HR Technology and Data Analytics
Domain 7: HR Technology and Data Analytics
This domain tests the HRIS landscape, the analytics hierarchy, data quality and privacy controls, and the metrics HR is expected to compute and defend.
Core rules and concepts
- Analytics maturity: descriptive (what happened), diagnostic (why), predictive (what will happen), prescriptive (what to do). Most HR functions sit at descriptive or diagnostic.
- Data quality - completeness, accuracy, consistency, timeliness - determines whether any downstream analysis is trustworthy. Duplicate employee records and inconsistent effective dates are the most common defects.
- Core HRIS modules: core HR and payroll, recruiting (ATS), learning (LMS), performance, compensation, benefits administration, and time and attendance.
- Privacy and security: GDPR and CCPA/CPRA impose purpose limitation, minimization, and access rights; employee data is personal data. Role-based access and audit trails are baseline controls.
- Common metrics: time-to-fill, cost-per-hire, offer acceptance rate, turnover rate, absenteeism, and training hours per employee.
Worked example
A company filled 20 roles last year. Total recruiting spend was $120,000 (advertising $40,000, agency fees $50,000, allocated recruiter time $30,000). Cost-per-hire = $120,000 ÷ 20 = $6,000. If two of the 20 hires were internal transfers with no agency fee, the metric should state whether internal moves are included; including them lowers the average but changes the meaning.
Traps that cost marks
- Reporting predictive claims from descriptive data.
- Using inconsistent denominators in turnover or cost-per-hire.
- Overlooking GDPR or CCPA when analyzing employee data.
- Assuming a dashboard is analytics; a dashboard is a display, not an explanation.