There is an ongoing data explosion transpiring that will make previous creations, collections, and storage of data look trivial. Big Data, Mining, and Analytics: Components of Strategic Decision Making ties together big data, data mining, and analytics to explain how readers can leverage them to extract valuable insights from their data. Facilitating a clear understanding of big data, it supplies authoritative insights from expert contributors into leveraging data resources, including big data, to improve decision making. Illustrating basic approaches of business intelligence to the more complex methods of data and text mining, the book guides readers through the process of extracting valuable knowledge from the varieties of data currently being generated in the brick and mortar and internet environments. It considers the broad spectrum of analytics approaches for decision making, including dashboards, OLAP cubes, data mining, and text mining. •Includes a foreword by Thomas H. Davenport, Distinguished Professor, Babson College Fellow, MIT Center for Digital Business and Co-Founder, International Institute for Analytics •Introduces text mining and the transforming of unstructured data into useful information •Examines real time wireless medical data acquisition for today’s healthcare and data mining challenges •Presents the contributions of big data experts from academia and industry, including SAS •Highlights the most exciting emerging technologies for big data—Hadoop is just the beginning Filled with examples that illustrate the value of analytics throughout, the book outlines a conceptual framework for data modeling that can help you immediately improve your own analytics and decision-making processes. It also provides in-depth coverage of analyzing unstructured data with text mining methods to supply you with the well-rounded understanding required to leverage your information assets into improved strategic decision making.

正在发生的数据爆炸，将使以前的数据创造、收集和存储显得微不足道。大数据、采矿和分析。战略决策的组成部分》将大数据、数据挖掘和分析结合起来，解释读者如何利用它们从数据中提取有价值的见解。它促进了对大数据的清晰理解，提供了来自专家贡献者对利用数据资源（包括大数据）来改善决策的权威见解。本书说明了从商业智能的基本方法到更复杂的数据和文本挖掘方法，指导读者从目前在实体店和互联网环境中产生的各种数据中提取有价值的知识。它考虑了用于决策的广泛的分析方法，包括仪表盘、OLAP立方体、数据挖掘和文本挖掘。包括由托马斯-H-达文波特的前言。介绍了文本挖掘和将非结构化数据转化为有用信息的过程 -探讨了实时无线医疗数据采集对当今医疗和数据挖掘的挑战 -介绍了来自学术界和工业界的大数据专家的贡献。本书充满了说明分析价值的例子，概述了数据建模的概念框架，可以帮助你立即改善你自己的分析和决策过程。本书还深入介绍了用文本挖掘方法分析非结构化数据的情况，为您提供了利用信息资产改善战略决策所需的全面理解。