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Understanding null dimensions

Sometimes when we look at results by a certain dimension, we'll see that one or more of the lines have no content in them:


There are a few reasons why this might be expected, and there are ways that we can use Analytics to improve the reporting if needed.

Some fields are expected to be null

For example, in Analytics, we can report on items by matrix, to gather all sizes and colors into one style, but if the items are not matrix items, their relative matrix is expected to be null.

We can improve in this particular reading by using the matrix or Item Custom Dimension. (See steps on Youtube.) 

Additionally, some fields may not need any definition:

  • Not all Sales Lines will have Discount descriptions
  • Not all Customers will have a Title or Type
  • Not all Order will have a Received date

Not all data in Lightspeed is defined

When anyone creates data in Lightspeed Retail, often there will be some fields that are not fully filled out: mostly on Items or Customers:

  • Sometimes items will be created without Categories or Vendors.
  • Not all Sales will have identified Customers.
  • Not all Customers will have filled out addresses

A few things we can do

If we want, we can actually filter our Analytics report just to look at the null instances, and then add another Dimension to extract the data.

Let's say we find a null Top Level Category in our results:


We could add the Top Level Category as a filter:


And filter for Top Level Category is null:


So now, if we add in an identifier Dimension, such as Description or System ID:


And run, we can now get a list of the Items that may need some action in Retail:


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