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The Confidence Gap: Why Verified Data Is the Foundation of Modern Recruiting
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Data quality has always been a challenge for organizations, but the rise of generative and agentic AI is raising the stakes dramatically. When AI systems can send thousands of emails or drive large-scale operational decisions automatically, poor data quality is no longer just an inconvenience. It becomes a strategic liability.
ZoomInfo VP of GTM Millie Beetham joined TDWI VP of Research Fern Halper, Ph.D., to break down the current state of data quality maturity and what organizations must do differently to prepare their data for AI.
Download this free TDWI research snapshot to find out what is changing, what most organizations are getting wrong, and how to build a data foundation that AI can actually act on, including:
Why most organizations are still managing data quality tactically rather than as a strategic, governed capability
The most important new data quality standard for AI and why traceability is now non-negotiable
The biggest mistake organizations make when using AI to solve data quality problems
How AI is shifting the definition of "good enough" from clean data to accurate, relevant, and timely data
What revenue leaders must prioritize to prepare their data environments for AI-driven automation
Download the snapshot now and start building the data quality foundation your AI strategy requires.
