# The Analytics Compass Blog

Twice weekly articles to help SMB companies optimize business performance with data analytics and to improve their analytics expertise.

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### A simple explanation of how entropy fuels a decision tree model

Decision tree models can be effectively used to determine the most important attributes in a dataset. The figure below shows an example of using a decision tree (in this case a C4.5 algorithm), to identify the most important parameters which would influence whether a banking customer would accept a personal loan offer during a campaign. The dataset contains 12 customer attributes ranging from Income, Education in years, mortgage, average credit card balance, family size, geographic data among others. The response (or target) variable is the binary condition of whether they would accept the loan offer - a "Yes" or a "No".

### The perils of complete absence of systems thinking in city planning

GK Chesterton once wrote that "life looks just a little more mathematical and regular than it [really] is". Clearly he had not visited the streets of the silicon plateau of India, Bangalore. Traffic (or life) here looks nowhere near mathematical! If there is any place on the planet that embodies the chaos which arises from uncontrolled entropy, this would be it. In fact, the city provides a text book example of how not to design a system. Forget systems thinking. That would be asking for too much.

### When Principal Component Analysis makes sense in business analytics

Principal component analysis (PCA) is a technique according to Wikipedia that "uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of uncorrelated variables called principal components."

### Variable reduction with chi-square and entropy based methods

One of the first steps in data mining or business analytics problem solving is the process of eliminating variables which are not significant. There are a couple of reasons for taking this step. The most obvious reason is that going from a few hundred variables to a handful will make the interepretation of the results easy. The second and probably more critical reason is that many modeling techniques become useless as the number of parameters increases. This is known as the curse of dimensionality.

### Building decision trees using information theory and shannon entropy

There is something really hypnotic about this picture. It is hard to explain, but the patterns we see in this image evokes a strange feeling of peace and equity(or maybe its just me!)

### 3 examples to show why correlations can fail

Scientists and engineers - not to mention economists, statisticians and people in general - look for correlations while searching for answers. While correlations do give us important information, it is dangerous to assume that they are also indicators of causality. Correlation does not necessarily imply causation.

### A simple way to measure Information in Risk and Business Analytics

This blog is about measurement of information content in data and how business analytics and risk professionals can adopt this simple and intuitive technique.

### Using Entropy for business analytics and risk management

We recently encountered a fairly experienced risk manager who was very well-versed in quantitative methods, unlike a vast majority of "check-box" risk managers. His question was, what new information does entropy give him that his current sophisticated Bayesian models and analyses don’t? Business analytics professionals may have the same question.

### Bubbles, Busts and the Economy versus Entropy

In the brilliant book by Eric Beinhocker of McKinsey Global Institute titled "The Origin of Wealth", there is a very powerful and dramatic statement which attempts to explain wealth creation, thus: "All wealth is created by thermodynamically irreversible, entropy-lowering processes". We could not agree more! If everything in this universe must subscribe to and abide by the laws of thermodynamics, so must the economy.

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