Data Quality Management Maturity Model: A Case Study in Higher Education's Human Resource Department

Data has increasingly become more imperative in organization's decision-making process. Low data quality can cause extensive organizational problems, such as inaccurate decision-making and dropped business possibilities. This is because low-quality data does not present a clear description of the actual situation. In Human Resource (HR) management, low data quality can cause recruitment, career development, remuneration, and retirement processes. Therefore, proper data quality management must be implemented to produce data that suits the organization's needs. To determine how far the implementation of data quality management in the organization, measurement of the maturity level in data quality management is conducted. This study presented an evaluation of data quality management maturity level in HR of higher education, applying the Loshin data quality management maturity framework. The results of this study indicate that the maturity level in the Data quality expectations area is 2.17, the maturity level in the Data quality dimensions area is 2.16, the maturity level in the Policies area is 1.22, the maturity level in the Data quality protocols area is 2, 11, the maturity level in the Data governance area is 1.77, the maturity level in the Data standards area is 1.67, the maturity level in the Technology area is 1.44, and the maturity level in the Performance management area is 1.67. The result shows that the Policies area is the lowest due to the lack of regulations and good documentation regarding data management. It can be a concern in conducting evaluations for improving data quality management.