The biggest data trends for 2026

Most enterprises have spent the past year chasing generative AI pilots. The problem is that most are stuck there. Experimental agents andRAG systemsstall before reaching production—not because the models fail, but because the data behind them isn’t ready.Edward Calvesbert, Vice President, Product Management of watsonx.data at IBM, has seen this pattern play out across industries. The core issue, in his view, is data quality and fragmentation. Companies are dealing with data trapped across silos and often lacking the structure, metadata and governance that agents need to use it effectively.But 2026 might be different. Calvesbert sees a few big shifts reshaping the data stack all at once. Hybrid cloud isn’t a stopgap anymore—it’s become the design pattern for enterprise scale, as companies look for more flexibility and cost control across providers. Zero copy integration (which lets you query data without duplicating it) saves both time and money while providing access to more data. And frontier models are making it easier to combinestructured and unstructured datato generate new insights and power agentic workflows.At the same time, the data platform market is consolidating around fewer vendors—but this time, they’re building on open standards instead of closed ecosystems.IBM Thinksat down with Calvesbert to dig into what “AI-ready data” actually means and what data leaders should prioritize in 2026.

Enterprises are typically missing unified access to both structured and unstructured data, with [up to] 90% of their data locked away in unstructured silos. They lack a unified knowledge and semantic layer to deliver consistent governance across data sources. And without that foundation, it’s difficult to combine new data with existing reference data to unlock insights and automation. Most critically, they lack a clear path frompilots to productionwith proper security, compliance, governance and cost-effectiveness at enterprise scale.

Data fragmentation prevents teams from accessing and combining information across sources and formats—and from delivering that data as reliable context and tools to models and agents. Missing enterprise readiness features like security, compliance and governance create barriers to deployment. Consistent accuracy and reliability as enterprises progress from informational use cases to analytics and agentic automation is still a significant challenge.