人员数据与分析 • 7 分钟阅读
HR 数据质量：为什么它很重要，以及如何修复它
MAY 22, 2026
Insights are only as good as the data behind them. Here is how to audit, improve, and maintain HR data quality across your HRIS.
数据质量差的成本
Poor HR data quality has both operational and strategic costs. Operationally: incorrect employee records lead to payroll errors, GDPR compliance risks, inaccurate management reporting, and failed system integrations. Strategically: analytics built on poor data produces confident-looking conclusions that are wrong, which is more damaging than having no analytics at all. A leadership team that makes workforce decisions based on an HRIS report and later discovers the data was unreliable is unlikely to trust HR data again in the near future.
开展数据质量审计
A data quality audit checks the accuracy, completeness, consistency, and timeliness of HRIS data. Pick the ten most critical data fields: employee name, job title, department, cost centre, manager, salary, contract type, FTE, start date, and work location. Export a sample of 100 employee records and verify each field against the source of truth (employment contract, payroll file, or HR decision record). Calculate an accuracy rate for each field. Any field with less than 95% accuracy should be investigated and corrected, and a root-cause fix implemented to prevent recurrence.
