You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Review on Business Intelligence and Big Data 
Chapter Title:  Enterprise Data 
1. Abstract of Cited Paper (BIB001):  It is already true that Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. As the speed of information growth exceeds Moore’s Law at the beginning of this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. A new scientific paradigm is born as dataintensive scientific discovery (DISD), also known as Big Data problems. A large number of fields and sectors, ranging from economic and business activities to public administration, from national security to scientific researches in many areas, involve with Big Data problems. On the one hand, Big Data is extremely valuable to produce productivity in businesses and evolutionary breakthroughs in scientific disciplines, which give us a lot of opportunities to make great progresses in many fields. There is no doubt that the future competitions in business productivity and technologies will surely converge into the Big Data explorations. On the other hand, Big Data also arises with many challenges, such as difficulties in data capture, data storage, data analysis and data visualization. This paper is aimed to demonstrate a close-up view about Big Data, including Big Data applications, Big Data opportunities and challenges, as well as the state-of-the-art techniques and technologies we currently adopt to deal with the Big Data problems. We also discuss several underlying methodologies to handle the data deluge, for example, granular computing, cloud computing, bio-inspired computing, and quantum computing.

Literature Review:
Enterprise data is the most prominent domain of big data. It was estimated in 2011 that volume of business data would double every 1.2 years BIB001 . Enterprise data is mainly structured data and managed by RDBMS and it holds every possible recordable action and activity of an enterprise like CRM, ERP, sales, financial, and production. For example, Walmart's 6000 stores all over the world generate approximately 267 million transaction data and in order to take advantage of this huge data, Walmart established petabytescale data warehouse (DW) BIB002 . As storage, processing, networking, and data management technology advances the data volume, variety and complexity increase. This increase requires more effective and advanced analysis techniques to infer decision making information.