The paper delves into various frameworks designed to address data quality concerns, highlighting their key components and methodologies. Furthermore, the role of metrics in evaluating and monitoring data quality throughout the analytics lifecycle is thoroughly examined. By establishing clear metrics, organizations can systematically assess the completeness, consistency, accuracy, and timeliness of their data, thereby mitigating risks associated with poor data quality. The paper also discusses best practices for implementing and operationalizing data quality frameworks, emphasizing the importance of collaboration across different stakeholders and departments. Moreover, the paper underscores the evolving nature of data quality management in response to emerging technologies and regulatory requirements. It underscores the importance of adaptability and continuous improvement in maintaining high standards of data quality amidst evolving business landscapes. Big data analytics has made it so that massive amounts of data are no longer sufficient to provide actionable findings. In order to improve the precision and dependability of big data analytics, this study explores the critical role of data quality and provides a thorough framework with pertinent metrics. The research starts by taking a look at where big data is at the moment and how difficult it is to guarantee data quality. Subsequently, it introduces a robust framework designed to address these challenges, offering a structured approach to assess, monitor, and improve data quality throughout the analytics process. Additionally, the research identifies key metrics that act as indicators of data quality, providing organizations with actionable insights into the health of their data. Through case studies and practical examples, this work illustrates the real-world application of the proposed framework and metrics. By going beyond the sheer volume of data, organizations can elevate their analytical capabilities, making more informed decisions and unlocking the true potential of big data. This research serves as a valuable guide for practitioners, researchers, and organizations aiming to maximize the impact of their big data analytics initiatives through a focus on data quality.

Data Quality, Big Data Analytics, Frameworks, Metrics, Reliability, Accuracy.

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