Jason Miles

Implementing Stars and Galaxies in Power BI

Power BI rewards clean dimensional models—but it also punishes sloppy ones. This post walks through how to implement star and galaxy schemas in Power BI semantic models, why ambiguous (multiple) filter paths cause headaches, why implicit measures don’t scale beyond the simplest star, and how tightly defined data products keep your BI ecosystem fast, correct, and governable. Because this is such an important topic, I’ve included links to references with each point.

Conway’s Law for Data Teams

Two Dashboards, One Truth

On Monday, Maya—head of a seven‑person data team—watched two dashboards disagree.

The executive dashboard showed $11.2M in MRR. Sales’ dashboard said $10.6M. Both pulled from “the warehouse.” Both refreshed nightly. Neither was “wrong”; they just measured different things.

Maya didn’t control how Sales Ops or Marketing were organized, who they reported to, or which tools they bought. She controlled only her data team—its models, interfaces, and operations. Yet the warehouse had clearly taken on the shape of the company’s communication patterns.

Conway’s Law, without asking permission, had moved in.

A Practical Introduction to Star Schema Data Architecture

Dimensional modeling remains the most effective way to make analytics fast, understandable, and resilient. The star schema sits at the center of that approach: a simple, denormalized structure where fact tables record measurable events and dimension tables provide descriptive context. In this post, we’ll ground the core ideas, clarify the often‑confused concept of snowflaking (and when it’s worth it), and show how to scale from a single star to a galaxy schema (a.k.a. fact constellation) without losing your footing.

Foundational + Derived Data Products in a Data Mesh

A data mesh is a sociotechnical approach to analytical data that decentralizes responsibility to business domains while standardizing the way data is produced and consumed. It’s grounded in four principles: domain ownership, data as a product, a self‑serve data platform, and federated governance. In practice, it asks each domain team to publish data as a product—discoverable, trustworthy, and operable—while a common platform automates cross‑cutting rules (access, lineage, quality, security).

Zhamak Dehghani frames a data product as an architectural quantum: the smallest independently deployable unit that bundles data, code, metadata, and policy, with a versioned contract and a clear interface (APIs or governed views). Treating both foundational and derived products as quanta is the key to decoupled evolution without breaking interoperability.

Saturday Film → Monday Growth: How you can use Microsoft Power Platform to level up your player grading experience.

It’s Saturday in the fieldhouse. You’re rolling through last night’s game with your staff. The goal isn’t to “get through the tape”—it’s to walk out with player‑by‑player statistics, clean per‑play grades, and a short list of reps each kid needs next week.

Here’s how to do it:

A simple grading and stats workflow that every position coach can run while you watch film, and
A practical Power Platform setup (Dataverse + model‑driven app + canvas app) that makes it quick to build and easy to maintain.

Data Products in Fabric, Part 3: Why Fabric Is Ideal, What to Expose, and How to Govern (with zero‑copy patterns)

In Parts 1–2, we framed a data product as a reusable, self‑contained package that bundles data, metadata, access methods, and governance to deliver an outcome—discoverable, interoperable, and managed like software. We also separated foundational (stable, domain‑anchored) from derived (composed/enriched for specific use‑cases) and showed how composition is the workhorse of value delivery.

This third part makes that guidance concrete on Microsoft Fabric: why Fabric is a natural home for data products, which product types you can expose, and how to govern and compose them—including zero‑copy patterns and two near‑term preview capabilities: Materialized Lake Views and Shortcut Transformations.

Improvement Science for Business Leaders: A Practical Playbook for Better, Faster Results

Most executives know Lean, Six Sigma, and Agile. Improvement science is the disciplined backbone behind those methods—a way to get measurable gains by learning quickly in the real world, not just in the boardroom. It’s been refined for decades in healthcare and education, but its core ideas translate cleanly to sales, operations, CX, finance, HR, and product. Here’s what it is—and how to start using it immediately.

“Zero Copy” Doesn’t Mean “No Copies.” It Means “No Unmanaged Copies.”

The rallying cry of modern data platforms—Zero Copy—is revolutionary because it flips the default: don’t move data unless there’s a good reason and the platform manages it for you. In Microsoft Fabric, that starts with in-place access via OneLake Shortcuts and an open storage layer, then selectively uses managed and automated copies (like Mirroring and Materialized Lake Views) when they deliver clear value. The result is less sprawl, more trust, and faster analytics—without hand-built duplication.

A Lightweight Ingestion Framework in Microsoft Fabric

Modern Fabric estates don’t need a forest of bespoke pipelines, but they do need metadata-driven tools to reduce time to insight. You can land data quickly in Bronze, promote it reliably to Silver and Gold with a metadata‑driven Spark Structured Streaming engine, and treat Gold as the foundation for your data products—semantic models, AI endpoints, and any other served formats.

How AI Can Help High‑School Football Between Friday Night and Saturday Morning

The lights go out, the sideline file finishes uploading, and the play list—your record of the plays actually called—lands in the same folder. While everyone sleeps, AI could take those two inputs (plus your existing playbook in the system) and quietly do the digital chores, so Saturday starts with coaching, not clicking.