This is an introduction to a series of posts and pages that will provide a comprehensive set of best practices for successful Power BI solutions. In previous posts, I have asked readers to suggest topics for future posts. Based on that and other feedback, I will be addressing questions and suggested topics. The topics list at the end of this post is a brainstorm list and I ask that you help me make sure it is complete. My goal is to provide a set of guidelines and practices that provide the best chance of success as you navigate many decisions about how to stage and transform source data, how and where to perform data shaping and calculations, how to model data and the best way to visualize the results. The biggest question of all may be how to make the right decisions so that the small project you design today will work when you add more data, more users and transition into a formal, managed solution.
There are many fantastic resources to learn about Power BI and rest of the Microsoft BI and reporting platform; but learning about Power BI and the choosing among design options can be like drinking from multiple firehoses at full pressure at the same time. I will be the first to admit that my “best practices” are “my opinions”. In many cases they work consistently for me but points are debatable and open for discussion. I’ll tell you when I have a very strong opinion about something being done a certain way, or when I have found a pattern that works for me and that I offer for your consideration. I’m not always right… just ask my wife :-). Please comment, ask and offer alternative points of view.
Rather than offering another training course or duplicating the contributions that so many others in the industry make through their blogs, courses, books and articles; this will be a condensed set of guidelines about the many choices you must make when designing a solution. In the posts to follow, I will reference other resources and discussions on various topics.
Best practice guidelines topics
The following topic list will serve as a link menu for future posts. Expect this list to be updated and completed:
- Futureproofing Power BI solutions
- Preparing, shaping & transforming source data
- Power Query design best practices
- Power Query in dataflows or Power BI Desktop
- Data modeling essentials and best practices in Power BI and AS tabular
- Validating data model results
- Planning for separation – data models and reports
- Power BI delivery options
- Choosing the right report type: analytic or paginated
- Designing and managing large datasets in Power BI
- Multi-developer and lifecycle management for Power BI
- Certified reports, certified datasets & the self-service mindset
Just tell me what to do
Any attempt to apply universal best practices to Power BI solution design is a slippery slope. The tools are so flexible and powerful, and the requirements of each project are so varied that it is challenging to establish a set of steps or rules that, if followed, will always yield the absolute best design for a given scenario. With that out of the way, I’ll say this: In my job, I see a lot of poorly-designed Power BI projects. I’ve worked with dozens or scores (maybe even hundreds?) of consulting clients who bring us projects – some partially completed, some finished, and many that are just broken – to be fixed or completed. My reactions range from “that’s just downright wrong” to “hmmm… I wouldn’t have done it that way but I guess it will work for the time being”. I try not to cast stones and do, on occasion, realize that others have found a better way to solve a problem. I don’t have all the answers but I do have a lot of experience with Microsoft Business Intelligent solution design, and have learned many good practices and design patterns from other community leaders and many successful projects over the past twenty or so years.
A little less conversation and a little more action
Let’s start with a simplified flowchart and condensed decision tree. This first whiteboard drawing is the first half of the Power BI design process, ending with the data model, before measures, visualization and solution delivery. There is a lot more but I think this is a good starting point.