With the maturity of AI technology comes a pivot toward higher levels of data quality.
This respect for better quality data has arguably always been there, but enterprises and vendors have focused more on large language modelHowever, AI vendors and enterprise users are beginning to realize that without the right kind of data, AI technology is incomplete,
This recent emphasis on data quality has led to a change in the way data is presented.
“We’re seeing a shift to viewing data as a product, not a by-product,” Yasmin Ahmed, managing director of product management for data and AI cloud at Google Cloud, said on the latest. Targeting AI Podcast from AI Business, “It’s treating data as a product, making sure there’s clear ownership of the data, making sure there’s guaranteed quality and governance around that data.”
Part of this change is made possible by generative AI, Hamad said. Generative AI makes it easy understand the dataAnd the next step is to ensure that the technology can be effectively integrated into the organization to refine data workflows and point them in new directions.
The second step is to ensure that data center It is more than just a keeper of data, but a brain for AI technology agentic ai,
“The data platform must be cognitive reasoning for agents,” Hamad said. “Over time, we are evolving these data platforms to match agents with the right tools and capabilities so agents can work across those platforms.”
Hamad said that to take AI and data to the next stage, the technology must be based on data, which is essential for enterprise security.
“AI has to be integrated into the data stack,” he said. “AI can’t be a bolt-on thing where you take all your proprietary, highly sensitive customer data, patient data and send it to an AI model.”
