MetaDAMA - Data Management in the Nordics

«We need source criticism more then ever now.»

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.

«Hvis folk tror at data faget er forskjellig mellom industrier så er det ikke det. Data er Data. Forretningskunnskapen er forskjellig. / If people think data work is different across industries, it isn’t. Data is data. Business knowledge is what changes.»

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

«A lot of things break with scale.»

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.

«It can’t be a centralized team. That is too dangerous, because you don’t know the business domain.»

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.

«Det at det er en team sport; det tror jeg er i hvert fall noe data verden kan lære av (software development). / The fact that it’s a team sport; that’s definitely something the data world can learn from software development.»

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.

«I consider this Data Governance as a cure. (…) Data Governance can make things better.»

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.

«Sometimes it feels like you have CIOs going on Julia Robe’s in Pretty Woman spending sprees.»

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.»

«You bring in the knowledge of what works in real life and what doesn’t. That is actually what you are being paid for.»

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.

"Dataetik handler også om den måde, vi opfatter brugeren og mennesket, vores demokrati og vores samfund på." / "Data ethics is also about how we perceive the user and the human being, our democracy, and our society."

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

«Leadership is about sowing the common vision and the common way forward, bringing the people with you.»

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 $$$.