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

5.0 out of 5 stars 1 customer review

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  • Big Data at Work: Dispelling the Myths, Uncovering the Opportunities
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  • Keeping Up with the Quants: Your Guide to Understanding and Using Analytics
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  • Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
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Product Details

  • Hardcover: 240 pages
  • Publisher: Harvard Business Review Press (Feb. 25 2014)
  • Language: English
  • ISBN-10: 1422168166
  • ISBN-13: 978-1422168165
  • Product Dimensions: 2.5 x 16.5 x 24.1 cm
  • Shipping Weight: 454 g
  • Average Customer Review: 5.0 out of 5 stars 1 customer review
  • Amazon Bestsellers Rank: #137,794 in Books (See Top 100 in Books)
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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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Format: Hardcover
Over the years, I have read and reviewed all of Thomas Davenport's previously published books as well as most of his articles and again acknowledge my substantial debt to him for all that he has helped me to understand -- or to understand much better -- in terms of the major issues, perils, and opportunities that business leaders have had to address during the last 12-15 years. His work in recent years has been especially valuable to me and countless others. More specifically, the information, insights, and counsel he has provided in Competing on Analytics (2007), co-authored with Jeanne Harris; Analytics at Work (2010), co-authored with Harris and Robert Morrison; Judgment Calls (2012), co-authored with Brooke Manville; Keeping Up with the Quants (2013), co-authored with Jinho Kim; and now Big Data @ Work (2014).

I agree with him: "Big data is here to stay and of substantial importance to many organizations. [Therefore] organizations and managers ignore it at their peril." It is also true that a number of myths about big data have developed, in part because of confusion about the term. As Davenport explains, "First, there is the issue that [begin italics] big [end italics] is only one aspect of what's distinctive about new forms of data, and for many organizations, it's not the most important characteristic...The term [begin italics] big [end italics] is obviously relative -- what's big today won't be so large tomorrow [and] what's big to one organization is small to another...but the only real way in which 'size matters' with data is in the amount of hardware you will have to buy to store and process it...
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Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: HASH(0xa5a71438) out of 5 stars 85 reviews
24 of 25 people found the following review helpful
HASH(0xa5925450) out of 5 stars Breezy overview -- for managers who want to start looking at big data, definitely not for techs. April 21 2014
By Ivy Reisner - Published on Amazon.com
Format: Hardcover Vine Customer Review of Free Product
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. Later on we get machine learning defined as "Software for rapidly finding the model that best fits a data set". Machine learning is so much larger as a field, and this is so small a subset of the possible applications, that it is hard to give it the same "just rounding the corners" courtesy. The book then implies that Python generates Java code (it can, but it would be far simpler to just write Java code to begin with). Mr Davenport calls software engineers "Hackers" then goes through this whole song and dance, divorcing the terms from the outlaw element while trying to get at the concept of a rapid development cycle approach, rather than simply referencing Agile. By his own admission, he's not technical. It shows.

So, it depends on your needs. If you're a manager and want a breezy introduction, it's great. If you're a tech and want information you can use, look elsewhere.
9 of 9 people found the following review helpful
HASH(0xa5910300) out of 5 stars Big Data requires smart data scientists Feb. 13 2014
By John Gibbs - Published on Amazon.com
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. For example, the author refers frequently to Hadoop as a preferred technology platform for Big Data, but never really explains how it differs from SQL databases, apart from the fact that it caters for unstructured data (but how?).

The book describes some large businesses such as banks which are making use of Big Data, and some small businesses which are analysing Big Data and using it to create and sell useful information, but never really answers the questions of how a normal business which does not have internal Big Data can get some, or how they could benefit from it, other than by hiring really smart data scientists and hoping that they can think of a way to use Big Data to reduce costs, speed up business processes, or come up with new products or services.

I found more useful ideas for the use of Big Data in Christopher Surdak's book Data Crush: How the Information Tidal Wave Is Driving New Business Opportunities, but this book does provide some interesting insights, particularly into the human elements of Big Data.
8 of 8 people found the following review helpful
HASH(0xa593fab0) out of 5 stars For the Davenport fan! March 26 2014
By Walter Smith - Published on Amazon.com
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.
10 of 12 people found the following review helpful
HASH(0xa593fb4c) out of 5 stars Excellent Analysis by a Leading Authority in the Analytics Field Feb. 11 2014
By Dave - Published on Amazon.com
Format: Hardcover Vine Customer Review of Free Product
"... 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".
10 of 12 people found the following review helpful
HASH(0xa5938948) out of 5 stars Too basic March 16 2014
By Anonymouse - Published on Amazon.com
Format: Hardcover
Gaining value from a book like this depends on your starting point. I know a bit about data collection and manipulation and I find little of value in Mr. Davenport's presentation. He seems more interested in allaying the reader's fears of big data than providing concrete examples of how we can use big data to improve the ways we manage our businesses. If you are new to data manipulation or are curious about how business data is seen these days then you might find the book useful. If not, then I suggest that you go on to something more practical.

I received a review copy of Big Data at Work through NetGalley.com.


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