Systems | Development | Analytics | API | Testing

The phrase “Big Data” may be out of trend, but data volumes keep climbing–and so do expectations. It’s estimated that in 2026, the global volume of data is expected to exceed 221 zettabytes. With AI tools and agents making it easier to consume, the pressure is on to deliver faster, more responsive insights on massive datasets to more users than ever.

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.

Today, we are unveiling a new ThoughtSpot. Not because we needed a new logo. Because our brand needed to catch up to the company we already are. If you know ThoughtSpot, you know us as more than dashboards or static analytics. You know us as the place where every question has an actionable answer. We are an AI company. We lead in Agentic Analytics. And it is time for our brand to make that unmistakably clear. And yes, I am excited. We are all excited.

In today’s product landscape, analytics isn’t just about showing insights. It’s about moving data into the tools where work actually happens, so that users can make informed decisions in context. Users expect analytics to integrate with their systems, whether that means sending a lead to a CRM, creating a Jira ticket, or triggering a workflow downstream. As product owners, that means one thing: your analytics experience should enable seamless data flows across systems.

In the world of early-stage startups, the real enemies aren’t competitors. It’s time, focus, and credibility. You’re racing to ship fast, protect runway, and look enterprise-ready long before you actually are. And in 2025, every serious buyer expects your product to have native, AI-powered analytics built in. Not basic dashboards, but a polished, conversational, ask-anything, data experience that delivers trusted answers.

Your executives are asking for AI-powered customer experiences, but your data teams are still playing catch-up with basic self-service requests—and that gap is costing you competitive advantage every day it persists.

Snowflake Intelligence and ThoughtSpot are more than complementary tools—they complete the picture for AI-powered analytics and BI that serves everyone everywhere, from data scientists to business executives. Together, they turn data into decisions through one connected experience that works the way people do: intuitive, secure, and fast. No silos, no copies—just a single source of truth your entire business can trust.