What's New in ThoughtSpot's Latest Release | 26.2
Check out what’s new in ThoughtSpot’s latest release!
Supercharge Retail Growth: Get Qlik & Snowflake's Expert Guide
According to a recent report, 90% of retail leaders say they began experimenting with gen AI solutions and scaling priority use cases. Retailers are looking to update data systems and boost AI for risk, cost reduction, and growth need an all-encompassing approach — and that means efficiently blending AI and analytics into operations. Learn how to use data and AI to manage risk and drive growth for your organization, see how Qlik and Snowflake are making this happen today.
The Five Pillars of AI Compliance Excellence
The AI revolution in finance is no longer a question of “if” but “how fast” and “how responsibly.” While our previous posts explored AI auditability frameworks, agentic workflows that transform finance operations, and building AI native Finance teams, today’s CFOs face an equally critical challenge: successfully navigating the complex and rapidly evolving landscape of AI compliance.
Build agentic AI in minutes on Snowflake
Agentic AI doesn’t have to mean months of architecture work, custom orchestration layers, or external platforms. In this hands-on workshop, you’ll build Snowflake Intelligence agents using native Snowflake capabilities to reason over structured data, retrieve context from unstructured sources, and execute multi-step analysis directly inside Snowflake within minutes.
Cloudera Account 360: New Self-Service Administrative Platform Demo
Cloudera Account 360 is designed to resolve this by providing a single pane of glass from which customers can manage their users and accounts. It offers robust, flexible, and secure account as well as user management capabilities, helping you avoid delays by eliminating the need to raise support cases with Cloudera for simple administrative tasks. Foundational Features Available Now Cloudera Account 360 includes two core feature sets.
How to Choose Between Server-Side and Web-Based Reporting
Any time an organization closes the books or practices the governance needed to meet regulatory demand, it relies on reporting. Reporting technology is designed to save time while increasing accuracy, but many long-standing reporting vendors are existing or de-emphasizing this still-essential space. This leaves a gap for teams that need a modern, production-grade reporting solution. And when choosing between reporting tools, architecture is important.
insightsoftware Data + Analytics Wins 2025 Dresner Technology Innovation and Application Innovation Awards for Embedded BI
For the fifth consecutive year, Dresnor Advisory Services has recognized insightsoftware Data + Analytics as a top Embedded Business Intelligence solution. This year’s dual recognition, winning both the 2025 Technology Innovation Award and 2025 Application Innovation Award, reinforces Logi Symphony’s position as the market’s premier solution for embedding analytics directly into applications.
Ep 60 | How AI FOMO and Lost Focus on Culture Harm ROI with Jen Stirrup
As AI and BI projects race to deliver quick wins, many organizations overlook what truly determines long-term success: the people behind the data. In this episode of The AI Forecast, Jen Stirrup, author, speaker, and founder of Data Relish, joins host Paul Muller to dissect the difference between data literacy (reading the numbers) and data fluency (speaking the language of business). Jen challenges the industry’s obsession with AI FOMO, warning that rushing to deploy models on shaky cultural foundations is the fastest way to derail ROI.
How Nasdaq Architected a $90 Trillion Data Ecosystem
Discover how Nasdaq uses data platforms at a massive scale to power markets and prepare for AI. Angie Ruan, Nasdaq’s CTO of Capital Access Platforms, explains how large-scale data systems support market integrity, transparency, and decision-making across public and private markets. She defines what it really means to be AI-ready, how leaders should modernize data platforms, and how market fundamentals help separate real AI value from hype.
AI Analytics with Databox
You know the feeling. It’s Monday morning, and someone asks, “How are we doing?” Suddenly, you’re toggling between six tabs, exporting CSVs, and trying to remember which dashboard has the number they actually need. By the time you’ve pulled everything together, the meeting’s over. This was the problem we originally built Databox to solve: centralizing scattered data into dashboards that actually make sense. But dashboards were only the first step.