Systems | Development | Analytics | API | Testing

Ever wonder why so many enterprise AI projects never make it past the pilot stage? It’s not the AI—it’s the foundation. In this video, we break down why rushing into complex models without fixing inconsistent data, fragile pipelines, and afterthought governance is a recipe for failure. Fix the basics first!

Are you actually building an open data platform, or are you just using open source file formats inside a new type of vendor lock-in? Many organizations assume that migrating to Apache Iceberg or Parquet automatically makes their data architecture open. However, true architectural freedom requires a strategy that spans across your entire data estate—not just the storage layer.

Is your organization actually driving value with AI, or just scratching the surface? USC's Founder of the AI Trust Lab, Shub Agarwal, joins to share his definitive 9-step playbook to take AI from concept to production without burning your budget.

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.

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.

As organizations rush to scale AI, many are learning that better models can’t compensate for weak data foundations. AI hype is everywhere, but operational readiness still isn’t. In this episode of The AI Forecast, Paul Muller sits down with Ravit Jain, founder of The Ravit Show and one of the leading voices in the global data and AI community, to explore the trends shaping the future of enterprise AI.

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.

Welcome to Part 7 of the “Data Products for Qlik Analytics” series! In this episode, we take Data Quality to the next level by building advanced data validation rules using IF / THEN / ELSE logic within Qlik Analytics. You’ll learn how to create conditional validation logic that evaluates relationships across multiple columns, enabling smarter and more dynamic data quality checks for your data products.