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MetaDAMA - Data Management in the Nordics

Winfried Etzel VP Activities DAMA Norway

This is DAMA Norway's podcast to create an arena for sharing experiences within Data Management, showcase competence and level of knowledge in this field in the Nordics, get in touch with professionals, spread the word about Data Management and not least promote the profession Data Management. / Dette er DAMA Norge sin podcast for å skape en arena for deling av erfaringer med Data Management​, vise frem kompetanse og kunnskapsnivå innen fagfeltet i Norden​, komme i kontakt med fagpersoner​, spre ordet om Data Management og ikke minst fremme profesjonen Data Management​.

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Top 10 MetaDAMA - Data Management in the Nordics Episodes

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

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

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

The Insight Factory

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

Product Management

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

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

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

Here are my key takeaways:
The Airline sector

  • Airline industry is reliant on partner «A Flight is just a subset of an end-2-end journey»
  • Data privacy and ethics are important topics for Norwegian and are faced with a systematic approach with an aim for automation.
  • Norwegian is building a cloud based analytical platform to ensure a greater visibility of the data analytics setup.
  • The first improvement should be on data discoverability, closely connected to data observability.
  • «A Data Catalog will raise awareness of what we have of data assets.»
  • There is a clear goal to ensure an automated observation of all data assets in Real time.
  • A central team needs to be able to deliver cross-domain use cases, also across domains with different data maturity.
  • «With this crisis-domain approach we are putting away some of the legacy discussions.» We can engage with each domain.
  • It is ok to have specific crawlers on local data, but you need to synchronize it into the central data catalog.
  • The organization needs to have a way to stay aware of everything that is produced.

Data Availability

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


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


  • How can you retain knowledge in a distributed architecture?
  1. Ensure domain knowledge is fostered in the domains. Build a documentation repository
  2. Infrastructure as code. Technical knowledge supplied with context
  • Domain knowledge is the most tricky and most difficult to replace
  • Great documentation can be both motivating and time saving. Motivating to reach a higher standard and time saving for problem finding, onboarding, etc.
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"The data lifecycles collides with the system lifecycles. It’s a classic."

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

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

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

Here are my key takeaways:

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

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

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

Here are my key takeaways:

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

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

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

Here are my key takeaways:

The Startup / scaleup setting

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

The Business Value of Data

  • You need to find balance in delivering fast (time to insight, speed and accuracy).
  • Speed is there no matter what, you need to ensure the right level of accuracy at that speed.
  • If you want to have impact, you need good data quality. If not, the numbers will not be trusted or used.
  • The goal is data that is trustworthy and easy to use.
  • Long term commitment to Data Quality and Data Governance, whilst speedy day-to day operations with little time to insight, have been key to success.
  • The role of CDO is to ensure that be business derives value from Data Science and Analytics, that counts for a Startup as much as for a large enterprise.
  • The combination of business and engineering becomes more and more important.
  • The data stack is moving towards speed and scalability, which makes it easier to handle large volumes of data.
  • The key innovation will happen on automated data quality, self-serve analytics, even API-contracts for click-steam data, as well as tracking and lineage.
  • Data is not always the goal. It can be a means to create value.
  • For Kahoot! And reMarkable, data is used to make a better product and to improve the user experience, not to monetize or even mine that data.
  • User should see and feel enhancements in the product through their provided data right away.
  • To show the business value of Data Management you have to argue with "What if...?" What if we don't do it? You need to show the consequences of not acting to the C-level executives.
  • Data quality problems grow exponentially over time. If you do not act, data-driven decision making will eventually be replaced by gut feeling.
  • Not having control and metrics in place is like going to the casino: You can win once or twice3, but in the long run you will lose.
  • Data products should have an assigned value - so Data as a product can help us argue for the business value of data.
  • Step 1 is to treat data as an API contract.
  • Self-service is dependent on a good structure and governance at the data producer side.
  • Self-service can cover 60-70%, not everything can be self-service.
  • A dataset tells the story of what happened in the business. You need business context to understand it, you need to phrase a business question into a data question, you need to know how to manipulate the data correctly.
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Welcome to Season 2 of MetaDAMA!
The first episode is dedicated to DAMA. I talked to Marilu Lopez, Leader of the Presidents Council for DAMA, Peter Aiken, President of DAMA International, and Achillefs Tsitsonis, President of DAMA Norway.
We had a great conversation about the vision and mission of the voluntary, vendor-independent organization DAMA and its value for the knowledge worker community, as well as society as a whole.
We also talked about what Data Literacy is, how we can operationalize the term, and to what means. The best definition of Data Literacy so far is “the ability to read, write and communicate data in context, with an understanding of the data sources and constructs, analytical methods and techniques applied, and the ability to describe the use case application and resulting business value or outcome.”
Here are my key takeaways:

The Data Quest

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

The aspect of Change

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

Data literacy in society

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

Knowledge workers

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

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

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

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

We talked about:

Eu digitalization strategy

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

Eu regulations

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


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


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

What I've learned:

  • EU is trying to take an active part in democratize data.
  • EU defines data is a non-competitive good.
  • A data collector has no exclusive rights to the data collected.
  • All open governmental data should be accessible for everyone
  • The Data Act arranges for private sector data, to secure sharing and end user rights.
  • Private organizations will have a duty to share information with public sector in times of crisis
  • These three regulations for sharing data are interesting to see in combination with the AI Act, that ensures use of data that now is available.
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Are we living "The Truman Show"? I had a chat with Mads Flensted Hauge, Chairman of DAMA Denmark and DPO and Data Governance Manager at a Danish health care provider.

We looked at Data Privacy from four different perspectives:

The society perspective

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

The company perspective

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

The data worker perspective

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

The personal perspective

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

Here are my key takeaways:

  • Convenience drives change and digitalization in public sector - and sometimes privacy becomes the victim for efficiency.
  • To apply GDPR you need to apply different knowledge areas of Data Management. That is why these two are closely combined.
  • It has always been hard to show the value of a data management journey from the start, but with GDPR and the ominous notion of fines, data management got the ear of the C-level.
  • Since GDPR is framed as compliance, it leads companies to do just the bare minimum to be compliant.
  • GDPR forces you to get a deeper knowledge about your business.
  • There is an ethics dimension to data privacy, and DPOs are on the forefront to instigate this ethics site.
  • A DPO does not just write policies and procedures but must navigate company culture to promote ethics and privacy actively.
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Everyone who ever played with Legos knows that the bricks don't just fall into place. It takes dedication, finding the right brick at the right time, maybe even sorting your bricks.
The same goes for Data Governance.
So whom better to ask about Data Governance, then the Director of Data Governance at the Lego Group, Michael Bendixen?
Michael was really clear in his message to all of us, by given us his 13 commandments for Data Governance:
1. Drop the data management "lingo".

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

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

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

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

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

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

8. Data governance is not about technology and tools.

9. Communication is key.

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

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

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

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

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

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

Here are my key takeaways:

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


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

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

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