What is Data Mining and why is it Important - Benefits, Applications, Techniques

Storing data and analyzing it is no new concept. You may be surprised to know that the earliest example of humans using data goes back to 18,000 BCE. Back then, humans used tally sticks to track their activity. And while, over the centuries, the way of recording data and analyzing it has undergone substantial changes, one thing has remained the same. Our world thrives on data. 

With today’s technology, large volumes of data are collected every day. However, the more data we collect, the more challenging it is to analyze. The time required to find meaningful insights from it also becomes longer. This is where data mining comes in. It provides a solution to this problem and allows businesses to make data-driven decisions. 

In this blog, we have discussed the nitty gritty of data mining. You will have an overview of everything from its meaning to its techniques and applications. Read on to learn about the applications, advantages, and disadvantages of data mining. 

What is Data Mining?

When you hear the word mining, you may think of people looking for valuable resources underground. Data mining is somewhat like that. Instead of looking in a mine, you look into colossal amounts of data. And instead of finding natural resources, you look for valuable information to help you achieve your goals. 

Data mining is defined as analyzing large datasets to find meaningful information that can help organizations find solutions to challenges by identifying trends and patterns, establishing relationships, and creating actionable information. It also helps organizations predict future trends and identify new opportunities. The key is to find information that enables them to make informed decisions. 

Data mining is also referred to as knowledge discovery in databases (KDD). However, data mining is a step in KDD and is distinct from it. Similarly, it is often also confused with machine learning. Now that you know what data mining is, let us move forward to its working. 

How does it work?

Data scientists are responsible for data mining, among other data professionals. They understand the organization’s goals and challenges. This helps them determine the kind of data they would need to achieve those goals. The data is gathered, prepared, mined, and analyzed to find meaningful insights. Based on those insights, solutions are developed and deployed. 

While the entire data mining process is long, these are the main steps:

  • Data gathering: The first step is to gather data from different sources. These sources may be a data lake or data warehouse. 

  • Data preparation: At this stage, the data scientist prepares the data for mining. This involves data exploration, preprocessing, and cleansing. Data transformation is also done.

  • Data mining: After preparing the data, data scientists use the most suitable data mining technique and mine the data. 

  • Data analysis and interpretation: Analytical models are created based on data mining results. Data visualization is used to communicate the findings with stakeholders and decision-makers. 

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Importance of Data Mining

Data mining is a growing industry. Many vendors, such as AWS, Oracle, Microsoft, SAP, and SAS Institute, provide tools used for data mining. Its importance is undeniable. While data can help organizations achieve their goals, it needs to be mined first. Raw data cannot be used for any purpose. Data mining ensures that useful information can be derived from raw data and used to benefit both the organization and its customers. 

Some of the areas where data mining helps are detecting fraud, spam filtering, managing risks, and cybersecurity. In the marketing sector, it helps in forecasting customer behavior. In the banking sector, it can help in determining fraudulent transactions. Data mining is not only beneficial for organizations. From governments to healthcare, it is used everywhere. 

Benefits of Data Mining

Data mining has many benefits. Here are some of them.

  • Enables informed and data-driven decision-making.

  • Helps in analyzing substantial amounts of data quickly.

  • Businesses can get reliable information through data mining.

  • Helps in identifying patterns and trends and detecting fraud.

  • It is a cost-effective and efficient option.

Now, let us discuss some of these benefits in detail. 

Better Customer Service

Data mining helps organizations provide better service to their customers. By identifying potential issues, companies can provide quick solutions to customers. It also helps them find the most appropriate communication channel to reach their customers. They can also update their agents with the developments and facilitate quick support. 

Marketing and Sales

Data mining enables more effective sales and marketing. It enables marketers to understand their customer's preferences. They can also predict customer behavior. This helps them in creating targeted marketing campaigns. The sales department also uses data mining to improve lead conversion, upselling and cross-selling.

Cost-effective Solution

Data mining is a cost-effective solution. It helps businesses save costs by enabling operational efficiency. Businesses can better assess their customers' needs and focus on fulfilling them.

Risk Management

Data mining enables strong risk management. It helps in fraud detection and threat identification. Businesses can improve their cybersecurity and risk protection by using the insights obtained through data mining. 

Quick Analysis

Data mining can help analyze massive volumes of data efficiently. Businesses all across the globe use it to move forward and identify new opportunities. 

Drawbacks of Data Mining

Everything in this world comes with its benefits and drawbacks. Data mining is no exception to this. Here are some of the major drawbacks of data mining.

  • Data analytics tools are often complicated to use. It takes highly trained and skilled personnel to analyze data properly.

  • It is also complicated to determine which tools should be used.

  • There are many privacy concerns surrounding data mining. 

  • The information obtained through data mining may not be completely accurate. 

Now, let us discuss some of these drawbacks in detail.

Complex Tools

Data mining tools can only be effectively used by someone with specialized training. Most small-scale businesses cannot use data mining due to these constraints. It is also vital that the data analyst has the ability to determine the technique suitable for different algorithms.

High Cost

Data mining is cost-effective in the long run. However, it is difficult to get started with data mining as it requires specialists and advanced software. The initial investment is too huge for many businesses to consider. 

Privacy Concerns

More and more people today are worried about the safety of their personal information. They are concerned about their data getting sold and used without their consent. This data can be used by companies to target them for their marketing campaigns. It can also leak to fraudulent companies looking to earn profit by selling the data.

Inaccurate Results

Data mining is not 100% accurate. It requires a large amount of data to identify precise patterns and trends. Preprocessing errors can also lead to inconclusive and inaccurate results. 

Applications of Data Mining

Data mining is used across all industries. It is an essential tool for businesses that want to thrive and succeed in their industries. Let us discuss some of its applications.

Healthcare and Insurance

Data mining is incredibly useful in the healthcare sector. By bringing together the entire medical history of a patient, doctors can give a more accurate diagnosis. Similarly, pharmaceutical companies also use it to improve drug discovery and delivery quality cost-effectively. 

Data mining enables companies to predict customer behavior and identify fraudulent activities in the insurance sector. Insurance companies also use it to price policies and approve policy applications. It also helps in identifying prospective customers for their policies.


In the education sector, the Educational Data Mining method is used. This method looks for information from educational data. It is used to improve performance for both learners and educators. It can help determine the changes in students' behavior and the effective involvement of teachers. Other areas where it helps include predicting student profiling, aiding in curriculum development, and evaluating students' and teachers' performance.


Streaming services make use of data mining to determine their viewers’ preferences. Through data mining, they analyze their viewers’ choices, hours spent on the service, etc. Based on this data, they make personalized recommendations. 


Data mining is used to develop financial risk models and detect fraudulent transactions in the banking industry. Banks and financial services also use it to understand market risks and determine the chances of loan repayment while lending money to a customer. Credit card companies also use data mining to identify prospective customers, identify loyal ones, and determine their credit card spending. 


Marketing is undoubtedly among the sectors that have reaped the incredible benefits of data mining. Marketing campaigns are successful only when they target the right customers at the right time with the correct method. Data mining helps marketers analyze customer behavior and their spending habits and aids in creating personalized campaigns. 


In the manufacturing sector, data mining helps companies improve their uptime in their production plants. It enables them to ensure operational efficiency and product safety. It is also beneficial in determining the patterns that can cause potential equipment failure. 

Techniques of Data Mining

There are many types of data mining techniques. Data professionals must determine the right tool to get the most out of their datasets. Here are some of the most popular data mining techniques. 

Association Rules

This is also known as market basket analysis. Under this, the tool looks for relationships between dataset variables. For example, association rules can search for the most purchased combination of products in the company’s sales history. 

Association rules are if-then statements that identify the probability of interactions between different data elements. It is mostly used in sales and medical datasets. The three measurement techniques are life, support, and confidence. 


Under classification, data elements are assigned different predefined categories in datasets. It helps in gathering relevant information from the data. These categories typically describe the characteristics of data items. Some common classification criteria include classification as per the type of data sources mined, databases involved, kind of knowledge discovered, and data mining technique used. 


Similar to classification, clustering groups data elements with the same characteristics. These groups are called clusters. This technique works by recognizing the differences and similarities in the data. 

Unlike classification, groups are not predefined in clustering. Let us take an example to understand the difference between classification and clustering. Using classification may categorize a dataset into groups such as toothpaste, foundation, and sunscreen. In the same dataset, clustering can form groups such as dental health and skin care.


Under regression, relationships are identified in datasets based on a set of variables. It is primarily used to predict a number. Some examples where regression is used include predicting profit, sales, distance, house values, etc.

Regression is most commonly used in financial forecasting. The different types of regression include linear regression, logistic regression, lasso regression, ridge regression, and polynomial regression. 

Predictive Analysis

As the name suggests, the predictive analysis uses historical data to build mathematical models and predict outcomes. It is possible to use predictive analysis with other techniques, such as clustering and classification.

These are not the only data mining techniques. Some other popular data mining techniques include neural networks, decision trees, K-nearest neighbor, and sequence and path analysis.

Data Mining Vs. Machine Learning

Data mining is sometimes interchangeably used with machine learning. This is mostly because machine learning is often used as a tool in data mining. However, these are two unique concepts. Let us understand the differences between the two.

  • As you have read earlier, data mining is analyzing datasets to find useful information. On the other hand, machine learning refers to discovering algorithms that have improved with the experience gained from data. 

  • Data mining was discovered decades before machine learning. Machine learning, in comparison, is a newer technology. 

  • Data mining works with large amounts of raw data. Machine learning, on the other hand, uses algorithms. 

  • Data mining can only work with human intervention. Experts must be involved in the data mining process. In comparison, machine learning was created to function without much human intervention. Its algorithms learn from experience and improve themselves. 

Machine learning and data mining are both parts of data science. They are both used to find solutions to complicated challenges. But as you have read, they are two unique concepts. 


Data mining is essential in today’s world. Data is at the core of organizations, and data mining helps them use this core for their growth and profits. If you are interested in becoming a data analyst or data scientist, it is beneficial to learn about data mining. We hope this blog helps you understand the meaning, benefits, importance, and applications of data mining.

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Armin Vans
Avni Singh has a PhD in Machine Learning and is an Artificial Intelligence developer, researcher, practitioner, and educator as well as an Open Source Software developer, with over 7 years in the industry.



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