Whitepaper
As AI utility increases, organizations are rethinking how they monitor and maintain data quality at scale to deliver trusted data for confident decisions. But traditional approaches often fall short—failing to keep up with chaos of data sprawl, silos, and ever-growing business demand.
Enter agentic AI.
Agentic AI shifts the paradigm from manual data quality rule creation to intelligent automation. Alation’s Data Quality Agent uses metadata, usage patterns, and generative AI to recommend and run the right data quality checks—at scale and with minimal effort. More importantly, it helps close the data trust gap—the divide between data producers and consumers—by surfacing and fixing the issues that matter most.
Download this whitepaper to discover:
Why today’s data quality challenges demand more than GenAI and ML
How agentic AI helps teams pinpoint critical data to monitor
Use cases from global enterprises tackling alert fatigue, scaling issues, and trust gaps
How Alation Data Quality accelerates remediation and builds trust—without adding complexity
Whether you’re starting from scratch or scaling an existing data quality program, this guide offers a smarter, faster path to clean, trusted data.
Webinar Registration
Poor data quality costs organizations millions in lost trust, wasted time, and bad decisions. Traditional data quality approaches are manual, slow, and often miss what matters most.
Webinar On-Demand
Data products bridge raw data and business outcomes - but most organizations struggle to define, build, and scale them.
Datasheet
Alation Data Quality leverages agentic AI and existing data intelligence to establish AI-recommended data quality rules and gauge overall data health across an entire data estate. Offering immediate visibility into the health of an organization’s most critical data assets, data consumers have access to data they can trust is accurate, up-to-date, and fit-for-purpose.