Data mining: Definition, techniques, benefits, and risks in 2023

Data mining is a process in which a large set of data is analyzed for the purpose of looking for specific behavioral patterns. By paying attention to certain patterns in data, an organization can adapt its practices to better suit its needs. If the data sample is large enough, a company can use it in an effort to make accurate predictions.

Data mining uses computers and automated processes to analyze huge data sets in order to identify meaningful patterns and derive useful information. Businesses apply it to form insights, predict future trends, and improve user experience, for example, by analyzing what parts of a website are used more than others. Or by collecting and picking apart student data, a teacher could predict which students might fall behind early and devise a strategy to keep them afloat.

Data mining can employ the use of machine learning to automate many of the processes. Machine learning and artificial intelligence helps to easily collect a massive amount of data and organize it into different categories and classifications.

Once an organization collects the data and identifies a trend, it can finally be put to use. How the information is utilized depends entirely on the organization that mined it. It can be used internally to provide better workplace efficiency, or it could be sold on to whoever would benefit most from the information — retailers, airlines or politicians, for example.

No matter what data mining is used for, it typically follows a similar process. Let’s break it down into a few steps:

1. An organization harvests unstructured data and stores it on physical or cloud servers. It can harvest the data by asking for it directly in the form of a questionnaire, or indirectly, like tracking user activity.
2. Analysts or management will determine which patterns they want to look for in this large clump of data.
3. Then they will pass it on to tech professionals, for example, data analysts, who will make sure the data is processed accordingly to fit the end use.
4. Finally, the data analysts will present the organized data in an easy-to-digest format — usually a chart or graph.

Different data mining processing models have a different number of steps, but the process is usually very similar. For example, the widely used cross-industry standard process for data mining (CRISP-DM) contains six steps:

• Understanding the business. First, the company determines its goals, objectives, and the problems it wants to solve. Also, it must have a clear idea of what data it needs for solving the problems. Otherwise, mining results can be inaccurate or not answer the intended questions.
• Understanding the data. The company should collect only relevant data. The data can come from different sources, like sales records, consumer data, documents, surveys, questionnaires, and geodata.
• Preparing the data. Data scientists extract the relevant data from various sources and pre-process it. They clean it and fix errors and other issues. Afterwards, they transform it to make it consistent and load it into a database.
• Modeling the data. In this step, data scientists choose the right techniques (described in the section below) for answering the questions raised in the initial step.
• Evaluating the models. After creating and testing the data mining models, data analysts evaluate them in terms of their efficiency in answering the questions raised in the business understanding step. This is where human input is absolutely necessary — the person(s) in charge of the project must decide if the questions have been successfully answered or if different data is needed or different models should be built.
• Deployment. If the mining results are deemed successful, the analysts present them to the end user, who puts it to use. Data mining results come in easily understandable forms, like a report or a visual presentation, so that they can be utilized in making better business decisions and devising strategies.

You can mine data in several ways and for a plethora of reasons. Here are six of the most common data mining techniques that a data miner will use to sort data:

Classification

The organizer of the data determines the predefined classes and sorts the raw data into classes based on their characteristics. A simple example is having one classification for people who are allergic to peanuts and another one for those who aren’t. This example shows two predetermined classifications used to organize a set of data.

Clustering

Clustering is similar to and easy to confuse with classification. Clustering is where groups are defined based on their similarities then sorted accordingly to those similarities. Whereas the classification technique will already have determined how the data is to be designated, clustering will create classes based on what the data collectively has in common.

Association

Retailers and those looking to sell a product to their users typically use the association technique. It identifies data based on the relationship between an item purchase and what other items were purchased simultaneously. It’s a useful technique to determine the spending habits of a user base.

Regression

Regression analysis is about determining which factors within a data set are most important, which can be ignored, and how they interact with each other. This technique can, for example, help predict how many snow removal tools customers will purchase after a snow storm is forecasted. Regression helps determine the relationship between the amount of snow, the severity of the temperature, and the units and types of snow removal tools that customers are most likely to buy.

Sequential pattern

Companies use sequential patterning to find patterns or behavioral traits in data over a specific amount of time. In other words, they classify the data by the “sequence” of events that happened in the collection time window. By using the sequential pattern method, a shop can find out what products are often bought together during certain times of year.

Predictive analysis

Organizations typically use the predictive technique, which also employs regression modeling, to justify new business actions. Predictive data mining analyzes previous data and finds patterns that can be used to predict the future of a market.

Many businesses have used social media data mining as an effective tool. Some platforms collect an individual’s data (search history, shares, likes, number of followers, etc.) and create a profile for each user. In that profile is all the data that has been mined over the user’s time on the platform. Companies use this information for sending targeted ads throughout the user’s online session or sell to third parties for another use.

Healthcare institutions can process the large amounts of data that they accumulate to provide better services. Hospitals sometimes use healthcare data mining to predict illnesses, foresee risks, and improve diagnostics. However, it’s crucial to protect the data so that it does not end up in the wrong hands, where it can be traded or used for illegal purposes.

Even though data mining is a useful tool that can yield great results for businesses, it can also be used inappropriately if a business gathers user data without the user’s consent or for illicit purposes.

A prominent example of inappropriate data mining is the Facebook and Cambridge Analytica case, reported in 2015, which raised serious concerns about data privacy. For years, the British political consulting firm harvested obscene amounts of data belonging to millions of Facebook users. The data was infamously used to influence election results.

An example of appropriate data mining is the way eBay uses the data generated on its platform to analyze relationships between products, determine price ranges and product categories, and analyze purchase patterns. EBay mines data about listings, buyers, sellers, and items, incorporating both current and historical data to improve its services.

Whether data mining is “bad” all depends on how sensitive the collected data is, who can access it, and for what purposes it is used. However, even if a company or an individual is cautious and mindful about the usage and collection of such information, nobody is safe from security breaches. If the large amount of data that businesses collect is leaked, the consequences may be devastating to both individuals and businesses.

Data mining history begins at the end of the 18th century with the discovery of Bayes’ theorem (1763) and the development of regression analysis (1805). But the foundation for present-day data mining was laid by multiple discoveries in the 20th century: the universal Turing machine (1936), the development of databases (1970s), the discovery of neural networks (1943) and genetic algorithms (1975), and knowledge discovery in databases (1989). With the expansion and development of computer technologies and data storage in the 1990s and the 2000s, data mining became accessible, widely used, and useful for businesses and state agencies.

Both data mining and machine learning fall under the category of data science. They are both analytics tools that data scientists use for detecting patterns in big data.

Data mining is the process of extracting previously unknown “rules” — patterns, relationships, and anomalies — from existing data sets (like a data warehouse) by using data mining algorithms. This allows you to discover new insights that you were not aware of or even looking for. It is a manual process that requires human intervention and decision making.

Machine learning is the application of artificial intelligence (AI). It is the process of teaching a computer to comprehend the given parameters and learn like a human. Having been programmed and having done the initial learning on a “training” data set, the machine continues learning by itself, with minimal or no human interference. Machine learning is especially useful in predicting outcomes.

Retailers use data mining for the following purposes:

Basket analysis

Retailers use data mining to analyze what their customers buy — their “baskets.” By applying the association technique, they get a clearer picture of their customers’ buying habits and can recommend them relevant purchases.

Customer loyalty

Loyalty programs are a goldmine for many retailers, let alone a great way to collect data on their customers, like their shopping frequency, typical basket contents, and how much they spend in one go. By using this data for mining purposes, businesses can develop and improve customer relationships and offer relevant discounts.

Database marketing

Companies build databases of consumer data in order to better direct their marketing strategies and offer their customers personalized communications. Database marketing allows businesses to gather more data for exploring consumer behavior and engage more customers.

Inventory planning

Data mining helps businesses keep track of the latest information regarding product inventory, production requirements, transportation, storage, and stock of their products. It can also help to streamline their supply chain and avoid potential issues.

Sales forecasting

Companies forecast their sales and set targets by applying predictive modeling to their historical data, such as sales records, financial reports, product documentation, consumer habits, and trends. Most businesses consider predictive data to be one of their most important analytical tools.

Most jobs that deal with big data, database administration, information systems, and information security use at least some of the data mining methods. The top positions that use data mining are:

• Data analyst
• Data scientist
• Database administrator
• Information security analyst
• Computer network analyst
• Market research analyst

Businesses that operate in sales, marketing, manufacturing, and other sectors can make use of data mining as long as they have a large batch of data to analyze and a set of goals they want to achieve with the help of the data mining results.

Sales

You can log and analyze sales data to strategically adjust your production. Let’s say you own a bakery. Each time a customer buys any of your baked goods, you can record the time of purchase, what goods were bought together, and which are the most popular to tailor your supply accordingly.

Marketing

Continuing with the example of a bakery, you can analyze your marketing data to understand where your customers come across your ads, where to place them, which groups of customers to target, and which marketing strategies are most likely to be successful. Then you can align your marketing campaigns, offers, and loyalty programs to the results of the data analysis.

Manufacturing

If you own a manufacturing company, data mining can help you analyze your raw material needs and costs, their usage efficiency, the time and costs of the manufacturing process, and the obstructions to the process. Data mining can help you keep a steady and efficient flow of goods.

Human resources

Human resources teams deal with large amounts of data, including data on salaries, promotions, retention, benefits, and employee satisfaction. They can utilize and process all of it to gain a better understanding of what employees need, why they leave, and what attracts potential new hires.

Customer services

Companies gather and analyze data on customer satisfaction regarding the quality of their goods and services, shipping times, and communication with customer service representatives (call wait times, email response times, conversation quality) to determine weak points and strengths and ultimately to offer better services for their customers.

Fraud detection

Analysis of large data sets can help companies identify correlations that should not exist and should be investigated. For example, an enterprise could analyze its cash flow to detect fraudulent transactions and other signs of mismanaged funds.

Businesses benefit from data mining by discerning patterns, trends, correlations, and anomalies in data sets. Then they use this information to make better decisions and improve their strategy. Specific benefits include:

• Improved marketing and sales. Data mining helps businesses understand customer behavior and preferences, which facilitates the creation of targeted advertising and marketing efforts. They can use the results to boost conversion rates and sell additional products to their customers.
• Better customer service. Data mining results can help companies identify customer service issues and work on solving them, which facilitates better customer service.
• Improved supply chain management. Companies can better foresee market trends and product demand to improve their inventory management. Supply chain teams can use mining results to optimize logistics operations, including warehousing, distribution, and shipping.
• Timely risk management. Risk management teams can better assess and predict legal, financial, and security risks and come up with plans to address these issues.
• Lower costs. Data mining helps make the manufacturing, sales, logistics, and overall business operations more efficient, which in turn saves costs and reduces downtime and expenses.

In the context of social media, data mining involves extracting and analyzing large amounts of data from social media platforms such as Facebook, Twitter, and Instagram, with the goal of uncovering patterns and trends in user behavior, preferences, and opinions.

Companies then use these mining results to improve their marketing strategies, increase customer engagement, and gain insights into consumers’ opinions on a particular topic. However, the analysis of user data by mining the data on social media platforms raises ethical concerns around data privacy and security.