on an article from Davenport 2017.
• There is no one-size fits all.
• You need to understand your motivation? Is it build due to risk mitigation needs or for business value creation?
• You need to understand your sector-driven differences.
• Look at this as a spectrum, where your approach can differ between offensive and defensive based on the criticality of the dat for the use case you are working with.
• You always have to show your value, the value needs to be measurable.
• AI ready data can be both offensive and defensive.

Identifying Scope & key stakeholders

• Identify your stakeholders and scope based on the strategic alignment on offensive vs. defensive.
• Use business stakeholders rather than IT, to gain a better understanding of the underlying problem.
• Data Governance is rather a business value enabler than a cost saving activity.

Determining the role of external consultants.

• You have to sell your solution. Selling is something you cannot outsource.
• If you are looking at tooling, consider if you can find consultants with the right knowledge and capabilities.
• Try to understand what experiences the consultants can bring to the table.
• Ensure that you are aligned on a methodological basis.

Have you ever wondered why data governance often becomes so complicated that no one really understands what it’s about?
In this episode, we take a refreshing deep dive with data expert Rasmus Bang, who shows how to make data governance simple and relevant.

We explore the delicate art of engaging middle management, which is often the key to successful implementation of data governance. Through data governance committees and a focus on concrete business challenges, you can create both transparency and accountability that drive real change. Rasmus also explains how to bridge the gap between process excellence and data governance—how these disciplines can reinforce each other rather than compete.

Here are our hosts' key takeaways:

• The level of regulation in your industry determines your approach to. Data governance.
• Spent time in exploring what is necessary and relevant from a business point of view.
• Identify pinpoints you have today, to tackle them beyond compliance. This is where you show your value to the business.
• Storytelling is essential in data governance.
• Be able to see and communicate how your work impacts the business.
• Data governance is a peoples game.

How to start your initiative?

• Do analysis to translate your data pain points to business pain points.
• Assess the size and complexity of the challenge ahead.
• Adjust your lingo to make it understandable for the business.
• Dont be dogmatic - use existing structures.
• You need to adapt to your environment instead of hoping that your environment adapts to you.
• Make sure you find a strategy to talk to the conflicting priorities of middle management.
• Watch out for the experienced professionals- they can become bottlenecks.
• Make the value proposition understandable for everyone, also talking to these priorities on middle management.
• How can my department become better, more efficient, etc.?
• Include middle management representatives activity around the data itself.

Process Excellence

• Processes are seen as a precondition, while they are actually a result of the organizations culture, organization and people.
• Process owners often have a broad network in the organization and are therefore important to make data governance work.
• «If you do data governance right you get de facto business process ownership.» - you get the right people in place.
• Processes can give you a repeatable structure that is foundational for your success in e.g. AI.

• Have an approach and build a model that your business stakeholders can recognize and see themselves in.
• Dont focus too much on theory. Focus on what matters for your business and for your stakeholders. It needs to resonate.
• Dont try to fix a complex problem with simple solutions.
• «There is a difference between quick fixes and fixing small things.»

With a year behind him as a solo entrepreneur in his own company, Datakor Consulting, Juha Korpela takes us on a journey through fact-finding-missions at what he calls "the middle layer" of organizations — the strategic area between high-level business strategy and tactical project execution. It is here, he believes, that data consultants can create the most significant and lasting value.

We discuss the pitfalls of standardized frameworks and "blueprint" approaches offered by many consulting firms, and why tailored solutions based on a deep understanding of organizational culture always yield better results. Juha shares his methods for knowledge transfer that ensure organizations can continue succeeding with their data work long after the consultant has left the project.

Here are Winfried´s key takeaways:

Skills

• The key skill as a data consultant, no matter if on a strategic or solution, project level is to understand «what the customer really needs.»
• The key skills are:
   
  • Listening. Active listening is the key to understanding.
  • Create mental models: when talking to stakeholder you need to be able to put the information you capture together in a mental model.
  • Understanding.
  • Tech comes after.
• Working with data modeling is about listening to stories about how business work. Understanding business processes are key.
• Understanding stories about business and what is relevant for data modeling is a skill that everyone can profit from, but that is seldom taught.
• Data Modeling is a fact-finding-mission.
• It is about understanding what the organization does, how it does things, and where this could be improved.

Impact

• A data consultants impact is dependent on the organization, the structure, and the level of maturity.
• If there is a CDO or CIO to connect to it can be a good way to create results and visibility.
• Also as a data consultant it is important find a place in the organization where you have shared views and understanding.
• If you begin bottom-up you need to be ready to sell this upwards in the organization.

Limits

• Consultants can help with the initial projects to get you started.
• Consultants can help figuring out processes and operating model and design what is needed.
• Organizations need to create long-term ownership in house.
• Running and maintaining needs to fit with the organizations culture, its s structure, needs, maturity, etc.
• Models, blueprints, frameworks that you get from the outside can get you started, but do not work in the long run.

Patterns

• Data Consultants can see certain patterns emerging across an industry.
• That knowledge on patterns, lessons learnt, experiences is valuable to apply.
• That knowledge you bring in is what defines your value, more than specific skills.
• It is easy for people in organizations to get stuck. Consultants can help as a fresh wind.

Knowledge transfer

• As a consultant you bring in new knowledge, and you need to account for that organizations want to transfer that knowledge to internals.
• Find ways to create custom training packages to facilitate knowledge sharing.
• You aim for the organization to succeed with their work, also after the consultants are gone.

Consultant aaS

• Do we move from being consultants to becoming a service offering?
• Service models can crate a distance between consultants and clients.
• You need to have a clear understanding the impact of models that include ownership and responsibility transfer as eg. Outsourcing operational tasks.

In this episode, we dive into the complexities of data ethics with Gry Hasselbalch, a leading expert on the topic. With experience shaping EU regulations on data and AI ethics, she shares insights on why human values must remain at the core of digital development.

We explore the principle of “humans at the center” and why people should be seen as more than just data points or system users. Gry discusses how artificial intelligence and big data challenge this idea and why human interests must take priority over commercial or institutional goals.

Here are our hosts' key takeaways:

Humans

• When we talk about data ethics we need to relate to a value set - in out case a European value set, based on human rights.
• Data Ethics is built around humans - a human-centric principle. That means that human interests are always prioritized, above organizational interests, commercial interests, or machine interests.
• User is not enough if we talk about human in the center: this will mean different things once the discussion includes AI.
• We need to talk about the whole human, not just the user or the data about the human.
• Systems have an influence on our life, and therefore the human needs to be seen as a holistic being.

Regulations

• EU is seen as a «regulatory superpower» that has an ethical starting point when regulating.
• All cultures will have different interpretation and starting point of what ethics means.
• But through history we have been able to agree on an ethical baseline, like the charts of human rights.
• Human dignity is a central part of what ethics mean internationally.
• Regulation is not everything - remember that regulation happens due to an identified need.
• Regulations and laws are a guideline, but they do not cover (and cannot cover) the entire topic of data ethics.
• To ensure a value based approach to data handling, we need to go beyond regulations - talk about this as a societal challenge.

Socio-technical

• Technology is not neutral - it is developed, applied within a certain cultural setting.
• Technical systems are part of society as much as society is part of the technical systems we develop and use.
• Maybe we should rather talk about «socio-technical infrastructure».
• There is a dichotomy in talking about data as something valuable and at the same time as a liability.
• Data ethics can be viewed as a competitive advantage, a way to induce trust and better an organizations reputation.

AI and ethics

• AI is accelerating the need for ethical data decisions.
• AI is not created out of the blue, it is very much based on our data, our societal norms, developed by humans.
• AI is becoming a solution for «everything» - but what does that nean for human-machine relationship?
• AI is a tool, not a solution.
• What interests are pushing AI and what impact does AI have on our social systems and our culture?

Data Ethics of Power - A Human Approach in the Big Data and AI Era

Data Ethics - The New Competitive Advantage

Human Power - Seven Traits for the Politics of the AI Machine Age

How can a nuclear physicist transform into a data leader in the industrial sector? Kristiina Tiilas from Finland shares her fascinating journey from leading digitalization programs at Fortum to shaping data-driven organizations at companies like Outokumpu and Kemira. Kristiina provides unique insights into navigating complex data-related projects within traditional industrial environments. With a passion for skydiving and family activities, she balances a demanding career with an active lifestyle, making her an inspiring guest in this episode.

We focus on the importance of data competence at the executive level and discuss how organizations can strengthen data understanding without a formal CDO role. Kristiina shares her experiences in developing innovative digitalization games that engage employees and promote a data-driven culture. Through concrete examples rather than technical jargon, she demonstrates how complex concepts can be made accessible and understandable. This approach not only provides a competitive advantage but also transforms data into an integral part of the company’s decision-making processes.

Here are my key takeaways:

• The AI hype became a wake-up moment for Data professionals in Finland taking the international stage.
•  As a leader in dat you need to balance data domain knowledge and leadership skills. Both are important.
• Leadership is important to provide an arena for your data people to deliver value.
• As a leader you are in a position that requires you to find ways of making tacit knowledge explicit. If not you are nit able too use that knowledge to train other people or a model.

CDO

• The Chief Data Officer is not really present in Nordic organizations.
• An executive role for data is discussed much, but in reality not that widespread.
• Without CDO present, you need to train somebody in the top leadership group to voice data.
• CDO is different in every organization.
• Is CDO an intermediate role, to emphasis Data Literacy, or a permanent focus?
• You can achieve a lot through data focus of other CxOs.
• Make data topics tangible, this is about lingo, narratives, but also about ways of communicating - Kristiina used gamification as a method.
• Creating a game to explain concepts in very basic terms with clear outcomes and structure can help with Data Literacy for the entire organization.

Data in OT vs. IT

• Predictions and views on production should be able to be vision also in Operational Settings on all levels. There should not be any restriction in utilizing analytical data in operational settings.
• Security and timeliness are the big differentiators between OT and IT.
• These are two angles of the same. They need to be connected.
• IoT (Internet of Things) requires more interoperability.
• Extracting data has been a one way process. The influence of Reverse ETL on OT data is interesting to explore further.
• There are possibilities to create data driven feedback loops in operations.

Data Teams

• If you start, start with a team of five:
   
  • One who knows the data (Data Engineering) 
  • One who knows the business
  • One who understands Analytics / AI
  • One who understands the users / UX
  • One to lead the team
• You can improve your capabilities one step at a time - build focus areas that are aligned with business need an overall strategy.
• If you expect innovation from your data team, you need to decouple them from the operational burden.
• Show your value in $$$.

• Standardization is a starting point to build a foundation, but not something that let you advance beyond best practice.
• Use standards to agree on ground rules, that can frame our work, make it interoperable.
• Conceptual modeling is about understanding a domain, its semantics and key concepts, using standards to ensure consistency and support interoperability.

Data Modeling

• Modeling is an important method to bridge business and data.
• More and more these conceptual models gain relevance for people outside data and IT to understand how things relate.
• Models make it possible to be understood by both humans and machines.
• If you are too application focused, data will not reach its potential and you will not be able to utilize data models to their full benefits.
• This application focus which has been prominent in mainstream IT for many years now is probably the reason why data modeling has lost some of its popularity.
• Tool advancement and new technology can have an impact on Data Management practices.
• New tools need a certain data readiness, a foundation to create value, e.g. a good metadata foundation.
• Data Modeling has often been viewed as a bureaucratic process with little flexibility.
• Agility in Data Modeling is about modeling being an integrated part of the work - be present, involved, addressed.
• The information architect and data modeling cannot be a secretary to the development process but needs to be involved as an active part in the cross-functional teams.
• Information needs to be connected across domains and therefore information modeling should be connected to business architecture and process modeling.
• Modeling tools are too often connected only to the discipline you are modeling within (e.g. different tools for Data vs. Process Modeling).
• There is substantial value in understanding what information and data is used in which processes and in what way.
• The greatest potential is within reusability of data, its semantics and the knowledge it represents.

The role of Information Architect

• Information Architects have played a central role for decades.
• While the role itself is stable it has to face different challenges today.
• Information is fluctuant and its movement needs to be understood, be it through applications or processes.
• Whilst modeling is a vital part of the work, Information Architects need to keep a focus on the big picture and the overhauling architecture.
• Information architects are needed both in projects and within domains.
• There is a difference between Information and Data Architects. Data Architects focus on the data layer, within the information architecture, much closer to decisions made in IT.
• The biggest change in skills and competency needs for Information Architects is that they have to navigate a much more complex and interdisciplinary landscape.

Metadata

• Data Catalogs typically include components on Metadata Management.
• We need to define Metadata broader - it includes much more than data about data, but rather data about things.

The healthcare industry is undergoing a significant transformation, driven by the need to modernize health registries and create a cohesive approach to data governance. At the heart of this transformation is the ambition to harness the power of data to improve decision-making, streamline processes, and enhance patient outcomes. Jørgen Brenne, as a technical project manager, and Marte Kjelvik’s team, have been instrumental in navigating the complexities of this change. Their insights shed light on the challenges and opportunities inherent in healthcare data modernization.

Here are my key takeaways:
Healthcare data and registry

• Its important to navigate different requirements from different sources of authority.
• To maintain comprehensive, secure, and well-managed data registries is a challenging task.
• We need a national standardized language to create a common understanding of health data, what services we offer within healthcare and how they align.
• Authorities need also to standardize requirements for code and systems.
• National healthcare data registry needs to be more connected to the healthcare services, to understand data availability and data needs.

Competency

• Data Governance and Data Management are the foundational needs the registry has recognized.
• Dimensional Modeling was one of the first classes, they trained their data team on, to ensure this foundational competency.
• If the technology you choose supports your methodology, your recruitment of new resources becomes easier, since you don’t need to get experts on that very methodology.

Models

• User stories are a focus point and prioritized. Data Lineage (How data changed through different systems) is not the same as Data Provenience (Where is the datas origin). You need both to understand business logic and intent of collection) - User stories can help establish that link.
• Understanding basic concepts and entities accounts for 80% of the work.
• Conceptual models ensured to not reflect technical elements.
• These models should be shareable to be a way to explain your services externally.
• Could first provides an open basis to work from that can be seen as an opportunity.
• There are many possibilities to ensure security, availability, and discoverability.
• Digitalization in Norwegian public services has brought forth a set of common components, that agencies are encouraged to use across public administration.
• Work based on experiences and exchange with others, while ensuring good documentation of processes.
• Find standardized ways of building logical models, based on Data Contracts.
• By using global business keys, you can ensure that you gain structured insight into the data that is transmitted.
• Low Code tools generate generic code, based on the model to ensure effective distribution and storage of that data in the registry.
• The logical model needs to capture the data needs of the users.
• Data Vault 2.0 as a modeling tool to process new dats sources and adhering to a logical structure.
• There is a discipline reference group established to ensure business alignment and verification of the models.
• Data should be catalogued as soon as it enters the system to capture the accompanying logic.

Data Vault

• Adaptable to change and able to coordinated different sources and methods.
• It supports change of formats without the need to change code.
• It makes parallel data processing possible at scale.
• Yet due to the heterogeneity of data vault, you need some tool to mange.

Make Data Management tangible

• Data management is not clear enough to be understood, to have feedback loops, to ensure responsibility to understand what good looks like.
• Because Data Management is not always clear enough, there is a pressure to make it more tangible.
• That pressure is also applied to Data Governance, through new roles like Data Governance Engineers, DataGovOps, etc.
• These roles mash enforcing policies with designing policies.

Data Contracts

• Shift Left in Data needs to be understood more clearly, towards a closer understanding and collaboration with source systems.
• Data Contracts are necessary, but it’s no different from interface files in software. It’s about understanding behavior and expectations.
• Data Contracts are not only about controlling, but also about making issues visible.

Data Governance

• Think of Data Governance as political parties. Some might be liberal, some more conservative.
• We need to make Data Governance lean, integrated and collaborative, while at the same time ensuring oversight and accountability.
• People need a reason to care about governance rules and held accountable.
• If not Data Governance «(...) ends up being that committee of waste.»
• The current way Data Governance is done doesn’t work. It needs a new look.
• Enforcing rules, that people don’t se ant connection to or ownership within are deemed to fail.
• We need to view ownership from two perspectives - a legal and a business perspective. They are different.

Data Modeling

• Business processes, domains and standards are some of the building blocks for data.
• Data Modeling should be an intentional act, not something you do on the side.
• The literature on Data Modeling is old, we are stuck in a table-centric view of the world.

Join us for an engaging conversation with Shuang Wu, Mesta's lead data engineer. We delve into the concept of platforms and explore how they empower autonomous delivery teams, making data-driven decisions a central part of their strategy.

Shuang discusses the intricate process of evolving from a mere data platform to a comprehensive service platform, especially within organizations that aren't IT-centric. Her insights emphasize a lean, agile approach to prioritize use cases, focusing on quick iterations and prototypes that foster self-service and data democratization. We explore the potential shift towards a decentralized data structure where domain teams leverage data more effectively, driving operational changes and tangible business value in their pursuit of efficiency and impact.

My key learnings:

• It’s not just about gaining insights, but also about harmonizing and understanding data in context.
• Find your SMEs and involve them closely - you need insight knowledge about the data and pair that with engineering capabilities.
• Over time the SMEs and the central data team share experiences and knowledge. This creates a productive ground for working together. 
• The more understanding business users gain on data, the more they want to build themselves.
• Central team delivers core data assets in a robust and stable manner. Business teams can build on that.

The Data

• You can integrate and combine internal data with external sources (like weather data, or road network data) to create valuable insights.
• Utilizing external data can save you efforts, since it often is structured and API ready.
• Dont over-engineer solutions - find you what your user-requirements are and provide data that match the requirements, not more.
• Use an agile approach to prioritize use cases together with your business users.
• Ensure you have a clear picture of potential value, but also investment and cost.
• Work in short iterations, to provide value quickly and constantly.
• Understand your platform constrains and limitations, also related to quality.
• Find your WHY! Why am I doing the work and what does that mean when it comes to prioritization?
• What is the value, impact and effort needed?

Service Platform:

• Is about offering self-service functionality.
• Due to the size of Mesta it made sense to take ownership for many data products centrally, closely aligned with the platform.
• Build it as a foundation, that can give rise to different digitalization initiatives.
• If you want to make data actionable they need to be discoverable first.
• The modular approach to data platform allows you to scale up required functionality when needed, but also to scale to zero if not.
• Verify requirements as early as you can.

Working with business use cases

• Visibility and discoverability of data stays a top priority.
• Make data and AI Literacy use case based, hands-on programs
• You need to understand constrains when selecting and working with a business use case.
• Start with a time-bound requirements analysis process, that also analyses constraints within the data.
• Once data is gathered and available on the platform, business case validity is much easier to verify.
• Gather the most relevant data first, and then see how you can utilize it further once it is structured accordingly.
• Quite often ideas originate in the business, and then the central data team is validating if the data can support the use case.

Step into the world of data science and AI as we welcome Victor Undli, a leading data scientist from Norway, who shares his  insights into how this field has evolved from mere hype to a vital driver of innovation in Norwegian organizations. Discover how Victor's work with Ung.no, a Norwegian platform for teenagers, illustrates the profound social impact and value creation potential of data science, especially when it comes to directing young inquiring minds to the right experts using natural language processing. We'll discuss the challenges that organizations face in adopting data science, particularly the tendency to seek out pre-conceived solutions instead of targeting real issues with the right tools. This episode promises to illuminate how AI can enhance rather than replace human roles by balancing automation with human oversight.

Join us as we explore the challenges of bridging the gap between academia and industry, with a spotlight on Norway's public sector as a cautious yet progressive player in tech advancement. Victor also shares his thoughts on developing a Norwegian language model that aligns with local values and culture, which could be pivotal as the AI Act comes into play. Learn about the unique role Norway can adopt in the AI landscape by becoming a model for small countries in utilizing large language models ethically and effectively. We highlight the components of successful machine learning projects: quality data, a strong use case, and effective execution, and encourage the power of imagination in idea development, calling on people from all backgrounds to engage.

Here are my key takeaways:
Get started as Data Scientist

• Expectations from working with cutting edge tech, and chasing the last percentage of precision.
• Reality is much more messy.
• Time management and choosing ideas carefully is important.
• «I end up with creating a lot of benchmark models with the time given, and then try to improve them in a later iteration.»
• Data Science studies is very much about deep diving into models and their performance, almost unconcerned with technical limitations.
• A lot of tasks when working with Data Science are in fact Data Engineering tasks.
• Closing the gap between academia and industry is going to be hard.
• Data Science is a team sport - you want someone to exchange with and work together with.

Public vs. Privat

• There is a difference between public and privat sector in Norway.
• Public sector in Norway is quite advanced in technological development.
• Public sector acts more carefully.

Stakeholder Management and Data Quality

• It is important to communicate clearly and consistently with your stakeholders.
• You have to compromise between stakeholder expectation and your restrains.
• If you don’t curate your data correctly, it will loose some of its potential over time.
• Data Quality is central, especially when used for AI models.
• Data Curation is also a lot about Data Enrichments - filling in the gaps.

AI and the need for a Norwegian LLM

• AI can be categorized into the brain and the imagination.
• The brain is to understand, the imagination is to create.
• We should invest time into creating open source, Norwegian LLM, as a competitive choice.
• Language encapsulates culture. You need to embrace language to understand culture.
• Norways role is a sa strong consumer of AI. That also means to lead by example.
• Norway and the Nordic countries can bring a strong ethical focus to the table.

• You need to understand the cost of error of a ML/AI application
• Cost of error limits the usability of AI
• Decision Science is a broader take on Data Science, combining Data Science with Behavioral Science.
• Decision Science covers cognitive choices that lead to decisions.
• Decision Science can just work in close proximity to the end user and the product, something that has been a challenge for many.

From Use Case to product

• Lots of genAI use cases are about personal efficiency, not to improve any specific organizational target.
• Differentiating between genAI and analytical AI can help ton understand what the target is.
• genAI hype has created interest from many. You can use it as a vessel to talk about other things related to AI or even to push Data Governance.
• When selecting use cases, think about adoption and how it will affect the organization at large.
• When planning with a use case, find where uncertainties are and ability for outcomes.
• It’s easy to jump to the HOW, by solving business use cases, but you really need to identify the WHY and WHAT first.
• Analysis-paralysis is a really problem, when it comes to move from ideation to action, or from PoC to operations.
• «Assess your impact all the time.»
• You need to have a feedback loop and concentrate on the decision making, not the outcome.
• A good decision is based on the information you had available before you made a decision, not the outcome of the decision.
• A learning culture is a precondition for better decision making.
• If you correct your actions just one or two steps at a time, you can still go in the wrong direction. Sometimes you need to go back to start and see your entire progress.
• The need for speed can lead to directional constrains in your development of solutions. 
• Be aware of measurements and metrics becoming the target.
• When you build a product, you need to set a treshold for when to decommission it.

Strategic connection

• The more abstract you get the higher value you can create, but the risk also gets bigger.
• The biggest value we can gain as companies is to adopt pur business model to new opportunities.
• The more organizations go into a plug-n-play mode, the less risk, but also less value opportunities.
• Industrial organizations live in outdated constrains, especially when it comes to cost for decision making.
• Dont view strategy as a constrain, but rather a direction that can provide flexibility.

• Mathematics is a combination of beauty, art and structure.
• Find your way in data and digitalization before jumping on the AI-train.
• Ensure that people can excel at what they are best at - this is what Tine tries to do for the farmers.
• Data only has a value, when it can be used - find ways to use data from analytics to prediction to more advanced algorithms.
• Create a baseline through a maturity assessment to see how you can tailor your work to the different business units.
• Follow up and monitor the usage of your data tools in the different areas of your business
• Create a gateway into data for your business users: Once that gateway is established it is also easier to introduce new tools.
• Data Literacy has a limit - not everyone in the business needs to be a data expert.
• Yet you need someone you can trust to enable and provide guidance - the Data team.
• Business users need to understand the difference between concrete answers and probability.

• How do you transform a complex organization without breaking the culture?
• Your data/digital/AI transformation team is key in ensuring good transformative action without breaking culture.
• Ensure you have good ambassadors for your data work in the Business Units, that what to transfer their knowledge in their respective units.
• Create a network of data-interested people, that help to drive adoption.
• Engage people by showing an initial value.
• Offer courses and classes for people to learn and understand more, but also to spread the word about your focus points.
• Inhouse courses provided by your own staff can increase the confidence in your data team.

• AI can mean different things to different people. It is important to define AI in your setting.
• Don’t replace existing work process with AI-driven solutions, just for the sake of it. Find ways to focus on where improvement actually provides business value.
• When you think of a new AI project, you have several options:

1. Develop in house
2. Buy off the shelf
3. Do nothing

•  Option two should be your preferred solution
• AI strategy is part of a larger ecosystem, with conditions to adhere to.
• Data and algorithms should become interconnected, also visually represented.
• «Always remember your core business.»

• The way we talk about data as a profession has changed, the lingo has changed and we adopt to trends.
• To display and evaluate data from different sources that are not connected, excel becomes the tool of choice.
• There is a very calculated amount of resources, that limit your ability to set up substantial teams to work exclusively on eg. Data Governance.
• Data Governance in SME (Small Medium sized enterprises) can be modeled as a repeatable process that incrementally enhances your data governance maturity.
• Identify sizable initiatives, ensure that they can be handled with a set amount of resources, and create metrics that enable you to track your progress.
• You need to find ways to ensure observability and monitoring over time.
• Don’t create something that you have no resources to maintain and improve going forward.
• To identify the right initiatives at the right time, you need to ensure a close collaborating with your business users.
• Ensure transparent and traceable ownership of the initiatives from the business side.
• To create a movement and engagement in data requires continuous and structured communication.

Data Maturity Assessment

• There is a need for speed and agility in SME, to ensure compatibility.
• Data Maturity Assessments are a welcome introduction to ensure that you create a baseline when working with data.
• There are advantages to both an internal view and to get some external perspective on your data maturity.
• Results from a maturity assessment can be a reality check that is not always easy to convey, yet you need to be realistic.
• Maturity assessments should ideally be both
   
  • Modeled/tailored to the needs of the organizations in question.
  • Repeatable and comparable over time and across organizations.
  • Good assessments cover both.
• To initially increase your maturity you can pick different tasks:
  • Low hanging fruits
  • «Duct-taped» operations that you can finally rectify
  • Find known problems that are visible
  • Find pinpoints for your business users
• It is good to start with cases that are understandable for business users, create interest, and can easy show value to leadership - this is what creates buy-in.
• You need to ensure that you keep a clear communication towards bigger, more substantial tasks, so your resources are not limited to quick win actions.

Data Strategy

• Data Strategy needs to be closely aligned with business strategy.
• Have a clear vision of where you want to go.
• To have a structure approach run our data strategy on how to handle both people, process, and technology is important for any work with data.
• Technology is not the staring point, but rather a consequence of your strategic choices, your organizational setup and your available resources.
• You need to include well-defined metrics to track progress.
• Find metrics that are closely connected to business outcome and value creation.

• Always when a new technology arrives, you need to adopt and figure out how to apply the new technology - often by using the new tools for the wrong problem.
• There is substantial investment in AI, yet the use cases for applying AI are still not clear enough in many companies.
• There is a gap I how we understand problems between technical and business people. Part of this problem is how we present and visualizer the problem.
• You need to create space for innovation - if your team is bugged down with operational tasks, you are canibalizing on innovative potential.
• Incubators in organizations are valuable, if you can keep them close to the problem to solve without limiting their freedom to explore.
• The goal of incubators is not to live forever, but top become ingrained in the business.
• CEOs need a combination of internal and external council.
• Find someone in the operational setting to take ownership from the start.
• The more data you have to handle the better and clear should your Data Governance strategy be.
• Small companies have it easier to set clear standards for data handling, due to direct communication.
• You want to make sure that you solve one problem really well, before moving on.
• Before intending to change, find out what the culture and the string incentives in your organization are.

LLMs as the solution for Data Management?

• ChatGP already today very good at classifying information.
• It can create required documentation automatically, by feeding the right parameters.
• It can supersede key value search in finding information.
• This can help to scale Data Governance and Data Management work.
• Data Management will become more automated, but also much more important going forward.
• RAG architecture - first build up your own knowledge database, with the help of vectorizing the data into a Vector-database.
• The results from querying this database are used by the LLM for interpretation.
• Find a way to consolidate all your input information into a single pipeline to build your knowledge database.
• Building strong controls on naming conventions will be less important going forward.
• Vectorized semantic search will be much faster.
• Entity matching will become very important.
• Fact tables and dimensional tables become less important.

Data to value

• Be able to benchmark your internal performance to the market
• undertand trends and how they affect you.
• How to use and aggregate third party data is even harder than internal data.
• You need to find ways to combine internal and third party data to get better insights.

• Azure services can be tailored to use cases and service needs. But you need to understand your requirements and needs.
• Once you understand what you need to do, you need to gain perspective in the how - what methods and processes are supported?
• Think security at every step.
• Security with integrations is an important part, we need to focus more on.
• Bringing different competencies together is a vital ingredient in building resilient applications.
• Cloud is about where your data resides, how you protect it and how you handle big data.
• Cloud should support the entire data lifecycle.

Cloud and AI

• «Cloud computing is the backbone of AI.»
• AI pushed for Edge AI, in addition to cloud. Reasons for Edge AI are latency, but mainly security.
• Cloud can provide an attack surface for eg. data poisoning, lack of control for training data, etc.
• AI tools can pose concerns on what and how you are exposing data.
• Awareness and education are important, when building something with AI.
• You need to at least understand your input to track your output - explainability starts with understanding of your data sources.
• There is a risk to Model Governance by on-perm due to the level of competancy needed.

Multi-Cloud vs. Single Cloud

• This is one of the questions to consider at the beginning of a cloud migration.
• Drivers for multi cloud strategy are:
   
  • Avoiding proprietary vendor lock-in,
  • Existing applications or infrastructure in another platform,
  • Choosing according to the quality of services offered by cloud vendors.
• If you choose multi cloud for automated resource management, you need to consider support platforms.

Cloud Migration

• Reason for cloud migration boil often down to gaining resiliency in the cloud, due to redundancy.
• You need to uphold Data Quality not just after the migration but also during the transit.
• Cloud migration requires strategy.
• There are great resources to help with your cloud migration, like the Cloud Adaption framework or the Well-Architected framework.
• Use observability and orchestration tools for your migration process.
• Ensure you understand your cost, and can optimize it to fit with your needs.

How can the sense of community support Data Professionals? We dive deep into this question with Tiankai Feng, a prominent figure in data governance and the Data Strategy and Data Governance lead at ThoughtWorks Europe. In this season four premiere of MetaDAMA, Tiankai shares his unique journey and how his passion for music plays a pivotal role in his professional and personal life. His story underscores the multidimensional nature of data professionals and the importance of a supportive community.

Building and nurturing internal communities is crucial. Tiankai and Winfried discuss how data governance conferences serve as therapeutic spaces, offering more than just professional development—they provide emotional and communal support. We explore various community models like grassroots movements and rotational leadership, highlighting the indispensable role of leadership in fostering these spaces. Recognizing and valuing community leaders is essential for sustaining these supportive networks within organizations.

Lastly, we delve into practical strategies for building strong data management communities. From integrating community introductions into onboarding processes to using these groups as recruitment tools, we cover it all. We also examine how company culture shapes the type of communities that flourish and the support provided by external organizations like DAMA. Joining communities helps alleviate isolation, share solutions, and foster a connected environment. Tune in to learn how to make community engagement a cornerstone of data governance and elevate both personal and professional growth.

Here are my key takeaways:
Communities in organizations

• Community is needed as a counterpart to the transactional behavior in a workplace.
• Communities of Practice is an established model, that comes from a technical side, methodology focuses.
• Communities can create new lines of communication, that can help spread a sense of belonging in an organization, beyond a specific department or team.
• Leadership needs to accept that being in a Community is also part of the job.
• Community leaders need recognition and to be valued for their work.
• The «smartest person in the room» should not be the leader of a Community - this can turn a community into a lecture setting.
• Ensure that organizational hierarchies are «flattened» in a Community, to support physiological safety and freedom to speak.
• Ensure you have some rules of engagement or code of conduct in place.
• Breakout groups can be a way to get everyone to participate actively in the Community.
• Leadership plays an important role to promote Communities in an organization.
• Well functioning Communities of Practice can become a selling point for recruitment.

DAMA as a Community

• A Community for Data professionals outside their organizations.
• The most outstanding impact DAMA can have is networking in a broad community, both local/national, but also internationally across sectors.
• There is an element of mentioning and coaching that a community of this size can offer.
• Another factor can be talent-sourcing: both for organizations, but also for job-seekers.
• Upscaling and learning are a great part of the DAMA Community, also including the CDMP certification.
• You need to find your balance between domain or sector specific communities and large data communities like DAMA.

SOME Community

• You need to be conscious about what you are reading on SOME.
• It can be a great place to provoke some new thoughts and get perspective on your work.
• There is certainly an entertainment factor to using SOME. Humor can heal a lot, and laughing about challenges we face as Data folks is like therapy.

• our security, economy, the national and international system relies on data.
• How data is collected, stored, protected, used, transferred, retained.. happens as much across boarders as within.
• Data Strategy on this geopolitical level is about creating a digital autonomy, not being reliant on big international enterprises, but for our political system to stay sovereign
• US is based on a liberal, free market model that is very innovation friendly.
• China is based on a very controlled environment, with limited access to their domestic market. Incubation of local companies, shield from global competition.
• The EU is setting the regulatory standard. Freedom is balanced with other values, like fairness or democracy.
• We need to talk about the role that big tech has on the global scene.
• Geopolitical impact on digital policies.
• Ingrid has a role between policy and business, coordinating and finding opportunities between both.
• EU has set the global standard in how we could deal with data and AI from a regulatory perspective.
• Politics are the decisions we make to set the direction for society.
• «Policy is the plan and implementation of what is decided through politics.»
• Cultural differences influence how we perceive, utilize and establish global policies, but also how we work with data in a global market.
• We have an issue if we only think in 4-5 year election cycles for tackling long term issues.

The EU

• Regulation is the biggest tool the EU has.
• «We are always in competition with technology, because technology develops so fast, and legislation develops so slowly.»
• You can see a change in responsibility for enforcement of EU rules and regulations, where implementation is moved from national responsibility to EU responsibility.
• The EU system is not any easy system to understand from the outside.

The rise of Big Tech

• We can go back to the anti-trust laws from the 1980s that opened for much more monopolistic behavior.
• The rise of the internet had a large influence on big tech.
• The liability shield was a prerequisite for social media platforms to gain traction.
• Big tech has created dependency for other organizations due to eg. their infrastructure offerings.
• We need to be aware of that concentration of power in the market.
• Big Tech is not just leading but also regulating the development of the market.
• Bigger companies that are competing with Big Tech, feel their influence and size the most.

• Yngvar has build and is leading the as he calls it «Moon-shoot unit at DNB».
• What do we need to do to actually implement and adopt to new technology and ways of working?
• How do we think tech for people in tech?
• We can identify three needed dimensions for change: 
  • a data / tech component, 
  • a business component and 
  • a change component.
• There is a difference between necessary and sufficient - just because a change is necessary, doesn’t mean that the proposed solution is sufficient.
• You need to find ways to navigate uncertainty, be active beyond concrete hypothesis testing, or tech-evaluation.
• For organizations to be successful, you need to coordinate both maintenance, improvement and innovation - it’s not one of those, but all there in concert that can ensure success over time.

• Innovation and digital transformation is not a streamlined process.
• Uncertainty offers a space for opportunity.
• We use the term agile without grasping its true meaning - an inspect-and-adapt mindset is key to agile.
• The development from GPT-1 through GPT-2 to GPT-3 is an example for the exponential development of technology.
• The digital infrastructure in Norway, that can utilize data and technology for value creation across public and private sectors is a reason for our success.
• The difference to the US market is that there are large cooperations that take on societal challenges.
• How our society is structure has an influence on how we perceive the need for innovation.
• It is natural to meet resistance in change and innovation.
• To iterate effectively you really need to live a mindset build around FAIL - First Attempt in Learning.
• We overestimate the effect of technology in the short term and significantly underestimate the long term.

• Data science needs a strong connection to business development 
• You need to embed Data Science in a cross-functional environment
• Business acumen needs to be ingrained in the work with data
• Data Science needs to start from a Business side - ensure that you work on the problems that generate value for your organization.
• Data Science works with probability, not certainty - this notion is not yet understood by everyone in business.
• Data organizations are often build on an engineering mindset, that can be contradictive to an exploratory mindset.
• Even when designing Data Warehouse, you need to understand the business impact, have a business development mindset.

Norway & AI

• Norway has a great AI and ML research community.
• The public discourse on AI portraits a quite narrow view, that doesn’t reflect the broad application and research done in the field.

Research & Business

• Responsible AI is not a one-size fits all. Different organizations have different needs, for either certainty, security, reliability of outcome, etc. So a rAI approach needs ton be tailored to the business need.
• Startups and companies that have products related to the AI research environment, have the advantage that products are improved in tact with research development.
• In addition to in-house R&D, organizations can collaborate directly with research environments at universities.
• You cannot do R&D just as a pocket of excellence, if you want to operationalize results in your organization.
• We need to shorten the distance between R&D and operations.

For the Data Science Student

• If you apply knowledge on different challenges, you will get an intuition on how to solve a broad variety of challenges.
• When selecting a task within an organization as a Master thesis, make sure the task is delimited.
• Traits to succeed as a student working in industry:
  • Interest in your discipline
  • Interest in the organization and its sector
  • Problemsolving
  • Creativity

How can Data Diplomacy transform an organization into a data-driven organization? This episode brings Håkan Edvinsson, a visionary in data management and governance, into the conversation, revealing the intricacies and impacts of Data Diplomacy in Nordic organizations. Håkan's journey from business data modeling in the 90s to robust governance practices today offers a treasure trove of insights. Together, we dissect the evolution of enterprise architecture and its role in business innovation.

Discover how data governance is not just about maintaining quality but is a dynamic force that propels organizations forward with each structural change. We discuss the concept of data design and how this approach is shaping the future of responsible data usage in companies like Volvo Penta and Gothenburg Energy. Our dialogue uncovers the importance of integrating governance into decision-making and planning, ensuring data is not just managed but used as a strategic asset for innovation.

The finale of our discussion broadens the horizon, touching upon artificial intelligence and its relationship with traditional data practices. We challenge the status quo, urging businesses to embrace a leaner governance model that aligns with Lean and Agile methodologies. Alongside this, we unravel the subtle yet crucial distinction between data and information, arguing for a proactive business ownership in data design and governance.

Here are my key takeaways:

• If you want an organization to last, someone has to define key terms.
• Data Governance and Data Quality should not be done reactively, but rather by design.

Enterprise Architecture

• Connecting the work of EA to certain project gates, is underpinning a reactiveness in EA.
• EA claims to be the master interpreter of business needs, yet EA artifacts are based on second hand knowledge.
• Architecture as well as Governance are supporting a development, not dictating it.
• EA is NOT the business designer, just an interpreter, a facilitator, that enables those with 1st hand knowledge.
• Don’t generalize away from business reality.

Data Diplomacy

• As long as you are working with operational data, you need to embrace business data design.
• You need to bridge Business with IT.
• The «gravity for change», mainly through external factors provide management attention.
• Use these external triggers to create more with less.
• Dont talk solutions and technology - too many opinions. Stick to the data.
• Focus on what data should look like. Base your work on the facts.
• Enable people to understand data, requires Data Governance to take a facilitator role, not an excellence role.
• «Being a hero once doesn’t mean you are lasting.» - you need to find a sustainable way of doing data work, beyond task based, checklist compliance.
• Establish a Data Governance network that represents the entire organization.
• A common language and established tacit knowledge can speed up processes.
• You need to be ready, prepared, and on the edge to ensure you are resilient to change.
• Integrate your data decisions into the management structure.
• Firefighting gets more credit then fire prevention.
• Traditional Data Governance is too focused on operational upkeep, laking a future outlook.
• Data Governance don’t rely have the means to state: What should it look like in tomorrows world?
• Entity Manager: taking charge of definition, label and structure of a certain data entity, of the data that we should have.
• A Facilitator works with these entity mangers in their respective area.
• Advice against top-down, classical Data Governance implementation.

Join us as we sit down with Elisabeth M.J. Klaussen from DoMore Diagnostics, who are on a mission to transform cancer diagnostics with artificial intelligence to improve patient care and make drug development more effective. With a rich background in quality assurance and R&D within Pharma, Biotech, and MedTech, Elisabeth shares how AI is revolutionizing patient care and the pathway to personalized medicine.

Navigating the complexities of starting a healthcare venture can be as intricate as the regulations that govern it. In this episode, we discuss the maze of regulations across continents, the implications of the European AI Act for innovators, and the non-negotiable necessity of protecting patient data.

Wrapping up our dialogue, we emphasize the importance of a Quality Management System (QMS), especially when developing AI models. As we delve into the EU's AI Act and its potential to harmonize standards, Elisabeth offers invaluable advice to health startups: the development of a robust QMS is not just a regulatory tick box but a foundational pillar for market readiness.

Here are my key takeaways:
AI in Health Care:

• Personalized medicine requires to analyze a lot of data and set it in a personalized context.
• To create value with AI in health care is challenging, due to the high density of regulations, yet benefits can be huge.
• AI can enable us to use investments in pharmaceuticals, biotech as well as patient care more effectively.
• You need to ensure you can constrain AI models, not only on the data input, but also through use of parameters or model-architecture.
• The product from DoMore Diagnostics is i.e. a static model, not generative, that gives an output on leanings only.
• There is a need to apply for a new CE marking, if model would change.

Regulations in Health Care:

• You need to understand both your product and its intended purpose to understand what regulation will apply to you.
• You need to set up a team with the right people and competency.
• Try to find generalists - People that have a core competency, but are really good at adopting and learning new surrounding competencies at a more generalist level to complement each other.
• Laws and regulations in the industry are getting more and more globally standardized.
• If you adhere to the area with the most stringent rules, you can basically introduce your product to any market you like.
• If you set up your organization for regulatory compliance, you have two perspectives to keep in mind:
   
  • Internally - how do you set up your principles, polices and processes internally?
  • How do you act towards your sector and market?
• The regulation on EU level provides a framework, within you can find national regulations and laws that go beyond. One example is product labeling that can vary between EU countries.

The EU AI Act:

• The EU AI Act introduces requirements that the heavily regulated industry is following already. (E.g. quality systems, documented design and development of your product, validations, performance studies)
• EU regulations are political documents, that are build on compromise.
• There is a huge constraint within the EU commission as well as on the authority side to take on the workload that results from the AI Act and other new regulations.
• The more cumbersome regulations are and the more regulations you build in, the more expensive will products get.
• Standards and regulations can help to structure your ways of working, ensuring efficiency, not wasting time and money in doing things over and over again.
• «You can be more creative, if you have a structured way of working.»

Get new perspectives on Data Governance with Valentina Niklasson from Volvo Penta as she talks about certain patterns, stages in the acceptance of Quality Management or Lean, that Data has to go through. Her rich experience in making Data Governance business-centric emerges, showcasing how you can get an organization engaged in Data.

Gain insights on the synergy between lean methodology and effective Data Management. We explore the application of the PDCA Deming circle in Data and discuss how common languages and methodologies bridge the gap between Data, IT and business. This convergence is not just theoretical; it's a practical pathway to tapping into customer insights, translating needs into strategies, and fostering a culture where continuous improvement reigns.

Finally, we delve into the human aspect of Data and Data Stewardship, emphasizing the importance of people over technology in cultivating a data-driven culture. By engaging the curious early and involving them in the development of business information models, we build ambassadors within the business, ready to champion change. Valentina and I talk about the dynamic role of Data Stewards and the approach to involving business personnel, ensuring the smooth adoption of new processes and strategies.

Here are my key takeaways:
Quality management as inspiration

• Data is still treated as an IT problem, but should really be treated as a business problem.
• We need to find a better way to communicate across data, IT and business.
• Use the same methodology wherever possible and try to reduce complexity in processes.
• Try to adapt to the ways of working in the business. Not creating own ways on digital, data or IT.
• You need to understand customer relations, end customers and the entire value chain to define needs correctly.
• Standardized ways of working can help to do right from start.
• Deming Cycle, PDCA, can be directly adopted to data. Think of data as the product you are building, that should have a certain quality standard.
• Don’t make it hard to understand for the business:
  • Using the same forms and approaches.
  • Business data driven process.
  • Let the business take part in the entire process.
• Lean Methodology should take a bigger place in data.
• A product management mindset makes data quality work easier.

Data Stewardship

•  You need to ensure owning the problem as well as the solution.
• High data quality is vital for data-driven organization. Someone needs to ensure this.
• Stewardship can have a negative connotation. 
• The technical demands on Data Stewards are really big today.
• Data Stewardship works if the Data Steward is part of a broader team.
• The role of Steward needs to be adjusted to the fast-speed reality.
• Data Stewards need to be able to solve problems, not only report to a central organization.
• Data Stewards should be approached in the business. You need that domain knowledge, yet they cannot perform the entire stewardship role.
• Most important to empower Data Stewards to start working and analyzing the challenges ahead.
• Don’t force Data Stewards to be technical data experts. That should be a supportive role in the Digital / data organization.
• If you build something new, engage Data Stewards from the beginning. 
• You cannot take responsibility for something you don’t understand.
• If you want to be sustainable in Data, you need to help the people in your organization to be part of the journey.
• It’s not only about hiring new competency, but engaging with the knowledge you have in your organization.

• Norwegian police is working actively to analyse risks and opportunities within new technology and methodology, including how to utilize the potential of AI.
• But any analysis has to happen in the right context, compliant within the boundaries of Norwegian and international law.
• Data Scientists are grouped with Police Officers to ensure domain knowledge is included in the work at any stage.
• Build technological competency, but also ensure the interplay with domain knowledge, police work, and law.
• Juridical and ethical aspects are constantly reviewed and any new solution has to be validated against these boundaries.
• The Norwegian Police is looking for smart and simple solutions with great effect.
• The Norwegian Police is at an exploratory state, intending to understand risk profiles with new technology before utilizing it in service.
• There is a need to stay on top of technological development of the Norwegian Police to ensure law enforcement and the security of the citizens. This cannot be reliant on proprietary technology and services.
• Prioritization and strategic alignment is dependent on top-management involvement.
• Some relevant use cases:
  • Picture recognition (not necessarily face-recognition) - how can we effectively use picture material from e.g. crime scenes or large seizure.
  • Language to text services to e.g. transcribe interrogations and investigations. 
  • Human errors are way harder to quantify and predict then machine errors.
• This is changing towards more cross-functional involvement.
• The IT services is also moving away from project based work, to product based.
• They are also building up a «tech-legal staff», to ensure that legal issues can be discussed as early as possible, consisting of jurists that have technology experience and understanding.
• Data-driven police is much more than just AI:
  • Self-service analysis, even own the line of duty.
  • Providing data ready for consumption.
  • Business intelligence and data insights.
  • Tackling legacy technology, and handling data that is proprietary bound to outdated systems.

Join us as we unravel the complexities of data management with Olof Granberg, an expert in the realm of data with a rich experience spanning nearly two decades. Throughout our conversation, Olaf offers insights that shed light on the relationship between data and the business processes and customer behaviors it mirrors. We discussed how to foster efficient use of data within organizations, by looking at the balance between centralized and decentralized data management strategies.

We discuss the "butterfly effect" of data alterations and the necessity for a matrix perspective that fosters communication across departments. The key to mastering data handling lies in understanding its lifecycle and the impact of governance on data quality. Listeners will also gain insight into the importance of documentation, metadata, and the nuanced approach required to define data quality that aligns with business needs.

Wrapping up our session, we tackle the challenges and promising rewards of data automation, discussing the delicate interplay between data quality and process understanding.

Here are my key takeaways
Centralized vs. Decentralized

• Decentralization alone might not be able to solve challenges in large organizations. Synergies with central departments can have a great effect in the horizontal.
• You have to set certain standards centrally, especially while an organization is maturing.
• Decentralization will almost certainly prioritize business problems over alignment problems, that can create greater value in the long run.
• Without central coordination, short-term needs will take the stage.
• Central units are there to enable the business.

The Data Value Chain

• The butterfly effect in data - small changes can create huge impacts.
• We need to look at value chains from different perspectives - transversal vs. vertical, as much as source systems - platform - executing systems.
• Value chains can become very long.
• We should rather focus on the data platform / analytics layer, and not on the data layer itself.
• Manage what’s important! Find your most valuable data sources (the once that are used widely), and start there.
• Gain an understanding of intention of sourcing data vs. use of data down stream
• «It’s very important to paint the big picture.»
• You have to keep two thoughts in mind: how to work a use-case while building up that reusable layer?
• Don’t try to find tooling that can solve a problem, but rather loo for where tooling can help and support your processes.
• Combine people that understand and know the data with the right tooling.
• Data folks need to see the bigger picture to understand business needs better.
• Don’t try to build communication streams through strict processes - that’s where we get too specialized.
• Data is not a production line. We need to keep an understanding over the entire value chain.
• The proof is in the pudding. The pudding being automation of processes.
• «Worst case something looks right and won’t break. But in the end your customers are going to complain.»
• «If you automate it, you don’t have anyone that raises their hand and says: «This looks a bit funny. Are we sure this is correct?»»
• You have to combine good-enough data quality with understanding of the process that you’re building.
• Build in ways to correct an automated process on the fly.
• You need to know, when to sidetrack in an automated process.
• Schema changes are inevitable, but detecting those can be challenging without a human in the loop.

Unlock the secrets to the legal sector's digital transformation with our latest guest, Peter van Dam, Chief Digital Officer at Simonsen Vogt and Wiig. We promise you a journey into the innovative realm where data management and artificial intelligence redefine the traditional practices of law. Peter offers us a glimpse into his professional trajectory from legal tech provider to digital pioneer, emphasizing how data and application integration are revolutionizing legal services.

Discover the unique challenges and opportunities that come in a new era of digital sophistication in the law profession. Our conversation dives into the significance of roles like Chief Digital Officer in shaping a progressive future for a historically conservative field. We share stories of how to catalyze excitement for technology among legal eagles and clients alike, and we explore the strategic vision needed to navigate the balance between innovation, confidentiality, and compliance.

The episode examines the expanding potential for automation within legal services. Here, the focus shifts to how digital tools enhance, rather than replace, the human expertise of lawyers. Rounding off the discussion, we shine a light on how law firms are upgrading their data access protocols, ensuring that sensitive information remains under lock and key.

My key takeaways:

LegalTech

• Legal might seem as a conservative section, but on the insight everyone, from lawyer, to staff to paralegal is working on continuous improvement and growing more and more efficient.
• Low code, citizen development, hackathons, etc. are ways to quickly iterate on ideas and applying them.
• Internal and external marketing of the importance of technology in law is important.
• You have to lift those first step barriers, an get first hand knowledge of using AI and tech, to really embrace it.

Document & Content Management

• Optimizing interoperability and data exchange between different document management tools is an interesting journey.
• There is huge, untapped potential in unstructured data.
• The biggest challenge for document management is to find ways of cutting through the noise of redundant, obsolete, and trivial data.
• You need a certain quality of data sources to utilize LLMs and genAI.
• Methods of AI Governance need to work in concert with classical methods of data and Information Management.
• Data volumes are growing exponentially, and so does the cost. Records Management is important to structure data, create retention schedules and ensure that datahis available according to need and regulatory requirements.

AI and trends in Technology

• Find a way to balance need and investment in a way that you have the relevant tools available when needed but are not exclusively reliant on those tools.
• Development in technology, data, AI, sustainability, etc. creates more demand for legal services - technological development accelerates legal demand.
• For the practice of law, human interaction is vital. There might be a more differentiated service offering going forward, but human interaction with a lawyer will still be at the core of the practice.

The role of CDO

• The role of CDO is challenged, because it can mean so many different things in different environments.
• A Chief Digital Officer is important to get enthusiasm about new technology and to actually get it implemented and used.
• Communication is the most important skill and tool.
• As a CDO or Digitalization department you need to think 6 month ahead, elicit trends and find out what can become relevant for your firm.

• «When we talk about product mindset its all about how we work as a team.»
• It is important to ensure aligned autonomy, when working in a compartmentalized organization with product management. You are delivering a piece to the totality.
• «Now, we need to have an adaptive Strategy everywhere.»
• Digital is the totality, the ecosystem that you are creating. Data has to flow in that ecosystem.
• There is no digital without data, but there is data without digital.
• People are coming and going within your company, and are bringing data along.

One Strategy

• The goal of strategy is to create one clear direction for the company.
• If you have multiple strategies, you will pull people in different directions.
• Break down strategies in where you deliver the value.
• Organizational models and actual value creation do not always overlap.
• There are transversal strategies that stretch throughout the entire organization (eg. HR, product), whilst there are specific strategies that strive towards one goal (eg. marketing).
• You can no longer afford to have business and digital separated.
• Digital tools do not deliver any value unless they are part of a process and used by the business.
• Ensure that you measure that matters, what is the value that you are creating.
• You need to work on a mindset for the totality of the organization, not a digital vs business mindset.
• OKRs can help to get that forward leaning mindset and to become more process oriented.
• The strategic part is really the choices you have, while plan is the actions you take towards these choices.
• A plan is about creating transparency in the company, so everyone understands what they are delivering and how it fits together.
• You need to have a goal to work towards. Your Strategy is laying out the logic to get there.
• «Culture eats strategy for breakfast»

• Think about how you make data available - design thinking, finding new was to visualize data is important for inclusion.
• Its the responsibility of public sector to make as much of their data openly accessible.
• The role of CDO is important, because you need someone to see the bigger picture and how data effects everyone.
• Managing data, especially for public services, comes with a social responsibility.
• The difference between a CDataO and a CDigitalO - data requires a different skill set than digital transformation.
• Data professionals need to ask the correct questions in a service design process.
• Data access and ownership should be discussed already at the design phase.
• People have expectations towards digitalization in public sector: you want to access the data you need at the time you need it, from where you are.
• «Data is a valuable societal asset, where we all have the shared responsibility to ensure data quality.»
• Data quality is a precondition for using data to its purpose and its potential.
• You need to think digital universal accessibility, when it comes to data and visualization.
• With data stories the city of Reykjavik uses visual, verbal and sound effects to convey messages through data.
• There is a focus on using accessible language, and to not over-complicate texts.
• Data, especially in the public sector, has not been collected and curated with trains AI language models in mind.
• There is a great risk that historical biases and previous lack of awareness is transmitted into our models.

Data Buffet:

• Open data visualization platform and an open data portal.
• Make as much of the city’s data easily accessible.
• Access to a wide variety of correct and reliable data is an enabler for innovation in societal services.

• 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.

• 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?

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!

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

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?

• 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/

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/

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

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

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

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

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.

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.»

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

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?

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 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

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/.

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.

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.

• «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

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.

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).

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.

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.

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

 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.

 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.

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.

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.

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.

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.

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.

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

 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".

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.

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.

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? 

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.

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.

•  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?

• 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?

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

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.

 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.