The Scenario
A system migration is eight weeks out. Your data steward team needs to complete a data quality audit before the cutover — specifically, personal information records for all 3,000 employees: names, email addresses, emergency contacts. The migration team needs to know what's complete, what's missing, and what's duplicated before they move anything.
You are the HR data steward. The audit deadline is in three weeks.
The bad version:
- Navigate to People Profile in SuccessFactors, find the PerPersonal entity export — it's not in the main export menu, it's in Report Center under a category called "Foundation."
- Build a custom report for PerPersonal records, wait for it to generate, export the file.
- Open the file: 3,000 rows, but email address is split across three fields (workEmail, personalEmail, loginEmail) in the export, and the audit template only has one email column. You spend an hour building a coalesce formula.
Three weeks. 3,000 records. One hour just to normalize the email column.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent inside your Google Sheet. It connects to SAP SuccessFactors and can pull PerPersonal records for all employees and write them into the structure your audit template uses.
Fetch all SAP SuccessFactors PerPersonal records and write each employee's personId, firstName, lastName, email, and emergencyContact into this sheet for a data quality audit
What You Get
- One row per employee in Sheet1.
- Columns: personId (A), firstName (B), lastName (C), email (D), emergencyContact (E).
- Email is coalesced from the work email field — the primary address, not three separate columns.
- Any row missing an email address gets flagged in column F automatically so the audit template's completeness count is accurate from row 1.
What If the Data Is Not Quite Ready
The audit also needs to flag missing emergency contacts
Fetch all SAP SuccessFactors PerPersonal records, write personId, firstName, lastName, email, and emergencyContact into this sheet, flag rows missing email in column F and rows missing emergencyContact in column G
Some records have name fields with extra spaces or encoding issues
Fetch all SAP SuccessFactors PerPersonal records, write personId, firstName, lastName, and email into this sheet, trim all name fields, and flag any row where firstName or lastName contains non-alphanumeric characters in column E
The migration team needs records compared against an existing system export
Fetch all SAP SuccessFactors PerPersonal records into Sheet1 with personId, firstName, lastName, and email, then compare against the existing system export in Sheet2 by personId and flag any row in Sheet1 where the email doesn't match the corresponding row in Sheet2
Full data quality audit in one prompt
Fetch all SAP SuccessFactors PerPersonal records, write personId, firstName, lastName, email, and emergencyContact into Sheet1, flag missing email in column F and missing emergencyContact in column G, then create a Sheet2 audit summary: total records, records missing email, records missing emergencyContact, records missing both, and percentage complete for each field
Three weeks, 3,000 records, audit summary ready.
Try It
Open your data quality audit workbook in Google Sheets and get the 7-day free trial of SheetXAI. Ask it to pull all PerPersonal records from SAP SuccessFactors and flag the incomplete ones. The migration cutover starts with clean data.