Recent blog entries

Big Data is a buzzword, but is it real: does it address real business issues or is it just an excuse to sell more computers, software, and consulting services?

We argue that it is real and it does matter, but only in some well-defined circumstances: it is not a universal solution or requirement to every problem. We provide a framework for determining where the Big Data applications are within your work and where traditional approaches apply.

Get this article as a PDF: When Big Data matters.

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.

Usually we talk about this and we are happy to talk to you about it (just contact us) but we recently had occasion to make a slide pack that covered some of the materials as a stand-alone presentation. This article is based on that pack which is also available for download.

We have seen data mining and other analytics projects fail; we have seen insights teams unable to deliver the insights needed to actually improve the business; we have seen marketing teams unable to use data effectively to guide and quantify their activities; we have seen business leaders who are sitting on piles of data but are effectively flying blind because they can not get from the data to the knowledge they need to inform their decisions.

Below we have listed five common pitfalls of analytics in a commercial environment, their warning signs, and what you can do differently.

We continue working our way through the examples, case studies, and exercises of what is affectionately known here as “the two bears book” (Swedish björn = bear) and more formally as Non-Life Insurance Pricing with Generalized Linear Models by Esbjörn Ohlsson and Börn Johansson (Amazon UK | US).

At this stage, our purpose is to reproduce the analysis from the book using the R statistical computing and analysis platform, and to answer the data analysis elements of the exercises and case studies. Any critique of the approach and of pricing and modeling in the Insurance industry in general will wait for a later article.

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. 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.

They have finally pulled that buggy unreliable piece of code that was doSMP from the CRAN mirrors while (I hear) Revolutions are re-writing it. To use all your cores for analysis on the Windows platform, you can try doSNOW instead; my code is something like the fragment below. Neither option is as attractive as doMC on anything-but-Windows platforms, but sometimes you have to work with legacy systems.

This is a note for people who are using the Mason system for high-performance, dynamic web site authoring with Apache, mod_perl, and a relational database like PostgreSQL accessed through DBI, and who want to be utf-8 Unicode clean in all their data.

You want to be able to write accented letters in any language in your web pages. You want your users to be able to enter any characters in web forms, and you want that data to get in and out of your relational database and still display correctly and be handled correctly by perl.

That is, unfortunately, not how it works out of the box, at least not on Red Hat Enterprise Linux 5 or on Fedora 10. This article shows how we made it work right.