Data Quality Management (DQM) is crucial in ensuring data accuracy, completeness, consistency, and reliability throughout its lifecycle, supporting data-driven decision-making and enhancing the value of analytics insights across organizations. DQM practices involve data profiling, cleansing, validation, standardization, and monitoring, addressing dimensions like accuracy, completeness, consistency, timeliness, and validity. Different methods like data profiling, cleansing, standardization, deduplication, validation, enrichment, and monitoring, along with data governance frameworks, are employed to maintain data quality. DQM is particularly important in managing the challenges posed by diverse data sources and big data, ensuring reliable data for analytics and predictive modeling. Tools such as PubNub Illuminate, Talend, Informatica, Ataccama ONE, and Trifacta offer various functionalities to enhance data quality, including real-time monitoring, anomaly detection, data cleansing, and enrichment. Implementing DQM leads to benefits like improved decision-making, operational efficiency, increased trust in data, regulatory compliance, enhanced customer experience, better analytics, cost savings, and scalability. DQM is essential in today's data-driven landscape, enabling organizations to make trustworthy decisions and maximize the value of their data assets.