Data management is becoming a strategic discipline

For decades, data management was treated as a technical necessity: store the data, secure it, back it up, and move on. That mindset no longer holds. According to SURF’s Tech Trends 2026 report, data management is rapidly turning into a strategic discipline that determines how education and research can collaborate, innovate, and retain autonomy in a geopolitically charged digital world.

The reason is scale and complexity. Scientific instruments such as the Large Hadron Collider or the Vera C. Rubin Observatory already generate tens of terabytes of data per day. Future experiments will push that to hundreds. At the same time, AI systems, IoT devices and sensor networks are adding new layers of automatically generated data. The challenge is no longer collecting data, but making it usable, trustworthy and sustainable over time.

FAIR as infrastructure, not aspiration

One of the most important shifts described by SURF is the growing adoption of FAIR principles — making data Findable, Accessible, Interoperable and Reusable — as a foundation for European digital ecosystems. FAIR is no longer limited to datasets. It is increasingly applied to software, workflows and scientific models through concepts such as FAIR Digital Objects and knowledge graphs.

This matters because it changes how research operates. Instead of isolated datasets stored in silos, FAIR enables interconnected webs of data and services that can be automatically interpreted and reused across domains. For education, this opens the door to adaptive learning environments that link learning objectives, materials and competencies. For research, it accelerates discovery and improves reproducibility.

FAIR, however, is not a technical checklist. It requires shared standards, metadata discipline and long-term commitment. Without coordination, FAIR risks becoming another label rather than a working infrastructure.

Data spaces: trust by design

As data sharing becomes essential, data spaces are emerging as a key architectural concept. These environments allow organisations to share and use data under agreed conditions, while retaining control over privacy, security and compliance. The European Commission is actively promoting common European data spaces as part of its digital strategy.

SURF highlights that large-scale data sharing often fails not because of technology, but because of trust. Standardised data space architectures are meant to address exactly that problem. They embed governance, access control and legal compliance into the technical design.

For education and research institutions, this has tangible implications. Data spaces can enable AI-driven learning while safeguarding data sovereignty. They can support cross-border research collaboration without forcing institutions into dependence on non-European platforms. But maturity differs per domain, and aligning legal frameworks, metadata standards and institutional practices remains a major challenge.

TRUST: shifting the focus from data to stewardship

Where FAIR focuses on data itself, the report introduces TRUST principles as a complementary framework for the repositories that store and manage data. TRUST stands for transparency, responsibility, user focus, sustainability and technology.

This shift is subtle but crucial. Long-term access to research data depends not only on formats and metadata, but on the reliability of the organisations that steward them. Who is accountable? How transparent are decisions? How sustainable is the infrastructure — financially and environmentally?

In a world where digital technology already accounts for an estimated 5–9% of global electricity use, sustainability is no longer optional. SURF explicitly links TRUST to energy efficiency, green data centres and responsible use of resources. Data stewardship, in other words, is becoming part of an institution’s public credibility.

Augmented data management: AI enters the back office

As data volumes explode, manual data management simply does not scale. This is where augmented data management comes in: AI-driven automation of tasks such as data orchestration, metadata enrichment and quality assessment.

The promise is significant. Researchers and educators spend less time on repetitive tasks and more time on analysis and interpretation. Institutions can manage increasingly complex data flows with smaller teams.

But SURF is clear about the precondition: data quality. AI systems trained on poor data will amplify errors rather than fix them. This places new demands on data professionals, who must become fluent in semantics, metadata vocabularies and ethical governance. Automation does not remove responsibility — it shifts it upstream.

DNA storage: thinking in centuries, not years

Perhaps the most striking long-term trend is the emergence of DNA-based data storage. While still firmly in the research phase, DNA offers extraordinary storage density and durability, making it a potential solution for preserving data over centuries.

For now, DNA storage has little direct impact on education. But for research domains that require permanent preservation — from national archives to endangered languages — it represents a radical rethinking of infrastructure. Data management is no longer just about efficiency, but about cultural and scientific memory.

A collective European challenge

Across all trends, one theme recurs: data management is inseparable from digital sovereignty. Decisions about where data is stored, how it is shared and under whose rules it operates are increasingly political.

SURF positions data management as a collective responsibility. No single institution can build FAIR ecosystems, trusted repositories or interoperable data spaces alone. Coordination, standardisation and shared infrastructure are essential — not to slow innovation, but to make it resilient.

In the coming years, the success of AI, immersive technologies and digital education will depend less on flashy applications and more on something quieter but fundamental: whether data is managed as a strategic public asset, rather than a technical afterthought.