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Predictive Analytics, Data Mining and Big Data: Myths, Misconceptions and Methods (Business in the Digital Economy), by S. Finlay
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This in-depth guide provides managers with a solid understanding of data and data trends, the opportunities that it can offer to businesses, and the dangers of these technologies. Written in an accessible style, Steven Finlay provides a contextual roadmap for developing solutions that deliver benefits to organizations.
- Sales Rank: #1702411 in Books
- Brand: imusti
- Published on: 2014-07-29
- Released on: 2014-07-29
- Original language: English
- Number of items: 1
- Dimensions: 8.81" h x .86" w x 5.56" l, .0 pounds
- Binding: Hardcover
- 260 pages
Features
Review
'A welcome addition to the literature on data driven decision making. Finlay's book gives a commendably non-technical discussion of the business issues associated with embedding analytics into an organisation and how data, big and small, can be used to support better decision making. It is peppered with case studies from the author's experience and is a great source of insight for technicians and business people alike.'
-Paul Russell, Director of Analytics, Experian UK&I
'A fully immersive introduction to the world of predictive analytics and its application to Big (and small) Data. Full of interesting stories and case studies, it provides a fascinating real world perspective of these technologies and how best to apply them. A must read for managers and data scientists alike.'
-Ioannis Stamatopoulos, Director for Moody's Enterprise Risk Solutions and Services, RiskMatrix
'Analytics is the latest organizational enthusiasm harnessing data, both internal and external, to add value is no easy task. This introduction hits all the right notes with case studies and insight gathered from Steve Finlay's considerable experience. The challenge which he meets is to explain in clear non-technical language the various methods and how they can be implemented; nor does he neglect the problems of embedding quantitative expertise into organizations that aren't used to its logic. Recommended for the manager or MBA student wanting an overview of this exciting new area.'
-Professor Robert Fildes, Distinguished Professor, Director, Lancaster Centre for Forecasting, Lancaster University, UK
'Mr Finlay has written a very readable, business friendly book that goes well beyond the formula. His real world experience and practical discussions would be of great benefit to industry practitioners.'
-Naeem Siddiqi, Global Product Manager, Banking Analytics Solutions, SAS Institute
"If you want an excellent nontechnical overview of the predictive analytics process, this book should meet your needs... Finlay, a data scientist with decades of experience, provides an excellent introduction for readers, equipping them with the knowledge
to manage both the implementation and use of predictive analytics models in their organizations."
-Neil Desnoyers, Decision & System Sciences Department, Saint Joseph's University, Philadelphia, Pennsylvania: review in Interfaces, INFORMS
About the Author
Steven Finlay is one of the UK's leading experts on predictive analytics and its application within Big Data environments. He has extensive experience of developing predictive analytics solutions within Financial Services, Retailing and Government organisations. Steven is currently Head of Analytics at HML, the UK's largest provider of mortgage administration services. Previously he has worked as a data scientist, consultant and project manager for a variety of organizations in both the public and private sectors. Steven has a PhD in predictive analytics and is an Honorary Research Fellow in the Management Science Department at Lancaster University in the UK.
Most helpful customer reviews
0 of 1 people found the following review helpful.
Sound and clear but boring
By JohnVidale
Focused on business applications. Instructive, but unfortunately that also means repetitive and boring. Frankly, I think the author should have stayed on the data analytics, where he is providing a good primer on the underlying math and the way the analytics often do or don't work. He ventures too often into organizational structure, personnel motivation, and power plays, when his lessons learned did not sound profound or cutting edge.
Still, probably an excellent start to judging when and when not to apply data analytics,
2 of 2 people found the following review helpful.
Mapping the interface between predictive analytics and business
By Dr Ross Gayler
There are plenty of books available on predictive analytics (and data mining and big data - depending on your tolerance for hype), so why should you buy this one? (That's why you are reading this review. Right?)
This book is about predictive analytics as a tool to aid business (in the broadest sense). If that's not your interest this book is not for you. The point is that if predictive analytics is approached as an isolated technical exercise it won't deliver the expected business benefits. In order to be successful the analytics needs to be done with proper recognition of the business context and the business needs to be an informed consumer of analytics. You'd think that would be obvious, but project failures due to a poor interface between the business and the analytics are distressingly common – hence the need for a book like this.
The intended readership appears to be the people on either side of the business/analytics boundary (with a bit of a bias towards the business side). For a reader with an analytics background (but no business experience) the overviews of modelling techniques will probably be superfluous, but the sections on analytics, organisation, culture, ethics, and legislation may be essential eye-openers. On the business side, I would expect the reader to be a middle manager responsible for an analytics-based project or establishing an analytics function (or possibly a business owner who is willing to invest the effort to understand the problem).
There is nothing in this book that is new or revolutionary. It's not that kind of book and that's not the problem it's addressing. Rather, it's more a consciousness-raising check-list. There are a wide range of issues that need to be addressed on the business/analytics interface. This book touches pretty much all of them without giving definitive answers (because the best choice for any specific project is always going to be very context dependent). Consequently, the reader is expected to go off and do other reading in response to the issues raised. The book supports this with extensive appendices, notes, and reading lists.
Steve is well qualified to write this book. He has been a hands-on, pragmatic model builder for many years and has built models for a wider variety of organisations than most. Consequently he has extensive first hand experience of the problems that can arise on the interface between the business and analytics.
In summary, this is not a detailed technical, how-to book. Rather, it is a big picture, “What are we trying to achieve and where does it fit in the organisational context?” book. I would recommend it for anyone who has a hands-on involvement in a business predictive analytics project unless they are an old trouper who has already fallen into every potential trap. The reader must be prepared to do subsequent work, because this book will point out the issues but it's up to the reader to develop the solutions.
(Disclaimer: I know the author. We have been crossing paths at industry conferences for the last 20 years.)
1 of 1 people found the following review helpful.
Gem of a Book - Great Intro to Analytics and Data Mining
By Alan F. Noel
I found this little book to be very informative. Although I have read several other books in this area the past few months none of them have provided so much useful insight into what to do and what to look out for. There are some very good explanations of the basic technology. The book truly dispels some myths and provides practical tips on what the area is about. Yes, there are lots of books around already to drill down into the various technologies but this book is the top of my list for getting a useful introduction.
See all 4 customer reviews...
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