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He believes that growing efforts will result in enterprises consolidating work across multiple data lakes and focusing on more automated operations to improve data discoverability.

“AI data lakes will lead to more flexible data centers,” says Deepto Chakravarty, chief product officer at Cloudera, a Hadoop pioneer that continues to deliver new data-centric tools.

AI challenges the existing rules of the game, he says. This means that data lake tools can scale up and down. This means supporting flexible computing in data centers and in the cloud.

", data teams want to move data on-premises. Other times, they want to move it back to the cloud. But there's a 'tax' on moving all those data workloads back and forth," Chakravarthy says.

CFOs are thinking about the “taxes” of AI, or its impact on costs, and the data center will become a testing ground. CIOs will focus on implementing data-crunching capabilities that can truly scale elastically.

“The key is customizing the output of the fundamental AI models,” says Edward Calvesbaert, vice president of product marketing for IBM Watsonx Platform. If you configure the AI ​​correctly based on your data, it will effectively represent your enterprise the way you want it to, in terms of use cases and quality, he says.

As an example, Calvesberth cites Watsonx.data, the central data repository in the Watsonx ecosystem, which now allows for customization of AI models that can be deployed across an enterprise's IT environment.

In the new era of AI, customization efforts must be benin mobile database by data governance. “Governance is what ensures lifecycle management and compliance with your own corporate policies, as well as any regulations,” says Calvesbaert.

"There's going to be more local data processing in the near future," says Justin Borgman, chairman and CEO of Starburst, which has turned an early development of the Trino SQL query engine into a full-fledged storage lake offering that can mine data from it.

Well-organized data lakes and data lakes are essential to support AI workloads, including those related to generative AI, he said. And we will see a surge in interest in hybrid data architectures, driven in part by the rise of AI and machine learning.