What Is Data Mining? How It Works, Benefits, Techniques, and Examples

What Is Data Mining?

Data mining is the process of searching and analyzing a large batch of raw data in order to identify patterns and extract useful information.

Companies use data mining software to learn more about their customers. It can help them to develop more effective marketing strategies, increase sales, and decrease costs. Data mining relies on effective data collection, warehousing, and computer processing.

How Data Mining Works

Data mining involves exploring and analyzing large blocks of information to glean meaningful patterns and trends. It is used in credit risk management, fraud detection, and spam filtering. It also is a market research tool that helps reveal the sentiment or opinions of a given group of people. The data mining process breaks down into four steps:

1. Data is collected and loaded into data warehouses on site or on a cloud service.
2. Business analysts, management teams, and information technology professionals access the data and determine how they want to organize it.
3. Custom application software sorts and organizes the data.
4. The end user presents the data in an easy-to-share format, such as a graph or table.

Data Warehousing and Mining Software

Data mining programs analyze relationships and patterns in data based on user requests. It organizes information into classes.

For example, a restaurant may want to use data mining to determine which specials it should offer and on what days. The data can be organized into classes based on when customers visit and what they order.

In other cases, data miners find clusters of information based on logical relationships or look at associations and sequential patterns to draw conclusions about trends in consumer behavior.

Warehousing is an important aspect of data mining. Warehousing is the centralization of an organization's data into one database or program. It allows the organization to spin off segments of data for specific users to analyze and use depending on their needs.

Data Mining Techniques

Data mining uses algorithms and various other techniques to convert large collections of data into useful output. The most popular types of data mining techniques include association rules, classification, clustering, decision trees, K-Nearest Neighbor, neural networks, and predictive analysis.

• Association rules, also referred to as market basket analysis, search for relationships between variables. This relationship in itself creates additional value within the data set as it strives to link pieces of data. For example, association rules would search a company's sales history to see which products are most commonly purchased together; with this information, stores can plan, promote, and forecast.
• Classification uses predefined classes to assign to objects. These classes describe the characteristics of items or represent what the data points have in common with each other. This data mining technique allows the underlying data to be more neatly categorized and summarized across similar features or product lines.
• Clustering is similar to classification. However, clustering identifies similarities between objects, then groups those items based on what makes them different from other items. While classification may result in groups such as "shampoo," "conditioner," "soap," and "toothpaste," clustering may identify groups such as "hair care" and "dental health."
• Decision trees are used to classify or predict an outcome based on a set list of criteria or decisions. A decision tree is used to ask for the input of a series of cascading questions that sort the dataset based on the responses given. Sometimes depicted as a tree-like visual, a decision tree allows for specific direction and user input when drilling deeper into the data.
• K-Nearest Neighbor (KNN) is an algorithm that classifies data based on its proximity to other data. The basis for KNN is rooted in the assumption that data points that are close to each other are more similar to each other than other bits of data. This non-parametric, supervised technique is used to predict the features of a group based on individual data points.
• Neural networks process data through the use of nodes. These nodes are comprised of inputs, weights, and an output. Data is mapped through supervised learning, similar to how the human brain is interconnected. This model can be programmed to give threshold values to determine a model's accuracy.
• Predictive analysis strives to leverage historical information to build graphical or mathematical models to forecast future outcomes. Overlapping with regression analysis, this technique aims to support an unknown figure in the future based on current data on hand.

The Data Mining Process

To be most effective, data analysts generally follow a certain flow of tasks along the data mining process. Without this structure, an analyst may encounter an issue in the middle of their analysis that could have easily been prevented had they prepared for it earlier. The data mining process is usually broken into the following steps.

Step 1: Understand the Business

Before any data is touched, extracted, cleaned, or analyzed, it is important to understand the underlying entity and the project at hand. What are the goals the company is trying to achieve by mining data? What is their current business situation? What are the findings of a SWOT analysis? Before looking at any data, the mining process starts by understanding what will define success at the end of the process.

Step 2: Understand the Data

Once the business problem has been clearly defined, it's time to start thinking about data. This includes what sources are available, how they will be secured and stored, how the information will be gathered, and what the final outcome or analysis may look like. This step also includes determining the limits of the data, storage, security, and collection and assesses how these constraints will affect the data mining process.

Step 3: Prepare the Data

Data is gathered, uploaded, extracted, or calculated. It is then cleaned, standardized, scrubbed for outliers, assessed for mistakes, and checked for reasonableness. During this stage of data mining, the data may also be checked for size as an oversized collection of information may unnecessarily slow computations and analysis.

Step 4: Build the Model

With a clean data set in hand, it's time to crunch the numbers. Data scientists use the types of data mining above to search for relationships, trends, associations, or sequential patterns. The data may also be fed into predictive models to assess how previous bits of information may translate into future outcomes.

Step 5: Evaluate the Results

The data-centered aspect of data mining concludes by assessing the findings of the data model or models. The outcomes from the analysis may be aggregated, interpreted, and presented to decision-makers that have largely been excluded from the data mining process to this point. In this step, organizations can choose to make decisions based on the findings.

Step 6: Implement Change and Monitor

The data mining process concludes with management taking steps in response to the findings of the analysis. The company may decide the information was not strong enough or the findings were not relevant, or the company may strategically pivot based on findings. In either case, management reviews the ultimate impacts of the business and recreates future data mining loops by identifying new business problems or opportunities.

Applications of Data Mining

In today's age of information, almost any department, industry, sector, or company can make use of data mining.

Sales

Data mining encourages smarter, more efficient use of capital to drive revenue growth. Consider the point-of-sale register at your favorite local coffee shop. For every sale, that coffeehouse collects the time a purchase was made and what products were sold. Using this information, the shop can strategically craft its product line.

Marketing

Once the coffeehouse knows its ideal line-up, it's time to implement the changes. However, to make its marketing efforts more effective, the store can use data mining to understand where its clients see ads, what demographics to target, where to place digital ads, and what marketing strategies most resonate with customers. This includes aligning marketing campaigns, promotional offers, cross-sell offers, and programs to the findings of data mining.

Manufacturing

For companies that produce their own goods, data mining plays an integral part in analyzing how much each raw material costs, what materials are being used most efficiently, how time is spent along the manufacturing process, and what bottlenecks negatively impact the process. Data mining helps ensure the flow of goods is uninterrupted.

Fraud Detection

The heart of data mining is finding patterns, trends, and correlations that link data points together. Therefore, a company can use data mining to identify outliers or correlations that should not exist. For example, a company may analyze its cash flow and find a reoccurring transaction to an unknown account. If this is unexpected, the company may wish to investigate whether funds are being mismanaged.

Human Resources

Human resources departments often have a wide range of data available for processing including data on retention, promotions, salary ranges, company benefits, use of those benefits, and employee satisfaction surveys. Data mining can correlate this data to get a better understanding of why employees leave and what entices new hires.

Customer Service

Customer satisfaction may be caused (or destroyed) by many events or interactions. Imagine a company that ships goods. A customer may be dissatisfied with shipping times, shipping quality, or communications. The same customer may be frustrated with long telephone wait times or slow e-mail responses. Data mining gathers operational information about customer interactions and summarizes the findings to pinpoint weak points and highlight what the company is doing right.

Advantages and Disadvantages of Data Mining

Pros Explained

• Profitability and efficiency: Data mining ensures a company is collecting and analyzing reliable data. It is often a more rigid, structured process that formally identifies a problem, gathers data related to the problem, and strives to formulate a solution. Therefore, data mining helps a business become more profitable, more efficient, or operationally stronger.
• Wide applications: Data mining can look very different across applications, but the overall process can be used with almost any new or legacy application. Essentially any type of data can be gathered and analyzed, and almost every business problem that relies on qualifiable evidence can be tackled using data mining.
• Hidden information and trends: The end goal of data mining is to take raw bits of information and determine if there is cohesion or correlation among the data. This benefit of data mining allows a company to create value with the information they have on hand that would otherwise not be overly apparent. Though data models can be complex, they can also yield fascinating results, unearth hidden trends, and suggest unique strategies.

Cons Explained

• Complexity: The complexity of data mining is one of its greatest disadvantages. Data analytics often requires technical skill sets and certain software tools. Smaller companies may find this to be a barrier of entry too difficult to overcome.
• No guarantees: Data mining doesn't always mean guaranteed results. A company may perform statistical analysis, make conclusions based on strong data, implement changes, and not reap any benefits. This may be due to inaccurate findings, market changes, model errors, or inappropriate data populations. Data mining can only guide decisions and not ensure outcomes.
• High cost: There is also a cost component to data mining. Data tools may require costly subscriptions, and some data may be expensive to obtain. Security and privacy concerns can be pacified, though additional IT infrastructure may be costly as well. Data mining may also be most effective when using huge data sets; however, these data sets must be stored and require heavy computational power to analyze.
  

Data Mining and Social Media

One of the most lucrative applications of data mining has been undertaken by social media companies. Platforms like Facebook, TikTok, Instagram, and X (formerly Twitter) gather reams of data about their users based on their online activities.

That data can be used to make inferences about their preferences. Advertisers can target their messages to the people who appear to be most likely to respond positively.

Data mining on social media has become a big point of contention, with several investigative reports and exposés showing just how intrusive mining users' data can be. At the heart of the issue is that users may agree to the terms and conditions of the sites not realizing how their personal information is being collected or to whom their information is being sold.

Examples of Data Mining

Data mining can be used for good, or it can be used illicitly. Here is an example of both.

eBay and e-Commerce

eBay collects countless bits of information every day from sellers and buyers. The company uses data mining to attribute relationships between products, assess desired price ranges, analyze prior purchase patterns, and form product categories.

eBay outlines the recommendation process as:

1. Raw item metadata and user historical data are aggregated.
2. Scripts are run on a trained model to generate and predict the item and user.
3. A KNN search is performed.
4. The results are written to a database.
5. The real-time recommendation takes the user ID, calls the database results, and displays them to the user.
   

Facebook-Cambridge Analytica Scandal

A cautionary example of data mining is the Facebook-Cambridge Analytica data scandal. During the 2010s, the British consulting firm Cambridge Analytica Ltd. collected personal data from millions of Facebook users. This information was later analyzed for use in the 2016 presidential campaigns of Ted Cruz and Donald Trump. It is suspected that Cambridge Analytica interfered with other notable events such as the Brexit referendum.

In light of this inappropriate data mining and misuse of user data, Facebook agreed to pay $100 million for misleading investors about its uses of consumer data. The Securities and Exchange Commission claimed Facebook discovered the misuse in 2015 but did not correct its disclosures for more than two years.

The Bottom Line

Modern businesses have the ability to gather information on their customers, products, manufacturing lines, employees, and storefronts. These random pieces of information may not tell a story, but the use of data mining techniques, applications, and tools helps piece together information.

The ultimate goal of the data mining process is to compile data, analyze the results, and execute operational strategies based on data mining results.