3369: Precisely Explains Why AI Fails Without Data Integrity

Tech Talks Daily

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1d ago 25:16

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In today’s episode of Tech Talks Daily, I sat down with Andy Bell, Head of Data Product Management at Precisely, to explore a challenge that many organizations continue to underestimate: the role of data integrity in AI strategies. With only 12 percent of businesses expressing confidence in the quality of their AI data, it's clear that the rush to implement AI is often outpacing the readiness of the data that supports it.

Andy and I unpack what happens when enterprises leap into generative or agentic AI without addressing foundational data issues. From hallucinations to bias to unreliable outputs, the risks are significant. As we discussed, these risks don’t just impact models — they erode trust with customers and complicate accountability, especially in regulated industries where traceability is non-negotiable.

We then explored the power of third-party data enrichment and how it can offer much-needed context that internal datasets often lack. Andy shared real-world examples, including how a major delivery company saved 65 million dollars by optimizing address accuracy and how San Bernardino County used Precisely’s wildfire risk models to improve emergency planning. These aren’t abstract use cases — they show measurable business value.

Andy also introduced the Precisely Data Link program, a solution designed to make it easier to connect, manage, and query multiple third-party datasets. With persistent IDs and flexible delivery methods through APIs, managed services, and platforms like Snowflake and Databricks, Precisely is helping organizations speed up time to value while reducing integration headaches.

Looking ahead, Andy shared how Precisely is building AI capabilities that allow users to query third-party data using natural language. This shift aims to make complex data interactions more intuitive and accessible to business users who may not be data engineers.

If data is the fuel for AI, then the quality and context of that data will define the road ahead. Is your organization doing enough to ensure its data can be trusted by the AI it deploys?

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