The Norwegian Continental Shelf has always been shaped by better data, better ideas and better decisions. From the earliest exploration wells to today’s digital subsurface workflows, progress has come from connecting observations across disciplines and turning scattered data into trusted understanding.

This presentation follows Equinor’s subsurface data journey and explores how a stronger data foundation can help unlock the next chapter of value on the NCS. The easy barrels are no longer easy. Future opportunities are smaller, more subtle and more complex, which means we need greater precision: better data, better integration, better technology and faster decisions.

But this is not just about new data or new tools. Some of the most valuable insight may already exist in old reports, legacy databases, physical samples, spreadsheets or specialist knowledge across the organisation. The challenge is to make that information easier to find, trust, connect and use, across applications, disciplines and teams.

By liberating data from silos, improving quality, creating clearer ownership and enabling more connected workflows, we reduce friction in technical work and increase the speed of insight. Emerging AI capabilities make this even more important, because the quality of the answer will only ever be as good as the data and information behind it.

In line with the conference theme, “Connecting Data, Information and People,” this presentation argues that future success depends on bringing those three things closer together, strengthening the creativity, judgement and challenge that come from people working across disciplines.

Ultimately, the future of the NCS will depend on our ability to keep generating new ideas. A trusted data foundation can help make that possible.

The Open Subsurface Data Universe (OSDU) promises a single, open, vendor-neutral home for subsurface and well data — but turning that promise into a working reality inside an operating company is a journey, not a switch you flip. In this presentation, Aker BP shares an honest account of our OSDU adoption so far: the wins worth celebrating, the obstacles we hit, and the work still ahead of us.

We will walk you through where we are today — what we have deployed, which data domains we have onboarded, and how OSDU is starting to change the way our teams find, trust, and use data. We will be open about where we struggled: the effort of data ingestion and mapping, aligning legacy systems and ways of working with OSDU's data model, governance and ownership questions, and the organizational change that technology alone cannot solve.

Looking forward, we will share what is on our roadmap — the capabilities, data types, and use cases we are prioritizing next — and what it will take to get there. Beyond our own four walls, we will reflect on our collaboration with other companies in the OSDU Forum: what works well in working together on a shared platform, what we have learned from others, and where we would like to see the community and the standard improve.

High-quality data products rarely start in the data platform, they start in the operational systems where data is created. Many modern platforms struggle not because of technology, but because of weak upstream data modeling and the lack of of metadata.

Data models and metadata together form the foundation. Models give data its structure and meaning. Metadata gives it context — legal basis, security, quality, ownership, and the full range of properties needed to govern data responsibly. Modeling has its own discipline, but the broader work of metadata across data quality, legal, security, and ethics requires organization through an operational model. "At the Norwegian Tax Administration, we complement the modeling foundation with an operational governance model: data leaders (stewards), legal expertise, and analytical capability are placed close to the domains and their data products, supported by structured collaboration around domain knowledge.

Together, the foundation and the organization turn upstream investment into downstream reuse, automation, and interoperable data products that consumers can actually understand and use.

Key takeaways:

- Information models and metadata together form the foundation for interoperable, well-governed data products

- Modeling discipline gives structure and meaning — semantics are more stable than technology

- Operational governance models matter — data leaders, embedded expertise, and collaboration around domain knowledge

It wasn’t too long ago when we were working with, or better said: constrained by the adage of: Garbage in, Garbage out. Recently I came across that again, but now in the context of modern day AI: “with all the AI, we still deal with Garbage In: Garbage Out”

We live in an age where economies become circular and a world player like Renewi uses the “Waste No More” in their company strap-line. And with modern AI, that is true for data as well.

In this talk we’ll talk about how, with these days of AI, Data Management has fundamentally changed. How we can actually embrace the data we used to call garbage, but were afraid to throw away “just-in-case” For this purpose we will dive a little bit into the following topics:

• What is data management in this age of generative AI?

• Does the adage of Garbage-in and Garbage-out still hold?

• A little bit of data-architecture to help you out

* A little bit of OSDU Data Standards in the mix as well

• And some cool projects you can download and run for yourself to become the AI enabled data manager

• And finally: how you too can stop worrying and love your data

xx xx xx