Updated April 29, 2026
The Math Behind the AnswerI'm hosting a session on May 20. Here's what we'll dig into. Agentic AI looks impressive in a demo, until you ask it a question your CFO actually cares about. If an AI agent can't reliably answer “What was net…
Updated April 28, 2026
Why I Stepped Off the Board and Joined AtScale as CMSOWhat I saw from the board, and why it matters if you’re betting your career on AI. When I joined AtScale’s board, I saw what looked like a best-in-class semantic layer: a way to make analytics faster, more consistent, and…
Updated April 23, 2026
5 Reasons Enterprise AI Leaders Should Attend Semantic Layer Summit 2026If your organization has invested in AI and isn’t seeing returns, the problem probably isn’t the LLM. It’s the foundation underneath it. The enterprises getting AI into production built semantic infrastructure first: consistent business definitions, governed metrics, and shared context…
Updated April 1, 2026
Not All Semantic Layers Are Built for AIThe semantic layer has moved from niche infrastructure to a boardroom priority. Snowflake announced one. Databricks announced one. Google repositioned Looker as one. Microsoft repositioned Power BI as one. This is genuinely good news. As ISG analyst Matt Aslett observed…
Updated March 27, 2026
AI vs. BI: Differences, What Changed, and Why Semantics Became CriticalEnterprise analytics was never just about data. It was about trust and confidence, getting the right people the right information so they could make intelligent decisions and keep things moving forward. The BI stack was made for that purpose, and…
Updated March 26, 2026
Power BI & Excel on Snowflake Without Data Movement | Semantic Layer for AIMost enterprises have already standardized where their data lives. What they haven’t standardized is how that data is interpreted. Power BI and Excel users still rely on a workaround: moving data out of Snowflake into extracts, cubes, or Microsoft Fabric…
Updated March 17, 2026
Why Moving Data for BI and AI Creates Architecture ProblemsIn modern data architectures, analytics tools often replicate data from the warehouse into secondary systems to improve performance. While this can speed up dashboards, it introduces new problems: duplicated datasets, inconsistent metrics, fragmented governance, and rising cloud costs. Modern analytics…
Updated March 12, 2026
Hits and Misses from Gartner Data & Analytics Summit 2026Gartner opened the Data & Analytics Summit 2026 with a memorable metaphor. The keynote framed the current moment as a raft ride through a roaring, treacherous river. The river represented AI. Fast-moving. Powerful. Difficult to control. The message was clear.…
Updated March 11, 2026
AI Can’t Run on Untrusted Data: Why Agentic Analytics Requires Live Semantic ConnectivityA logistics company deploys an AI agent to monitor inventory and automatically trigger reorders. The model is solid. The prompts are well-engineered. The agent connects to their analytics environment and starts running. Three weeks in, it fires a reorder for…
Updated March 5, 2026
Autonomous AI Without a Semantic Layer Is Just Expensive GuessingI've been in the BI and data space long enough to recognize when something fundamental shifts. Denny Lee and I first worked together when he was at Microsoft on SQL Server Analysis Services (SSAS), and I was a customer building…
