Since the release of Power BI Desktop this past week, I’ve been really spending my extra time digging into the application focusing on learning and experimenting as much as I can. When my wife has been watching Law and Order: SVU reruns at night after the rug rats are in bed, I’ve been right there next to her designing Power BI dashboards like the total data nerd that I am. When my kids have been taking their naps during the weekend, I’ve been writing calculations in the model for my test dashboards. Or when I’ve been riding in the car back and forth to work I’ve been thinking of new things to do with Power BI Desktop.
Since I’ve been spending a decent amount of time with Power BI Desktop, I thought I’d take a moment to share three things to know and remember when designing your Power BI models and dashboards that I think will help you make the most of this tool and be effective at providing the data your business needs to succeed.
1. Optimize your Power BI Semantic Model
It probably hasn’t taken you long to figure this one out if you’ve built Power Pivot/Tabular models or at least it won’t when you do start developing Power BI dashboards. The visualizations in Power BI and Power View are heavily meta-data driven which means that column names, table or query names, formatting and more are surfaced to the user in the dashboard. So if you using a really whacky naming convention in your data warehouse for your tables like “dim_Product_scd2_v2” and the column names aren’t much better, these naming conventions are going to be shown to the users in the report visualizations and field list.
For example, take a look at the following report.
Notice anything wonky about it? Check the field names, report titles and number formatting. Not very pretty, is it? Now take a look at this report.
See the difference a little cleaned up metadata makes? All I did was spend a few minutes giving the fields user-friendly name and formatting the data types. This obviously makes a huge difference in the way the dashboard appears to the users. By the way, I should get into the movie production business. ;)
My point is that the names of columns, formatting, data types, data categories and relationships are all super important to creating clean, meaningful and user friendly dashboards. The importance of a well-defined semantic model cannot be understated in my opinion. A good rule of thumb is to spend 80% to 90% of your time on the data model (besides, designing the reports is the easy part).
I’d also like the mention the importance of the relationships between the objects in the semantic model. Chance are you will have a small group of power users that will want to design their own dashboards to meet their job’s requirements and that’s one of the beauties of Power BI. But when users began developing reports, they may query your model in unexpected ways that will generate unexpected behaviors and results. I only want to mention this because the relationships between the objects in the model will impact the results your users will see in their reports. Double check your relationships and ensure that they are correct, especially after you add new objects to the model since the Power BI Desktop will sometimes make an incorrect guess at creating the relationship.
2. Choose the Right Visualizations
The best dashboards are those that tell a clear story within seconds. Your data should tell a story that is easy to read and can communicate the tale of the data to the users without a lot of extra work on their part. If your users have to look at the report for a long time in an attempt to decipher the visualizations plastered across their screen, chances are they won’t want to use your dashboard.
Let’s look at two different charts that I think will illustrate my point on the importance of choosing the right visualization for the story. The chart below shows a comparison of Domestic Sales and International Sales for different movie genres. If the purpose of this chart is to determine from which market most of the money comes from for the various film genres, then this chart isn’t doing that great of a job because we can’t clearly see the difference between the markets for Westerns.
Is there a better way to tell the data’s story? What about the pie/donut chart?
Goodness, no. Stay away from pie and donut charts. The problem with pie/donut charts is that even with only a few categories it can be very difficult to compare the slices in the pie. And if the purpose of our dashboard is for the users to quickly gain insights into the successes and failures of the business, I recommend you stay away from the pie/donut charts.
Now that’s what I’m talking about! With a clustered bar chart, we can clearly see from which markets most of the money comes from the different genres. This is a much better visualization choice for the data. We don’t have to stare and squint in order to determine the differences between the bars.
Visualization choice is critical with designing an effective and useful dashboard, so always make sure you choose the best visualization for the job.
3. Remember the User!
We as developers can oftentimes find ourselves lost in the minutia of data processing times, ETL performance, writing code, documenting the solution and all the other things that go along with designing and building a business intelligence solution. In the midst of all that awesome and glorious development work, it can be easy to forget that the whole purpose of this solution is to make the user’s job easier, faster, better, etc.
I only mention this because too many times I’ve encountered solutions that did not make the user’s job easier. Users are crafty and resourceful people. They’re (mostly) good at their job and will find a way to do their job without having to use your crappy dashboards and reports that are confusing and difficult to use. And once you start down the path of having your users work around your solution instead of with your solution, your solution has failed because at that point its not a solution; It’s an impediment.
Meet with the users as frequently as necessary to constantly gather feedback. During the requirements gathering phase its important to ask lots of questions especially if you’re unfamiliar with the data. And once its time to start designing reports, you may meet with the users even as frequently as daily since this will be the user’s primary way to interact with your solution. I’ve been on projects where my team and I worked in a conference room with a few power users. This was excellent as we were able to get immediate feedback on any reports developed and make the required changes as desired.
So in a nutshell, here are my three best practices for designing and building a killer Power BI reporting solution:
- Optimize the data-model by doing the following:
- Set data types correctly
- Apply user-friendly formatting to the data including explicit measures.
- Rename fields, measures, and tables with user-friendly naming conventions.
- Validate relationships between tables are created correctly.
- Use the right visualization that communicates the story of the data as clearly as possible.
- Remember the user and their experience with your solution! If the user likes to use your solution then its a success!
Here’s a few more Power BI related resources you may find useful:
So what do you think? What best practices did I leave out that you thought I should have included in this list? Leave a comment down below and let me know! And as always, thanks for reading. :)