Publisher's Synopsis
Ever since the beginning of the digital age, data in digital form has received a growing importance, first primarily in the business domain and later also in the private domain. Any business organization needs to continually monitor its business environment and its own performance, and then rapidly adjust its future plans. This includes monitoring the industry, the competitors, the suppliers, and the customers. The organization needs to also develop a balanced scorecard to track its own health and vitality. Executives typically determine what they want to track based on their key performance Indexes (KPIs) or key result areas (KRAs). This volume compiles state of the art information with real-world examples to build a theoretical and practical understanding of key data mining methods in business analytics. There is more data from customers' responses and on the industry as a whole. All this data can be analyzed and mined using special tools and techniques to generate patterns and intelligence, which reflect how the business is functioning. These ideas can then be fed back into the business so that it can evolve to become more effective and efficient in serving customer needs. This volume also reviews the issues, techniques, and applications of big data, with an emphasis on future business intelligence architectures. Business intelligence includes tools and techniques for data gathering, analysis, and visualization for helping with executive decision making in any industry. Ever since the emergence of big data concept, researchers have started applying the concept to various fields and tried to assess the level of acceptance of it with renown models like technology acceptance model (TAM) and it variations. In this regard, this volume tries to look at the factors that associated with the usage of big data analytics, by synchronizing TAM with organizational learning capabilities (OLC) framework. This volume covers some of the most important modeling and prediction techniques, along with relevant applications. Showing how data mining can improve the performance of organizations, this book and its R-based software provide the skills and tools needed to successfully develop advanced analytics capabilities.
