When your team asks the same question in Power BI, in Excel, and through an AI agent, the answers should match. That consistency comes from defining your business rules once, upstream in the lakehouse, so every tool reads from the same place.

In this episode, Brick Thompson and Landon Ochs walk through the platinum layer, which takes the business logic sitting in your semantic models and the cleansed data in your gold layer and bakes both into materialized tables an LLM can query directly. They cover what stays in the report for runtime behavior like security and date slicers, how to settle a definition of revenue that sales, finance, and the CEO all recognize, and how role-based context can serve the right version of a metric to whoever is asking.

They also get into authentication, since MCP servers can run on the same Entra security groups you already manage, and into the other agents that can read your data once it is in a consistent state.

Chapters

00:00 Same question, same answer across Power BI, Excel, and NLQ
00:53 One place where the logic lives
01:40 Where the platinum layer sits in the medallion architecture
02:20 What belongs in the semantic model at runtime
03:00 Combining gold layer data with semantic business logic
03:39 Materialized tables and defining revenue for your business
04:46 Handling competing metric definitions, including role based
05:30 Context files, MCP servers, and measures in the lakehouse
05:54 Entra authentication, tool permissions, and agents beyond NLQ
07:35 Updating business logic in one place

We're Blue Margin, a managed data services firm working with mid-market and PE-backed companies.

Learn more: https://bluemargin.com

Microsoft is folding AI into nearly every part of Fabric, and the Fabric Data Agent is now generally available after a long run in preview. Brick and Landon talk through what they've found in testing: where natural language querying holds up, what it takes to get consistent answers, and why we've been adding a platinum stage to the medallion architecture with context files that tell the model how the business actually works.

Landon also covers Microsoft's open-source Fabric skills, the PBIP and PBIR report format that finally puts Power BI into source control, and the validation loop where the AI generates a report, screenshots it, catches its own broken visuals, and corrects them. Both come back to the same conclusion: the results depend on a strong data foundation and someone who knows their way around Power BI.

The Dashboard Effect is hosted by Blue Margin. We build the data foundation that mid-market and PE-backed companies run their reporting and AI on.

Learn more: https://bluemargin.com

An MCP server gives an LLM a standard way to reach your data and run tools against it. Every API is built differently, and the Model Context Protocol puts one interface over all of them, which is why Anthropic's standard gets compared to USB C.

This episode covers what the standard defines, how tools and resources set the boundaries on what an AI can do with your systems, and what you get by writing a server yourself. We built one for our own AI-ready data model and hit a context problem along the way: the model would skip the business context files and bluff its way through an answer, then admit it never read them when asked directly. The fix was a token buried in those documents that the server checks before it will run any other tool.

If you work in data, BI, or analytics engineering and you are weighing a vendor's MCP server against building your own, start here.

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Blue Margin builds and manages data platforms on Microsoft Fabric and Power BI for mid-market and private equity backed companies. Our Managed Data Service consolidates your systems into one governed source, delivers the dashboards your team runs on, and maintains them as your business changes. Learn more at bluemargin.com.