Big Data, Mining, and Analytics: Components of Strategic Decision Making (Hardcover)

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.

數據爆炸正在發生，使以往的數據創建、收集和存儲顯得微不足道。《大數據、挖掘和分析：戰略決策的組成部分》將大數據、數據挖掘和分析相結合，解釋讀者如何利用它們從數據中提取有價值的見解。它提供了專家貢獻者對利用數據資源（包括大數據）改善決策的權威見解，從而促進對大數據的清晰理解。

本書從基本的商業智能方法到更複雜的數據和文本挖掘方法，引導讀者從實體和互聯網環境中生成的各種數據中提取有價值的知識。它考慮了用於決策的各種分析方法，包括儀表板、OLAP立方體、數據挖掘和文本挖掘。

本書的特點包括：
- 包含Thomas H. Davenport的序言，他是巴布森學院的傑出教授、麻省理工學院數字商業中心的研究員和國際分析學院的聯合創始人。
- 介紹文本挖掘和將非結構化數據轉化為有用信息的過程。
- 探討當今醫療保健和數據挖掘挑戰中的實時無線醫療數據獲取。
- 提供來自學術界和行業的大數據專家的貢獻，包括SAS。
- 強調大數據最令人興奮的新興技術，Hadoop只是個開始。

本書充滿了例子，以說明分析的價值，並概述了一個數據建模的概念框架，可以幫助您立即改善自己的分析和決策過程。它還深入介紹了使用文本挖掘方法分析非結構化數據，以提供您所需的全面理解，從而將信息資產轉化為改進的戰略決策。