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One year ago, we introduced Analyst Studio, ThoughtSpot’s unified workspace for preparing and managing AI-ready data, with a vision: to transform analysts from report generators into business catalysts. SQL, Python, and visual analysis finally worked together in one workspace, letting data teams move seamlessly between ad-hoc queries and advanced modeling, all while preparing data for the AI revolution we knew was coming.

Customers don’t buy software because they feel loved. They buy it because the product works, solves a real problem, meets security, scalability, and reliability requirements, and fits their budget. No amount of empathy or friendliness can compensate for missing features or poor performance. So at first glance, it’s easy to assume that great products alone win customer loyalty. But once the contract is signed and the product is in use, the rules change.

The original promise of AI was that it would write most of the code for us. In reality, we’re not there yet. So where can AI meaningfully improve developer productivity today? In this post, we look at how AI powers development productivity across the SDLC, practical tools to use and frameworks for overcoming AI operationalization bottlenecks.

The preview of global queries in BigQuery lets you query data stored in different geographic locations with a single, standard SQL query, with no ETL.

If you're already using an analytics tool today, here are seven questions that will reveal whether it’s still the right one, not just for insight, but for risk control and long-term scalability.