What Is Data Mining?

Data mining is a process that allows someone to find patterns, anomalies, and correlations in large sets of data and predict potential outcomes. This process uses many different techniques to allow businesses to reap numerous benefits.

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Mining data is not new. However, the industry has changed drastically since the term was coined in the 1990s. Since then, the discipline has had to go beyond basic statistics and incorporate AI and machine learning. This is allowing mining to stay current with the trends of large data sets and computing power.

Today, the technicians performing these tasks are now using automated analysis software. This allows them to process more complex data sets and discover relevant insights. The result is businesses from all industries use mining and analysis to gain insights into the information they need to make informed decisions.


Now you know the answer to “what is data mining?” you probably want to know why it is important. The amount of data devices are collecting is increasing exponentially. In fact, about 90% of digital information is unstructured data. However, more data alone does not mean businesses have access to the knowledge they need.


Data mining techniques can be used to sift through data, identify relevant information, and make informed decisions about your business. The use of data mining to gather insights and make informed decisions is called predictive analysis. For example, mining and predictive analysis are used in the healthcare, retail, and advertising industries excessively. They are important in these industries for shaping workflows, approaches, and techniques used.

For example, in healthcare, data mining helps with treatment planning for patients, while also finding fraud within medical insurances. Similarly, in retail data mining helps predict customer trends.


While the biggest data sets may seem like a challenge that requires a professional to overcome, this is no longer the case. INE prides itself in training individuals in mining software to ensure the best results for businesses. Their courses include Python programming, machine learning, data analysis, and much more.


Mining is used to uncover connections between billions of different data points. The result is many companies use it to shape the way they make decisions. For instance, telecom, technology, and media companies often use mining to find the answers they need amid a bustling market with intense competition. The resulting models help businesses in this industry make sense of their data, predict consumer behavior, and offer relevant campaigns. Banking and education are other industries that use mining to enhance their success by understanding data. For example, educators often use predictive analysis to identify students that may need extra attention, review student data, and predict achievement levels.

On the other end of the spectrum, insurance companies often rely on data analysis to solve problems around fraud, risk management, compliance, and customer attrition. They use the information to price their policies and offer unique products that appeal to their customers. Likewise, manufacturing companies use mining to predict what will appeal to their customers and detect potential problems. For instance, they can predict the wear of their machines, anticipate maintenance, optimize their processes, and maximize production efficiency.


The most common solution that an IT department can create is descriptive modeling, which examines historical data to find similarities or other correlations that can explain successes and failures. Some descriptive models include clustering, anomaly detection, association rule learning, principal component analysis, and affinity grouping. Another solution is predictive modeling, which goes a step further than descriptive modeling by making predictions. Businesses often rely on this to predict customer churn, credit defaults, and campaign responses. Some of these models include regression, decision trees, and support vector machines.

Lastly, prescriptive modeling is predictive modeling that incorporates any text data from eBooks, emails, comment fields, PDFs, and other sources. This has grown in popularity because it enhances the predictive capabilities of mining. It also differs from predictive modeling because prescriptive modeling offers a post-analysis solution. In other words, it recommends courses of action based on data. The most common recommendation it makes is marketing optimization.

Mining data is a crucial process that allows businesses to make informed decisions. Knowing more about this process can help you reap its benefits and implement mining strategies in your company. INE helps people in the industry, no matter their skill level, increase their knowledge of data science through a series of in-depth courses.