This hands-on ECIM 2026 workshop is designed for data managers, data professionals, and energy domain experts who want to work smarter, faster, and with far more confidence in OSDU. Building on the success of previous OSDU bootcamps, we are raising the bar — combining practical workflows, live demos, AI-assisted “vibe coding,” and real industry use cases that solve everyday challenges.

Over three energetic hours, participants will explore how modern tooling and AI-driven workflows can dramatically improve data quality, accelerate validation processes, and simplify interaction with OSDU services and external APIs.

Data management professionals are fluent in the how of the discipline: catalogs, lineage, quality rules, contracts. We are often less fluent in the why. We tell stories about pipelines and platforms when sponsors need stories about production regularity, reporting confidence, safety, maintenance execution, and trust in operational decisions. The result is familiar: work is treated as technical overhead instead of a business capability. This three-hour, hands-on bootcamp closes that gap. Drawing on best practice in Storytelling, stakeholder management, and visualization, participants learn to translate data work into language that executives, domain leaders, and technical teams can act on. We move from tool-first descriptions to outcome-based narratives that state the operational or reporting problem, the decision at stake, and the measurable result. We map the room with the power-interest grid, identify the three sponsors every initiative needs, and draft a one-paragraph case a sponsor can repeat to a peer. Every module is interactive. Tables and pairs work, pressure-test, and role-play. Participants leave with a drafted Minimum Viable Sponsorship package, a tested pitch, and one concrete next conversation to run in their own context.

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.

National Data Repositories (NDRs) have long served as trusted systems for preserving, governing, and distributing subsurface data. For decades, success was measured by the ability to ingest, manage, and provide access to growing volumes of information. Today, a new opportunity is emerging. As artificial intelligence, cloud-scale computing, and standards-based data ecosystems mature, the NDR can become more than a repository. It can become the foundation for a new generation of data-driven workflows, analytics, and decision support across the energy industry. This presentation explores the evolution of the Norwegian National Data Repository, DISKOS, and examines how modern data architectures can transform national repositories from systems of record into platforms for intelligence. Drawing on operational experience from managing one of the world's largest subsurface data repositories, the presenters will discuss how trusted data, common standards, and scalable architectures create the conditions for advanced search, automated quality control, digital workflows, AI applications, and future agent-based systems. The session will also examine the strategic value of NDRs beyond regulatory compliance and archival storage. As energy companies seek to accelerate exploration, improve recovery, support carbon storage initiatives, and reduce operational risk, national repositories are uniquely positioned to provide a shared foundation for innovation across the industry. Attendees will gain practical insights into the technologies, governance models, and architectural decisions that can help transform NDRs from passive data stores into active enablers of industry intelligence.

Are we trying to run before we can walk? While AI offers vast opportunities for workflow efficiencies and enhanced insights, it needs to be built on a sound data foundation. Subsurface data are difficult and expensive to acquire, manage and store. To maximize the return on these investments, we need to ensure that the right data are at our fingertips; whether we’re running traditional subsurface workflows or complex machine learning algorithms, data is currency. AI adds to the jeopardy of poorly managed data, owing to its “black box” nature. We can rapidly build models and make decisions based on vast quantities of data that may never have been curated or verified. As we know it’s “garbage in, garbage out,” so the consequences could be disastrous. To mitigate this risk and safely leverage the power of AI, we need a subsurface data ecosystem that is fit-for-purpose — the right data, in the right place, deduplicated, curated. We can reach this goal by first scanning all data repositories to identify issues, then building a data strategy suited to our aims, and finally using a tech-driven approach to implement this accurately and efficiently. Only then can we fully embrace the value of AI without fear of the consequences.