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Home SiliconANGLE

Snowflake doubles down on AI innovation and data trust

SiliconANGLE by SiliconANGLE
June 4, 2025
in SiliconANGLE
Reading Time: 7 mins read
129
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Victoria Gayton
2025-06-04 15:44:00
siliconangle.com

The enterprise data landscape is transforming rapidly as organizations lean into advanced analytics and artificial intelligence to drive decisions and streamline operations. But innovation only scales when data trust, quality and governance keep pace.

Snowflake Inc. is taking a pragmatic approach to the tension between AI acceleration and responsible data governance. That balance is foundational to how the company enables access, fosters trust and builds internal tools designed to serve both product teams and customers, according to Anahita Tafvizi (pictured), chief data analytics officer of Snowflake.

Anahita Tafvizi, chief data analytics officer of Snowflake Inc., talks with theCUBE about data trust at Snowflake Summit – 2025.

Snowflake’s Anahita Tafvizi talks with theCUBE about building trustworthy systems that scale with confidence.

“I really think of trust and governance as foundations to enable innovation versus against innovation,” Tafvizi said. “It does take time, but it then creates better solutions and better trust with your users. We have been very strong on ensuring that there is the right governance … the right foundation on the data side, and then providing that solution to our customers.”

Tafvizi spoke with theCUBE’s Dave Vellante and Rebecca Knight at Snowflake Summit, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed overcoming legacy data silos, accelerating enterprise AI adoption and building trustworthy systems that scale with confidence. (* Disclosure below.)

Elevating data trust through internal application

The responsibilities of today’s data leaders go far beyond traditional back-office data management. They’re expected to drive real-time insights, integrate AI into workflows, promote data literacy and ensure data trust remains foundational, according to Tafvizi.

“The role is not only do you bring the right data and insights, but how do you accelerate decisions?” she said. “How do you bring the right AI technology also to the organizations? How do you help with the data and literacy across the organization? And then how do you balance the technology and the speed of innovation with the governance and the foundations that’s needed to get there?”

Snowflake’s internal use of its own products — acting as “customer zero” — is central to both product innovation and reinforcing data trust before public release. Product teams receive feedback directly from internal users, surfacing friction points and uncovering what Tafvizi calls “magic moments” that inform broader product development.

“We do use all of our products before they go to the hands of our customers,” she said. “One of the examples of that … was Snowflake Intelligence, which we have been working with the … product team over the last few months.”

Snowflake Intelligence is an internal AI-powered assistant that helps go-to-market teams query both structured and unstructured data using natural language. Sales teams can ask questions about revenue, customer references or training materials without writing structured query language or navigating multiple systems, according to Tafvizi.

“The Snowflake Intelligence that we use internally for our go-to-market team has access to both your structured and unstructured data through a natural language interface,” she explained. “You go in as a seller and you can ask questions of your data, whether it’s your unstructured data, such as your sales enablement materials, your training materials [or] your latest pitch decks, and you want to know where to find them.”

To ensure reliable outputs and reinforce data trust, Snowflake applies a semantic layer that translates queries into business context. This layer, along with verified queries, helps ground AI-generated responses in trusted, accurate information, according to Tafvizi.

“This is basically what we call the semantic layer,” she said. “Where … how do you translate your data with the business context? Then, when you ask an actual language question, it can actually then understand what you’re talking about.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the Snowflake Summit event:

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

Photo: SiliconANGLE

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July 3, 2025
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