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What is Data Mining? - Definition & Process

Instructor: Kaitlin Oglesby
While big data may be getting a lot of buzz, for many companies data mining is a more attractive option. In this lesson, we learn what exactly data mining is, and its applications, from TV sales to baseball.

What is Data Mining?

Companies amass a great deal of data in the course of their business. For the company that can use data properly, there is a great deal that can be learned as a result. This means spending time actually looking at the data, manipulating it in new ways, and trying to tease out as much information as possible from the same collection of information. This process is known as data mining. As we will see, the basics of data mining have been around for quite some time, even though technology to allow to fully implement those theories is only now becoming widely available.

Data Mining vs. Big Data

Before we go any further, let's take a minute to make sure that we understand the basic differences between data mining and big data. Both are useful, and there is some overlapping. However, data mining is more about intensively looking at the information at hand, getting to know the ins and outs of it and how it relates to other information within the data set. On the other hand, big data is about looking at a gigantic amount of information and seeing if there are some basic truths that seem to hold firm about it.

Finding New Perspectives

In short, data mining is about finding new ways to look at the same data. To provide the technical term, this is about establishing relationships across relational database tables. In terms that make more sense, it's about being able to compare apples and oranges in a meaningful way by creating as many statistical analyses as possible.

Chances are that you have a friend who is obsessed with sports, particularly baseball. He or she knows everything about each player on every team in their chosen league, from batting averages to pitching statistics. They can make a reasonable assumption based on a pitcher's ERA and a batter's on base percentage how each at bat will end. Failing that, they can rattle off statistics about how the team in question has done on rainy days, holidays, or even opening days of a leap year. The scary thing is that they are right a fair amount of the time.

That is the type of person who excels at data mining.

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