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

A deep dive into the complex balance between data, ethics, and commercial operations in a modern media organizations. Robert Børlum-Bach from JP/Politikens Hus takes us on a fascinating journey through the media industry's data landscape, where AI is revolutionizing journalism while simultaneously raising critical questions about democracy and public discourse.

What does data management maturity actually mean in an organization? Robert challenges traditional thinking as he explains why his team avoids using the very term "maturity" and instead focuses on visibility and understanding the real challenges faced by teams. He introduces the innovative "stamp model," which visualizes how different departments require varying levels of support on their data journey.

In a world where media organizations must balance dependence on major tech platforms with the need for editorial integrity, Robert shares practical approaches to data contracts and data products that bridge the gap between technical and business needs. We explore how JP/Politikens navigates between centralization and decentralization with a "demarcation line" that is designed to be broken when teams demonstrate higher maturity.

Whether you work in data, media, or leadership, this conversation offers new perspectives on how organizations can develop a healthy data culture that balances innovation and standardization. The most important advice? Stop telling teams they have "low maturity"—start listening to their challenges and questions instead. Because that’s where true data governance begins.

Here are some key takeaways:

Social Media

• Social media is originally not designed for news consumption. Journalists and media houses need to ensure the same quality of news on SOME.
• There is an unequal dependence between media houses and SOME platforms.
• Think about then ethical implications of driving traffic to your side and platform through click bate, or rage bate, which might nit be the way you want to present your organization.

Maturity Assessments

• If data is a support function to the business it might be strange if you demand to assess the businesses dat maturity,
• Sometimes maturity is reflected in the questions you get back from whom you are assessing.
• There is a natural variety of maturity between different parts of the business.
• Showcasing this variety can be a good step towards learning from each other.
• Start with a common baseline, but try to adjust your approach in how you support the different business units to their level of maturity.
• 4 deliverables of a maturity assessment:

1. As-is
2. To-be
3. How to close the gap between as-is and to-be?
4. Measure your progress 

How to increase maturity?

• Data governance sets up the playing field, but you need to play on the field.
• Set a baseline to get a starting point. This needs to be a common determinator that is not too low and not too high, but relevant for all business units.
• «We are seeing more and more data enabling teams.»

Strategic perspective on maturity

• Maturity assessments are a snapshot in time and the world around you is constantly evolving.
• Assessments should be an organic and living thing.
• Keeping updated information on maturity should be the teams themselves.

Federated and domain oriented

• Stated with data contracts to identify producer and consumers and expectations between them.
• Understand usage, requirements, ans success criteria for data.
• Contract as skeleton and products as the flesh around it.
• Understanding roles and responsibilities is a constant challenge you need to face.