1. Defining Analytics User Roles and Permissions
Decision: Who will have access to Advanced Analytics and what level of detail should each user be able to view?
Why it is critical for implementation: Assigning the right permissions ensures that only authorized users can access sensitive analytics data and that users see only the information relevant to their roles, supporting data security and effective decision-making.
Example: A business analyst is given permission to drill into detailed candidate data, while a recruiting manager views only high-level summary reports.
2. Planning Status Mapping for Analytics Reporting
Decision: How will candidate statuses in SAP SuccessFactors Recruiting be mapped to the five standard statuses in Advanced Analytics?
Why it is critical for implementation: Accurate status mapping ensures the integrity of analytics reports and prevents integration failures, as all candidate statuses must be accounted for in the mapping process.
Example: Multiple "not hired" statuses are mapped to the ATS Capture status, and "Background Check" is mapped to "Offer Made" for reporting purposes.
3. Preparing for Cost Data Integration
Decision: What external recruiting cost data will be reported and how will it be structured for submission to SAP Support?
Why it is critical for implementation: Providing well-organized cost data allows for accurate analysis of recruiting spend and ROI, but only if the costs are tied to sources tracked by Advanced Analytics.
Example: The HRIS team prepares a spreadsheet with vendor names, products, annual costs, and contract dates to submit as a support ticket for cost data loading.
4. Scheduling Analytics Training and Support
Decision: When and how will users be trained on Advanced Analytics reports and features, and will consultation with SAP Professional Services be required?
Why it is critical for implementation: Timely training ensures users can interpret analytics correctly and maximize the value of the data, especially after sufficient hiring cycle data has accumulated.
Example: The HR team schedules a training session with SAP Professional Services three months after go-live to review hiring trends and report usage.
5. Establishing Data Localization and Language Strategy
Decision: How will non-English data be handled, given that Advanced Analytics is only available in English?
Why it is critical for implementation: Proper planning for data localization and translation ensures all relevant data is mapped and usable in analytics, supporting organizations with global operations.
Example: A global company maps non-English requisition data to English equivalents before importing them into Advanced Analytics to ensure consistent reporting.