Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Steve Leeper, VP of Product Marketing, Datadobi
The core challenge facing enterprise data leaders is now firmly focused on control. In particular, rising data volumes and growing AI dependence are exposing the limits of traditional approaches to storage and data management. Get these wrong, and there can be some serious cost and governance consequences. But how will these issues play out over the next 12 months, and what factors will drive strategy and investment decisions?
1. Unstructured data will become one of the biggest drains on IT budgets.
It’s well known that unstructured data already accounts for up to 90% of enterprise information, with accumulation rates accelerating rapidly, largely due to machine-generated data. To cope, many organizations default to simply adding more storage, an increasingly unsustainable approach, mainly due to spiraling infrastructure costs. At the same time, SaaS adoption and shadow IT growth rates continue to further fragment data across clouds and endpoints.
The cost challenge is exacerbated by scale, with enterprises not only managing petabytes of unstructured data but also billions of individual files and objects. What’s more, a significant proportion of stored unstructured data has not been accessed or modified for years, yet continues to consume primary or near-primary storage. Many organisations also lack reliable insight into data ownership, age and activity, making it difficult to enforce consistent retention or archiving policies.
These represent significant challenges for 2026 and beyond because, without lifecycle-based control, unstructured data will continue to accumulate, turning storage into a permanent and painful cost centre rather than a manageable and worthwhile investment.
2. Data governance will become a strategic AI requirement
Even though GenAI initiatives depend on access to high-quality data, most organisations struggle with the practicalities of managing unstructured data. It’s not uncommon, for example, for businesses to lack consistent insight into data ownership, lineage, and usage – a situation which undermines confidence in the outputs these systems produce. But, as AI projects inevitably scale, governance needs to support continuous auditing and refinement as new datasets are introduced, rather than relying on static policies.
The answer lies in building governance frameworks that will evolve beyond compliance into a foundation for enterprise intelligence. Those who can also break down silos and provide enterprise-wide visibility will unlock faster collaboration and more complete intelligence, turning governance into a driver of competitive advantage. In contrast, organizations that fail to adapt governance for AI run the very real risk of investing heavily in initiatives that can’t deliver reliable, explainable, or repeatable outcomes.
3. Legal and risk teams will play a critical role in shaping enterprise data strategies
Over the last 12 months, regulatory pressure around data protection, sovereignty and AI ethics has continued to intensify. Complicating matters for legal and risk teams is the unprecedented scale and distribution of unstructured data, especially when there is little to no reliable insight into where data resides and how it is being used.
Elsewhere, inconsistent retention and deletion practices increase exposure during audits, investigations, and potentially, litigation proceedings, particularly in large, hybrid data environments.
In 2026, proactive collaboration between legal, IT and data leaders will improve. Legal teams that once reacted to risks hidden in file shares or outdated records will increasingly partner with IT and data leaders to proactively shape governance policies. By ensuring visibility and defensible processes across petabyte-scale environments, legal teams will become strategic enablers, reducing litigation risk, supporting compliance and embedding resilience into the wider business.
Whichever of these issues applies, many organizations will face an either/or choice: continue absorbing rising cost and risk as data accumulates, or take deliberate control of how data is retained and governed.
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