These are heady times in the data management world. Organizations of all types are embracing AI and real-time capabilities as key to future growth, and data managers are leading the way in delivering. In this special report, BDQ canvassed a range of industry leaders and activists for their views on the cutting-edge technologies they see emerging.
Not surprisingly, AI dominates many of the new initiatives shaping the Big Data world—that’s why we asked our participating industry leaders to drill down and share what aspects of AI are making the jobs of data managers more interesting and productive. Let’s look at some key technologies to put on your data watchlist.
CONTEXT-AWARE AI AGENTS
A compelling development that promises a positive impact on data management is the emergence of “AI agents that don’t just read enterprise data but write enriched context back into it,” said David DuChene, senior manager of data and AI at SHI.
Why it matters—With traditional integration approaches, “data was as a static asset to be moved and queried,” DuChene pointed out. Now, agentic AI “turns the data estate into a living substrate that humans, applications, and AI agents are continuously enriching together. Each agent interaction surfaces hidden relationships, fills gaps in context, and makes implicit knowledge explicit.”
Potential issues—Ungoverned, inconsistent data can hamstring the impact of agents. “Without entity resolution and embedded governance in place first, agents amplify existing data problems instead of resolving them,” said DuChene.
Advice—“The organizations getting this right aren’t trying to transform everything at once,” said DuChene. “They’re picking one high-value data domain, building the governed foundation for that domain, deploying agents, and then extending the pattern to the next domain.”
ENTERPRISE-AWARE AI AGENTS
Autonomous AI agents are extending capabilities across the entire data life cycle, beyond just “copilots that suggest and wait,” said Ashwin Patil, principal of data engineering and analytics leader for Deloitte. These autonomous agents can “discover, decide, act, and verify across the entire data life cycle, continuously and at enterprise scale.”
Why it matters—Agentic AI is becoming a tool for building a well-designed data foundation.
Potential issues—Building a data foundation on autonomous AI agents requires a fundamental shift in data management and its underlying operational foundation. “Structural risks should be addressed before deployment,” said Patil. In addition, “organizations may struggle if they design agents to provide recommendations without enabling closed-loop execution and verification. This makes later retrofitting difficult.” Adoption can also be slowed by “unclear roles, ownership, and governance, since agents may expose organizational friction rather than resolve it.”
Advice—Data and IT teams need to “stop processing tickets and start making decisions,” Patil advised. “Stewards should be reviewing exceptions, not routine updates. Owners should be approving risk-tiered batches, not individual assets. Governance should become something the organization sustains, not something it periodically catches up on.”