What is Data Mining?

As companies embrace digital transformation, many want to leverage data as a key differentiator. However, the volume and variety of data have many of them overwhelmed. Data mining is a strategy that helps companies overcome this information overload and extract meaningful business insights from it.

Here we’ll answer questions such as “What is data mining?” “Why is it important?” and “How can I use it in my business?” 

What Is Data Mining and Why Does It Matter?

Data mining is the process of extracting knowledge from data. The process involves discovering and analyzing patterns in large volumes of data. Companies use the information to make predictions, improve business processes and make other decisions.

Data mining has two main functions: exploratory and predictive. Exploratory data mining techniques are used to find interesting correlations in data sets. Predictive data mining techniques use existing correlations to suggest new correlations for making predictions.

How Data Mining Works

Data mining encompasses a variety of techniques for analyzing data. These include association rules (also known as regression algorithms or pattern-matching algorithms), clustering algorithms, feature selection algorithms, outlier detection, prediction algorithms and visualization methods. 

Descriptive Data Mining

Descriptive data mining is the process of describing the dataset. Descriptive mining helps extract useful information from large datasets. Companies use the process to understand the data. Descriptive mining consists of two approaches: supervised and unsupervised.

Supervised

Supervised data mining uses supervised learning algorithms trained using labeled datasets on how they should classify or categorize the new incoming observations (or unlabeled observations). The supervised learning algorithm predicts outcomes from these observations. For example, a credit card company may have an algorithm to determine if a person will pay their credit card bill or if a person will default on their payment. The algorithm would use past data to predict future events.

Unsupervised

Unsupervised data mining is a process without prior knowledge about the dataset. The information has no labels or categories. The information is unorganized. Unsupervised learning discovers patterns in the data without human guidance or input. For example, a computer program may analyze large amounts of financial transactions to locate trends. Human experts in the company then use these trends to determine how they should classify or categorize new incoming observations (or unlabeled observations).

Predictive Data Mining

Prescriptive data mining is a process where the mining algorithms are used to discover patterns in the data to make predictions about future events based on past events. For example, using the algorithm to predict which of two products will be more popular. In this case, we would use a supervised learning algorithm to predict which product will be more popular based on existing data.

Prescriptive Data Mining

Prescriptive mining builds upon the findings gathered from predictive analysis. Prescriptive mining suggests the appropriate steps to take based on the analyzed data. This approach also provides the potential outcome of each action. For example, we may want to optimize sales for a certain product by recommending actions that we think will increase sales. We can also recommend additional actions based on how well the first set of actions performed and their impact.

What Is Data Mining’s Role Across Industries?

Data mining is used in many fields. The approach applies to any type of data to discover hidden patterns. Data mining reduces costs. Data mining also increases efficiency across the board. Specific examples include: 

• What is data mining’s role in marketing? Data mining helps marketing companies predict who will respond to their campaigns. The information helps develop an appropriate marketing approach.
• What is data mining’s role in health care? Data mining can help health care professionals predict the best course of action for a patient based on their medical history. Data mining can also help identify potential medical risks influencing a patient’s treatment.
• What is data mining’s role in insurance? Data mining can help insurance companies predict who will file a claim based on their personal history and lifestyle. The information helps insurers develop an appropriate approach to coverage.

•  What is data mining’s role in supply chain management? Data mining helps supply chain managers predict the best course of action for their products and services based on historical data. The information helps improve efficiency.

• What is data mining’s role in government? Data mining can help government officials predict the effects of proposed policies. For example, data mining can show how new legislation will affect unemployment rates.

• What is data mining’s role in manufacturing? Manufacturers use data mining to detect faulty equipment and determine optimal control parameters. It can also predict how a product will perform when used in the real world.
• What is data mining’s role in political research? Political researchers often use data mining to predict the outcome of elections based on historical data on voter behavior. The information helps political leaders make better decisions about campaign tactics.

• What is data mining’s role in publishing? Publishers often use data mining to determine what topics are most likely to interest their readers based on historical data on their reading habits. The information helps publishers decide which books will sell well and which should be discontinued.

• What is Data Mining’s Role In Finance/Banking? Data mining helps financial institutions determine good and bad loans, detect fraudulent credit card transactions, and protect credit card owners.

Data mining is a powerful technique useful for analyzing any type of data. The process provides an efficient way to discover potential relationships and trends in data. Overall, the technique aids companies in making informed business decisions.