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By Jessica Hwang
Your data engineers have spent months getting your metric definitions right: revenue recognized the way finance approved it, churn calculated the way your exec team aligned on it, and pipeline logic that your rev ops team actually agrees on. And then a new tool arrives, and someone has to do it all again.
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By Katie Flynn
More business teams are doing their thinking inside Claude and ChatGPT than ever before. Research, planning, analysis, content: it's all happening inside LLM platforms now. But the moment someone needs an answer grounded in actual enterprise data, the workflow breaks. They leave the AI, open the BI tool, run the query, copy the result back. Context lost, momentum killed. That's the problem we set out to solve when we launched ThoughtSpot's Agentic MCP Server back in July.
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By Antonio Scaramuzzino
Most teams deploying AI agents on their data are watching the wrong things. They check whether the query ran and whether the number looks plausible. When both checks pass, the agent gets credit for a correct answer, and the output flows into dashboards, decisions, and the next agent in the chain. There's a gap between those two checks and actual correctness, and it's where the expensive mistakes live. Getting to a correct answer requires more than a formally valid calculation.
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By Ashok Anand
4,096 Tasks completed 89.8% success rate 302 Active users 4× growth Jan→Mar 86 Agents deployed 73 built by engineers 72 days In production 15,896 messages Modern engineering teams face a familiar paradox: the bigger the system, the more time engineers spend managing the work rather than doing it. Bugs pile up faster than they can be triaged. PRs wait days for review. On-call engineers spend hours reproducing what someone already debugged six months ago.
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By Damian Waldron
In 1799, soldiers near Rosetta, Egypt, unearthed a stone carved with the same decree in three scripts: hieroglyphs, Demotic, and Ancient Greek. Because scholars already understood Greek, it unlocked a language—and with that, a civilization’s worth of knowledge that had been dark for over a millennium. We’re at a similar inflection point in enterprise data.
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By Lindsay Lukens
AI-powered analytics is everywhere right now. But the payoff? Not so much. Two patterns show up again and again. The first is an “AI everything” backlog that expands faster than teams can deliver. The second is an insights bottleneck that still forces the business to wait in line for basic answers while analysts drown in ad hoc requests.
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By Jessica Hwang
Your data’s never lived in one place. Customer records might be in your CRM, while sales and operational metrics are split among data platforms. And somewhere, there's critical budget data living in a spreadsheet, owned by a single person on the finance team. Bringing it together has always come at a cost of speed vs. governance.
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By Peeyush Vardhan
Every analyst has been there: Deadline looming, data in hand, and a BI tool that either requires a workflow you haven't learned or a colleague you can't reach. So you open Excel. It's familiar, it's flexible, and it works right now. So that's where the work happens—and now where insight stays, ungoverned and invisible to your team, your analytics stack, and your agents.
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By Nishant Taneja
We launched Spotter with one goal: give every enterprise team their own analyst—an agent that reasons through business complexity, validates its own outputs, and surfaces answers you can actually act on. The response from customers made one thing clear: the ThoughtSpot foundation works. Teams trust Spotter, because it doesn’t only rely on an LLM to reconstruct your business logic on the fly—a process that produces different answers depending on how a question is phrased.
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By Damian Waldron
AI can now generate SQL, build dashboards, and answer questions in plain language. But generating queries isn’t the same as understanding a business. The model might not know which revenue definition finance approves, how your fiscal calendar works, or which fields require restricted access. As AI agents become the front door to analytics, the real challenge isn’t query generation; it’s semantic grounding. That’s where the Agentic Semantic Layer becomes essential.
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By ThoughtSpot
Check out what’s new in ThoughtSpot’s latest release: Spotter learns from your Liveboards and conversations to deliver smarter, more context-aware answers over time. It's now easier than ever to turn complex business questions into trusted, beautifully designed Liveboards with SpotterViz Seamlessly import formulas, measures, and dimensions from Snowflake into ThoughtSpot for a single source of truth across your stack.
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By ThoughtSpot
Shub Agarwal (Founder of the AI Trust Lab at USC) flips the script. Stop over-investing in massive data overhauls. Instead, reverse your approach: start with a brutal business problem, pull only the specific data needed to solve it, and build incrementally. Chief Data & AI Strategy Officer Cindi Howson agrees that true value comes from scaling immediate business impact, not waiting for a flawless architecture that will never arrive.
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By ThoughtSpot
Understand how to close the gap between AI experimentation and enterprise production. Shub Agarwal, Founder of the AI Trust Lab at USC and author of Successful AI Product Creation: A Nine-Step Framework, shares his AI product management framework for taking enterprise AI strategy from demo to production, drawing on two decades of product leadership at Amazon and Fortune 50 firms. He breaks down why experimentation must tie directly to business OKRs, the four mindset shifts leaders need to scale AI responsibly, and how the AI Trust Lab is building a benchmark evaluation framework for AI model trust and governance.
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By ThoughtSpot
AI fixed the M&A data nightmare, but @SPGlobalMarketIntelligence 's Saugata Saha says the real win is shifting your mindset. Stop treating data as a byproduct...make it your competitive edge! Catch the full discussion of podcast on any podcast platform.
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By ThoughtSpot
Is your system performance taking a hit from hidden data clutter? Admins are constantly looking for ways to streamline their environments, and finding unused content is the fastest way to get results. In this video, we dive into how you can utilize the User Adoption Liveboard to instantly track down abandoned Liveboards and Answer Cards that haven't been accessed in over 90 days. You'll learn how to easily copy and recalibrate existing answer cards, set up custom organizational filters, and pull the exact lists you need to perform a high-impact cluster cleanup.
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By ThoughtSpot
Is your organization actually driving value with AI, or just scratching the surface? @SPGlobalMarketIntelligence's Saugata Saha joins to share the 4-part journey to real AI maturity. He warns that without a deliberate action plan to completely transform day-to-day employee workflows, any productivity gains will just be "squishy" metrics that fail to impact the bottom line. Head over to your favorite listening platform to catch the full episode!
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By ThoughtSpot
Want your data strategy to actually drive revenue? @SPGlobalMarketIntelligence’s Saugata Saha and ThoughtSpot’s Cindi Howson break down why data strategies fail when they disconnect from business goals. To win, you need to solve real customer pain points and move past the bottleneck of report prep. Watch the new episode of on your preferred listening platform! Music: “The Clermont” by Flash Fluharty Licensed via PremiumBeat, ID: P9IHFMDYNZCKLEFZ.
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By ThoughtSpot
Learn how ThoughtSpot Sync enables you to operationalize insights from ThoughtSpot for the business apps you know and use every day, including Slack, Google Sheets, Microsoft Team, ServiceNow, Salesforce, HubSpot, and more.
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By ThoughtSpot
Enjoy this lesson from the Customizing Charts and Visualizations course in the Business User learning path on ThoughtSpot U. This lesson covers the basics of Customizing Visualizations to get the desired results.
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By ThoughtSpot
Learn what happens when the executive accountable for data strategy is also the executive accountable for the business results that depend on it. Saugata Saha, President of S&P Global Market Intelligence and Chief Enterprise Data Officer at S&P Global, shares how he manages one of the world's largest financial data estates while driving business outcomes across public and private markets. He breaks down the four pillars of S&P Global's data strategy, the federated organizational model that connects data teams to business value, and why capturing ROI from AI requires deliberate workflow transformation.
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By ThoughtSpot
For more than 20 years, dashboards served as a foundational element of business intelligence, helping leaders visualize and share valuable data across their organization.
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By ThoughtSpot
Businesses today run on apps, and those apps run on data. Too often, however, the technical complexity required to surface and explore that data for additional analysis prevents users from doing so. With ThoughtSpot Everywhere, organizations are easily building new data apps powered by the simplicity and ease of use of ThoughtSpot, or adding ThoughtSpot services to their existing SaaS offerings. This is giving them the unprecedented opportunity to create product experiences that stick, monetize data in new ways, and harness data right within existing tools.
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By ThoughtSpot
We are living in an unprecedented time driven by rapidly changing economic scenarios, the rise of digital native organizations and growing digital revolution, and the emergence of transformative business models. At the heart of much of this revolution is data. Organizations are collecting, analyzing, and mining data at an accelerated rate, creating new opportunities for powerful insights that deliver significant business impact.
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By ThoughtSpot
Today, just 24% of organizations say they've succeeded at becoming data-driven.* This is a challenge many data leaders are still struggling to solve despite increasing demand for data-driven insights from business users. Migrating to a cloud data warehouse is a good first step-and many have done so-but introducing new technology is not the same as ensuring adoption. To truly reap the benefits of your cloud data warehouse investment, you need an equally fast, scalable, and easy-to-adopt analytics solution to make your cloud data available to all.
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By ThoughtSpot
Despite huge investments in data and analytics over the last two decades, many companies are still struggling with how to become truly data-driven. What are data leaders doing at the organizations that have figured it out? In this white paper, DATAcated Academy's Kate Strachnyi explores four key strategies for critically evaluating your entire data and analytics stack and systematically removing the barriers that exist between their business users and business-critical insights.
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By ThoughtSpot
Although making predictions about the future is difficult even under the best of circumstances, it's never been more important for business leaders to focus, prioritize, and act in order to stay ahead of the technological curve-and the competition. The strategies you used to innovate and grow your business in the past will not be the same ones you use today. Rethinking how you use data to react and proactively adapt to change will be critical to your bottom line.
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ThoughtSpot is the Modern Analytics Cloud company. With ThoughtSpot, you can put the full power of your modern data stack in the hands of every employee and customer with consumer-grade analytics.
Why Everyone Loves ThoughtSpot?
- Instant Insights for all: Stop waiting for custom reports from data experts and instantly answer ad-hoc data questions on the fly.
- Unleash the value of your cloud data: Maximize the value of your cloud data warehouse and accelerate speed-to-insight for everyone across your business.
- Build Interactive Data Apps: Drive adoption by embedding search and insight-driven actions into your apps using our low-code developer-friendly platform.
- Bye-bye backlog: Empower non-technical people to answer their own data questions, while you build a single source of truth with security and governance at scale.
Welcome to the Modern Analytics Cloud.