We are in the era of big data. So much data is collected every second across the world that data applications and automated processes cannot keep up. While the endless volume of data creates opportunities for businesses, it also creates opportunities for data professionals.
Data affects every aspect of a business. Data analysts, engineers, and scientists collect (mine) and interpret data to find patterns that tell the story of how a business operates and why. Through data mining in business, a company can create a data-driven strategy that saves it time, resources, and money.
What Is Data Mining?
Data mining in business refers to the process of extrapolating key patterns from large data sets to create operational and strategic insights. Data mining is useful in almost every aspect of business, such as marketing, operations, management, sales, and production. Data analysis can be performed using a number of different techniques, including the following.
Classification assigns categories to a collection of data to make more accurate predictions and analysis. This method is used in everyday life across a variety of platforms and businesses to help define customers and their relation to a business. Classification is found in supervised learning algorithms, where a learning algorithm uses sample inputs to make its classifications.
An example of classification could be the “suggested items” feature used in online shopping. The system recognizes that a user is interested in one product and offers other associated ones. For example, if a shopper buys dog food, their “suggested items” might feature dog bowls, toys, and other accessories.
Clustering, also known as descriptive modeling, is similar to classification in that the end goal is to categorize and label data points under one naming convention. However, clustering is an unsupervised learning technique, meaning the data is grouped by an analyst or data professional based on similarities. Conversely, classification uses supervised learning, which has machine algorithms set to classify a data point based on its attributes.
Banking companies use clustering for fraud detection. If a customer consistently uses their card in a certain location, that location is perceived as normal. However, when the account is used outside that area, it triggers a warning because it deviates from normal purchasing behavior.
Regression predicts the quantitative values of one variable based on the relationship of other variables in a data set. Regression can be predicted through simple linear regression and multiple linear regression statistical methods.
An example of regression could be the value of a house based on the square footage of the property. The linear regression model would show a simple linear regression between the variables of square footage and value, with a positive trend between increased square footage and housing prices.
Data Mining Applications in Business
Classification, clustering, and regression can all be applied to extrapolate insights for business operations such as marketing, customer relations, fraud detection, and more. While data mining in business hasn’t yet become standard practice, many companies are actively leveraging data analysis and business intelligence to create competitive advantages.
Online retailer Amazon.com is a prime example of a company using data to its benefit. Amazon enhanced its marketing strategy through data mining by creating a feature that classifies potential customers in like-minded customer sets. When a potential customer is browsing Amazon, they will see what products other like-minded customers have purchased, increasing their likelihood to view further products and make additional purchases.
Capital One is another company leveraging data to make smarter marketing decisions. It generates a list of products and services a customer is likely to purchase based on their buying history. This list is given to customer service representatives who then use it to create customer relationship strategies and marketing opportunities.
Data mining can also influence creative marketing. Coca-Cola uses data to drive customer retention through its online loyalty program. It collects feedback from customers on what they would like to see featured in advertisements, where advertisements should be placed, and why. Coca-Cola found that using data enabled it to connect its brand messaging to its consumers’ passions, creating more effective marketing and garnering stronger brand loyalty.
The Skills Behind Effective Data Mining
Careers in data analysis consistently rank among the top U.S. jobs. However, an individual who is considering a role in data mining, analysis, engineering, or science must obtain a variety of skills to be effective. Of these skills, the most important include being numbers-savvy, highly analytical, a critical thinker, able to decipher patterns in data, and able to communicate and tell a story though data. A potential data analyst must also have strong technical and computer science skills, including advanced computer language and operating system competencies.
Many soft and hard skills are needed to enter the data field. Prospective students can learn these skills through a college education, such as the one offered by the Online Master of Business Analytics degree from Ohio University.
Become an Expert in Data Mining in Business
Data mining is critical to organizational growth. Businesses that use data mining have an on-average profit increase of 8-10%, and a 10% reduction in overall costs. In the era of big data, companies need analysts, engineers, scientists, and marketers who can effectively use data to make informed decisions.
Learn the foundations of data mining with Ohio University’s Online Master of Business Analytics program. The curriculum is specifically designed to enhance data skills through courses such as:
- Descriptive Analytics
- Predictive Analytics
- Business Intelligence
- Strategic Use in Analytics
Enroll now to gain the skills and knowledge to successfully leverage data mining techniques. You’ll join an in-demand profession that is building effective analytics-driven strategies for companies large and small.