Steve Leeper, VP of Product Marketing, Datadobi
To operate in the modern digital economy, businesses have little choice other than to be data-centric. For most, this means collecting and storing information on an unprecedented scale but often without a corresponding level of visibility or control.
It comes as little surprise that many are struggling to cope with the management and compliance issues this situation brings. Add to that the sense of urgency driven by data-hungry AI, and enterprises are rapidly approaching the limitations of existing technology and processes.
But, in 2026, how will the challenges associated with data dependency manifest themselves, and what will businesses need to do to strike a balance between extracting value from their data and maintaining effective control?
1. The escalating cost of unstructured data will hit home
Businesses everywhere are already storing vast amounts of unstructured data, with AI workloads only adding to accumulation rates. Lacking a better option, many organizations are simply adding more storage, an increasingly expensive approach that only serves as band-aid for a more serious strategic issue.
Even worse, a significant proportion of enterprises remain blindly committed to storing unstructured data on primary infrastructure, even though much of it hasn’t been accessed for years. This can be accompanied by a partial or complete absence of reliable insight into data ownership, age, and activity, making it almost impossible to enforce consistent retention or archiving policies.
Whilst these challenges aren’t necessarily new, what’s different about the year ahead is that they are becoming increasingly unsustainable. Without more effective lifecycle-based control, unstructured data will continue to accumulate at exponential rates, turning storage into a significant cost centre that delivers little real ROI.
2. Effective AI integration will depend on better data governance
GenAI initiatives depend on high-quality data, but most organisations still haven’t developed a practical approach to integrating unstructured data into their training processes. Without accurate insight into fundamentals such as data ownership, lineage and usage, there can be little true confidence in the outputs AI systems produce. But, as projects consume more data, governance processes must adapt to ensure businesses aren’t blindsided by the sheer complexity of what they are attempting.
In 2026, governance frameworks will evolve beyond compliance into a foundation for enterprise intelligence. Doing so will offer a pathway to faster collaboration and more complete intelligence, enabling governance to become a driver of competitive advantage. Those organizations that opt for the status quo are in danger of falling foul of governance requirements, with the potential to materially impact the success of their wider AI strategy.
3. Regulatory pressure will force a rethink of enterprise data strategy
For many very valid reasons, AI regulation continues to increase in scope. For compliance teams, the challenge is not just keeping pace with new rules but doing so at a time when enterprise data environments are expanding faster than existing governance frameworks can realistically support.
In the year ahead, we can expect to see further growth in enforcement activity as regulators exercise their expanding powers and businesses struggle under the sheer volume of data that is being collected.
To proactively and effectively shape governance policies, legal teams will need to deepen their collaboration with IT and data leaders to ensure visibility and defensible processes across enterprise-scale environments.
For those organizations that fail to get ahead of these issues, 2026 could become a very expensive exercise in managing the cost of data dependency.
