Paper Contents
Abstract
In today's data-driven landscape, the integrity and reliability of large-scale data warehousing systems are paramount for informed decision-making. This paper explores best practices in data quality and control, emphasizing methodologies that enhance the accuracy, consistency, and completeness of data. With the exponential growth of data volumes, organizations face significant challenges in maintaining data quality throughout the data lifecycle. We propose a framework that integrates automated data validation, cleansing, and profiling processes to systematically address common quality issues. The study highlights the importance of establishing robust governance policies, including data stewardship and accountability, to foster a culture of quality across all levels of the organization. Furthermore, we examine the role of advanced analytics and machine learning techniques in identifying anomalies and predicting potential data quality issues. Case studies demonstrate the successful implementation of these practices in various industries, illustrating the tangible benefits of improved data quality, such as enhanced operational efficiency and more accurate reporting. Ultimately, this paper advocates for a proactive approach to data quality management, emphasizing that investing in comprehensive data control strategies not only mitigates risks but also unlocks the full potential of large-scale data warehousing initiatives. Through these best practices, organizations can ensure that their data remains a reliable asset, driving strategic insights and competitive advantage in an increasingly complex business environment
Copyright
Copyright © 2023 Satish Vadlamani, Rahul Arulkumaran, Aayush Jain, Shreyas Mahimkar, Dr. Shakeb Khan, Prof.(Dr.) Arpit Jain. This is an open access article distributed under the Creative Commons Attribution License.
