- Author:
- Frank J. Ohlhorst
Unique insights to implement big data analytics and reap big returns to your bottom lineFocusing on the business and financial value of big data analytics, respected technology journalist Frank J. Ohlhorst shares his insights on the newly emerging field of big data analytics in Big Data Analytics. This breakthrough book demonstrates the importance of analytics, defines the processes, highlights the tangible and intangible values and discusses how you can turn a business liability into actionable material that can be used to redefine markets, improve profits and identify new business opportunities. Reveals big data analytics as the next wave for businesses looking for competitive advantageTakes an in-depth look at the financial value of big data analyticsOffers tools and best practices for working with big dataOnce the domain of large on-line retailers such as eBay and Amazon, big data is now accessible by businesses of all sizes and across industries. From how to mine the data your company collects, to the data that is available on the outside, Big Data Analytics shows how you can leverage big data into a key component in your business's growth strategy.
Cited By
- Sarkar B (2017). Big Data and Healthcare Data, International Journal of Knowledge-Based Organizations, 7:4, (50-77), Online publication date: 1-Oct-2017.
Caldarola E, Picariello A and Castelluccia D (2015). Modern Enterprises in the Bubble, ACM SIGSOFT Software Engineering Notes, 40:1, (1-4), Online publication date: 6-Feb-2015.- Kim G, Trimi S and Chung J (2014). Big-data applications in the government sector, Communications of the ACM, 57:3, (78-85), Online publication date: 1-Mar-2014.
Index Terms
- Big Data Analytics: Turning Big Data into Big Money
Recommendations
Reviews
John M. Artz
Big data is a big deal, and publishers are scrambling to take advantage of this emerging phenomenon by providing new titles to a marketplace looking for answers to questions such as, "What is big data__?__" and "Why does big data matter to me__?__" This is reflected in the number of Kindle books on Amazon.com addressing these questions, as well as in the number of titles that have been announced but have yet to be released. This suggests that we currently have no definitive answer to these questions, in part because the phenomenon is still emerging and is often confused by the also-emerging phenomenon of data science, which overlaps big data but is not the same. One somewhat standard answer to the first question is that the term refers to datasets that exceed the capacity of existing database technology. The author of this book uses that as one of the possibilities. Another somewhat standard answer is given in terms of three Vs-volume, variety, and velocity-which is to say that it is a lot of complicated data coming at you very fast. Some authors, including this one, add a fourth V for veracity, although the definition the author provides in this book seems at odds with other sources. This is all to say that the world of big data is a bit of a jumble and the definitive text on the subject still lies in the future. This book provides a general sense of what big data is about. Its strengths are that it is short and relatively easy to read. Its main weakness is that it reads like a long magazine article, suggesting that there are things you may want to know more about but not telling you much about them. For example, the author states that a starter set of Hadoop components may consist of the following: HDFS and HBase for data management, MapReduce and OOZIE as a processing framework, Pig and Hive as development frameworks for developer productivity, and open-source Pentaho for BI. These terms were neither introduced before this statement nor explained after it. If you are familiar with these terms, the author is not telling you anything you don't already know. If you are not familiar with these terms, this statement is useless. In addition, the author has a distracting habit of making gratuitous statements such as "knowing what big data is and knowing its value are two different things," or "like any other technology or process, there obviously are best practices that can be applied to the problem of big data." I recall a writing teacher telling me years ago that if something is obvious, then you don't need to say it. If you are looking for substance and an in-depth understanding of big data, then this book is probably not for you. If you are looking for an overview that provides a general idea, then it may suffice. More reviews about this item: Amazon Online Computing Reviews Service
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