Creating reliable people data : advancing HR through structured quality-management practices

This case study investigates how Human Resource (HR) data quality can be enhanced within a multi-system environment by applying the DMAIC (Define-Measure-Analyse-Improve-Control) framework to key workforce disclosure requirements under the European Sustainability Reporting Standards S1 Own Workforce standard. HR data are widely recognised in the literature as fragmented, inconsistently governed and shaped by human-centred processes rather than stable transactional systems, which limits their reliability for both analytics and organisational decision making. These challenges are particularly pronounced in knowledge-intensive service organisations, where decentralised routines, limited automation and heterogeneous tools make data quality difficult to stabilise at scale. Drawing on five metrics within a Nordic IT services firm, the study identifies recurring structural hindrances such as manual oversight, fragmented lineage, semantic inconsistency, unstructured text fields and external-input dependencies, and traces each metric through the DMAIC cycle. The findings demonstrate that workflow design, shared definitions and governance routines are more decisive for data reliability than the choice of system alone. Analytical techniques (rule-based lexicons and logistic regression) are shown to supplement governance controls, but only when upstream processes are sufficiently stable. The study contributes theoretically by expanding empirical understanding of HR data quality at the process level, a domain where peer reviewed research remains sparse due to sensitivity and access constraints. It also offers sector-specific value for service-based organisations that rely on hybrid digital workflows and human-mediated capture. Practically, the research provides a methodological blueprint for integrating governance and analytics and delivers actionable insights for building repeatable, auditable workforce reporting pipelines.