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The April 2019 update to Power BI has many good features, as this blog explains
Part four of a nine-part series of blogs

The April 2019 update to Power BI introduces a new concept: the ability to make the value of a property dynamic. Other welcome new changes include M Intellisense, data profiling in query editor and cross-report drill-down.

  1. The April 2019 Update to Power BI Desktop
  2. Conditional titles for visuals and conditional URLs
  3. Intellisense in the M Query Editor Language
  4. Data profiling in Query Editor (this blog)
  5. Cross-report drill-through
  6. Fuzzy merging
  7. Power BI Dataflows
  8. Other changes in the April 2019 update
  9. Power BI features waiting in preview, as of April 2019

For a cumulative list of all of the updates to Power BI Desktop in the last few years see this blog, or have a look at the Power BI courses that we run.

Posted by Andy Brown on 18 April 2019

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Data profiling in Query Editor

This has emerged from a few months in preview darkness, blinking into the light. 

Suppose you've loaded a list of Abba songs (surely a typical use for Power BI?), and you want to know at a glance how reliable the data is:

Abba songs

Mamma mia! The songs look good, but what are all those gaps?

You can let your mouse linger over the thin bluey-green strip to see an analysis of the quality of any column:

Album names

Two album names are missing.

From here it's really easy to decide what to do about the missing (or in some cases, duplicate) data:

Removing empty rows

You could choose this option, for example, to remove any rows with no album names.

But you could go further.  How about a histogram showing the distribution of column values?

Colum profile

These column distributions show the distribution of values in each column.

Or even a full breakdown for any column you click on?

Column profile

This is called a column profile.

You can turn these - and more - options on by ticking the appropriate boxes on the View tab of the Query Editor ribbon:

The view options

These are the two options I ticked to get the features shown above.


All in all, a really nice, quick, simple way to check for obvious errors in your data before you start trying to tidy it up!

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