An illustration of a computer's data being pulled away from it in data mining.
Image: ZinetroN/Adobe Stock

Data mining is an important big data management strategy that is gaining steam, especially as organizations realize how many patterns and problems data mining operations can detect across their data sets. In this guide, learn what data mining is, how it operates and why it might be the next data management strategy you need to incorporate into your business.

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What is data mining?

Data mining is used to identify patterns, correlations and anomalies in large data sets for data analysis. This helps turn raw data into actionable information to make informed business decisions, predict outcomes and develop business strategies.

Although the term “data mining” wasn’t coined until the 1990s, data mining techniques were used long before that. As the quality and complexity of data increased, software applications were used for data mining. The potential of data mining continues to increase with technological advancements in computing power and the enormous potential of big data.

Benefits of data mining

Data mining helps organizations analyze a large amount of data, deriving useful insights that allow an organization to become more efficient or profitable. With increases in data complexity and the volumes of data that are available to an organization, data mining provides a semi-automated way to process large data sets.

SEE: Data governance checklist for your organization (TechRepublic Premium)

An organization can make informed decisions and improve its strategic planning by uncovering data patterns, data anomalies and data correlations. Business executives can also use data mining to reduce legal, financial, cybersecurity and other types of risks to the organization.

How data mining operates

Data mining works by exploring and analyzing large volumes of data to derive meaningful trends, relationships and patterns. Data mining software solutions are versatile tools that can be used for different objectives and functions like fraud detection, customer sentiment analysis and credit risk management.

Although data mining can be used in various ways, the process includes a few common steps. The first step is to gather and load the data. This step is followed by preparing the data through methods such as data cleansing or data transformation.

Once the data is prepared, it is ready to be mined. Computer applications with data mining algorithms are most frequently used to perform data mining. From there, data mining results are often translated into visual or statistical representations for further analysis.

Different types of data mining

There are several types of data mining techniques that businesses can apply to their big data. The right data mining technique to use depends on several factors, including the type of data and the objective of the data mining project. Here are some of the most common types of data mining:

Affinity grouping

Data elements that share the same characteristics are grouped. For example, customers that have the same buyer intent, interests or goals can be grouped. This type of data mining is also known as clustering.


Predicting data values based on a set of variables. This type of data mining is often used to find relationships between data sets.

Neural networks

Computing systems that are inspired by biological neural networks, such as the human brain. The algorithms in neural networks are useful for recognizing complex patterns in data.

Association rule

Association rules are established to determine the relationship between data elements. This includes determining co-occurrences and patterns in data.

Data mining examples

Telecommunications and media

Several industries use data mining, including the telecom and media industries, where it is often used to analyze consumer data. These companies use data mining to map customer behavior and run highly targeted marketing campaigns.


Similarly, data mining is commonly used in the insurance industry, where it helps companies solve complex problems related to compliance, customer attrition and risk management. Health insurance companies use data mining to map the patient’s medical history, examination results and treatment patterns. This helps them develop and execute an efficient health resource management strategy.


Data mining is also used in the manufacturing industry to align supply chains with sales forecasts and for early detection of future problems. Through data mining, manufacturers are able to anticipate maintenance and predict the depreciation of production assets.


Finally, the banking industry uses data mining algorithms to detect fraud and other anomalies in their data. Data mining helps banks and other financial institutions achieve optimum ROI on marketing investments, meet compliance requirements and have a better view of market risks.

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