Big Data at Work: Dispelling the Myths, Uncovering the Opportunities Hardcover – Feb 25 2014
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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 opportunitiesand avoid the pitfallsof 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.”
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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...Read more ›
Most Helpful Customer Reviews on Amazon.com (beta)
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.
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.
If you are a Davenport fan then this book might be for you. If now, get another one. There are many out there.
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".
I received a review copy of Big Data at Work through NetGalley.com.
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