Data Governance for Generative AI in Analytics

The democratization of data governance for generative AI is reshaping the landscape of analytics and artificial intelligence.

As organizations strive to leverage AI for actionable insights, a focus on maintaining high-quality, timely and diverse data becomes paramount, according to Sharad Kumar (photo), Field Technology Manager for Data at QlikTech International AB.

QlikTech’s Sharad Kumar talks to theCUBE about data governance for generative AI.

“What we’ve realized, and I’ve talked to a lot of CIOs and seniors, they realize that to do AI and AI generation, you need good data,” Kumar said. “It has to be of good quality. What I presented yesterday was the need for six principles to ensure what I call the quality of your data and its suitability for AI.

Kumar spoke to CUBE Research John Furrier And Savannah Peterson At Data + AI Summit, in an exclusive broadcast on theCUBE, SiliconANGLE Media’s live streaming studio. They discussed the convergence of data management and generative AI, focusing on integrating open data formats, democratizing data governance, and creating a trusted database for effective AI applications. (*Disclosure below.)

Building reliable databases for generative AI

The discussion highlighted the crucial role of unified data formats and governance in the advancement of AI applications. Such integrations aim to create a single, open format that improves compatibility and flexibility for various applications, according to Kumar.

“A couple of things that we saw coming with the Tabular acquisition is really the convergence of the Delta Lake format and the Iceberg format,” Kumar said. “If you look at the last couple of years, there were sort of divergent formats, and even though Databricks was pretty uniform… you end up bringing the two formats together into a singular format. So now you… can plug different apps and different things into the same lake house.

The discussion also addressed the issue of data fragmentation and legacy systems, which often hinder seamless data integration. Consolidating data onto a unified storage platform, such as a lake house, can help overcome these challenges, Kumar added.

“If I look at customers, they have multiple databases…now they’re going to import the data into, say, a lake house. This is the first step where you create a unified storage space. You bring all your data together in one place,” Kumar said. “Databricks has a wonderful, diverse, thriving and growing partner ecosystem. But that means if I’m a customer, if I’m building an end-to-end platform around Databricks, you often have to choose another tool to ingest the data, another tool to transform the data, something else to secure. the data.”

Here’s the full video interview, part of SiliconANGLE and theCUBE Research’s coverage on the Data + AI Summit:

(*Disclosure: TheCUBE is a paid media partner for the Data + AI Summit. Neither Databricks Inc., the sponsor of theCUBE event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE .)

Photo: SiliconANGLE