Data Quality at Equifax with Anomalo | What Actually Works

Data quality is still one of the most underestimated problems in AI. Everyone talks about models. Very few talk about whether the data behind those models can actually be trusted. At Gartner D&A, I sat down with Nick Oldham, COO at Equifax, and the conversation was very real. Equifax operates at the center of the global financial ecosystem. That means data integrity is not just a technical problem. It is a business risk. A regulatory risk. A trust problem.What stood out was this shift:- They are not treating data quality as a one-time cleanup anymore.- They are building systems to monitor, detect, and fix issues continuously.That is where platforms like Anomalo come in.Instead of reacting to broken dashboards or failed pipelines, the goal is simpleCatch issues before they impact the business. And this becomes even more critical as AI enters the picture. Because AI systems do not fail loudly. They fail quietly when the data is wrong.Nick also shared how their journey was not just about tools. The harder part was organizational.- Aligning teams on what “good data” actually means- Moving from siloed ownership to shared accountability- Bridging the gap between data strategy and real executionLooking ahead, with investments in Google Cloud and tools like Dataplex, the direction is clearMore automationMore observabilityLess manual firefightingIf you are building AI without solving for data integrity first, you are building on weak foundations.If this topic interests you, Nick will be going deeper into this at an upcoming session: The Road to Self-Driving Data, April 2 go for ithttps://engage.anomalo.com/the-road-to-self-driving-dataThis is one of those conversations that is less about hype and more about what it actually takes to make data work at scale.#data #ai #anomalo #dataquality #theravitshow