Much like the way a prospector would sift through dirt to find nuggets of gold, data mining is the process of sifting through large sets of data to find pertinent information that could be used for a specific purpose. As a sub-discipline of computer science, data mining is essentially all about patterns.

Once data has been harvested and stored, the next step focuses on making sense of the data — otherwise, it's all meaningless.

Data analysis is carried out in a number of ways, including using concepts like machine learning, where complex adaptive algorithms are used to artificially analyze the data.

More traditional methods involve data scientists — experts trained specifically to make sense of complex information — producing reports for management to act on.

Who Participates in Data Mining?

In its safe, legal form, data mining is widespread and used by a large range of industries, from finance to retail.

When browsing the internet, user data is recorded based on websites that are visited, searches that are made, personal details that are entered, and products that are explored.

That data — created by millions of users — can then be examined at a granular level by companies who use it to make informed operating and marketing decisions.

What Can Data Mining Be Used For?

Data mining is used for many purposes, depending on the company and its needs. Possible uses include:

  • Forecasting and Risk: Analyzing data to determine where something went wrong in the past — the number of online visitors that didn't purchase an item after looking at it, for example — could help a retailer make better decisions about inventory to purchase in the future. Similarly, seeing what time of day a system has been overloaded with web traffic in the past could help a business prepare by assigning more resources or investing in server upgrades.
  • Grouping: Data provided by customers allows companies to group users together in a range of ways, including demographically based on gender, age, income, where they live, and their spending habits. This allows them to efficiently target the appropriate users for specific offers or messages.
  • Analyzing Behavior: Examining data allows companies to understand the kind of stimuli customers respond to. Do certain groups respond to specific offers or emails at a certain time of day or on a certain day of the week, for example? Or maybe it provides clarity on why users visit one website and not another or why they abandon sales at the last minute. Analysis helps them determine what they can do to prevent negative consumer behaviors that hurt their company.

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