Building AI-Ready Data Foundation with James Serra

What does it really take to build AI-ready data systems people can trust? In this episode of Data Builders Club, James Serra shares lessons from 40+ years in data and AI, covering data quality, trust, Data Mesh trade-offs, real-time systems, and why strong foundations matter more than hype in the AI era. Featuring insights from James Serra, Data & AI Solution Architect at Microsoft and author of Deciphering Data Architectures.

Enterprise AI Security with ClearML: A Complete Series Summary

Over a seven-part series of posts and videos, ClearML’s Enterprise AI Security series covered every layer of securing an AI platform in production, from who gets in to what gets recorded. This post brings it all together in one place: what each layer does, why it matters, and how the layers connect.

Why 90% of Data Strategies Fail to Make Money

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.

From Analytics Platform to an AI Operating System: Data Lakehouse in the Agentic AI Era

The lakehouse architecture was developed with the mission to combine the unstructured scale of the data lake with the structured performance of the data warehouse. This shift unified enterprise data and delivered the first true "single source of truth". But in 2026, the mission has expanded.

Why Enterprise Teams Are Doing xP&A Planning Directly in Their BI Tools

Most enterprise finance teams already have a BI platform they trust. Power BI and Qlik Sense power the dashboards that executives review every day. They’re where analysts spend their days, where the business goes to answer questions, and where the organization has invested years of development and governance work. So why, when it comes time to planing, forecasting, and budgeting, does everyone abandon that environment and disappear into a tangle of spreadsheets?