New: Trusted data for the people and the AI making decisions on it

Ask three people in your company to pull the number of active customers this month, and you’ll probably get three different answers, even though each person labeled the metric the same way. One counts everyone who logged in, another counts only paying users, and a third filters down to a single plan tier. Nobody is wrong here. They’re all working from real data; they just never agreed on a single definition. Do that enough times, and the data itself becomes the thing everyone argues about.

How to Measure Embedded Analytics ROI for Busy End Users

Most analytics programs fail the ROI test for one simple reason: they measure dashboard output, not workflow impact. A team can ship reports, charts, and alerts, yet still miss the real question: does the analytics change what busy people do next? That is the core issue for embedded analytics ROI. How do we measure whether embedded analytics actually delivers business value for busy end users, frontline teams, and executives?

How Booking.com Scaled Agentic Analytics for Self-Service

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.

Ep 79 | Why Some AI Products Strike a Chord (and Others Don't)

You recognize the tune, but something feels off. That's how Marlon Davis describes many of today's AI initiatives: AI karaoke. Organizations are rushing to add AI to products, but too often they're layering technology onto solutions without fully understanding the customer problems they're trying to solve. In this episode of The AI Forecast, Paul Muller sits down with fractional Chief Product Officer at Devlnio, Marlon Davis, to explore how organizations can move beyond superficial AI efforts and build products that deliver meaningful customer value.

How to Eliminate Training-Serving Skew With a Unified Real-Time Streaming ML Pipeline (2026 Guide)

The problem. Predictive ML pipelines that maintain separate batch and streaming code paths for the same features carry training-serving skew, the gap between the features a model was trained on and the features it sees at inference time. Skew silently degrades model accuracy and doubles infrastructure cost. The recommendation. Adopt a unified streaming (kappa) architecture.