MetaDAMA - Data Management in the Nordics

“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: 

• 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.

• 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.

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.

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?

Challenges

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

Possibilities

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

What I've learned:

• EU is trying to take an active part in democratize data.
• EU defines data is a non-competitive good.
• A data collector has no exclusive rights to the data collected.
• All open governmental data should be accessible for everyone
• The Data Act arranges for private sector data, to secure sharing and end user rights.
• Private organizations will have a duty to share information with public sector in times of crisis
• These three regulations for sharing data are interesting to see in combination with the AI Act, that ensures use of data that now is available.

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.

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.

Can you put a value on quality data? Definitely! But how?

Who better to ask than Kristin Otter Rønnevig and Espen Hjelmeland? They both dedicated their master thesis to explore this topic with really interesting results. Their thesis "An Investment Perspective on Data Quality in Data Usage" asks "How can an organization optimize its investments in data quality?"
To answer the question Kristin and Espen posed three Research Questions:

1. What are the main drivers for willingsness to pay for data quality?
2. What part of data quality aspects should one invest in to maximize opportunities and minimize risk?
3. How can the quality of data be improved, and what are the costs?

Here are some of their key observations, I found particularly interesting:

• Increasing confidence in data in order to enhance company operations is one of the
  motivations for willingness to pay for data quality.
• A greater level of knowledge in data quality gives a higher willingness to pay for data quality.
• Demonstrating to the customer how quality improvements may enhance profit at each step
  of the value chain contributes to the willingness to pay for data quality.
• Prioritizing improvements are based on the time and cost of the particular improvement. A way to reverse-engineer the impact of various improvements
  can help to identify how to improve the quality.
• It is critical to invest in a professional, highly skilled team environment to succeed
  in data quality investments.
• To cope with data quality, it is necessary to invest in security.
• To know whether the organization is investing in data quality optimally, it will need a deep understanding of the business and experience with it.

Espen Langbråten is leading Europris' bet on digital platform, data warehouse, and analytics.

We had a great talk about everything data, but at the core we discussed three questions: 

1. How do you build your team?
2. How to recruit and build competencies in an ever-changing environment whilst keeping an eye on cost?
3. What resources do you need in house? What can you outsource to a consultancy?

Here are my key takeaways:

• It is not only hard to find competency in the marked and recruit, but also to retain valuable knowledge in the company,
• Give your employees the responsibilities they want and need to thrive on the job,
• Mentor-programs give the new employees a change to get integrated fast and at the same time the existing employees a boost of their sense of value and the chance to share their knowledge
• It is important to recruit basic technical skills and a hunger for learning, business acumen and domain knowledge is something you learn on the job
• You need to have core-competencies in house and work in close relation with your consultants to transfer knowledge
• The conceptual and even logical architecture needs to be owned in house
• Most problems the business faces are process based. Tech can aid to solve these, but it is seldom a tech problem by definition

What has been the hot topic in Norway this winter? Energy prices. The prices for energy skyrocketed. But how does this relate to data? Can we save energy with Data Management? The answer might be on the infrastructure side.

And since "the color of Data centers is Green!", I took this question to Espen Bjarnes, who works as VP Sales for Green Mountain, one of the most sustainable data centers in the world according to Data Center Magazine.

 Green Mountain was founded in 2009 and opened their first Data Center in Rennesøy (Stavanger) in 2013 in an old NATO facility.

Norway has an unique standing compared to other countries, because 

1. we use close to 100% renewable energy sources.
2. We have cold climate, which reduces cooling expenses
3. We have relatively low energy costs

 With that, there is a chance to reduce CO2 emissions by storing data in Norway.

An on top of that there are political incentives in place to support the data center industry in Norway, such as reduced taxes. So, for Norway, "Data Center is the new oil" - because this might become the driving force for Norwegian industry going forward.

The amount of data in the world doubles every second year, streaming of live data grows exponentially. All this needs data center to support. That translates to a natural growth of the data center industry. At the same time there is a potential for the Nordic data center industry to take the lead, due to sustainability advantages in the Nordics. Espen calls it the "sustainable edge".

How can you democratize data from Norway’s public services?

What does it mean to have a user centric approach to public services?

What needs to be in place to organize your organization, the public services as a whole, while maintaining focus on good services to your citizens?

I had the pleasure of chatting with Gustav Aagesen, Chief Data Officer at Lånekassen. The Norwegian State Educational Loan Fund, who is celebrating its 75th anniversary in 2022. 

Gustav started as Information Architect in Lånekassen in 2012, became analysis manager before taking the position as CDO at Lånekassen. Today, he sees his responsibility in supporting the entire organization and to institutionalize information management.

 We looked at 3 different Perspectives on “Orden I eget hus” a Data Governance framework for public services and Democratization of data in Norway:

 1.       Lånekassen. An internal view on automation and structures, data citizenship, and culture.

2.       Public Norway. A perspective that includes valuable work on the common data catalogue, “orden i eget hus”, common concepts and datasets.

3.        Citizen perspective. Thoughts about finding ways to make use and consumption of data easier and with less barriers, and provide citizen-centric services.

Here are some of my key takeaways:

-          Information Management is not a goal by itself, but a way to create and gain value

-          Information Management has to start with a purpose!

-          Data and Information has a longer lifecycle then applications.

-          Data Lineage is important, with the objective in mind to create services and gather data based on consumer demands, or the needs of the citizens. 

-          The value for the citizen is an end-to-end- value stream that is traceable and can create trust in the data.

-          If you want to give the citizens access to proactive public services, information has to flow between different institutions.

-          It seems easier to get funding for technology then work-processes. That is also a reason automation is in high demand.

-          Data Sharing needs to be balanced with trust and privacy to ensue good solutions for the consumer and citizens.

How can you set up your services to have technology support your data journey? How do you work with tech procurement? And what is the impact of Data Mesh, especially for data governance? How to evaluate which consumer need to satisfy first? How to prioritize what is important to create value?

I chatted with Bente Busch, who is leading the Service Platform Team at NAV, the Norwegian Labour and Welfare Administration. 

Bente and her team are responsible for platform services for the product teams at NAV, the application platform, design toolbox, as well as good practice around digital product development.

Bente is driven by being a product director, helping the users of data to minimize their cognitive burden and deliver an attractive and easy to use service.

Modernization of digital systems and ways of working has been a priority for NAV. In the last 5 years they fundamentally changed the way they deliver projects and programs, by focusing on ongoing product-development. This was done in combination with breaking down technological one-size-fits-all suits, to a micro-service oriented architecture.

With that, NAV provides technology and systems tailored to the consumer-needs.

Here are some of my key takeaways:

• Good technological services can help foster a data culture.
• The goal is for the consumers and citizens to connect with public services easier, faster, and more user friendly.
• Framework agreements can help to liberate resources, gain agility, and tailor to specialized needs.
• Data analysis can help in procurement processes to ensure consistency and integrity in future needs.
• NAV views the consumer needs as digital products to be developed and design products that can be improved and changed if needed.
• Specific solutions that need to be tailored to provide best possible service to the consumer needs to be built, whilst more generic services can be based on "our-of-the-box" solutions.
• Open-source should be a stable part of the architecture, both usage of open-source libraries as well as solutions
• Data is moving in the same direction as software, to a more decentralized architecture.
• A network of actors and collaborators needs to agree and enforce governance in a distributed architecture. 
• Technology can help to codify and automate Data Governance. "Lett å gjøre rett" - Easy to make the right decision.
• The data domains in NAV are building autonomous architectures. They are independent in their architectural decisions but need to account for their surroundings.
• NAV is inspired by "team topologies" to create "APIs" between teams.