The Shift Toward AI Data Quality as a Core Product
With the maturity of AI technology comes a pivot toward a higher level of data quality.
This regard for better quality data has arguably always been there, but enterprises and vendors have focused more on large language models. However, vendors and enterprise users of AI are starting to realize that without the right kind of data, AI technology remains incomplete.
This recent emphasis on data quality has led to a change in how data is presented.
“We’re seeing the shift of seeing data as a product, not a byproduct,” said Yasmeen Ahmad, managing director of product management for data and AI cloud at Google Cloud, on the latest Targeting AI podcast from AI Business. “It’s treating data as a product, ensuring data has clear ownership, ensuring there’s guaranteed quality and governance around that data.”
Part of this change is made possible by generative AI, Hamad said. Generative AI has made it easier to understand data, and the next stage is to ensure that the technology can be effectively integrated into the organization to refine data workflows and point them in new directions.
Another stage is ensuring that the data center is more than just a keeper of data, but rather a brain for AI technology and agentic AI.
“The data platform has to be this cognitive reasoning for agents,” Hamad said. “Over time, we’re evolving these data platforms to meet agents with the right tools and the capabilities so that agents can operate on those platforms.”
To take AI and data to the next stage, the technology must be grounded in data, which is essential for enterprise security, Hamad added.
“AI has to be integrated into data stacks,” she said. “AI can’t be this bolt-on thing where you take all of your proprietary, highly sensitive customer data, patient data, and ship it off to an AI model.”
