Do you have a Power BI report that has a row of card visuals on it, something like this:
It’s a very common thing to do, but if your report is too slow to render you may find that a design like this is part of the problem. Now look at the following report which contains a single matrix that has been configured to look as un-matrix-like as possible but shows the same data:
This is likely to perform faster, but why?
Before we go any further, I don’t want you to go and change your reports if you’re not going to get any benefit from doing so. Use Performance Analyzer (as shown here) to determine which visuals on your report are the cause of slow performance – there’s no point redesigning visuals that are fast anyway.
As a general rule the more visuals you put on a report page the slower it’s going to get. It’s logical if you think about it: the more visuals there are, the more queries have to be run against your dataset and the more work Power BI has to do to render the report. I know there is a tendency to try to pack as much information onto a page as possible and this often happens when someone else has designed the report you’re trying to build, but you should always try to resist this. Splitting a single large page into multiple smaller pages, using slicers or filters to reduce the amount of data shown at any one time and avoiding gigantic Excel-like tables are a good idea.
Once you’ve done that you need to see whether you can reduce the number of visuals needed to display the same amount of information: in the example above, the four separate cards show the same data as the single matrix. In part this is because the cards generate four separate DAX queries against the dataset to get the data they need whereas the matrix only generates one DAX query. There is a certain overhead to running a DAX query, so reducing the number of DAX queries needed to get the same amount of data is a good thing. More importantly, in this example Power BI can get the four values required for the single DAX query generated by the matrix much more efficiently than it can in the four separate DAX queries needed by the cards. This is because of something called “DAX Fusion”, which my colleague Phil Seamark blogged about in great detail here, and this can make a signficant difference to performance (Marco Russo’s post on why Analyze In Excel reports may be slower than Power BI reports here also contains some useful information on DAX Fusion).
This is only a simple example; if you want to see a really sophisticated demonstration of replacing several visuals with a matrix I suggest you watch this video by another colleague of mine, Miguel Myers. And of course it doesn’t just apply to cards: any time you can replace multiple visuals with a single visual (remember to be careful using custom visuals though) you’re likely to gain some performance.