What Is Data Mining? (With Applications and Examples)
By Indeed Editorial Team
Published June 1, 2022
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
Data can support your conclusions on a topic and improve decision-making. If you have a large data set, mining it can help you better understand its uses. By learning about data mining, you can discover patterns, anomalies, and trends in your data sets. In this article, we answer the question, "What is data mining?", describe how it works, discuss its importance, present various applications, answer frequently asked questions, and share two examples of data mining.
What is data mining?
Knowing the answer to the question "What is data mining?" can help you understand whether this concept applies to your work. Data mining, or knowledge discovery in data (KDD), is the process of finding valuable insights in data sets to make decisions. You may also mine data to forecast business outcomes and analyze previous situations. Because it involves working with many data records, you typically mine data using computers and automated systems.
How does data mining work?
Here are the steps you can follow when working on a data mining project:
Define business objectives
Outlining a business's reasons for data mining is typically the first action to take because it can influence subsequent steps. You can work with stakeholders to define the business problem and outline data questions and parameters. Conducting additional research to understand the business context may also be necessary. For example, if you're working on a data mining project for another department, you may research its practices, policies, and standards.
Collect and store the data set
Once you clarify the business's objectives, you can start gathering relevant data records. Uploading these records to temporary storage locations, such as data lakes and warehouses, can help you keep them safe. While a data lake is a central location that enables you to store data at any scale, a data warehouse is a storage location for filtered data. It's essential you prepare the data set by handling duplicates, missing values, and outliers. Once you have a complete data set, you can move records to permanent storage locations, such as internal servers or secure cloud storage.
Manage the data set
While in its permanent storage location, you can make your data set accessible to professionals who can manage it. These individuals may include business analysts, information technology professionals, and managers. Managing a data set typically involves organizing it by category or source to make it more interpretable.
Build data models and identify patterns
Next, you can build models to answer previously identified data questions. There are various modelling techniques to consider, such as classification, clustering, and regression analysis. You may also use different models on the same data set to address specific business objectives. The results from data modelling typically reveal patterns, correlations, or association rules in a data set.
Evaluate your results
After data modelling, you can determine whether your preferred model provides answers to your data questions. For example, suppose a company wants to determine whether it might have more customers in the future. If your chosen model provides a reliable answer, you can advance to the next project stage. Otherwise, you can consider changing the model or reevaluating the data set.
Present your findings
This step involves using the data model to recommend or make decisions. You may create visual presentations or reports to share your findings with stakeholders. For example, if you collect data on internal operations, you might share it internally. Otherwise, your findings might be valuable in annual reports for public review.
Why is data mining important?
Data mining enables you to examine data sets efficiently and effectively. It involves identifying valuable data records to determine a business's most favourable outcomes. For example, data mining can help a business understand the most relevant factors influencing its sales or campaign efforts. Companies may gain a competitive advantage by using this process to explore new markets and stay current with customer trends.
Applications of data mining
Here are various fields where you can use data mining:
Education: Data mining can help educators access student data, predict achievement levels, and identify students requiring more assistance to develop intervention strategies.
Insurance: Using data mining, insurance companies can solve problems related to compliance, risk management, fraud detection, and customer churn.
Manufacturing: Data mining can help manufacturers align their supply plans with demand forecasts. This process may also be valuable for predicting maintenance actions, which can ensure timely production.
Sales and marketing: Many companies collect data about their customers and prospects. Using these records, they can optimize their marketing campaigns, enhance customer loyalty, and generate higher returns on marketing efforts.
Management: With data on business functions, managers can reduce costs across departments, enabling a business to operate more efficiently.
FAQs about data mining
Reviewing the following answers to common questions can help you learn more about data mining:
What is the difference between data mining and machine learning?
Data mining is the process of finding patterns in data. It generally requires human interpretation of insights to apply them to businesses. In comparison, machine learning is the process of teaching a computer to learn as humans do. While machine learning may use data mining as part of its process, it typically doesn't require human intervention.
What careers involve data mining?
If you're considering a career that involves data mining, you can explore the following options:
Database administrator: This professional stores, secures, and restores a company's data, ensuring analysts' database access whenever required. The national average salary for a database administrator is $68,931 per year.
Computer scientist: This professional uses technology to solve problems, creating computer software and hardware. The national average salary for a computer scientist is $66,916 per year.
Market research analyst: This professional gathers and analyzes data on consumers and competitors. The national average salary for a market research analyst is $21.16 per hour.
Network architect: This professional designs, develops, and maintains computer networks, including intranets, local area networks (LAN), and wide area networks (WAN). The national average salary for a network architect is $93,962 per year.
Information security analyst: This professional plans and implements security measures to protect an organization's computer networks and systems. The national average salary for an information security analyst is $74,675 per year.
What tips can you use when mining data?
Here are the best practices for data mining:
Ensure data mining projects align with a company's goals and mission.
Collect sufficient data from multiple, credible sources.
Ensure data protection and privacy.
Consider working on data mining projects as part of a team.
Approach model building in data mining as an iterative step.
Examples of data mining
Here are two practical examples of data mining to gain more business insights and aid decision-making:
Example on gaining insights into customer preferences
This example describes how a company might mine data to make customer decisions:
RoastVeg is a local grocery store that establishes a loyalty program, offering reduced grocery prices and select items. Upon acceptance into the program, customers typically receive loyalty cards to make purchases. These cards enable the store to track customer demographics and purchases. RoastVeg plans to use this data to offer coupons targeting customer purchasing habits. This data set can also inform managers whether to put an item on sale or sell them at their regular prices.
After collecting, sorting, and managing the data set, RoastVeg uses algorithms and data mining techniques to answer its data questions. It finds that many loyal customers prefer leafy green vegetables. This implies that it may be best to sell items not desired by loyal customers at their regular prices. Using visual representations, the data mining team makes these recommendations to upper-level managers and seeks strategy approval before implementation.
Example on gaining insights into retargeting prospects
This example describes how a school might mine data to make decisions:
Harrow Vermont University is looking to attract promising candidates who recently finished high school. On its website, it has a request for information (RFI) form to obtain data on a prospective student's age, location, and preferred program. Whenever candidates submit their RFI form, the website's system collects and stores their data records.
The university's team manages these records and builds data models to identify patterns. It discovers that prospective students who finished top of their class preferred the humanities and applied sciences. It also found similarities between a student's location and their preferred academic program. Harrow Vermont University can now use these insights to retarget prospects based on their locations and high school performances.
Salary figures reflect data listed on Indeed Salaries at the time of writing. Salaries may vary depending on a hiring organization and a candidate's experience, academic background, and location.
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