I am excited to be giving the Analytics for Marketing online training course on 25-28 September 2012. Sign up before 25 August 2012 for the early bird discount.
Big can be a qualitative as well as a quantitative difference. The gas in the ill-fated Hindenburg airship, the gas that formed our Sun, and the gas that formed the Milky Way galaxy were just lumps of hydrogen atoms (with varying impurities). The difference was in the number of atoms. But that difference in numbers made the three structures into different things. You simply cannot look at them in the same way. If you try to model the galaxy in the way you model a balloon you will fail.
Insurance pricing is backwards and primitive, harking back to an era before computers. One standard (and good) textbook on the topic is Non-Life Insurance Pricing with Generalized Linear Models by Esbjorn Ohlsson and Born Johansson (Amazon UK | US). We have been doing some work in this area recently. Needing a robust internal training course and documented methodology, we have been working our way through the book again and converting the examples and exercises to R, the statistical computing and analysis platform. This is part of a series of posts containing elements of the R code.
We have created and managed analytics teams in commercial organizations (mainly telecommunications) across Europe. The teams were using SAS or SPSS. Our company now has a commercial analytics as a service offering and we mainly use R.
Commercial Analytics is the kind that makes money. From data to dollars, insights to income, this is all about how to run the business better. To do it and to do it well you need certain capabilities in place. This article builds a map of those business capabilities to help you assess, understand, and plan your business.
We have 20 years of experience of big data environments within a variety of industries including Research, Banking, Insurance, and Telecommunications. We have especially worked with customer data: Marketing, Risk Management, customer segmentation and -profitability, and customer-driven product development.
John Kay muses on interpreting statistical data:
save() function in the R platform for statistical computing is very convenient and I suspect many of us use it a lot. But I was recently bitten by a “feature” of the format which meant I could not recover my data.
Because it is Friday and because we collect quotes, here is one on statistics being the best and worst of disciplines. Which one of the two views are closest to your opinion?