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Author's profile photo Sivakumar Mohan

SAP BW Analytics for SAP Success Factor

Abstract: Through this blog, would like to share my experience of SAP BW Analytics for Success Factor.

Target Audience: BI Analysts, BI-Focused IT and SAP Professionals

Author: Sivakumar Mohan

Company: Cognizant Technology Solutions

Published: May 2018


The reader would get an insight of following areas which would help them to leverage their thought process towards Success Factor (SF) analytic solution in SAP BW.

  • The benefit of SAP BW analytics for SF?
  • How do we connect SF with SAP BW?
  • How do we acquire data from SF?
  • How do we build the model to integrate on-premise HCM data?
  • The high-level insight of Business-driven reporting KPI’s

Why we need BW analytics for SAP E-Recruitment?

  • Large workforce, difficult to manage and monitor
  • Lack of holistic view of SAP HR and Success Factors Data (e-recruitment)
  • Need for improved governance in the hiring/recruitment process

Major Metrics in Recruitment Life Cycle Management Process are below.

  • Recruitment
    • Number Position against LOB (Open, Filled and Rejected)
    • Number Requisition for Position (Open, Filled and Rejected)
    • Average time to fill the Position –> Requisition –> Applicant
    • Average time to fill Requisition by phase
    • Requisition status by phase
  • Candidacy
    • Number of application for Position (Open, Filled and Rejected)
    • Number of application against Requisition (Open, Filled and Rejected)
    • The average time is taken to evaluate applicant by phase
    • Candidate rejected by phase
  • Performance:
    • The average time is taken by Hiring Supervisor, Recruiting Manager, Lead Recruiter, Recruiting Coordinator and Recruiter to complete the hiring process.
  • Anomalies
    • In addition to the above metrics, the solution also indicates scenarios, when a process breaches the recruitment compliance. Example – Application received before job posted, offer sent before background check completed, etc.

Business Benefits : ( The percentage figure may vary place to place based on the scope. Reader may take it as a tentative figure

Project Realization:

As part of project realization, we have come across following three important milestones.

  • How do we acquire the entire recruitment information (From requisition, candidacy and candidate and their respective audit and history) from SF to BW?
  • How do we integrate SF with SAP BW?
  • How do we extract data from SF to BW where the data format is big challenges?
The high-level data flow of proposed architecture.

How do we accomplish the milestones?

Milestone #1: How do we acquire the entire recruitment information (From requisition, candidacy and candidate and their respective audit and history)?

  •  In SF we could view the table with information in two ways
    •  ODATA Dictionary
    •  API Dictionary.
  •  SAP has delivered the recruiting report named as ADHOC_RECRUTINGV2
  •  It has around 820 fields with schematically grouped by 71 groups which cover wide spectrum information across requisition, candidate, application and recruiting team.
  • Do a thorough analysis of this structure and come up with your own semantic structures which would help us to build the data source.
  •  As per our requirement, we have analyzed and come up with 40 BW data sources.

Milestone #2: How do we integrate SF with SAP BW?

  • BODS play an important role to perform the following activity.
    • Integration SF and BW using standard SF API adopter.
    • Data type conflict handling.
    • Creation of JOB.
  • Refer following the link which has created by one of my team member who worked as part of the BW analytics for SF project.

Milestone #3: How do we extract data from SF to BW where the data format is big challenges?

  • As I mentioned above, as an outcome of ADHOCRECRUTINGV2 reports analysis we have come up with the 40 data source structure.
  • Challenging part is data type declaration for fields, we are unable to inherit the field properties of SF since almost all the fields defined using varchar. Hence, we need to study the nature of the data and based on that we have to define the data type. Whether it would come as DATE or TIMESTAMP or NUMC or CHAR or AMOUNT or STRING.
  • Once data sources creation has completed then import all data source in BODS. Now the target is ready.
  • On the other end, using SF API import the ADHOCRECRUTINGV2 structure. Now the source is available in BODS.
  • Create a Job based on the schematically grouped target data source with the ADHOC_RECRUTINGV2 source. Do the same exercise for until all the data sources job creation has completed.
  • Create an EDW layer based on the data sources. Now data flow is ready to load the SF data till EDW layer targets.
  • Do the BW modelling to integrate HCM data (0JOB, 0HRPOSITION and 0EMPLOYEE) with SF data to enrich the SF data.
  • Create the Reporting Layer to cater the information to BOBJ ( Web Detail Report) and Lumira ( Dashboards)Reports.


Below tables highlighted KPI’s would help to the decision maker to govern the recruitment process effectively.



Through this blog, I have brought an insight on how we could use the BW analytics for SF to govern the recruitment lifecycle management effectively. We could also use the predictive analytics to anticipate the new position based on the company growth forecast plan and position vacancy based on the past history records, which would help to plan for budgeting and effective management of recruitment life cycle process.





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      Author's profile photo Former Member
      Former Member

      Very good blog useful for SF data modelling people!

      Author's profile photo Vikash C Agrawal
      Vikash C Agrawal

      very well explained, Thanks.

      Author's profile photo RAVINDER PAL VASHIST

      Good one