Current Challenges of Big Data Quality Management in Big Data Governance: A Literature Review

Advent of big data has changed the way data-driven organizations collect, process and analyses the data. However, considering how data is generated from heterogenous sources, which gives the ample amount of available information to be generated dramatically. This has forced the data-driven organizations to invest more in technologies that will determine the quality of their data through an effective data governance process. However, the lingering issues on the challenges of big data quality management in big data governance remains an alarming issue that requires much attention from the researchers and practitioners, because studies on this area are still in its infancy stage. Therefore, the main objective of this study is to identify the current challenges of data quality management in big data governance from previous studies and address them accordingly. To achieve this objective, we conducted a literature review on the recent studies that address these challenges. This study has also examined the available data quality dimensions used by the individual studies. Moreover, a total of 41 recent studies published between (2020–2023) were utilized by this review. The results of our findings including the authors recommendations on these challenges, we proposed a framework for big data quality management based on ISO 8000–61 for data quality reference model, ISO 8000–62 for organizational process maturity assessment, and “Plan-Do-Check-Act” approach for big data quality assessment. Subsequently, we proposed affordability, Operationability, and data literacy to be included in the big data quality dimensions.