Blog Archives

Commercial Analytics: The Capabilities

October 5, 2011
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Commercial Analytics: The Capabilities

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.

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5 common pitfalls of commercial analytics projects

September 5, 2011
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5 common pitfalls of commercial analytics projects

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.

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Friday quote: what is the question to which this number is the answer?

August 26, 2011
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Friday quote: what is the question to which this number is the answer?

John Kay muses on interpreting statistical data: Always ask of such data “what is the question to which this number is the answer?”. “Earnings before interest, tax, depreciation and amortisation on a like-for-like basis before allowance for exceptional restructuring costs” is the answer to the question “what is the highest profit number we can present without attracting...

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A warning on the R save format

August 23, 2011
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A warning on the R save format

The 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. I recommend that you save data in a data format (e.g. CSV or CDF), not using...

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Friday quote: the handmaiden and the whore

August 19, 2011
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Friday quote: the handmaiden and the whore

Because it is Friday and because we collect quotes: If mathematics is the handmaiden of science, statistics is the whore: all that scientists are looking for is a quick fix without the encumbrance of a meaningful relationship. Statisticians are second-class mathematicians, third-rate scientists and fourth-rate thinkers. They are the hyenas, jackals and vultures of the scientific ecology: picking...

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Spreadsheet errors

April 20, 2011
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Spreadsheet errors

For my sins, I have done more than my fair share of analysis in Excel. I am quite capable of building and maintaining 130Mb spreadsheets (I had a dozen of them for one client). Excel is pretty much installed everywhere, so it is sometimes the only way to get started getting commercial value of the data in the...

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Getting started with the Heritage Health Price competition

April 8, 2011
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Getting started with the Heritage Health Price competition

The US$ 3 million Heritage Health Price competition is on so we take a look at how to get started using the R statistical computing and analysis platform.

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Benchmarking feature selection with Boruta and caret

November 25, 2010
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Benchmarking feature selection with Boruta and caret

Feature selection is the data mining process of selecting the variables from our data set that may have an impact on the outcome we are considering. For commercial data mining, which is often characterised by having too many variables for model building, this is an important step in the analysis process. And since we often work on...

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Feature selection: Using the caret package

November 16, 2010
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Feature selection: Using the caret package

Feature selection is an important step for practical commercial data mining which is often characterised by data sets with far too many variables for model building. In a previous post we looked at all-relevant feature selection using the Boruta package while in this post we consider the same (artificial, toy) examples using the caret package. ...

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Feature selection: Using the caret package

November 16, 2010
By
Feature selection: Using the caret package

Feature selection is an important step for practical commercial data mining which is often characterised by data sets with far too many variables for model building. In a previous post we looked at all-relevant feature selection using the Boruta package while in this post we consider the same (artificial, toy) examples using the caret package. Max Kuhn kindly listed...

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