I do that too! Data Mining and Data-Analytic Thinking: Data Science for Business
Foster Provost and Tom Fawcett have set out to write the go-to reference on Big Data. ‘Data Science for Business’, what you need to know about Data Mining and Data-Analytic Thinking, published by O’Reilly Media.
They have produced an authoritative book that is both a pathfinder and a lighthouse. It is a long, clearly-written book that shows what can be done using Big Data, where to go and what techniques to use to get it done, and what to watch out for.
Thank you for writing this book. The authors and their many references are already established and respected. The book brings the issues and their business applications together in one essential place. Already in just 1 month since release (25th July 2013) the eBook has gathered praise quotes from a dozen industry names. I am honoured to receive a complimentary review copy.
So to add to the recommendations, I pitch my review slightly differently: Who in business should buy this book? What does this book add to what we are already doing in business with Data and Data Mining?
On first reading, if you work in analysis, IT, Business Intelligence, Management Reporting, Marketing or SEO, I guarantee your reaction at some point will be ‘I do that too’.
For me the ‘Aha!’ realisation came a few pages into chapter 2. The authors discuss database searches for the most profitable items in a business. All businesses do that every day! But not always in the way the academics think.
The book surprised me in covering a broader range of topics than I previously considered were Data Science. Here are some great success stories to illustrate what data science is. Buy the book to see how these things really work and how the leading companies are applying themselves to these challenges. These studies border on the commercially sensitive.
- How a supermarket can use their sales analysis to predict when people are expecting a baby, and so gain an advantage by making offers before their competitors.
- How advertisers use Facebook Likes to profile and segment their audience
- How Netflix make their movie recommendations
- How to compare web pages for plagiarism
- How to tell how far away a customer is from their mobile app
Chapter 10 talks about text analysis. In contrast to most of the book, I would say here that small and medium sized businesses are ahead of Google and the academics. While the search engines refine their algorithms to extract news and meaning from bare text, there is whole industry sector manipulating the source data to fool the algorithms and keep one step ahead: it is called Search Engine Optimisation.
If you are just starting out in using Big Data for your business decisions, you need to know the importance of Maths. In particular there are 2 challenges in the mathematics that underpin Data Science that I should warn you about even if you do not read the book:
- One is causation and correlation. When you find the beer-buying customers are also the nappy-buying customers, that is just the first step towards some very careful thinking before you draw any conclusions about which is cause and which is effect and how you might adjust your marketing or product mix to assist your customers accordingly
- The other is what is now called ‘Overfitting’. Gaze hard enough and you will find trends in data just like you can find shapes in clouds or patterns on the back of your eyelids. If you search too hard through too much data, you invalidate correlation co-efficients and confidence calculations. Or to put it another way, every cloud looks like something.
A great book. For everyone who can still manage their high-school level maths, I recommend you buy this book. For everyone else, I recommend you be aware of the book and the issues within it and get it on the corporate bookshelf. For myself I look forward to checking back regularly for future editions as the science develops. Five stars.