Tirupathi Rao Bammidi, Leela Manush Gutta, Anudeep Kotagiri, Laxmi Srinivas Samayamantri, Rama krishna Vaddy


As organizations increasingly rely on automated decision-making systems to derive insights and streamline operations, the importance of data quality becomes paramount. This paper delves into the critical role that data quality plays in the efficacy and reliability of automated decision-making processes. Recognizing that the outputs of these systems are only as accurate as the data they process, the study explores the challenges, best practices, and strategies for ensuring high-quality data within automated decision-making frameworks. The paper begins by elucidating the fundamental connection between data quality and the performance of automated decision-making systems, emphasizing how inaccuracies or biases in the input data can propagate and magnify within the automated decision-making process. It delves into the impact of poor data quality on decision outcomes, operational efficiency, and overall organizational effectiveness.Drawing from established literature and real-world case studies, the study highlights the challenges organizations face in maintaining data quality within the context of automated decision-making. It explores common sources of data errors, such as inaccuracies, incompleteness, and inconsistency, and their potential ramifications on decision accuracy and reliability. These practices encompass data governance frameworks, validation protocols, and continuous monitoring strategies. The study advocates for a proactive approach to data quality management, emphasizing the need for organizations to invest in robust processes and technologies that ensure the accuracy, completeness, and relevance of their data. In inference, the paper underscores that successful implementation of automated decision making systems hinges on the establishment and maintenance of high data quality standards. It urges organizations to view data quality as an integral component of their decision-making infrastructure and provides insights into mitigating risks associated with poor data quality. By embracing a comprehensive and proactive approach to data quality, organizations can optimize the performance and reliability of their automated decision-making systems, thereby enhancing their capacity to make informed and impactful decisions in an increasingly automated landscape.

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