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Big Data at Work: Dispelling the Myths, Uncovering the Opportunities Hardcover – February 25, 2014

4 out of 5 stars 84 customer reviews

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Editorial Reviews

Review

“It’s a required reading for managers that need a straightforward, hype-free introduction to big data, a clear and clarifying “signal” in the incredible noise around the confusing and mislabeled term.” — Forbes

“Davenport has written a thought-provoking book about a current topic that is becoming more important to business and individuals every day. Summed up: Highly recommended.” — Choice magazine

“The book covers all aspects of the issue, from what big data means, to whom you must hire, to what technologies to follow. It’s surprisingly easy to read, given the topic, and offers good examples to ponder from startups and large firm.” — Globe & Mail

“Davenport is a methodologically-sound researcher. His deep interviews and surveys of executives and data scientists set a standard for excellence in an industry where marketing bravado generally supersedes scientific rigor” — Information Management (information-management.com)

ADVANCE PRAISE for Big Data at Work:

Jane Griffin, Managing Director Analytics, Deloitte Canada and Americas—
Big Data at Work is the first and only book to describe how real organizations are using big data, extracting value from it, and combining it with other forms of data and analytics. It’s an invaluable guide to planning and action.”

Jonathan D. Becher, Chief Marketing Officer, SAP—
“Is Big Data a buzzword or does it have practical applications in business? Big Data at Work goes beyond tech-talk to help businesspeople turn Big Data into Big Decisions.”

Gary L. Gottlieb, MD, MBA, President and CEO, Partners HealthCare System, Inc.; Professor of Psychiatry, Harvard Medical School—
Big Data at Work provides a terrific foundation for thoughtful planning to exploit the business opportunities created by diverse and vast sources of information. Davenport’s clear approach will enlighten managers about the need to carefully mine these resources to improve operations and products while driving new and competitive strategies.”

Rob Bearden, CEO, Hortonworks—
“Thomas Davenport has supplied a smart, practical book for anyone looking to unlock the opportunities—and avoid the pitfalls—of big data.”

Adele K. Sweetwood, Vice President, Americas Marketing & Support, SAS—
“Conversational, engaging, and an exceptional guide for decision making in the big data world. Big Data at Work offers insight to the business and technology components of a big data strategy, a path to success, and best practices from across industry sectors.”

About the Author

Thomas H. Davenport is a world-renowned thought leader on business analytics and big data, translating important technological trends into new and revitalized management practices that demonstrate the value of analytics to all functions of an organization. He is the President’s Distinguished Professor of Information Technology and Management at Babson College, a fellow of the MIT Center for Digital Business, cofounder and Director of Research at the International Institute for Analytics, and a senior adviser to Deloitte Analytics. Davenport is the author or coauthor of seventeen books, including the bestselling Competing on Analytics, as well as the author of dozens of articles for Harvard Business Review.
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Product Details

  • Hardcover: 228 pages
  • Publisher: Harvard Business Review Press (February 25, 2014)
  • Language: English
  • ISBN-10: 1422168166
  • ISBN-13: 978-1422168165
  • Product Dimensions: 1 x 6.5 x 9.5 inches
  • Shipping Weight: 1 pounds (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (84 customer reviews)
  • Amazon Best Sellers Rank: #89,413 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

By Ivy Reisner VINE VOICE on April 21, 2014
Format: Hardcover Vine Customer Review of Free Product ( What's this? )
This slim volume provides an adequate, breezy introduction to big data. On the plus, it's a light book, easy to read, easy to digest. The tone is warm and friendly, and the book is quite a pleasure to read. If you just need an overview, and if you are willing to acknowledge that what you are reading barely scratches the surface of the topic -- and that is a legitimate purpose for a book -- then this is perfect. If you want a first, "get your feet wet" kind of book, this is perfect. I think you'd have a hard time finding an easier, more entertaining introduction to the field.

In chapter one, we soon encounter the line, "These aren't real facts about the dazzling nature of data volumes and types today -- I made them up -- but they're probably not that far off." Then in chapter five we get "My focus here is not on how Hadoop functions in detail, or whether Pig or Hive is the better scripting language (alas, such expertise is beyond my technological pay grade anyway)". That's a decent indicator of where this book falls on the breezy, indicator scale.

If you have a technical background, you will not like this book. It is mostly accurate, most of the time. That is not to say that it is incorrect, so much as incomplete. It defines scripting languages as "Programming languages that work well with big data (e.g., Python, Pig, Hive)". Yes, you can use scripting languages to deal with large data. You can also use compiled languages like C#. Cobol has been doing this kind of work for decades. Scripting languages can be used for things beyond data crunching -- a chat client or a game for example. So, here we get a kind of rounding of the corners -- a simplification for sake of clarity.
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Format: Kindle Edition Verified Purchase
Big data, at least today, requires some educated faith. ROI is difficult to define in advance--particularly when it involves new products and services or faster decisions, according to Thomas Davenport in this book. Nonetheless, some businesses are getting significant benefits from employing data scientists to work on Big Data, so it definitely seems to be something worth investigating.

Although the idea of Big Data is not precisely defined, the characteristics of Big Data described by the author include unstructured formats, volume of greater than 100 terrabytes, existing in a constant flow rather than a static pool, analysed by machine learning rather than hypothesis, and intended for data-based products rather than internal decision support. These are trends rather than absolutes, as Big Data includes more conventional types of data as well.

The key to deriving maximum advantage from Big Data seems to involve employing the smartest data scientists to analyse the data. Good data scientists are likely to be rare and expensive, given the ideal traits described by the author:

* Understanding of big data technology architectures and coding
* Improvisation, evidence-based decision making and action orientation
* Strong communication and relationship skills, particularly in dealing with senior management
* High level skills in statistics, visual analytics, machine learning, and analysis of unstructured data
* Good business sense and focus on commercial value

The book assiduously avoids using technical language, and as a result the book avoids answering some of the questions raised in readers' minds.
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Format: Kindle Edition
I had high hopes from this book but I felt it was too basic. The general problem with Davenport's books is that they offer little practical guidance but rather some high level advise and some examples, although interesting ones, one how analytics are applied across industries.

If you are a Davenport fan then this book might be for you. If now, get another one. There are many out there.
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Format: Hardcover Verified Purchase
This book is very high level and contains very basic information in big data. But It also contains horizontal informations not just big data and also infrastructure for hadoop, data scientist skill sets, out-sourcing vs in-housing etc... So this book is designed for managers who wants to put a strategy about big data and do not know where to start :)

For big data find another books. Nevertheless i suggest to read this book weather u are manager or not. Author has wrote his own decisions for example big data may not be used as a decision system due to political approaches. Business analytics systems were available for a while and no body used that for decision making .... :)
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Format: Hardcover Vine Customer Review of Free Product ( What's this? )
"... big data, despite my reservations about the name of the phenomena, is here to stay and of substantial importance to many organizations" Pg. 3-4

With this statement Davenport begins his analysis of big data. What's great about this is that we start at a point of credulity, asking the difficult questions that analytics as a practice demands we ask. big data is an emerging trend, and what is important lies beyond the current name we give it. The cover title drives this point home by denying "big data" the capitalization of a proper noun.

This approach is a welcome change in the literature of big data and is especially important to the primary intended audience: business leaders. After defining "big data" and placing it in the context of the analytics field, the focus is how it can be turned into a productive and valuable aspect of your business or organization.

The book is relatively short, very well written and organized, and full of important and useful information. If you're unclear as to what big data really is, if you're curious about what it can do for your organization then this is the book for you. Davenport is an expert in the field and has already produced very well regarded books detailing the value in the modern and evolving field of analytics and this book adds to that discussion with easily consumed academic insights that relate immediately to modern business challenges and opportunities.

Highly recommended to anyone that is interested in, or involved with, "big data". Those in IT, Finance, HR, or an industry that generates and consumes large quantities of data are especially encouraged to read this since the focus is on the "why", not the "how".
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