Building a cohesive, scalable data foundation requires translating technical architecture directly into active business value. Bringing together the focus areas of the Data 2030 Summit provides a clear roadmap, moving from the structural plans that unify scattered infrastructure to the operational models that transform raw assets into high-yielding data products.
Establishing the Strategic Vision
- The journey begins at the leadership level on the Data Strategy & Governance Stage. Before modernizing tools or writing pipelines, organizations must align executive leadership around data ownership, compliance, and culture. This track focuses on setting up the overarching framework needed to support rapid AI adoption while safeguarding critical enterprise assets.
Strategic track highlights:
- Operationalizing AI-Ready Governance: Aligning enterprise data stewardship, metadata, and ownership models to safely support autonomous AI operations and workflows.
- Embedding Quality & Responsibility at Scale: Practical frameworks for enforcing continuous data quality and responsible governance without stifling organizational agility.
- Modernizing Organizational Operating Models: Restructuring teams and governance frameworks to foster cross-functional collaboration and enterprise-wide data fluency.
Speakers address the operational models required to transition from traditional compliance into active, value-driven governance, ensuring that data fluency, data security, and strategic alignment extend across every business unit.
Smarter Infrastructure & Interoperability
- The Data Platform, Context & Architecture Stage is custom-built to inject deep meaning into disconnected technical infrastructure. Engineered specifically for the modern Cloud Architect, platform director, and data engineer, this track dives straight into the blueprints of modern Enterprise Data Architecture. Moving past basic debates between centralized Data Warehouses, distributed Data Lakes, or hybrid Cloud Infrastructure, speakers will demonstrate how to seamlessly stitch these environments together into a high-performance ecosystem ready for intelligent automation. Through deep technical explorations into deploying an elegant Semantic Layer, leveraging Knowledge Graphs, and mastering Master Data Management (MDM), this stage solves the complex puzzle of regional Data Interoperability, ensuring automated applications access clean, contextualized information across fragile, siloed systems.
Technical track highlights:
- Architecting the Modern Semantic Layer: A 30-minute guide to translating cryptic database schemas into clear, standardized business terms that models can understand instantly.
- Platform Deep Dive: MDM in a Multi-Cloud World: How to maintain an unshakeable, golden record across complex, highly distributed environments without stalling system performance.
- Unlocking the Power of Data Fabrics: Top engineers showing how an integrated data fabric dynamically handles query processing across separate cloud platforms.
Once an interoperable, context-rich infrastructure is established, organizations can elevate these reliable data flows into actionable, value-generating assets.
Shifting from Legacy Storage to High-Yielding Data Products
- That brings the narrative to the next evolution on the journey. On the Data Products, Data Quality & AI Enablement Stage, the conversation shifts completely away from passive storage costs and lands squarely on active revenue and efficiency gains. To truly democratize decision intelligence across an organization, the time has come to abandon sluggish, centralized pipelines and lean into decentralized models like the Data Mesh. This highly operational track uncovers the exact mechanics of treating corporate assets as standalone, reusable Data Products. By implementing formal Data Contracts between internal engineering teams and business domains, organizations can eliminate broken pipelines and schema changes. Combined with real-time Data Observability platforms to trace Data Lineage and catch anomalies, this track delivers practical strategies for robust DataOps and complete AI enablement.
Tactical track highlights: 
- Enforcing Data Contracts in Production: A 30-minute operational blueprint on creating and automated checking of pipeline agreements between teams.
- Moving from Pipeline to Product: Real-world transition stories from major Nordic enterprises detailing how engineering teams restructured into domain-driven data mesh setups.
- Advanced Observability and Lineage Tracing: How to get complete visibility across ingestion streams to confidently guarantee the integrity of AI inputs.
By aligning architectural interoperability with a product-centric delivery model, enterprises can eliminate operational friction and unlock the full potential of their data ecosystem. Together, these tracks provide the definitive blueprint for moving beyond fragile storage silos toward a resilient, intelligent enterprise ready for the future of AI and decision automation.
Data 2030 Summit 2026 | October 21–22, 2026 | Stockholm
Position enterprise data architecture for the era of intelligent automation. Review the Agenda or reserve seats on the Official Registration Page.
