ThoughtSpot

Webinar recap: Andy Cotgreave (co-founder, How to Speak Data) and Francois Lopitaux (SVP of Product, ThoughtSpot) on hallucinations, accountability, and what it actually takes to trust AI with your data. 📌 Key takeaways 1. Polish is not proof. A wrong AI answer looks identical to a right one, and we're wired to trust anything that looks clean and authoritative. 2. If you can't validate it, you're flying blind. Accountability lands on whoever signs off on the number, so any tool worth deploying has to show its work. 3. Determinism comes from architecture, not better prompts. Keep the LLM away from the step where it's most dangerous (generating the query) and the same question returns the same answer every time. 4. "Single source of truth" is marketing. The realistic goal is one governed, shared definition of what your metrics mean before the AI starts answering. 5.The analyst isn't disappearing; the job is changing. The role shifts from building dashboards to managing agents and owning

How PAYBACK Upgraded from Legacy BI to Agentic AI with Spotter At the recent data:unplugged 2026 in Münster, Europe’s biggest festival for data and AI, the stage was set for a masterclass in data transformation. Julian Stock, Analytics Reporting Team Lead, and Andreas Weiß, Senior Reporting Engineer, from PAYBACK , Germany’s premier loyalty program, shared the stage to detail a decade-long evolution: the journey from a strict, ticket-based reporting system to a thriving, AI-ready data culture. In a world where transactional data moves at lightning speed, PAYBACK’s story is a blueprint for any organization looking to scale. How to Break the "Ticket Bottleneck" Before 2016, PAYBACK faced a challenge familiar to many large enterprises. Every minor report adjustment required manual changes to the underlying Datamarts, causing weeks of delay. Analysts were buried under a mountain of tickets, and business users—frustrated by the wait—often reverted to "Email and Excel," c

Why your AI agents are only as smart as the data underneath them A CFO asks her AI agent a simple question: "What was our ARR at the end of Q3?" The agent finds the subscriptions table, spots a column called arr, sums it up, and returns $16.4M. Strong quarter. Everyone nods. The real number was $13.9M, but no one in the room knew it yet. I hear some version of this story from nearly every data leader I talk to right now, and it almost always starts the same way. They stand up an AI pilot. It looks sharp in the POC. Then it goes to production and starts confidently generating wrong answers at scale. So they go hunting for a better model. But the model was never the problem. The problem is what the model knew about the business, which is to say: almost nothing. It didn't understand the fiscal calendar, the product taxonomy, or that "ARR" means three different things depending on who's asking. That's not a model gap. That's a missing layer. I'v

With ThoughtSpot and Snowflake 📌 Key takeaways 1. AI-ready data architecture is the foundation of successful agentic analytics: governance and standardization must come first. 2. Removing the analyst bottleneck requires a semantic layer that makes data accessible to every business user, not just specialists. 3. Booking.com's shift to ThoughtSpot eliminated dashboard dependency and created a single, trusted source of truth across the organization. At Snowflake Summit '26 , Chris de Groot, Manager of Data Engineering Customer Service, and Jay Stricks, Group Product Manager, Insights Platform, took the stage to share Booking.com's massive data transformation. In their session, "Booking.com's Data Travels: Platform Foundations to Agentic Analytics," they laid out a masterclass on how to make a colossal, fragmented data landscape entirely AI-ready. At the heart of their presentation was a core realization: AI doesn't replace the need for data architecture—it ac

At the recent Big Data & AI World in Frankfurt , Midas Pharma shared a narrative of transformation that resonates with any organization buried under manual reporting. For Midas, a global B2B leader in the pharmaceutical value chain, the stakes are high: they manage everything from active ingredients to manufacturer audits and regulatory submissions. The Starting Point: Escaping “Excel Hell” In the beginning, Midas Pharma faced a classic "Excel Hell." As Max Maelike, Head of Data Science , recalled, the complexity of the B2B pharma world meant that every department had different requirements. Before ThoughtSpot, this led to a culture of ad hoc Excel analyses that were outdated as soon as they were saved. The business risk was tangible. "Identifying new value-generating opportunities was mostly a matter of luck," Max noted. There was no single source of truth, and hours were wasted cleaning data rather than analyzing it. The Evolution: From Reports to “Agentic” In

📌 Key takeaways 1. Static dashboards can't keep pace with modern foodservice. Strategic, tactical, and operational cycles are collapsing, and businesses need answers fast enough to change tomorrow's decisions 2. Foodstep embedded ThoughtSpot to give customers real-time access to Dutch foodservice market data. Questions about segment shifts, volume trends, and category performance are now answered in seconds, in plain language. 3. The result is a higher-value customer relationship, not just a faster tool. With manual reporting gone, Foodstep's Customer Success Managers focus entirely on strategic counsel. The world of foodservice is changing faster than ever. Consumer preferences are shifting rapidly, margin pressures are intensifying, and market elasticity demands continuous adaptation. In this highly dynamic landscape, having data is no longer a luxury or a rearview mirror—it is the vital compass used to steer strategic choices. As the premier knowledge expert in the Dutch foodservice

📌 Key takeaways 1. 150 years of football data, queryable in plain language. Spotter lets anyone interrogate international match history directly with no dashboards, no SQL. 2. Predicting the group winners: By crunching 6 years of historical form and official FIFA rankings, Spotter forecasts who will top each group before a single ball is kicked. 3. This updates throughout the tournament. New Spotter insights publish at each milestone as live match data flows in daily. BLOG UPDATE - 6/30 Trusting the Data: Spotter's Knockout Breakdown In our last post, we put our AI data analyst, Spotter, to the test by predicting which teams would navigate the group stage and claim a spot in the historic, expanded Round of 32. Spotter successfully predicted 29 of the 32 teams that punched their ticket to the knockout rounds! While most of the heavy hitters advanced as expected, soccer is defined by its chaos, and the data models caught a fascinating outlier. The Most Surprising Qualifier: South Af

I had the distinct pleasure of hosting a Snowflake Summit ‘26 session with Agustin “Augie” Del Rio , CEO and Founder of Gallus Insights, an analytics platform tailored specifically for mortgage lenders. As we sat down to discuss the future of analytics, one core truth echoed throughout the room: the most ambitious AI goals live or die by the quality of the underlying data. To truly harness the power of next-generation agentic analytics, organizations must ensure they have a high-performance data infrastructure. Gallus Insights has done exactly that. By building a robust, scalable architecture on Snowflake and seamlessly integrating ThoughtSpot Embedded, Gallus is delivering a conversational analytics experience to their end users (mortgage lenders), delivering intelligence where and at the speed they need it. Today, Gallus is experiencing 10x faster performance and reduced data costs , turning complex data into a massive competitive advantage for their enterprise clients. Here is my re

📌 Key takeaways 1. The BI backlog is a strategic liability. When critical questions require analyst intervention, insights arrive too late to act on. 2. Agentic analytics lets finance teams ask questions in plain language and get governed answers across data domains, without intermediaries. 3. Trust has to be designed into the data foundation—shared metrics, governed definitions, transaction-level detail—before the intelligence layer goes on top. 4. Finance teams seeing real results are redefining what analysts do: strategy and modeling, not report production. 5. The data foundation you build today determines how much value agentic analytics can deliver tomorrow. Finance leaders are operating in one of the most demanding macro environments in recent memory. Interest rates are moving faster than most models anticipated, reshaping the cost of capital almost overnight. Supply chain fragility has also turned working capital management into a moving target, and geopolitical uncertainty is c

Live from Snowflake Summit '26 , tech leaders from around the globe gathered to discover how the world’s most innovative companies are making AI real for business. But few sessions delivered as much raw, practical insight as the one presented by Frankie Woodhead , Chief Product & Technology Officer at Thrive Learning . Heading up a fast-growing, £20m ARR LearnTech business that serves over 500 global customers and 5 million users, Woodhead didn't give a standard product pitch. Instead, he delivered a deep dive on a critical strategic decision: when to stop building your own data tools and start partnering with trusted leaders. Here is the story of how Thrive Learning transformed its culture, slashed costs, and rolled out cutting-edge AI analytics to tens of thousands of users in a matter of weeks. The Enterprise Challenge for Thrive Learning: Proving Impact at Scale Thrive isn’t just a traditional Learning Management System (LMS)—it’s an all-in-one platform for deskless w

It's the day before your POC, and the embedded analytics demo still looks like it belongs to someone else. Your designer handed over a brand guide last week. Your developer has been buried in CSS variables ever since: cross-referencing token names, mapping changes across components, reloading the page after every tweak to see what broke. The UI is almost right. The nav color is close. The typography still isn't matching, but there's no time left. This is the hidden cost of embedded analytics customization. The styling system is powerful: ThoughtSpot exposes granular CSS variables , allowing you to style individual components with precision. But translating brand intent into working theme variables typically requires deep product knowledge, patient iteration, and more time than anyone budgets for. Getting it exactly right often means looping in a sales engineer, extending timelines, or shipping a POC that doesn't quite look the part. AI Mode in Theme Builder is built