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15 posts
August 4, 2026
Austin asks whether a hub-and-spoke composite model pattern is the right structure. Mike and Tommy say the shape is defensible but they'd reach for master-and-mini models instead — and that every composite model needs a data contract, change control, and git-backed monitoring.
July 14, 2026
Agents will not run your data governance program — most of governance is a committee, not a computer. What they will do is the mundane discoverability work: cataloguing models, surfacing ambiguous definitions, and making lineage human-readable.
July 2, 2026
A mailbag question about 30-60-90 day AI plans turns into an honest accounting of why organizations are stuck: IT doesn't know what an agent is, nobody has bought into the value yet, and there is essentially no training. Mike and Tommy separate AI strategy from AI theater and each give a real 30-60-90.
June 25, 2026
Part two picks up at the skills and validation layers of Anthropic's analytics stack, and Tommy arrives at a hot take he'll defend: no agentic solution on your Fabric data until governance is in place. Along the way — why perspectives are Power BI's most untapped feature and why Microsoft needs skills for models.
June 23, 2026
Anthropic published how they run self-service analytics internally, and the answer starts with dimensional modeling and a semantic layer — not with the agent. Mike and Tommy read it as validation that everything Power BI professionals already do well is now the thing that makes AI work at all.
January 21, 2026
Mike and Tommy unpack a viral LinkedIn article where a consultant saved a client $57,000 per year by moving from Fabric F64 to Premium Per User — sparking a bigger conversation about when Fabric actually makes sense, licensing confusion, and the tipping points that justify the investment.
December 31, 2025
Mike and Tommy unpack Fabric IQ's new ontology feature and why it will create friction in organizations—from cultural resistance to political turf wars over data definitions. They explore MCP vs. IDE workflows and debate whether businesses are mature enough for enterprise-wide semantic alignment.
December 24, 2025
Mike and Tommy explore the emerging 'Intelligence Developer' role—a new position shaped by AI and Fabric IQ that shifts focus from metrics to dimensions and data governance. They also recap the biggest SQL, Fabric, and Power BI developments of 2025.
December 19, 2025
Mike and Tommy ask the big question: who in your organization is going to build the Fabric IQ ontology? They explore a 'backwards' approach—starting from semantic models you already have—and debate whether the BI team, data governance, or a new role should own it.
December 12, 2025
Mike and Tommy explore what data governance looks like in the Fabric IQ era, unpacking how ontology changes the governance conversation from policing data to defining business meaning. Plus, notebooks in pipelines now support service principals and workspace identity.
May 20, 2025
Mike and Tommy debate whether the Power BI and Fabric ecosystem is still truly self-service or quietly drifting back toward corporate BI, exploring semantic model-to-lakehouse sync, data contracts, managed self-service, and where the line between central IT and business users really falls.
April 2, 2025
Mike and Tommy dive into data contracts and how they apply to Power BI and Microsoft Fabric environments. They explore why formalizing expectations between data producers and consumers is key to building trustworthy, scalable data platforms.
February 2, 2024
It's mailbag time. In Ep. 290, Mike, Tommy, and Seth answer listener questions on adopting Power BI in a Google/BigQuery environment, surviving terminology changes like 'dataset' to 'semantic model', and balancing process vs. technology as tools evolve.
July 14, 2023
In Ep. 232, Mike, Tommy, and Seth break down the symbiotic relationship between data governance and data storytelling—why trust, ownership, and clear lineage are what turn dashboards into decisions.
June 28, 2023
In Ep. 227, Mike, Tommy, and Seth explore what Fabric adoption really requires: governance, ownership, and cost visibility—not just new tech. They debate OneLake discoverability and the ‘lakehouse per dataset’ question, then land on a practical distinction: data quality is the condition of your data, while data governance is the operating model that keeps it trustworthy.