In today’s data-driven landscape, the reliability and accuracy of information are paramount for informed decision-making and operational efficiency. A robust data quality framework provides a structured methodology for managing and maintaining the quality of data as-sets throughout their lifecycle. It in-troduces a comprehensive data qual-ity framework encompassing four key pillars: data governance, data profil-ing, data cleansing, and data monitor-ing. The foundation of the framework lies in establishing clear data governance policies and procedures, defining owner-ship, responsibilities, and accountability for data quality across the organization. Data profiling techniques are then em-ployed to assess the quality of data, iden-tifying anomalies, inconsistencies, and inaccuracies. Subsequently, data cleans-ing processes are implemented to rec-tify these issues, employing techniques such as deduplication, standardization, and validation to ensure data integrity. Continuous data monitoring is integral to the framework, enabling proactive de-tection of data quality issues and facil-itating timely corrective actions. Ad-vanced analytics and machine learn-ing algorithms may be leveraged to au-tomate monitoring processes, flagging anomalies and deviations from prede-fined quality thresholds.
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Aditya Kamarthi (2024) studied this question.