«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 $$$.

"Vi modellerer for å forstå, organisere og strukturere dataene." / "We model to understand, organize, and structure the data."

This episode with Geir Myrind, Chief Information Architect, offers a deep dive into the value of data modeling in organizations. We explore how unified models can enhance the value of data analysis across platforms and discuss the technological development trends that have shaped this field. Historical shifts toward more customized systems have also challenged the way we approach data modeling in public agencies such as the Norwegian Tax Administration.

Here are my key takeaways:
Standardization

• 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.

"Den største utfordringen, det viktigste å ta tak i, det er å standardisere på nasjonalt nivå. / The biggest challenge, the most important thing to address, is standardizing at the national level."

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.

«Data Management is an interesting one: If it fails, what’s the feedback loop?»

For the Holiday Special of Season 4, we’ve invited the author of «Fundamentals of Data Engineering», podcast host of the «Joe Reis Show», «Mixed Model Arts» sensei, and «recovering Data Scientist» Joe Reis.
Joe has been a transformative voice in the field of data engineering and beyond.
He is also the author of the upcoming book with the working title "Mixed Model Arts", which redefines data modeling for the modern era. 

This episode covers the evolution of data science, its early promise, and its current challenges. Joe reflects on how the role of the data scientist has been misunderstood and diluted, emphasizing the importance of data engineering as a foundational discipline.
We explore why data modeling—a once-vital skill—has fallen by the wayside and why it must be revived to support today’s complex data ecosystems.
Joe offers insights into the nuances of real-time systems, the significance of data contracts, and the role of governance in creating accountability and fostering collaboration. 

We also highlight two major book releases: Joe’s "Mixed Model Arts", a guide to modernizing data modeling practices, and our host Winfried Etzel’s book on federated Data Governance, which outlines practical approaches to governing data in fast-evolving decentralized organizations. Together, these works promise to provide actionable solutions to some of the most pressing challenges in data management today.  

Join us for a forward-thinking conversation that challenges conventional wisdom and equips you with insights to start rethinking how data is managed, modeled, and governed in your organization.

Some key takeaways:

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.

«We want to make data actionable.»

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.

«I think we are just seeing the beginning of what we can achieve in that field.»

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.