Measuring Success: KPIs for Agentic AI in Data Quality Management
Agentic AI systems now monitor, correct, and negotiate data integrity across enterprise systems. They operate semi-autonomously, making decisions that once required human judgment. But here's the challenge: how do we know they're actually performing well? Success in this new paradigm isn't just about accuracy. It's about trust, speed, resilience, and measurable business impact.
The Impact of Bad Data on Modern AI Projects (and How to Fix It)
The enterprise AI conversation has been dominated by models. Which LLM should we license? Should we fine-tune or use RAG? What about open-source versus proprietary? These are the wrong questions to start with.
The AI boom is exposing a truth that data teams have known for years: most organizations are building on a foundation of poor-quality data. Decades of neglected data strategy are now coming due. The models are powerful, but they're only as reliable as what they're trained on and what they retrieve.