Big Data and Business Intelligence in Amazon (MO2-3) Amazon, one of the world's leading e-commerce platforms, serves millions of customers each day. The amount of data generated from customers' online activities is substantial, and Amazon uses big data analytics and business intelligence (BI) to drive its operations, decision-making, and customer experience improvements. Amazon collects large volumes of structured and unstructured data, such as customers' purchasing history, search queries, and clickstream data. This data is processed and analyzed to gain insights into customer behavior and preferences [1]. Amazon uses BI tools to visualize and interpret the data generated. Tools like AWS QuickSight provide valuable insights about sales trends, customer behavior, and product performance. Amazon's recommendation system is a prime example of big data analytics and BI. It analyzes customer behavior, past purchases, items in the cart, and items rated and reviewed by the customer. Using machine learning algorithms, Amazon predicts what customers might want to buy next [2]. The personalized recommendation system has significantly boosted Amazon's sales. A study by McKinsey stated that 35% of Amazon's revenue is generated by its recommendation engine [3]. Prepare a research report to address following questions: 1. How did Amazon leverage big data analytics and business intelligence to enhance customer experience and boost sales? 2. What role did machine learning play in Amazon's Bl strategy? 3. Can other e-commerce businesses replicate Amazon's success? What would be the potential challenges? References: [1] Davenport, T. H., & Dych, J. (2013). Big Data in Big Companies. International Institute for Analytics. [2] Linden, G., Smith, B., & York, J. (2003). Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing, 7(1), 76-80. [3] Mckinsey Global Institute. (2013). Big data: The next frontier for innovation, competition, and productivity.