What Is Data Mining: Definition, Examples, Tools, and Techniques (For Beginners)

Data shapes every corner of our world, and understanding how to use it properly is key to success in finance, commerce, education, and even sports and entertainment. According to the World Economic Forum, worldwide data production will reach 463 exabytes daily by 2025. An exabyte has 18 zeros; that’s an incomprehensibly vast amount of data to mine.

But what is data mining? In this article, we’ll explore data mining techniques and tools, important industry terms, and even explain its importance to a career in data science.

What Is Data Mining?

Data mining is the process of analyzing dense volumes of data to find patterns, discover trends, and gain insight into how that data can be used. Data miners can then use those findings to make decisions or predict an outcome. Data mining is an interconnected discipline, blending the fields of statistics, machine learning, and artificial intelligence.

How Does Data Mining Work?

Though the name evokes images of a miner digging for ore, data mining doesn’t focus on the dig. Instead, a data miner’s responsibilities revolve around analyzing that ore (i.e., the data) to predict its value or detect useful patterns within it. This process may seem complex, but it is not as difficult as it sounds, and the skills it encapsulates can greatly benefit those looking to become data scientists.

Because of the volume and differing types of data we create daily, assessing and determining their relationships can be difficult and time-consuming. How, for instance, can a florist use daily sales totals, online searches for their store, and comments on the store’s Facebook page to determine which flowers to order? Data mining can provide an answer.

To do their jobs, data mining experts have to know how to collect and store data, work with databases, and extract the proper data from them. Further, data mining requires knowledge of industry problems and the data that will help solve them.

Ultimately, organizations in every industry — from government and finance, to healthcare and technology — have questions to answer and projections to make. For this reason, data mining often begins with a question.

For example: How many flowers should a florist order prior to a major event?

Through data mining, the florist can assess past sales, check what customers are searching for online, gauge their interests through social media posts, and make projections based on the success of other recent events during the year. Asking the right questions, and collecting the right data to answer those questions, is critical to successful data mining.


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Data mining is vital to business operations across many industries. Companies use data mining to manage risk, anticipate demands for resources, project customer sales, detect fraud, and increase response rates to their marketing efforts.

According to a MicroStrategy report on the Global State of Enterprise Analytics (PDF 11 MB), 60 percent of respondents used analytics to save money, 57 percent used it to drive strategy and change, and 52 percent sought to improve financial performance.

Perhaps the best known data mining process is called CRISP-DM, or Cross-Industry Standard Process for Data Mining.

A visual depiction of the CRISP-DM method for data mining.

This is a six-step procedure for turning data into insight. The model works like this:

Business Understanding

This is the starting point. What questions do you have? What do you want to learn from your data? Companies and organizations first must identify their objectives, including what insights they want to extract or problems they want to solve using their collected data. Determining project goals is important for collecting the right data to be analyzed.

Data Understanding

Once the objective is defined, it’s time to define the data. Not every data point stored on a server or in the cloud is appropriate for every project. Determining the right data to be sourced saves time and the potential hassle of retracing steps later.

In this phase, data is collected from multiple sources based on the problem being addressed. Is the company looking for historical sales of a certain item? The type of credit card used to make a purchase? Whether items were bought in store or online? Each type of data may be relevant — or not — depending on the project.

This part of the process is important for verifying data quality as well. Missing, errant, or duplicate data can be corrected before moving to the next phase.

Data Preparation

Data preparation is considered the most demanding phase of data mining, often consuming at least half of the project’s time and effort. It’s in this step that the most helpful data is selected, cleaned, and sorted to account for errors or coding inconsistencies. Data from multiple sources can be merged, organized, or adjusted in different ways to prepare for the next phase: modeling.

Modeling

Now the data begins to take shape. Data miners can run a variety of models (ways of organizing data) to generate solutions. For instance, models can seek to detect patterns or anomalies in the data or use the data to predict an outcome. Companies will choose the model based on the type of data they’re analyzing, the project’s specific requirements, and the goals being pursued.

Several modeling techniques can be used on the same set of data to derive different results. Rarely do companies answer their data mining question with just one model.

Evaluation

At this point, data miners assess whether the models have produced a satisfactory answer to the question asked and whether the results contain any unexpected or unique findings.

If the initial question remains unanswered, a new model might be required, or the data might need to be changed. If the results meet their criteria, the project moves to its final phase.

Deployment

At this point, companies have answered the question they asked. In the flower shop example, perhaps the model suggested an increased order due to past sales and expected customer demand. The florist can deploy that knowledge to ensure they have enough flowers on hand when a major event arrives.

Why Is Data Mining Important for Businesses?

Put simply, data mining improves business; it can save money, drive competitive advantage, improve the customer experience, and identify new customers and revenue streams.

According to the MicroStrategy survey (PDF 11 MB), 63 percent of respondents said analytics had improved their company’s efficiency and productivity, 57 percent said it helps them make faster decisions, and 51 percent cited improved financial performance.

A chart showing the biggest benefits organizations have realized by using analytics.

Data mining is about discovery — hence the term and its relation to mining for precious materials. And, in a consumer world overwhelmed by data, companies need efficient ways to sift through that data to find relevant, actionable points. They can customize all the data they generate to learn who’s buying their products, where they’re buying them, and how to sell more.

One of the primary benefits of data mining is speed. Decades ago, large data sets required weeks or months to analyze. Banks and credit card companies had to sift through millions of records to detect fraud or errors. With advances in neural networks, machine learning, and artificial intelligence, those huge data sets can now be analyzed in hours or minutes. More advanced data mining tools and techniques have helped to bring together disparate data into usable groups like never before.

Data can be divided into two main formats: structured and unstructured. Structured data consists of the numbers we recognize in a table or Excel spreadsheet, such as last month’s sales records and this month’s inventory. Unstructured data, meanwhile, exists in different formats, such as text or video. It’s included in emails, social media posts, photos, and even satellite images.

Companies certainly need to evaluate structured data, but mining for insight in unstructured data is a booming enterprise. According to a Forbes survey, more than 95 percent of businesses say they need stronger ways to manage unstructured data.

How Is Data Mining Being Used by Different Industries?

What is data mining used for? And who uses it? In reality, data mining can be applied to every industry that generates data and wants to leverage it. As long as you have access to data and a curiosity to discover meaning or answer questions, data mining can help you find your way.

Here are some examples of how data mining is being used within specific industries.

Healthcare

Data mining has been embedded in healthcare for years. Physicians take advantage of more effective treatment methods based on data mined from clinical trials and patient studies. Hospitals and clinics can improve patient outcomes and safety while cutting costs and lowering response times. Data mining can even match patients with doctors based on reports of successful diagnosis rates.

Banking and Finance

Among the first uses of data mining was the detection of credit card fraud. Financial companies also mine their billions of transactions to measure how customers save and invest money, allowing them to offer new services and constantly test for risk.

Retail

Retailers have an enormous amount of customer data (purchase trends, preferences, and spending habits among them) that they attempt to leverage to boost future sales. Retail companies that don’t produce insight from data mining risk falling behind the competition.

Insurance

Fraud detection is a critical component of the insurance industry, but insurers also use data to manage risk, understand why they’re losing customers, and price their products more effectively. For instance, a car insurance company could study mileage and accident rates for a certain region to determine whether it should raise or lower rates for customers who live there.

Media and Telecommunications

Media and telecommunications companies have loads of data on consumer preferences, including the programming they watch, books they read, and video games they play. With that data, companies can target programming to consumers by taste, region, or other factors. They can even suggest media to consume — an approach companies like Netflix have mastered.

Education

By measuring student achievement data, educators believe they can predict when students might drop out of school before the students even consider it. Further, this data can help educators intervene with at-risk students and potentially keep them in school.

Manufacturing

Manufacturers use data to align their production schedules with demand, ensuring that products are on store (or virtual) shelves when they’re needed. This helps maximize production at critical times and predict when assembly lines might need maintenance.

Transportation

Safety is a primary driver of data mining in the transportation industry. Cities and communities can conduct traffic studies to determine the busiest roads and intersections. And public transportation entities can mine data to understand their busiest zones and travel times.


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Data Mining Definitions: Other Key Terms

Here’s a guide to some of the key terminology related to data mining. Interested in learning more? Check out our beginner’s guide to data science.

Clustering

Clustering is the process by which subsets of data, such as individual records or images, are grouped together for analysis. These clusters of data can be mined to discover patterns within them. For example, a retailer can cluster sales data of a certain product to determine the demographics of the customers purchasing it.

Machine Learning

Machine learning is a branch of artificial intelligence in which programmers essentially teach computers to analyze large amounts of data. Once programmers develop the initial algorithm, the computer “learns” by analyzing more and more data. Streaming services use machine learning, for example, to recommend programming based on what consumers have watched.

Predictive Analysis

Predictive analysis uses data mining and machine learning to project what might happen based on historical data. Organizations can address many issues with predictive analysis, including fraud prevention and risk management. The available computing power and software today make predictive analysis accessible to most businesses.

Business Intelligence

Business intelligence refers to the process of converting data into useful information for a business. Deriving business intelligence is a similar process to data mining. However, business intelligence usually refers to drawing conclusions from broader data sets rather than mining for specific patterns or answers in a data set.

Data Analysis

Data analysis focuses on turning data into useful information. It includes the processes of collecting, analyzing, interpreting, and visualizing data, which businesses then use to make better decisions. Generally, everyone practices data analysis daily; if you leave for work 15 minutes earlier than yesterday because traffic was heavy, that’s a simple example of data analysis in action.

Data Science

Data science is a broader field that includes analysis, statistics, machine learning, and more. Data science explores how to work with data — from capturing and storing it, to processing and analyzing it. Data scientists have strong skills in statistics and computer programming, along with deep knowledge of the industries in which they work.

What Are the Most Popular Data Mining Tools?

Data scientists employ several data mining tools to store, organize, and visualize data. Here are some of the most common ones used today.

Python

Python is a multi-purpose language often used for web development and app building. The language is versatile, considered easy to learn, and supports many internet protocols. And, because Python is compatible with many libraries and packages used for data analysis, visualization, and machine learning, it is one of the most important languages for data mining. Python is also open-source and free to install, which makes it a good first language to learn.

SQL

SQL, or Structured Query Language, is essential for data scientists. SQL (sometimes pronounced “sequel”) is the standard language used to communicate with relational databases. Tasks such as adding, deleting, and retrieving data and creating new databases are performed using SQL.

Since data mining requires the ability to work with databases, SQL is a prominent language. Further, it’s a very common language in business, particularly e-commerce, where websites store and relate large amounts of data about products and customers.

NoSQL

NoSQL (“Not only SQL”) is different from SQL in that it works with non-relational databases. Unlike relational databases, which store data in tables, non-relational databases can store data based on other methods (such as values or documents) and on the specific requirements of that data. NoSQL databases can capture both structured and unstructured data. As a result, organizations that gather different types of data use NoSQL to manage it.

R

R is a popular programming language for statistical modeling and graphics production. Essentially, R’s world revolves around data. It includes tools for data storage, handling, and analysis as well as those for displaying the results of that analysis.

Further, R offers an enhanced set of free packages (fundamental units of reusable code) that can be used for tasks such as visualization, statistical analysis, data manipulation, and more.

Apache Spark

Apache Spark calls itself a “unified analytics engine for large-scale data processing,” one that works in conjunction with many of the platforms mentioned here. Originally developed at the University of California, Apache Spark runs SQL queries, comes with a machine learning library compatible with other frameworks, and performs streaming analytics. Apache Spark also features a large community that contributes to its open-source code.

Hadoop

Hadoop is a framework for storing large amounts of data across different servers, creating a distributed storage network. The data is also copied to different networks as a safety measure. Hadoop’s collection of modules are used to process and analyze data and can be incorporated into many other software platforms (i.e., Microsoft Excel).

One benefit of Hadoop is that it can be scaled to work with any data set, from one on a single computer to those saved across many servers.

Java

Java is a well-known language that runs across multiple devices — from laptops, to large-scale datacenters, to cell phones. In fact, Java is used so widely, many data mining tools (including Hadoop) are written in and platformed on Java. Further, Java programs can be written on one system and work on any other system that runs Java.


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What Are the Most Common Data Mining Techniques?

Data miners employ a variety of techniques to extract insights. The type of data mining technique used depends on their data and their goals.

With that, here are the most common data mining techniques used:

  1. Descriptive Modeling
  2. Predictive Modeling
  3. Prescriptive Modeling
  4. Pattern Mining
  5. Anomaly Detection

1. Descriptive Modeling

Descriptive modeling answers the question, “What happened?” and focuses on past events. Organizations use descriptive modeling to answer questions such as: What were sales totals for last year? How much time do deliveries require? What kinds of products are people buying on weekdays as opposed to weekends?

Descriptive modeling, or clustering, summarizes data sets by creating groups of defined points. Want to know how many people responded to a Facebook post or signed up for a digital coupon? Descriptive modeling will deliver the answer. Organizations that want to explain something about their history, their relationship with customers, or their operations use descriptive modeling to do so.

2. Predictive Modeling

Whereas descriptive modeling primarily deals with analyzing what happened in the past, predictive modeling focuses on what is likely to happen in the future. This modeling method provides organizations with insights used to recognize risk, improve operations, and identify upcoming opportunities.

Through predictive modeling, data is collected based on a specific question or model, and a forecast is generated based on the results. For instance, retailers might want to explore consumer spending habits during certain times of year to address inventory or staffing needs.

3. Prescriptive Modeling

Prescriptive modeling takes descriptive and predictive modeling a step further by recommending actions based on the insight gleaned from data analysis.

Through prescriptive modeling, organizations seek to answer questions such as, “What actions should we take based on the data?” Machine learning is important to prescriptive modeling because computers not only use it to analyze data but also when making decisions.

With prescriptive modeling, retailers can tailor marketing strategies to specific consumers. Financial firms use prescriptive modeling to go beyond risk assessment; it can help them recommend specific stock trades to adjust to volatile market conditions. Another example of prescriptive modeling is the self-driving car. Just like a human driver, the car has to make thousands of instant calculations about when to go faster or slower, when to turn, and when to avoid potential harm.

4. Pattern Mining

Pattern mining is one of the key elements that distinguishes data mining from other types of analysis — examining a data set to see what sorts of patterns emerge rather than asking specific questions.

Organizations seek to find patterns in all kinds of data. For example, retailers may want to check the frequency with which consumers buy eggs or milk on weekends, and what other goods they buy in the same shopping trip. Identifying these data patterns and trends will enable them to tailor their pricing, display, and advertising strategies to maximize profits and customer satisfaction.

5. Anomaly Detection

Anomaly detection is a data mining technique that uncovers which data points might deviate from a data set’s normal pattern or behavior. Also known as outlier analysis, this process is essential to uncovering statistical anomalies that may impact strategic decision making.

For example, perhaps a salon focuses its business primarily on female clients. Then, one week, the salon sees an uptick in male customers. Were some male customers drawn to a particular social media post? Did a sale or special spark their interest? Anomaly detection can help the salon owner understand why the customer pattern changed and perhaps adjust their business plan accordingly.

Why Should I Consider a Career in Data Science?

Data mining is just one discipline within data science where job growth is outpacing the number of job candidates. In tracking this shortage, QuantHub found that job postings for data scientists are three times higher than searches for those jobs. Companies are having a difficult time finding enough qualified candidates to meet their expanding needs in data science.

According to the U.S. Bureau of Labor Statistics, the demand for computer and information research scientists, which includes those versed in data mining, will increase by 15 percent through 2029. And CareerOneStop, a site sponsored by the U.S. Department of Labor, reports that the U.S. median salary for these professionals reached $99,230 in 2020.

Data science is a supply-and-demand industry now, making it a desirable career. To get started, consider Georgia Tech Data Science and Analytics Boot Camp. This 24-week, part-time online program covers the necessary skills to pursue a career in data science and analytics. Participants are shown how to learn programming — including the best programming languages for beginners — as well as how to work with databases, statistical modeling, front end web visualization, and more.

Through the bootcamp, learners attend online classes that are instructor-led and backed by a team of teaching assistants and tutors for support. Applicants don’t need to have previous experience in data science — just a desire and devotion to learn something new.

Georgia Tech Data Science and Analytics Boot Camp works for learners new to data science, professionals looking for a career change, or business owners looking to gain a market advantage by advancing their technical skills. It’s also a good first step for beginners to explore the best way to learn to code. The bootcamp is short-term and fast-paced, offering an accelerated way to pursue a new career.

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