Supply: Shutterstock AI-Generated
For all of the advances in analytics, two issues proceed to plague information groups: misaligned or inconsistent metrics throughout instruments and the inaccessibility of unstructured information. These two points not solely decelerate decision-making but additionally drive groups to depend on handbook workarounds – introducing inefficiencies, inflicting information drift, and growing the danger of pricey errors.
When groups outline KPIs in several instruments, equivalent to dashboards, spreadsheets, and experiences, these numbers typically don’t match, undermining belief within the information. In the meantime, important enterprise info stays locked inside PDFs, contracts, and different unstructured recordsdata that almost all BI platforms can’t entry or analyze successfully.
Sigma, a warehouse-native BI and analytics platform, introduced two main updates on the Snowflake Summit 2025 that instantly tackle these ache factors. In partnership with Snowflake, the corporate unveiled native assist for Snowflake Semantic Views and integration with AI SQL, Snowflake’s breakthrough characteristic for querying unstructured information utilizing giant language fashions (LLMs).
These advances are designed to assist ruled semantic exploration and allow AI-powered evaluation of unstructured recordsdata, instantly inside Sigma’s spreadsheet-style interface. Sigma positions this as a major step ahead in unified analytics because it permits customers to question each structured metrics and uncooked human context side-by-side inside a single ruled system.
In accordance with Mike Palmer, CEO of Sigma, the combination with Snowflake Semantic Views is “actually native” and is constructed for flexibility, scale, and the following era of analytics. “By assembly the semantic layer the place it belongs, we’re giving enterprise groups on the spot entry to ruled metrics and logic with out compromise,” explains Palmer. “And that is just the start. From bi-directional syncs to visible semantic exploration, Sigma is constructing towards a unified modeling expertise that brings readability and management to each layer of the information stack.”
“This can be a main leap ahead in delivering a constant, ruled expertise powered solely by Snowflake,” added Palmer. “Sigma is constructing towards a future the place each layer of the information stack speaks the identical language – outlined as soon as, executed all over the place.”
The corporate presents this integration as a approach to streamline analytics by aligning enterprise metrics instantly with the information warehouse. It’s a part of a broader push to make sure that everybody, from analysts to enterprise customers, is working from the identical trusted definitions.
The collaboration with Snowflake additionally displays a broader business shift towards warehouse-native analytics. For Sigma, it’s an opportunity to assist notice a long-standing purpose within the information world: outline what you are promoting logic as soon as, maintain it ruled on the supply, and make it accessible wherever it’s wanted.
This implies no extra duplicating definitions throughout dashboards and spreadsheets. As an alternative, every thing stays anchored within the warehouse, giving groups a single, trusted basis to work from. That’s one much less supply of confusion when groups want to maneuver rapidly and belief their information.
The opposite main replace is Sigma’s assist for Snowflake AI SQL, a brand new set of LLM-powered instruments that make it doable to question unstructured content material, like photographs, scanned receipts, product manuals, or contracts, identical to how you’d in the event that they have been rows in a desk.
Supply: Sigmacomputing.com
This improve pairs nicely with Sigma’s just lately launched File Column Sort, which permits customers to add and work with recordsdata instantly within the platform. Paired with AI SQL, this opens the door to make use of instances that have been beforehand cumbersome, like extracting cost phrases from contracts or reviewing receipts as a part of claims workflows. The brand new capabilities scale back the necessity for customized scripts, third-party instruments, or handbook information entry.
“For many years, legacy BI instruments assumed your information was clear, structured, and ready politely in rows and columns,” mentioned Palmer. “However a number of the most essential enterprise selections are made with the messy stuff: authorized paperwork, compliance PDFs, screenshots, receipts, product specs, and annotated photographs.
“Traditionally, these codecs required a human within the loop: to learn, interpret, and manually extract insights. That’s the bottleneck AI SQL removes. Sigma and Snowflake flip human information into scalable programs, unlocking solely new kinds of evaluation throughout industries and groups.”
For Snowflake, the combination helps advance its imaginative and prescient of a unified, AI-powered information cloud. It validates its semantic layer technique and showcases how third-party platforms can faucet into Cortex AI SQL, with out heavy engineering.
Carl Perry, Head of Analytics at Snowflake, mentioned the combination “marks an essential step ahead in enabling enterprises to leverage the state-of-the-art AI options out there with Snowflake Intelligence and Cortex Analyst,” including that it helps groups “create extra environment friendly and highly effective workflows.”
The most recent updates to the Sigma platform aren’t nearly stacking on extra options – it provides a glimpse into what fashionable analytics will be. With native assist for Semantic Views and AI SQL, Sigma is displaying how structured metrics and unstructured context can coexist in a single interface.
Associated Objects
Why a Common Semantic Layer is the Key to Unlock Worth from Your Information
A Semantic Method to Massive Information Governance
AtScale Declares Main Improve To Its Semantic Layer Platform
