Data Mining and Why it’s Important

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  • April 5, 2018

In recent news, we’ve all heard certain words like “data” and “privacy” and “security” tossed around a lot, but what do they mean and why are they important? The following article explores the technicalities of data mining and the implications for society on a broad spectrum.

What is Data Mining?

Data mining is the process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis. Data mining tools allow enterprises to predict future trends.

In data mining, the analysis of data is based on if/then patterns; using the “support” and “confidence” criteria to locate the most important relationships within the data. “Support” means how frequently the items appear in the database and “confidence” is the number of times the if/then statements are accurate.

Other data mining parameters include:

  • Sequence is an ordered list of sets of items, and it is a common type of data structure found in many databases.
  • Classification parameter looks for new patterns, and might result in a change in the way the data is organized. Classification algorithms predict variables based on other factors within the database.
  • Clustering parameters find and visually document groups of facts that were previously unknown. Clustering groups a set of objects and aggregates them based on how similar they are to each other.

Fostering parameters within data mining can discover patterns in data that can lead to reasonable predictions about the future, also known as predictive analysis.

Benefits of data mining

In general, the benefits of data mining come from the ability to uncover hidden patterns and relationships in data that can be used to make predictions that impact businesses.

Specific data mining benefits vary depending on the goal and the industry. Sales and marketing departments can mine customer data to improve lead conversion rates or to create one-to-one marketing campaigns. Data mining information on historical sales patterns and customer behaviors can be used to build prediction models for future sales, new products and services.

Companies in the financial industry use data mining tools to build risk models and detect fraud. The manufacturing industry uses data mining tools to improve product safety, identify quality issues, manage the supply chain and improve operations.

Simply put, on a broader level, the access and use of information/data to create a more customized approach in relaying information is generally a good thing – but like anything, when in the wrong hands, can be used to cause more harm than good – so choose wisely!

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