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.

Like and subscribe for straight talk on data, AI, and running a modern business on real numbers.

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.

The platinum layer sits on top of the standard medallion architecture, built specifically to get data ready for LLMs. In this episode, Brick and Landon break down what it takes to build one well.

Landon walks through the two foundations that make a platinum layer work: markdown files that give the LLM business context and call out data gotchas, and a modeling approach that goes further than typical BI denormalization. They discuss why AI models need a single grain of data to avoid summing errors, why report-specific columns need to be stripped out, and why the platinum layer gets materialized nightly instead of served through views.

They also cover the role of MCP servers in this setup, including why Blue Margin builds tightly scoped servers for business users asking direct questions and more open ones for analysts building queries.

If you've been wondering what actually separates a working AI data layer from a frustrating one, this episode covers the fundamentals.

Key Moments:

1:00 — Markdown Files & Context
2:05 — What Happens Without Context
2:58 — Fabric Data Agent Test
3:19 — Modeling for AI
4:25 — The Grain Problem
5:11 — Extreme Denormalization
5:28 — Cleaning Columns & Tables
6:37 — Nightly Materialization
7:43 — Why You Need an MCP Server
8:22 — Two Types of MCP Access

About Blue Margin

Blue Margin is a Microsoft Fabric and Power BI consultancy based in Fort Collins, Colorado. We help mid-market and private equity-backed companies build data platforms that hold up: clean architecture, trustworthy reporting, and dashboards teams actually use. The Dashboard Effect is where we talk through the technical decisions behind that work.

Learn more: https://bluemargin.com

The gold layer is where cleaned, validated data lives in a medallion architecture, ready for reporting and analysis. In this episode, Brick and Landon break down what actually makes a gold layer good.

Landon covers what "good" means in practice: deduplicated data, validated numbers, and a structure business users can trust without checking behind the curtain. They discuss why star schema still holds up as the standard modeling approach, how to handle naming conventions that make sense to analysts instead of source systems, and what happens when six different systems all claim to have "the customer table."

They also get into the messier parts of building a gold layer: matching source system reports to catch hidden filters, deciding how to relate keys across systems without collapsing records that only look the same, and building exception reports to flag duplicates back to the business instead of quietly fixing them downstream.

If you've ever wondered what separates a gold layer that works from one that just exists, this episode covers the fundamentals.

Key Moments:

0:36 — What is the gold layer?
1:00 — Combining data silos
2:01 — Modeling the gold layer
2:22 — A preview of the platinum layer
2:30 — Naming conventions
3:39 — D_ and F_ naming standards
4:08 — Validating the gold layer
4:36 — Hidden filters and reporting errors
5:13 — Relating fact tables
5:57 — Handling duplicates
6:52 — Exception reports

About Blue Margin

Blue Margin is a Microsoft Fabric and Power BI consultancy based in Fort Collins, Colorado. We help mid-market and private equity-backed companies build data platforms that hold up: clean architecture, trustworthy reporting, and dashboards teams actually use. The Dashboard Effect is where we talk through the technical decisions behind that work.

Learn more: https://bluemargin.com

How does raw data from a system like Salesforce become a dashboard your team can actually trust? On this episode of the Dashboard Effect, Brick and Landon walk through medallion architecture and the three stages data moves through on its way to becoming useful.

They keep it simple: the first stage holds an exact copy of your source data, the middle stage cleans it up and combines data from different systems, and the final stage delivers a polished, analytics-ready view for tools like Power BI and Tableau. Landon also makes the case for keeping things lean rather than forcing every dataset through every step, and shares where speed and scale change the approach.

If you're building or refining how your data flows, this is a clear, approachable starting point.

About Blue Margin -

Blue Margin is a fractional data and analytics team for mid-market and PE-backed companies. Acting as an extension of your team, they build and manage your data platform, shape your data strategy, and deliver Power BI dashboards that give leaders a clear, real-time view of what's driving the business. The goal is simple: turn scattered data into decisions and build a culture of accountability and growth along the way.

Everyone's asking the same question: why can't I just run all my business intelligence through AI? In this episode of The Dashboard Effect, Brick and Landon break down the real answer. You probably can, but only if your data foundation is built for it.

They cover where AI analytics fit alongside traditional Power BI, why a strong medallion architecture is the difference between reliable answers and confident wrong ones, and the practical engineering that makes natural language query trustworthy. They also dig into rising token costs, vendor lock-in, and how to decide what belongs in a dashboard versus a chat window.

If you run a data-driven business and you're weighing how far to push AI into your reporting stack, this one is for you.

Subscribe for more conversations on data, dashboards, and getting real value from your BI.

About Blue Margin -

We build and manage the data foundation that turns scattered systems into reliable, AI-ready reporting, so your team gets trustworthy answers without the overhead of an internal data team.

Learn more: https://www.bluemargin.com/

In this episode, the team gets hands-on with vibe coding and what it actually looks like inside real data work. From building API connections to generating synthetic datasets, they walk through where AI is genuinely speeding things up and where it still runs into friction.

The big takeaway: writing code might be faster, but everything around the data still matters just as much.

Blue Margin helps growing companies make better decisions with their data. From building data pipelines to creating reporting and dashboards, we handle the technical side so teams can focus on using their data—not chasing it.