Modern data programs also face structural data challenges that manual approaches can’t rectify. Over 50% of organizations rely on three or more data integration tools, creating fragmented workflows and inconsistent logic across teams. That fragmentation cascades into broader problems: quality checks happen too late, governance rules drift across systems, lineage breaks go undetected and semantic definitions fall out of sync. In reality, 77% of organizations lack the talent to manage such complexity.
These pressures directly impact data teams. Engineers spend 10–30% of their time uncovering data issues and another 10–30% resolving them—over 770 hours per year per engineer, or more than USD 40,000 in wasted labor. Meanwhile, analysts and business users wait an average of 1-4 weeks for the data they need because integration tasks are siloed or stalled.
Agentic data management represents a shift in how enterprises ensure data accuracy, quality and integrity at scale. Rather than scripting every transformation or maintaining rigid rules, organizations can introduce AI agents to scale pipeline creation, streamline data operations, reduce bottlenecks and sustain high-quality data with far fewer manual interventions. With more efficient operations and trusted data across the entire lifecycle, data teams can focus on strategy rather than rework.