Everything You Wanted to Know About Data Mining but Were Afraid to Ask

A guide to what data mining is, how it works, and why it's important.

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Big data is everywhere we look these days. Businesses are falling all over themselves to hire 'data scientists,' privacy advocates are concerned about personal data and control, and technologists and entrepreneurs scramble to find new ways to collect, control and monetize data. We know that data is powerful and valuable. But how?

This article is an attempt to explain how data mining works and why you should care about it. Because when we think about how our data is being used, it is crucial to understand the power of this practice. Without data mining, when you give someone access to information about you, all they know is what you have told them. With data mining, they know what you have told them and can guess a great deal more. Put another way, data mining allows companies and governments to use the information you provide to reveal more than you think. 

Data mining allows companies and governments to use the information you provide to reveal more than you think.

To most of us data mining goes something like this: tons of data is collected, then quant wizards work their arcane magic, and then they know all of this amazing stuff. But, how? And what types of things can they know? Here is the truth: despite the fact that the specific technical functioning of data mining algorithms is quite complex -- they are a black box unless you are a professional statistician or computer scientist -- the uses and capabilities of these approaches are, in fact, quite comprehensible and intuitive.

For the most part, data mining tells us about very large and complex data sets, the kinds of information that would be readily apparent about small and simple things. For example, it can tell us that "one of these things is not like the other" a la Sesame Street or it can show us categories and then sort things into pre-determined categories. But what's simple with 5 datapoints is not so simple with 5 billion datapoints.

And these days, there's always more data. We gather far more of it then we can digest. Nearly every transaction or interaction leaves a data signature that someone somewhere is capturing and storing. This is, of course, true on the internet; but, ubiquitous computing and digitization has made it increasingly true about our lives away from our computers (do we still have those?). The sheer scale of this data has far exceeded human sense-making capabilities. At these scales patterns are often too subtle and relationships too complex or multi-dimensional to observe by simply looking at the data. Data mining is a means of automating part this process to detect interpretable patterns; it helps us see the forest without getting lost in the trees.

Discovering information from data takes two major forms: description and prediction. At the scale we are talking about, it is hard to know what the data shows. Data mining is used to simplify and summarize the data in a manner that we can understand, and then allow us to infer things about specific cases based on the patterns we have observed. Of course, specific applications of data mining methods are limited by the data and computing power available, and are tailored for specific needs and goals. However, there are several main types of pattern detection that are commonly used. These general forms illustrate what data mining can do.

Anomaly detection : in a large data set it is possible to get a picture of what the data tends to look like in a typical case. Statistics can be used to determine if something is notably different from this pattern. For instance, the IRS could model typical tax returns and use anomaly detection to identify specific returns that differ from this for review and audit.

Presented by

Alexander Furnas is a research fellow at the Sunlight Foundation in Washington, D.C.

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