«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 $$$.

"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.