Data Quality Assurance in the Age of Big Data

Abstract

As organizations grapple with the vast and complex landscape of big data, ensuring the quality and reliability of information becomes a critical imperative. This research investigates strategies and best practices for data quality assurance tailored to the challenges posed by the era of big data. The study explores advanced techniques for data validation, cleansing, and enrichment, emphasizing the role of automated tools and machine learning algorithms in enhancing data quality. Additionally, the research delves into the development of robust data governance frameworks, incorporating real-time monitoring to promptly detect and rectify data anomalies. The significance of high-quality data in analytics and decision-making processes is underscored, highlighting the impact on organizational success. This study provides actionable insights to guide enterprises in navigating the complexities of big data and maintaining elevated standards of data quality.