What Is Data Mining?

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Every business needs a wealth of data to be successful in the modern market. They need to collect, analyze, and understand data about their target audiences, the broader economic market, and even their performance to make wise decisions, avoid pitfalls, and bring in more revenue.

But collecting raw data, even in staggeringly large amounts, is not enough. Instead, that data has to be transformed into useful information through a process called data mining.

Data mining is a distinct process that turns raw data points into informative ones. Data mining involves finding different patterns, correlations, or anomalies within big data sets to predict outcomes or better understand the source of said data points. 

Let’s take a closer look at data mining, how it works, and how companies perform it every day. If you’d like to skip to a particular topic area, simply use the following clickable menu:

  1. How does data mining work?
  2. The data mining process
  3. Data mining techniques
  4. Data mining applications
  5. Advantages of data mining
  6. Disadvantages of data mining
  7. Data mining examples
  8. Data mining tools
  9. Summary

Ready to learn more about data mining? Let’s get started!

1. How does data mining work?

Data mining is when data analysts or scientists:

  • Collect data,
  • Compile that data into a large data set, then
  • Run different analyses or use different algorithms to extract important information from the data set, which can be difficult from just looking at the data points “raw.”

Depending on the needs of a business or client, data scientists may perform data mining using different modeling techniques, such as:

  • Descriptive modeling that can help to uncover similarities or groupings and historical data to explain failures or successes.
  • Predictive modeling that helps classify or predict events in the future or estimate outcomes.
  • Prescriptive modeling that helps organizations filter and transform unstructured data and use it for predictive models. This modeling can help to improve forecasting accuracy and make wise decisions for the future.

Note that data mining is not the same as crypto mining, although both processes rely on groups of people sometimes performing complex computations.

Related reading: The 4 Types of Data Analysis

2. The data mining process

The data mining process runs the length of data collection and analysis. It includes initial data harvesting and then proceeds to data visualization. In the visualization step, data analysts extract information from big data sets. They may use different techniques to generate predictions, descriptions, or other information about a targeted data set.

Furthermore, data scientists can describe the data they collect and mine using observations of correlations, associations, or patterns. They may also classify or cluster data through different regression or classification methods.

The data mining process usually includes four primary steps:

Setting objectives

Most organizations first decide what they want to learn about the data set, what questions they should ask, and what parameters they should set for the project. During this step, data analysts may perform extra research so they can understand the business context for their efforts.

Data preparation

Once data scientists know what they are looking for, they can identify the correct data set to mine or analyze. They then collect relevant data and “clean it” by removing data “noise,” such as outliers, missing values, and duplicate data points that were inputted by accident.

Model building and pattern mining

Data scientists investigate interesting or notable data relationships, like correlations or sequential patterns. High-frequency data patterns usually have broader applications for businesses. But in many cases, deviations from data sets may be interesting. For instance, an outlier financial data point could indicate the possibility of fraud. During the pattern mining step, scientists may leverage deep learning algorithms to classify, cluster, or organize data sets.

Data evaluation and conclusion implementation

As soon as the mined data is aggregated, the results are evaluated, interpreted, and used to draw conclusions. Those conclusions may then be used to influence policies, business decisions, or other actions depending on the initial goals outlined earlier.

3. Data mining techniques

Data scientists can use a variety of data mining techniques, as well as algorithms, to mine large quantities of data and extract useful information. A few of the most common data mining techniques are:

  • Association rules, which use different rules to find relationships between data points in a data set. Association rules are often used for “market basket analysis” so companies can understand the relationships between the different products, consumption habits of consumers, etc.
  • Neural networks, which are used for deep learning algorithms. These process training data and mimic how the human brain works using different layers of digital nodes.
  • Decision tree analysis. This technique uses regression methods or classification to predict outcomes based on predetermined decisions. It provides its conclusions with a treelike visualization so laypeople can understand the outcomes of different decisions.
  • K-nearest neighbor or KNN algorithms. These are algorithms that classify data points based on proximity and association to other relevant and available data points. They can be useful for calculating the distance or difference between data points (such as Euclidean distance).

While all of the above data mining techniques can be useful, data analysts must determine which techniques, algorithms, or models to use that will best suit their needs or the needs of their clients.

4. Data mining applications

Data mining is so widespread because of its many potential applications. In fact, data mining has applications in practically every industry, including:

Sales and marketing

Many companies use data mining to better understand their customers or leads, then develop marketing or sales techniques that better speak to those target customers.


Many educational institutions collect and mine data to better understand their students and construct environments or learning platforms that are conducive to academic success.

Operational optimization for organizations

Businesses use process mining to reduce operational costs and help their organizations run more efficiently or cost-effectively.


Specifically, finance organizations may use data mining for fraud detection. They can look at patterns in financial data and identify anomalies, which can help them track down financial criminals or prevent fraud from occurring on a wide scale.

5. Advantages of data mining

Data mining carries many advantages. It allows organizations to take the raw data they collect from their customers, users, or employees, then understand that data more deeply.

In a broad sense, data mining lets companies create value with the information they already have on hand or that they can gather without too much difficulty. It may help companies make smart decisions for the future, such as whether to expand or what types of products to manufacture.

6. Disadvantages of data mining

That said, data mining also has certain limitations. It’s very complex and requires trained specialists to perform properly. Furthermore, data mining doesn’t always produce results or accurate information.

Of course, data mining requires a regular source of high-quality data, which can be difficult to gather for some organizations without extracting subscriptions or data access permissions from their customers or users.

7. Data mining examples

There are many modern examples of data mining. For example, eBay – the widely known online marketplace – collects tons of data from its users and listings every day. eBay employs data scientists to perform data mining so they can understand the relationships between prices, products, consumer behavior, and more.

Facebook and the consulting firm Cambridge Analytica have also used data mining, though to a more morally dubious extent. These organizations collected millions of users’ personal data, extracted information or relationships from that data, then sold that data to organizations and presidential campaigns.

Overall, data mining can be used for good, but also for inappropriate (and unethical) goals.

8. Data mining tools

Many data analysts use a wide range of tools to both collect and analyze data sets. One such tool is Apache Spark, an IBM-related data mining tool. AI and machine learning tools and algorithms also regularly help data scientists perform accurate data mining. In the future, artificial intelligence data mining algorithms may take the place of most human-operated tools.

You can learn about other popular data mining tools in this article. Or you could check out the following video on general data analytics tools:

9. Summary

Data mining is an incredibly important practice and is not going away anytime soon. Competitive businesses will continue to use data mining to ensure their dominance in their niches and make smart decisions in turbulent economic conditions. Data mining will become even more accurate and sophisticated as new algorithms and techniques come into practice.

Interested in learning more about data mining, or about data analytics in general? Why not try out our free, 5-day data analytics course? Otherwise, you may be interested in the following articles:

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