Saving the Day from Delay: Recap & Series Kick-Off


Ironically, I’m a few months behind on a recap for my presentation on building and using a sustainable, dynamic date query in M code – Saving the Day from Delay. On a positive note, the delay means that I can do a series on the importance of date tables, how to effectively use them in DAX, and what I mean by sustainable and dynamic practices. At the end of this post is a link to a GitHub containing my most up-to-date M Query referenced in this series and the PowerPoint used in the aforementioned presentations.

Presentation Recap

Using a date query, whether from a SQL table or Power Query table (M), can aid in optimizing and honing your data model. A main culprit in sluggish Power BI data models are the native date hierarchies. Native date hierarchies are the default setting in Power BI and provide you with hierarchies to use in visuals that allow for easy drilldowns through year, quarter, month, and day. You can turn off this default under File > Options > Current File > Data Load (see below). You can also do this in global settings, but I recommend doing this file by file as the benefits reach diminishing returns on datasets containing under a 500,000 rows.

Where to change your default settings

If you’re unsure what I mean by native data hierarchy, the image below contains an example of a date hierarchy built by Power BI. To rebuild a date hierarchy from a standard date table, drag and drop columns on top of each other to create a custom date hierarchy. By right clicking on the Hierarchy title, you can rename and reorder your hierarchy accordingly. One of the biggest reasons to turn off the native date hierarchy is that you now only have one hidden DAX table in your data model for the date instead of however many date fields you have in your model (I’ve seen some models with up to 15 date fields!).

Native Date Hierarchy

In subsequent posts, I’ll cover the benefits of turning off this feature, reconnecting your data model, how to customize the date query, custom columns in M, and best practices for a lean, mean data model machine.

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