Today’s enterprises are generating, transmitting, analyzing and monetizing more of those precious zeros and ones than ever.

However, the sprawling amounts of data in play — coupled with breakthroughs in artificial intelligence and large language models — have brought with them a need to evolve the data stack. So, what signals are emerging from the data product realm on the topic?

“The modern data stack has been a boon essentially for data teams, which is very exciting,” said Lior Gavish (pictured), co-founder and chief technology officer of Monte Carlo Data Inc. “I think where it’s evolving right now or where I feel we’re maturing is we’re starting … after we’ve crammed all of our data in and built a lot of things … to figure out how to make sense of it all. We see companies kind of going back to the foundations and the basics and starting to think about, ‘How do we productize all these things? How do we really create data products that are trusted and discoverable and usable and that really drive impact in the business?'”

Gavish spoke with theCUBE industry analyst Rob Strechay during a CUBE Conversation from SiliconANGLE Media’s livestreaming studio in Boston. They discussed the modern data platform’s evolution, expanding on new capabilities/growth drivers and mounting complexities.

A functioning whole with several constituent parts

The modern data stack is primarily tool based. It’s made up of data storage, orchestration/workflow engines, data ingestion, business intelligence and a metrics layer, among other things. Each of these tools are independently evolving in line with advancements, such as AI and high-performance computing, according to Gavish.

“It’s a very exciting time, and I think most recently, one of the biggest ways people believe they can add value is by working with AI and LLMs. And that’s probably the new exciting frontier of the modern data stack,” he said. “All of these things are super exciting for me as someone that’s part of the industry.”

In terms of data platform usage in regard to mass adoption, it appears to be balanced between the big players, including Snowflake Inc., Databricks Inc. and BigQuery, as teams choose the best tools for their needs, personas and operating strategies within companies. Driving value with heterogenous toolsets requires certain parameters to be set first, according to Gavish.

“Once you have so many products in the hands of so many people, you really have to think about scale and how to actually expose it in a way that’s repeatable and trusted and findable,” he explained. “If they have the need, you need to make sure that they understand how to use it and how to consume it — you need to make sure it’s at the level of trustworthiness and quality that’s required for the job. ”

Organizations are increasingly adopting a new set of tools or a new set of processes for data teams to support data products and make them a success, driving value in the enterprise, Gavish added.

Here’s the complete video interview, one of many CUBE Conversations from SiliconANGLE and theCUBE:

Photo: SiliconANGLE