Agentic analytics platform ThoughtSpot launched its next generation of Analyst Studio, which it claims offers a “new suite of capabilities to revolutionize how data teams deliver AI-ready data with speed, flexibility and control.”
The analytics market has spent the last decade obsessing over usability. They wanted faster dashboards and queries, while keeping the interface easy to use. Each iteration aimed to bring data close to decision makers. However, one layer has remained stubbornly difficult. Data preparation still consumes time and coordination.
ThoughtSpot’s latest release aims to overcome this challenge with agentic data preparation. The company is now inserting intelligence into how data is structured inside the Analyst Studio, which is a workspace for analysts to shape, model, and explore datasets before they are exposed more broadly across the organization.
It is now essentially moving away from a consumption environment into more of a hybrid modeling and analytics space. The emphasis is now on shaping data (even before analysis begins).
It’s not that ThoughtSpot suddenly invented data preparation or was the first vendor to tackle modeling workflows. Every serious platform in the market already offers transformation capabilities. Snowflake, Databricks, Microsoft Fabric, they all address preparation in one form or another. Preparation itself is not new.
However, the difference is where ThoughtSpot is coming from. Most of those platforms are built around infrastructure first. More emphasis on storage, compute and pipeline. The analytics layer was on top. In contrast, ThoughtSpot started at the consumption layer.
Why does that matter? Well, it reflects how a company thinks about data preparation. A vendor that is infrastructure-first designs this process mainly as an engineering environment. It’s about performance and control for them. For ThoughtSpot it’s more about the user. It aims to embed preparation directly into the environment where the analytics already work.
You can’t replace infrastructure, but by reducing friction inside the analytics workflows, you can influence how the experience is built and who it is really designed for. The goal is to have a platform where analysts do not have to wait in an engineering queue to adjust models or have to switch tools. ThoughtSpot is trying to let them refine the structure inside the same workspace where they analyze results.
ThoughtSpot also introduced SpotCache, which is a performance-focused addition that reduces query latency. It does this by caching frequently accessed data so that the commonly used datasets get served more quickly from within the platform itself. This reduces the need to repeatedly send requests back to warehouse systems for every interaction. This complements its data prep strategy. One improves readiness. The other improves responsiveness. Together, they provide more control over end-to-end analytics.
The update to the platform comes only two months after ThoughtSpot shared plans to automate its analytics tools with AI agents. The update to Analyst Studio reflects a step forward in that direction.
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ThoughtSpot was founded in 2012 by former Google engineers. They wanted to position the platform differently from traditional BI vendors. Instead of another dashboard-focused platform, they focused on search, where users can interact using plain language.
As the analytics market matured, search alone wasn’t enough to stand apart. Some of the big players in the market, such as Snowflake and Databricks, were moving data stacks to the cloud. Instead of competing against them, ThoughtSpot deepened integration with these platforms. It evolved into a cloud-native analytics layer built to sit on top of modern data warehouses. Over time, it leaned more toward GenAI and what it calls agentic analytics.
“The journey to effective AI starts with data readiness, but for too many teams, that journey is stalled by rigid tools and unpredictable cloud costs,” said Anjali Kumari, Vice President of Product Management at ThoughtSpot.
“With the all new Analyst Studio and SpotCache capability, we are reimagining the process and simplifying data readiness. With a native spreadsheet interface and AI agent for data prep, we aren’t just improving productivity, we are giving every analyst the power to build the trusted data foundation required for the age of AI.”
The data prep agent and caching capabilities can help ThoughtSpot stand out in a crowded space. Many others in the space are moving toward agentic capabilities. A natural progression for ThoughtSpot would be to add multi-agent coordination capabilities to further reduce data prep friction and extend automation across the analytics workflow.
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The post ThoughtSpot Pushes Upstream With Agentic Data Preparation appeared first on BigDATAwire.
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Author: Ali Azhar
