Big data analytics : a management perspective

• 作者： Corea, Francesco, author.
• 其他作者：
  • SpringerLink (Online service)
• 其他題名：
  • Studies in big data ;
• 出版： Cham : Springer International Publishing :Imprint: Springer
• 叢書名： Studies in big data,volume 21
• 主題： Management information systems. , Big data. , Engineering. , Computational Intelligence. , Artificial Intelligence (incl. Robotics) , Big Data/Analytics.
• ISBN： 9783319389929 (electronic bk.) 、 9783319389912 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Introduction -- What Data Science Means to the Business -- Key Data Challenges to Strategic Business Decisions -- A Chimera Called Data Scientist: Why they don't Exist (but they will in the Future) -- Future Data Trends -- Where are we Going? The Path Toward an Artificial Intelligence -- Conclusions.
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• 摘要註: This book is about innovation, big data, and data science seen from a business perspective. Big data is a buzzword nowadays, and there is a growing necessity within practitioners to understand better the phenomenon, starting from a clear stated definition. This book aims to be a starting reading for executives who want (and need) to keep the pace with the technological breakthrough introduced by new analytical techniques and piles of data. Common myths about big data will be explained, and a series of different strategic approaches will be provided. By browsing the book, it will be possible to learn how to implement a big data strategy and how to use a maturity framework to monitor the progress of the data science team, as well as how to move forward from one stage to the next. Crucial challenges related to big data will be discussed, where some of them are more general - such as ethics, privacy, and ownership - while others concern more specific business situations (e.g., initial public offering, growth strategies, etc.) The important matter of selecting the right skills and people for an effective team will be extensively explained, and practical ways to recognize them and understanding their personalities will be provided. Finally, few relevant technological future trends will be acknowledged (i.e., IoT, Artificial intelligence, blockchain, etc.), especially for their close relation with the increasing amount of data and our ability to analyse them faster and more effectively.
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