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Author's profile photo Ajit Kumar Panda

Embedded Analytics for SAP Commissions – Blog Series – Part1 – Filters

This will blog post will provide information on different kind of filtering techniques available in Embedded Analytics for SAP Commissions.

The content of this blog post caters to following topics:

  1. Story Filter vs Page Filter vs Widget Filter
  2. Static vs Dynamic Filter
  3. Cascading Effect for Multiple Filters
  4. Date Time Range Filter
  5. Measure Based Filter

1) Story Filter vs Page Filter vs Widget Filter

Overall, there are 3 kinds of filters available to narrow down or restrict data set for analysis.

  1. Story Filter – This filter applies to all visualization of all pages in a story that are based on same model as filter.
  2. Page Filter – This filter applies to all visualization of the page of a story in which filter is defined and based on same model.
  3. Widget filter – On each individual visualization or chart, filter can be applied to restrict information for analysis.

To get a quick look about these filter, please refer to the video on microlearning site.

Filtering%20a%20Story%20in%20Embedded%20Analytics%20for%20SAP%20Commissions

Filtering a Story in Embedded Analytics for SAP Commissions

2) Static vs Dynamic Filter

A filter is called Static when its members are fixed, do not change with data set and is called dynamic if its members change if data set changes.

Example: Last year a story was created in embedded analytics with a filter on Credit Type. However, this year the compensation plan is changed, and one additional credit type is created. Static filter will show the same list of members for credit type filter. If the filter on Credit Type is dynamic, then automatically the newly added credit type will also be displayed.

A filter can be made dynamic, by clicking on ‘All members’ in the filter pop up and can be made static by individually selecting filter members.

3) Cascading Effect for Multiple Filters

When a story/dashboard have multiple filters, any change in one of the filters will affect other filters. This effect can be switched off under filter settings.

Example: A story has filters on Business Unit and Manager’s Full Name. When one business unit is selected, automatically the list of managers filter gets changed and inactive values are hidden due to cascading effect.

4) Date Time Range Filter

It is possible to define filters on dates like accounting date, compensation date, credit date etc. Time ranges based on years, quarters, months, or days cab be fixed or dynamic.

Examples-:

Fixed Date Time Range – This type date range will be helpful when a dataset needs to be filtered based on a specific date and time e.g. finding payee transaction data based on a specific date

Filter by from Past time period to Future/Current time period – Last 2 Year to Next 2 Year or Last  Quarter to Current Date

Offset: Shift range backwards or forwards – Last 2 quarter data excluding running quarter

Multiple Range:  It is also possible to define multiple range time filters and apply these together –  Filter with ranges of Current Year, Previous Year and 2 years back and user can select which ranges to apply

Reference date:  Analysis can be performed based on a reference date which can be chosen by user instead of today’s actual date – Last 30 days data prior to the reference date.

5) Measure Based Filter

Filters based on a range of measure values is also possible in embedded commissions for sap commissions.

Conclusion: Embedded analytics for SAP Commissions has different kinds of filtering techniques with rich set of features which can be used while designing Stories.

More information about embedded analytics for commissions can be found here . You can follow my profile to get notification of the next blog post on embedded analytics for commissions. Please feel free to provide any feedback you have in the comments section below and ask your questions about the topic in sap community using this link.

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      3 Comments
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      Author's profile photo Akhilendra Singh
      Akhilendra Singh

      Nice blog. keep writing !!

      Thank,

      Akhil

       

      Author's profile photo Ajit Kumar Panda
      Ajit Kumar Panda
      Blog Post Author

      Thank you Akhil 🙂

      Author's profile photo Shridhar Deshpande
      Shridhar Deshpande

      Thanks for posting this nice blog on embedded analytics. Will this replace the existing standard reporting and data sources in C4C?

      Waiting for next part..