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Big Data at Work: Dispelling the Myths, Uncovering the Opportunities
 
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Big Data at Work: Dispelling the Myths, Uncovering the Opportunities [Format Kindle]

Thomas H. Davenport

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Présentation de l'éditeur

Go ahead, be skeptical about big data. The author was—at first.

When the term “big data” first came on the scene, bestselling author Tom Davenport (Competing on Analytics, Analytics at Work) thought it was just another example of technology hype. But his research in the years that followed changed his mind.

Now, in clear, conversational language, Davenport explains what big data means—and why everyone in business needs to know about it. Big Data at Work covers all the bases: what big data means from a technical, consumer, and management perspective; what its opportunities and costs are; where it can have real business impact; and which aspects of this hot topic have been oversold.

This book will help you understand:
• Why big data is important to you and your organization
• What technology you need to manage it
• How big data could change your job, your company, and your industry
• How to hire, rent, or develop the kinds of people who make big data work
• The key success factors in implementing any big data project
• How big data is leading to a new approach to managing analytics

With dozens of company examples, including UPS, GE, Amazon, United Healthcare, Citigroup, and many others, this book will help you seize all opportunities—from improving decisions, products, and services to strengthening customer relationships. It will show you how to put big data to work in your own organization so that you too can harness the power of this ever-evolving new resource.

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Amazon.com: 4.1 étoiles sur 5  69 commentaires
9 internautes sur 10 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Excellent Analysis by a Leading Authority in the Analytics Field 12 février 2014
Par Amazon Customer - Publié sur Amazon.com
Format:Relié|Commentaire client Vine pour produit gratuit (De quoi s'agit-il?)
"... 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".
8 internautes sur 9 ont trouvé ce commentaire utile 
3.0 étoiles sur 5 Breezy overview -- for managers who want to start looking at big data, definitely not for techs. 21 avril 2014
Par Ivy - Publié sur Amazon.com
Format:Relié|Commentaire client Vine pour produit gratuit (De quoi s'agit-il?)
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.
5 internautes sur 5 ont trouvé ce commentaire utile 
2.0 étoiles sur 5 For the Davenport fan! 27 mars 2014
Par Walter Smith - Publié sur Amazon.com
Format:Format Kindle
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.
5 internautes sur 5 ont trouvé ce commentaire utile 
4.0 étoiles sur 5 Big Data requires smart data scientists 14 février 2014
Par John Gibbs - Publié sur Amazon.com
Format:Format Kindle|Achat vérifié
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 internautes sur 10 ont trouvé ce commentaire utile 
1.0 étoiles sur 5 Too basic 17 mars 2014
Par Anonymouse - Publié sur Amazon.com
Format:Relié
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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