Data Mining in Business Analytics: Definition, Techniques, and Benefits

Reviewed by Joe Dery (Ph.D.) from the College VP in the College of IT, Dean of Data Analytics, Computer Science, and Software Engineering

The term “data mining” may conjure images of hackers getting access to your information or spying on you. But the truth is that data mining plays a very important and positive role for many individuals and organizations.

Data mining helps professionals and researchers learn about all kinds of things, from helping with humanitarian work abroad and reducing the spread of diseases to managing climate change. Without data mining, it could take months or even years to acquire the data needed to make accurate predictions and solve problems around the world. Various types of organizations conduct data mining projects that have many applications, which in turn can offer profound meaning for the business world.

Data mining is an important focus for IT specialists, and a degree in data analytics can help qualify you for a career in data mining. But nearly everybody involved in business should have a basic understanding of data mining since it’s vital to how many business processes are performed and how information is gleaned. That means that current and aspiring business professionals should learn fundamental data mining principles.

This guide will help you learn more about what data mining is, how it’s done, and what it means for businesses. 

Simply put, data mining is the process that organizations use to turn raw data into useful information. For example, a tech firm may use programming languages like R or Python to uncover patterns in data to learn more about customers, products, internal processes, and more.

Data mining allows businesses to identify key sets of information and compare them to past data. This process, in turn, can lead to better decision-making and strategy. It can also help in other objectives like boosting sales and marketing more effectively.

Individuals may sometimes confuse data mining with machine learning and data analysis, but these terms are different and unique from one another. While both data mining and machine learning use patterns and analytics, data mining looks for patterns that already exist in data.  Machine learning can then predict future outcomes by applying newly-given rules, patterns, or variables to the data analysis. In data mining, the rules or patterns aren’t known from the start. In many cases of machine learning, the machine is given a rule or variable to better understand the data.

Data mining relies on human intervention, whereas machine learning is meant to be initiated by an individual and then learn on its own. However, machine learning processes are often utilized in data mining for automation purposes. 

Data mining, data analysis, artificial intelligence, machine learning, and similar methods are typically combined in business intelligence processes so that organizations can gain more insight about their customers and potential sales outcomes.

Today, nearly all businesses use data mining, so it’s important to understand the data mining process and how it can help businesses make better decisions. Below is a breakdown of each step in the data mining process.

The first step to successful data mining is understanding the overall objectives of the business and how it converts these objectives into a data mining problem and plan. Without an understanding of a business’s ultimate goal, you may not be able to design a good data mining algorithm. For example, a supermarket might want to use data mining to learn more about its customers. The business understanding comes when the supermarket discovers which products customers are buying the most.

After you know what a business is looking for, it’s time to collect data. There are many complex ways that organizations can obtain, organize, store, and manage data. Data mining involves becoming familiar with the data, identifying issues, gaining insights, and observing subsets of information. For example, a supermarket may use a rewards program where customers can input their phone number at purchase, giving the supermarket access to their shopping data.

Data preparation means readying information production, which tends to be the most intensive part of data mining. It typically includes converting computer-language data into a user-friendly and quanitifiable format. Transforming and cleaning the data for modeling is key during data preparation.

In the modeling phase, mathematical models are used to search for patterns in the data. Businesses may use one of several techniques for the same set of data. Even though modeling involves a fair amount of trial and error, it’s still a crucial phase in data mining.

When the model is complete, it needs to be carefully evaluated and reviewed to ensure that it meets business objectives. At the end of this phase, a final decision about the data mining results is made. In the supermarket example, the results will provide a list of relevant customer purchases that the business can then use for its operational planning and goals.

Deployment can be as simple or as complex as a business deems necessary, depending on the amount and nature of the data. For instance, it could entail generating a single report or creating a repeatable data mining process to occur regularly.

After the data mining process has been completed, a business can finalize its decisions and implement changes accordingly.

Why is data mining important for businesses? Organizations that engage in data mining can a gain a competitive advantage with a better understanding of consumer behavior, efficient oversight of business operations, improved customer acquisition, and new growth opportunities.

Some businesses may look for better ways to optimize their supply chain while others may opt to improve marketing outreach techniques. Whatever an organization’s goals might be, the process of data mining can help them make more effective decisions through comprehensive analysis. 

While they can differ across industries, the benefits of data mining commonly include the following: 

• Cost effective. Organizations that invest in efficient methods of data mining can save money in the long run.
• Reliable. Many—if not all—types of data mining are designed to produce dependable, actionable results.
• Quantifiable. Information pulled from data mining can be easily measured and compared against other sets of data.
• Strategy promoting. Data mining is instrumental in fostering new, improved strategies for businesses to test and prove.

How does data mining work specifically in business settings? Read on to learn about five effective techniques that many businesses practice.

Classification

This complex data mining technique takes attributes of data and moves them into discernable categories. For example, supermarket data miners may use classification to group the types of groceries customers buy, including produce, meat, baked goods, and more. These classifications help the business learn more about what customers prioritize as they shop.

Clustering

This technique is akin to classification as it involves grouping data sets together based on their similarities. Cluster groups are more generalized than classification groups, making clustering a more flexible option for data mining. In the supermarket example, simple cluster groups might be food and nonfood items.

Association Rules

Association in data mining is about tracking patterns based on linked variables. In the supermarket example, this may mean that many customers who buy one specific item may also buy a second, related item. The business then learns to group certain products together. You may encounter association when you see a “people also bought this” section while shopping online.

Regression Analysis

Regression analysis is used to plan and model to identify the likelihood of a specific variable. For instance, the supermarket may project price points based on product availability, consumer demand, and industry competition. Regression analysis helps identify the relationship between variables in a data set.

Anomaly and Outlier Detection

For many data mining cases, merely seeing an overarching pattern might not be enough. You may need to be able to identify and understand outliers in your data as well. For example, if most supermarket shoppers are generally female, but one week in February skews mostly male, the business can use anomaly detection to investigate that outlier to understand what’s behind it.

These data mining techniques are key for businesses to better understand extracted information and make adjustments as needed.

Data mining tools have a steep learning curve, so it’s essential to study and research different data mining techniques. A WGU degree program in data analytics could be just what you need to learn the skills and gain the knowledge required for a promising data mining career.

WGU’s competency-based education model lets you progress through your program as quickly as you master the material. You’ll learn only what you need—and not what you already know—at a pace that works for you. Get started today!