ThoughtSpot

View, Work, and Save Your Live, Governed Data, Without Ever Exporting It Every analyst has a version of this “Monday blues.” A leader pings asking for the quarterly forecast, wants it broken out by region, and needs it for a call in thirty minutes. The fastest way to do this? Pull the data out of your tool, paste it into Excel, and build the report there. Thirty minutes later, you've got the report: three new formula columns, a pivot view summarizing the regional split, and conditional formatting flagging the outliers. You save it as "Q3_Forecast_v2_FINAL.xlsx" and send it off. Nobody in that email thread stops to think about where the file came from, or can they find it when they need to verify the numbers in a month Now multiply that by every analyst on your team, every deadline of the quarter: that's hundreds of files living outside the analytics tool. The report didn't disappear. It just stopped being the governed data the rest of the business, and your AI

Most AI agents can write SQL. The problem is they write it against the wrong definition of revenue, for the wrong team, using the wrong business rules, and you won't catch that in a demo. Before you evaluate a single semantic layer vendor, there are four things worth getting right first. The semantic layer market has never been more crowded. Every major analytics vendor, data platform, and BI tool now claims to have one. What most of them won't tell you: the technology is only as trustworthy as the decisions you made before you opened the procurement spreadsheet. Why Does Governance Come Before the Vendor Demo? A semantic layer is only as trustworthy as the governance behind it. Before you evaluate tooling, confirm your organization has a program that unites business and technical teams with clear data ownership. Without that foundation, even the most sophisticated semantic layer will still produce inconsistent answers at scale. This isn't just a process recommendation,

You’ve rolled out a modern data stack, built self-service dashboards, and empowered your team to ask their own questions. Job done, right? Not quite. The data landscape is shifting rapidly beneath our feet, which makes it critical to understand how to build your AI for BI platform so you can scale and navigate technology evolutions. I had the pleasure of sitting down with Jane Smith, ThoughtSpot’s Field Data & AI Chief Officer (EMEA), for a fireside chat at the CDO Retail Exchange in London on The Power of Trust + Context for Agentic Analytics . We talked about what comes after self-service analytics: the transition to AI-driven, agentic analytics. At Huel, this journey hasn't just changed how we interact with numbers; it has fundamentally redefined the role of my data team and how we drive commercial growth. If you missed the session, here is my insider blueprint on how a fast-moving consumer brand scales AI analytics without a Silicon Valley budget. 1. Being "Commercial

Spotter Memory is now generally available. 📌 Key takeaways 1. Spotter Memory is generally available in ThoughtSpot. Spotter keeps the business definitions and query paths it learns instead of starting every session cold. 2. Memory comes in two forms: rules, which define what a metric means in your business, and recipes, which capture how a recurring question gets answered. 3. Spotter learns memory from your conversations, from the Liveboards your teams already trust, and from your connected apps. 4. Every memory is inspectable in Show work, correctable in the conversation, scoped to the data models you use, and governed by the permissions you already set. 5. Spotter can be set up at the personal user level, also along with data model level and organisation level. 6. You teach Spotter once. Spotter Memory makes the right answer reproducible across people, sessions, and repeat attempts. Imagine a retail analyst asks an agent for monthly active users. And the answer comes back in seconds,

ThoughtSpot’s Native Connector Brings Governed Agentic Insights to Every ClickHouse Customer Agentic analytics, embedded customer-facing reporting, and everyday business metrics now demand the same thing: performance at massive scale, and answers fast enough that a business user never notices the wait. Most generic databases were never designed for that combination. They assume a small population of analysts writing SQL and query latency measured in seconds, not AI agents and business users asking questions around the clock. That gap is exactly why ClickHouse, a database purpose-built for high-concurrency, high-volume analytical workloads, has grown its customer base so fast over the past few years. ThoughtSpot has been database-agnostic since day one. Being open by design means we connect natively to the warehouses, catalogs, and specialized databases teams already run, rather than asking anyone to move data into a proprietary store first. Our newest native connector extends that same

📌 Key takeaways 1. The field separates on challenging and hard questions: Spotter answered 70.6% of the challenging band, while the lowest engine managed 46.8%. 2. Accuracy slope, not headline accuracy, is what predicts performance on your data. 3. Swapping in a newer model added 3.4 points overall with no change to the agent harness underneath it. 4. Full protocol, confidence intervals, and per-question results are available on request. Analytics vendors claim their AI answers questions accurately, but almost none of them will show you how they checked. The standard move is a percentage with no denominator: "90%+ accuracy on internal benchmarks." No dataset you can download. No scoring method you can inspect. No competitor runs under the same conditions. You're asked to trust the grade without ever seeing the exam1 We ran the exam in public terms instead. This summer we put Spotter and four competing agentic analytics engines through BIRD , the most widely cited public

Putting Data to Work Requires Someone to Build for Everyone Every organization says it wants self-service analytics, but very few have it. What actually stalls true self-service analytics is the invisible work that has to happen before anyone can ask a data question: the semantic layer, query engine, security model, and analytics app. That work lands on your product and data teams, who are already time-strapped to deliver core features and strategy, and it lands on them again every time a new question, a new tenant, or a new dashboard request arrives. So the backlog grows, and business users wait. The promise of putting data to work across the business stays a slide in someone's strategy deck. The real bottleneck comes down to two problems: the cost of building and maintaining that analytics layer as definitions and context change, and the gap between a business question and an answer someone can actually trust. ThoughtSpot Is Now Live in the ChatGPT Work Plugin Directory ThoughtS

If you’re feeling overwhelmed by the pace of change, you should be. Innovation is outpacing organizations’ ability to adapt and even to absorb what’s new, what’s possible, and what’s important. We came across an article at the start of the year that suggested that a new top leadership skill is “critical ignoring." It’s an interesting suggestion. The one thing we do know: continuous learning is a must for data and AI leaders. Those who learn the fastest and ignore the noise will be well equipped to keep up with the latest innovations and strategies to lead their companies through this tumultuous time. The ThoughtSpot Analytics and AI Center of Excellence team has once again culled the best-seller lists, watched LinkedIn for great book reviews, and cultivated our annual must-read list. Be sure to check out our light-hearted beach reads too! The list is sorted alphabetically by title. If you think I overlooked a critical one, do let us know! We learn best from one another. Tune into

Imagine handing a new developer an SDK, a stack of docs, and a deadline. They read for twenty minutes, write ten lines, break something, go back to the docs, second-guess a prop name, and try again. Now multiply that by every component they'll embed, every agent, every color scheme, and every project they'll touch. That gap, between "I know what I want this to look like" and "I know the exact syntax to make it happen," is the tax every developer pays on embedded analytics. SpotterCode has been closing that gap in AI-native IDEs like Cursor, Claude Code, and more for a while now, connecting a developer’s editor to ThoughtSpot's SDK and API knowledge through an MCP server. Now, it’s also available in the Developer Playground, right where developers already go to prototype before they build. What It Does In the Developer Playground, pick a component and type what you want: “Embed a Liveboard” “Embed Spotter and rename the agent to Ava” “Make the header t

Foreign Filing filed 2026-09-03

Retailers are making heavy investments in AI. From interactive virtual shopping assistants to automated supply chain tools, the goal is simple: connect with buyers and drive growth. However, realizing real business value requires bridging a critical trust gap. So why did ThoughtSpot team up with YouGov to survey 4,833 adults across the US and the UK? It all comes back to trust. Because as an agentic analytics company, we recognize a reality many technology vendors overlook: an AI model is only as reliable as the underlying data. When retail AI fails (whether by pushing a bad product recommendation, misjudging a customer’s budget, or miscalculating delivery timelines), it is not just a technical error. It damages customer trust. Our survey report , titled Trust, Tested , reveals a clear split between retail strategy and customer expectations. The primary finding sets the tone for the entire study: 74% of consumers say retailers already collect too much of their personal data. When shopp