Recognition and Systemic Analysis of Data Quality Methodologies and Proposing a Comprehensive Framework Using the Meta-Synthesis Method

Despite abundant research on data quality, no research has so far been conducted which can provide a comprehensive view of data quality methodologies. In the present study, 3909 articles and related researches in the period before 2020 were selected from Web of Science (WOS) and Scopus citation indexes, from among which 27 articles were finally evaluated in line with the research goals, using meta-synthesis method and inclusion criteria. In this regard, while applying the system view and using the open coding method, the related codes to the three main categories of the systemic approach (input, process and output) were extracted. The similar concepts were categorized in sub-codes and then the sub-codes in main codes. The main inputs included the context and status of organization, data and information resources, and data quality dimensions. Steps of data quality methodologies were also classified into three main stages: state reconstruction, measurement/ evaluation, and improvement. In addition, data quality outputs fell into six general categories: list of activities and the related techniques for data quality improvement, controlled or redesigned processes, measured or improved flows and databases, data quality status results, revised data quality policies or rules, and costs and benefits. The results of this study can provide an appropriate instrument for identifying the existing data quality methodologies as well as evaluating the strengths and weaknesses of data quality methodologies.