The Big Data Paradox: Challenges and Opportunities in Analytics

Abstract

Big data analytics has revolutionized the way organizations extract insights from vast datasets, though it brings substantial challenges in areas like data quality, integration, processing, and security. Analyzing large volumes of data requires sophisticated analytical techniques that can efficiently assess and predict outcomes with high precision, complemented by advanced decision-making strategies. As data volume, variety, and velocity continue to rise, so too does uncertainty, which can reduce confidence in analytics and decision-making. Unlike traditional methods, AI techniques—such as machine learning, natural language processing, and computational intelligence—offer more accurate, faster, and scalable solutions for big data analytics. This case study examines the obstacles a major corporation faced in implementing effective big data analytics and the strategies it adopted to address these challenges. By analyzing practical solutions and lessons learned, this paper aims to provide valuable insights for other organizations tackling similar issues in their big data initiatives.