What Is Data Mining?

This is a the transcript of a podcast hosted by InetSoft on the topic of "Data Mining Basics." The speaker is Mark Flaherty, CMO at InetSoft.

What is data mining? Data mining means search for hidden information. The locating of previously unknown patterns and relationships within data, using a business intelligence application, is called data mining.

For example, it could be the locating of customers with a common interest in a retail establishments' database. Through a variety of techniques, data mining identifies nuggets of information in bodies of data. Data mining extracts information in such a way that it can be used in areas such as decision support, prediction, forecast and estimation.

The data is often voluminous but of low value and with little direct usefulness in its raw form. It is the hidden information in the data that has value. In data mining, success comes from combining your knowledge of the data, with advanced active analysis techniques in which the computer identifies the underlying relationships and features in the data. The process of data mining generates models from historical data that are later used for prediction, pattern detection and more. The technique for building these models is called machine learning or modeling.

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Data Mining and How It Is Used in Marketing

Here’s an interesting look at data mining and how marketers use when they learn about you. Imagine a young lady named Angie just ordered a new fall coat from her favorite retailer. Or a middle-aged couple named Sarah and Sam requested a car insurance quote to see if their current policy was competitive. Or a teenager named Mark filled out a fun, hey, what’s your favorite sports, survey online. It wasn’t long before unsolicited emails were showing up in their inboxes. Each had become targets of data mining. What is it?

Data mining, sometimes referred to as data or knowledge discovery, is the computer-assisted process of taking data from enormous databases and compiling it to detect consumer patterns. This profiling practice helps companies increase revenue, reduce costs, and market to people mostly likely to buy their products.

For example, Angie’s retailer data mines to analyze which products are selling best, when and where, and then sends targeted promotions based on an individual’s purchase history. Sarah and Sam’s insurance quote request did not go unnoticed by other providers once the couple clicked Submit. Mark began receiving promotional emails from professional football and basketball teams because of his sports survey preferences.

In addition to buying preferences, data mining has other applications too. Governments use it for national security or law enforcement purposes, confidentiality concerns. Data mining has the potential to uncover information or patterns that may compromise confidentiality and privacy obligations. The threat to an individual’s privacy may occur when the compiled data enables the data miner, or anyone who has access to the dataset, to identify specific individuals.

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While it may be difficult to avoid data mining, the best defense for protecting your confidentiality is to be choosy about what you share online. Never give out personal financial information to anyone in an email, and when shopping online, make sure you trust the website and look for security encryption icons. Good marketers respect the confidentiality of all consumers and believe in helping you protect your privacy and personal financial data.