Importance of Data Profiling

The analytics effort involves mashing and modelling data. In order to mash together the data we sometimes need to first understand the shape of the data.

Power Query

Power Query has built-in functionality that provides us with a visual representation on the shape of the data.

In order to demonstrate this, I have imported the Employee view from the Adventure Works 2017 OLTP database. Below is the Preview grid view in Power Query (you get here by clicking on the Edit Queries button)

There are 3 options for data profiling in Power Query, found under the “View” menu

  • Column Quality
  • Column Distribution
  • Column Profile

COLUMN QUALITY

By selecting “Column Quality” you are presented with a brief overview above each column on the percentage of data elements that are:

  • Valid (to the data type of the column)
  • Error (to the data type of the column)
  • Empty (number of records that are blank)

Error Values

If there are errors as is the case with the “CustomerID Error” column, then the Valid and Empty statistic will have a dash. See the example below where there are forced errors. In this case there is approximately 1% errors based on the current setting of the sampled first 1000 records (more on this later).

Empty Values

If any of the records in the column are null then they will be presented in the column quality statistic called “Empty”. See below example.

The Green Line Hover

In addition to being able to select “Column Quality”, you can achieve a similar outcome by hovering the cursor over the green line that sits under the column header (see above).

By doing this you will get a tooltip providing information about the column quality (see below image)

COLUMN DISTRIBUTION

Selecting the “Column Distribution” option provides a visual representation of the shape of the data, similar to a histogram. This option also presents us with some basic statistics regarding:

  • The number of distinct values
  • The number of unique values

What is the difference between Distinct and Unique?

Distinct: the typical meaning of individual record values after eliminated duplicates.

Unique: this is the individual record values where there is only 1 record in the data set (i.e. where there are no duplicates)

Ultimately, column distribution assists us in understanding, in combination with the defined model relationships, something about the cardinality of the model. In general the higher the cardinality the less performant your data model will be.

  • Hover over the green line – data profile
  • Column profiling based on only 1000 items. If larger table – select that message and change to “Entire data set”

Data Profile Actions for Column Quality & Distribution

Once you have reviewed the Column Quality statistics, then you can right click on the column quality preview and action a response such as removing empty rows or errors (etc).

COLUMN PROFILE

Clicking on the “Column profile” option gives us a pane below the data preview grid.

The Column distribution pane has 2 actions available:

HOVER ACTION
RIGHT CLICK ACTION

Approach to Data Profiling

I find the best approach to data profiling with Power BI Desktop is to turn on all 3 options and click on the column that is of interested when you need more detailed information from the “Column profile” option.

So basically “Column quality” and “Column distribution” provide at a glance view of all the columns in the query table and “Column profile” provides an in-depth view of a particular column that you need.

Important Reminder!

By default the profiling is done on the first 1000 records of the query result / table. As such if you want to profile the entire data set then you need to navigate to the bottom left of the data preview pane and click on the wording “Column profiling based on top 1000 rows” and change it to the entire data set (see below).

Have a great day !