Systems | Development | Analytics | API | Testing

If you're a business or data leader, you've probably felt the pressure to find new revenue streams while keeping partners and customers happy. What if your analytics could do more than just report on past performance? This implementation illustrates the true potential of Enterprise AI: shifting analytics from a passive back-office function to a frontline revenue driver.

When you’re building analytics for users, you quickly realize something: not every definition belongs on the Model. A lot of business logic sits in an awkward middle ground, too context-specific to hardcode into the Model but too important to leave scattered across one-off formulas. And in most tools, if the logic doesn’t live on the Model, every team ends up rebuilding the same thing over and over again. That’s where Query Sets and Column Sets in ThoughtSpot come in.

This is Part 2 of our WEX series. In this blog, we explore how the company scaled self-service analytics by embedding AI—read Part 1 on their people-first approach. You’ve got AI pressure from every angle: execs, customers, and competitors. But legacy analytics doesn’t just slow down development—it frustrates users and undermines the value your product is supposed to deliver.

Talk to anyone who works with data, and you’ll hear a familiar story: Data engineers are still bogged down cleaning, prepping, and untangling semantic models. Analysts are churning out dashboard after dashboard, with little time left for real analysis. Developers are hand-coding embedded analytics, turning every new feature into a months-long project. And business users are stuck in line, waiting for answers.