A blog on Nary_Max!
So, I have a simple question……Why do we use Google DataStudio? (Hint….the answer’s not the fact that its free)
Is it to show data and its different visualizations in a beautiful aesthetic format? Yes
Is it to show data in a more dynamic and organized way, thus helping in analysis and decision making? Yes
But the basic answer for this question is – Reporting. The google data studio is a reporting platform that lets you collate data from all platforms in which you are investing effort (Well, almost all).
It is important to represent that effort properly to see the current performance of the organization, as well as for efficient future decisions. However, let’s face it, sometimes some data just cannot be represented. It may not exist, it may not be gathered, but for that time period, that data value is just that – A null.
And in data studio, if you are using calculated fields to create another metric, one of your unpredictable problems could be that the data with which you are calculating another metric contains null values. And you definitely cannot show a “null” for your organization’s efforts.
So, the question is, how to translate your null value into a 0 (Zero) when calculating another metric?
For doing that, Nary_Max is your go to function!
Meaning & Example
Basically, this function among n variables, that is, among different rows of data, can pick up the maximum data set. This in itself is a useful application of Nary_Max
But we were discussing on how does it help null values, right?
Let’s take an example to explain that.
In the figure shown below,from data studio, there are 2 fields, that is cost and variable cost. In both the cost and the variable cost data there are null values.
Now, there is a requirement to aggregate these fields into a calculated field called total cost. For more information on calculated fields, you can refer to this blog post here
In the calculated field given above, Nary_Max function is used to differentiate between 2 sets of data and use the higher value. The 2 sets of data in this case mean the actual data and 0.
The entire formula used in the above field is: SUM(NARY_MAX(Cost,0))+SUM(NARY_MAX(Variable cost,0))
Therefore, it will differentiate between the values in both the the fields with o. If there is a null value in any one of the fields, then Nary_Max function will use 0, as it is a higher value than null.
The end result comes like this
The total cost now has 0, if the values involved in both the fields are null.
In this way, the main calculated metrics which are observed by the shareholders are calculated even if they have null values in them.
If you need help with this, then we are a crazy team of Google and Adobe Certified Analytics Experts who eat, breathe, sleep and dream Analytics. And we’ve made it our purpose and passion to electrify your business’s potential through Analytics.
Contact us here.
As I am pretty sure, the above image summarizes itself.
Your one and only web analytics Chica here – Garima Mathur