Dear SAP Analytics Cloud beginners,

This is a blog post for beginners in SAP Analytics Cloud. I also started learning SAP Analytics Cloud from August 2021. I’d like to share my trial and error from a beginner’s point of view, and hope it helps you get started easily!

 

Day2: Datatype and data transform 

I posted a blog “Day1:Time Series data” with how to set up a hierarchy when creating a chart. (Link to Beginner’s SAP Analytics Cloud Journey: Day1)

I was trying to create monthly, quarterly and weekly trend chart, but I had a problem when setting up a hierarchy for time series data.

I can’t find hierarchy. Where is hierarchy😨?

Dimensions without Setting Hierarchy

 

Dimensions with hierarchical settings are what I expected.

Dimensions with Setting Hierarchy

 

I found a solution that I need to change the datatype and transform the data.

Hierarchy setting for dimensions is unavailable when datatype is “Date and Time” format, thus you should change the datatype.

The conversion format for SAP Analytics Cloud is as follows:

• “Date and Time” Conversion Format: YYYY-MM-DD HH24:MM:SS
• “Date” Conversion Format: YYYY-MM-DD

You can easily change and transform your data by following these steps:

 

Step1: Go to dataset and check datatype

In this example dataset, datatype is “Date and Time” with Conversion Format, “YYYY-MM-DD HH24:MM:SS”.

The next step converts the datatype from “Date and Time” to “Date”.

Check datatype and Conversion Format

 

Step2: Duplicate column, “Date and Time”

Duplicate column

 

Step3: Create a Transform

You can transform datatype with concatenate, split, extract, replace and change functions.

Create a Transform

 

But, “Split” function is unavailable when datatype is “Date and Time”, thus before split the data, you should change datatype from “Date and Time” to “String”

Split is unavailable with datatype, “Date and Time”

 

Step4: Change datatype from “Date and Time” to “String”

In the detail section, you can change the datatype from “Date and Time” to “String”.

Change datatype to String

 

Step5: Split data on ” “

After changing the data to “String”, you can split the data in “blank”.

Split data

Step6: change datatype from “String” to “Date”

After splitting the data, you can change the datatype from String to Date with “YYYY-MM-DD” format.

Change datatype to Date

 

Step7: change datatype from “String” to “Time”

After splitting the data, you can change the datatype from String to Time with “HH24:MM:SS” format.

Change datatype to Time

 

Step8: Change column name

You can change the column name in the “viewing details for” in the detail section.

change viewing details for

 

Step9: Delete column

Please delete unnecessary columns using “Delete Column”.

Delete column

 

Step10: Save the dataset

Please save the transformed dataset.

Save the transformed dataset

 

Step11: Go to the stories and Set Hierarchy

Please go to the stories and you can find the “Set Hierarchy”.

Dimension with Set hierarchy

 

Step12: Select Hierarchy

You can select different types of hierarchies such as, “Year, Half-Year, Month, Day”, “Year, Quarter, Month, Day”, and others as follows.

Hierarchy options

 

Step13: Choose Level in the Hierarchy

You can set the date hierarchy by year, quarter, or month, depending on the hierarchy you choose.

Set Hierarchy: Level 2- Yearly

 

Set Hierarchy: Level 3- Quarterly

 

Set Hierarchy: Level 3- Monthly

 

Now you can now change datatype and transform data👏👏👏!

In my opinion, this is pre-required steps before creating charts and analyze data in SAP Analytics Cloud. If you’d like to learn more about charting and analyzing time series data?

Please refer to my blog posting: Beginner’s SAP Analytics Cloud Journey: Day1

Following on Beginner’s SAP Analytics Cloud Journey: Day3 – How to create Geo Map.  

 

Learn more about SAP Analytics Cloud

• Want to play around with SAP Analytics Cloud yourself? Download a trial version here
• Locate the “?” symbol on the top of your SAP Analytics Cloud window to access the help documents, including guided playlists, knowledge base, browse community, etc.