個案資料

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. Chapter 1 gives an overview of the field of business analytics, which uses data, statistical analysis, mathematical and statistical modeling, data mining, and advanced analytics tools to explore, investigate, and understand business performance. The tools of business analytics give insight to and drive business planning and decisions. Descriptive analytics tools use statistical, graphical, and numerical methods to understand the occurrence of certain business phenomena; predictive analytics tools are used to predict future business phenomena; prescriptive analytics tools have applications in optimizing and automating business processes. Business analytics helps with informed decision-making in business and can be used to automate and optimize business processes. Data is an asset and can be leveraged for competitive advantage. Successful business analytics depends on data quality and skilled analysts. Business analytics is an organizational commitment to data-driven decision-making.

• Business analysis
• Decision making
• Predictive analytics
• Technology and analytics
• Management performance