Assessing Data Quality Maturity Models: Enhancing Organizational Readiness and Improvement

Data quality is crucial in supporting accurate decision-making in today’s digital era. Key attributes such as data completeness, accuracy, consistency, and relevance must be maintained to ensure organizations across various sectors, including finance, healthcare, and education, can optimally utilize data. However, data quality issues such as incompleteness, inaccuracy, and inconsistency often pose significant challenges to accurate decision-making. Invalid or irrelevant data can lead to decision-making errors, ultimately harming business performance and strategy. If data quality is not maintained, decisions based on such data risk being inaccurate and ineffective. In this study, a data quality capability model is proposed and evaluated by six experts, with an average score of 3.7 and a Content Validity Ratio (CVR) of 1, indicating that the model is valid and relevant for implementation. The Fleiss’ Kappa value of 0.481 indicates good agreement among the experts, further supporting the model’s relevance for use within organizations. Implementing this model is expected to help organizations manage their data more effectively and efficiently, resulting in high-quality data that supports accurate decision-making. The application of this model is also expected to enhance organizational performance and competitiveness in the face of increasingly complex data management challenges. This model can be a foundation for achieving higher data quality standards, allowing organizations to strategically leverage data in innovation development and operational efficiency.