Big Data

Author: Bernard Marr
Publisher: Wiley
Date: February 1, 2015
Pages: 258
ISBN: 9781118965832
Print: 1118965833
Kindle: B00S4TBEJK
Audience: Managers and business people new to the idea of big data
Rating: 4
Reviewer: Kay Ewbank

There is much buzz around big data. But what is it and how does it work? 

In the opening chapter of this book, Bernard Marr points out that despite the noise around Big Data, most people still don’t really understand it, and that the real value isn’t in the amount of data, but what we can do with it. This introduction to big data is subtitled ‘using SMART big data analytics and metrics to make better decisions and improve performance’.  


The book opens with a chapter on titled ‘smarter business’, where Marr gives some examples of the companies using big data, and introduces the SMART model used for the rest of the book. Marr’s style of writing is easy to read and relies on interesting examples to keep you hooked. Unlike some titles (particularly in the database world), you’re unlikely to drop to sleep from boredom; if anything, you’re more likely to wish there was a bit more detail. What it definitely isn’t is a title aimed at developers; it’s more a light touch read for managers and business people who need a high-level view of what big data is all about.


 Having introduced the topic, the next few chapters each focus on an individual element of SMART, starting with S for ‘Start with Strategy’. Working out what you need to do to make use of big data in your company is undoubtedly tricky, and Marr chats about the problems and how various companies have overcome them. He also introduces a SMART strategy board that he’s designed to help analyze the ‘strategic information needs’ of a company. This has six panels to be completed, looking at aspects such as your customers, financial implications, and the resources needed. The majority of the rest of this chapter looks at each panel in turn and discusses what information is needed to complete it. The chapter closes with a case study on Google’s project oxygen, which started with a question familiar to many developers: ‘do managers make a positive impact?’

The M in Smart stands for Measure Metrics and Data, and that’s the topic of the next chapter. Marr starts with a discussion of structured versus unstructured data, then considers the data being generated (and gathered) in areas such as email conversations, online photos, GPS and other sensors, and the Internet of Things. The second half of the chapter more usefully looks at how to use metrics and data strategically. The data needs of the different parts of the SMART strategy board are each discussed in turn.

A for Apply Analytics forms the material for the next chapter, in the form of data analysis. Marr chats about techniques such as data clustering, sentiment analysis, document summarization, and analyzing speech and videos in ways that are interesting but with no detail of the mechanics. Other topics introduced include behavior analysis and facial recognition. There’s an interesting section on ways in which video analytics are already being used, and ‘the dark side’ of how our personal information is so easily available from our online habits is also well explained.

A chapter on ‘Report Results’ comes next. This chapter gets closest to being of direct use to developers, with brief descriptions of options such as the Google Maps APIs, D3.JS, and visitor trackers such as Crazy Egg. The final letter of SMART stands for Transform Business, and that’s what’s discussed in the next chapter, with discussions of how to understand and target customers, optimize business processes, increase security and reduce fraud.

This book is an easy read, and there are useful nuggets of information. It does read rather as though it could appear as one of those ’10 things you never knew about Big Data’ topics that you see while browsing the web, but that’s not necessarily a criticism. It would be a good introduction for someone who wanted a clear view of what big data is without ever needing to make the data really work.  

See Data Science for reviews of similar titles.


Grokking Machine Learning

Author: Luis G. Serrano
Publisher: Manning
Date: December 2021
Pages: 512
ISBN: 978-1617295911
Print: 1617295914
Kindle: B09LK7KBSL
Audience: Python developers interested in machine learning
Rating: 5
Reviewer: Mike James
Another book on machine learning - surely we have enough by now?

The AWK Programming Language, 2nd Ed

Author: Alfred V. Aho, Brian W. Kernighan and Peter J. Weinberger
Publisher: Addison-Wesley
Pages: 240
ISBN: 978-0138269722
Print: 0138269726
Kindle: B0CCJ1N4X3
Audience: Developers interested in Awk
Rating: 5
Reviewer: Kay Ewbank

The name Brian Kernighan among the authors of this updated classic raises  [ ... ]

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Last Updated ( Saturday, 07 May 2016 )