Author: Paul Turley

This post is part of a series of excerpts from my forthcoming book “Microsoft Power BI Data Analyst Associate Study…

My new book “Microsoft Power BI Data Analyst Associate Study Guide” from O’Reilly Press is really two books in one. When O’Reilly approached me about writing a study guide for the PL-300 exam, one of the most popular Microsoft certification exams, I said that I wanted to be a two-part book to help exam candidates join the thriving Power BI community followed by a guide for using Power BI in real-world applications in accordance with industry best practices. The second half of the book is focused on how to apply best-practice design after learning the Power BI building blocks. Some of the content in the second half of the book is updated and elaborated material from earlier blog posts and 2025 posts I will reference in this blog series. As of this post in January 2026, the online book is available as an early release through the O’Reilly subscription service and scheduled to be delivered through booksellers like Amazon early this year.

How do Copilot and Agentic AI change the way business users will interact with organizational data? Is the platform ready and are we ready to embrace it? AI agents and assistants are part of our daily lives. LLM-driven agents like Alexa, Siri, Gemini and Grok provide entertainment, perform requested tasks and answer questions with varying degrees of accuracy and reliability. Sometimes they misunderstand and provide out-of-left-field responses for simple questions. I might ask: “Show me the best practices for my model.” and get: “Of course! Here’s a detailed guide on runway walking techniques and how to maintain a confident posture during a fashion show.”

This series of posts will focus on how to prepare Microsoft Fabric and Power BI semantic models to support effective self-service reporting and analysis using Copilot.

My Dad and I did a lot of fishing when I was young. Back then, we fished for wild trout…

This post is part of a series of excerpts from my forthcoming book “Microsoft Power BI Data Analyst Associate Study…

It is no secret that the AI revolution has started. Most every modern business application has an agent, a copilot…

Both Azure DevOps and GitHub are supported Git hosts for Power BI and Fabric workspace integration. I will demonstrate using…

For the business intelligence professional, DevOps can be a confusing topic because it intersects with many disciplines and philosophies. I’m hopeful that a bit of reflection on my own experience over the years provides some valuable insight. I have seen DevOps and version control implemented on dozens and perhaps hundreds of projects, with different degrees of sophistication and control.

If you work in a software development environment where DevOps is practiced as part of your team’s development culture, you know that DevOps truly is a holistic methodology, including practices can be very extensive and regimented. Software development tends to be a linear process, whereas data and BI projects are more iterative in nature. Frankly, DevOps purists can be down-right militant about enforcing all the rules, which many BI specialists tend to skirt, in an effort to move quickly.

If you are a business intelligence analyst or developer and suddenly find yourself working on a team where DevOps is practiced, you will likely find the process to be more strict and less flexible than typical ad hoc BI development. The key is to strike the right balance for your organization’s needs.

There is certainly a flavor of DevOps that seem to be over-engineered and counter-productive. DevOps practices exist because they address critical needs in a software development environment, but you should find the right balance for your organization’s project needs. Be mindful that the very purpose for business intelligence is to deliver insights and reporting insights directly to business users, which entails quick iterations through the entire process – from requirement refinement to report delivery, with several steps in-between.

I just read that the Miss Universe contestant from Panama was evicted from the Miss Universe pageant. I don’t know…

I work with hundreds of consulting clients who go through the same cycles, having the same experiences, facing the same challenges, many making the same mistakes, and many learning some of the same lessons. The purpose of this series is to share those lessons with you.

Universal Best Practices

If we sum up Power BI best practices at the highest level, it is that all projects of any scale and size should adhere to the same general set of guidelines, categorically. Your Power BI project no matter how small or large should address every category in the following list. The specific design patterns and practices will vary significantly depending on scale and purpose. The following guideline categories will frame this and related future posts in this series: