In daily operations, a business collects data about sales, customers, production, employees, marketing activities and more. Data mining can help businesses extract more value from that critical company asset. The knowledge gained through data mining can become actionable information a business can use to improve marketing, predict buying trends, detect fraud, filter emails, manage risk, increase sales and improve customer relations.

Because data mining techniques require large data sets to generate reliable results, they have been used in the past mostly by big businesses. But the advent of large publicly available data sets — think social media posts, weather forecasts and trends, traffic patterns — can make data mining useful for many small businesses that can combine such external data with their own information and mine them together for valuable insights. At the same time, data mining tools are becoming less expensive and easier to use, making them more accessible to smaller businesses.

What Is Data Mining?

Data mining is a collection of technologies, processes and analytical approaches brought together to discover insights in business data that can be used to make better decisions. It combines statistics, artificial intelligence and machine learning to find patterns, relationships and anomalies in large data sets.

With data mining, a business can discover patterns in current customer behaviors that may not be apparent to a human analyst. It also can predict future trends. For example, applied to a new dataset of prospects, a model based on current customers could predict which prospects are most likely to become future customers.

Key Takeaways

  • Data mining combines statistics, artificial intelligence and machine learning to find patterns, relationships and anomalies in large data sets.
  • An organization can mine its data to improve many aspects of its business, though the technique is particularly useful for improving sales and customer relations.
  • Data mining can be used to find relationships and patterns in current data and then apply those to new data to predict future trends or detect anomalies, such as fraud.

Data Mining Defined

In a multistep, iterative process, data mining produces models that automatically look for patterns and relationships within large data sets, then use that information to describe relationships within the data or predict future trends. For this reason, data mining is also sometimes called knowledge discovery in data, or KDD. Often, the analysis is performed by a data scientist, but new software tools make it possible for others to perform some data mining techniques.

How Data Mining Works

Data mining works through the concept of predictive modeling. Suppose an organization wants to achieve a particular result. By analyzing a dataset where that result is known, data mining techniques can, for example, build a software model that analyzes new data to predict the likelihood of similar results. Here’s an overview:

  1. Start with historical data

    Let’s say a company wants to know the best customer prospects in a new marketing database. It starts by examining its own customers.

  2. Analyze the historical data

    Software scans the collected data using a combination of algorithms from statistics, artificial intelligence and machine learning, looking for patterns and relationships in the data.

  3. Write rules

    Once the patterns and relationships are uncovered, the software expresses them as rules. A rule might be that most customers ages 51 to 65 shop twice a week and fill their baskets with fresh foods, while customers ages 21 to 50 tend to shop once a week and buy more packaged food.

  4. Apply the rules

    Here, the data mining model is applied to a new marketing database. If the company is a packaged food provider, it will be looking for 21- to 50-year-olds.

What Can Data Mining Do?

Data mining finds hidden relationships and patterns in data that human analysts and other analysis techniques are likely to miss. The insights it reveals can help a business make better decisions, increasing revenue or making marketing more efficient, for example. But it’s important to understand that data mining finds patterns, not causal relationships. It doesn’t reduce an organization’s need for analysts who know the business, understand the data and are knowledgeable about data mining techniques and processes. Only such experts can assess the value of the patterns that data mining discovers and put them to good use on behalf of a business.

Why Is Data Mining Important?

More products are becoming digital, as are more payment transactions and customer interactions. As this happens, more companies are finding that their data, often already stored in a data warehouse waiting to be analyzed, is just as valuable as their products and services. In this context, data mining gives companies a competitive edge by helping to rapidly find business insights hidden in all the data from all those digital business transactions. The benefits are almost endless. Understanding customer behaviors can lead to new product, service or marketing ideas. Detecting intrusions can prevent a devastating theft of customer data.

Who Uses Data Mining?

Any company can use data mining, but those with large data sets will get more reliable results. The patterns and relationships discovered with thousands of customers are more likely to accurately predict future customer behavior than those discovered with only hundreds or dozens. But the market is also broadening as large data sets become publicly available and data mining technologies become less expensive and more accessible to even those without a background in data analysis.

So, while data mining has traditionally been used in industries that generate a lot of data, such as in the credit card industry, health care or oil and gas exploration, it’s also gaining ground in education, customer relationship management and marketing, among many others.

Key Data Mining Concepts

As in many fields, data mining uses its own vocabulary as shortcuts to identify important concepts. Knowing these concepts is important to master data mining and understand what it can do for a business.

  • Data cleansing: Also called data scrubbing. The process of correcting errors and omissions in data before analyzing it.

  • Model: The knowledge discovery of relationships among data, often expressed as rules.

  • Target: The goal of data mining, for example, identifying high-value customers.

  • Predictors: The related data that leads to the target.

  • Case: A specific instance of data, such as a particular customer’s information, that is plugged into the model to determine its relationship with the target. For example, is this customer likely to return for repeat sales?

  • Market basket analysis: Discovering buying behaviors of customers based on past buying patterns, often using data collected from company loyalty programs.

  • Machine learning: Algorithms that use known cases to discover other similar or identical cases in large data sets.

Data Mining Techniques

Depending on the company’s goals for data mining, different techniques are used to produce models that fit the desired outcomes. The models can be used to describe current data, predict future trends or aid in finding data anomalies.

  1. Descriptive model: Descriptive analytics finds patterns and relationships in current data.

  2. Predictive model: Used to predict future outcomes, such as whether a loan applicant is a good risk, or to make financial forecasts, such as upcoming sales.

  3. Outlier Analysis: Used to find anomalies, that is, data that doesn’t fit neatly into patterns. Outlier analysis is especially useful in fraud detection, network intrusion detection and criminal investigations.

Advantages of Data Mining

Data mining can deliver big benefits to companies by discovering patterns and relationships in data the company already collects and by combining that data with external sources. Here are just a few of the potential advantages data mining can bring to a business. The results of data mining are often demonstrated in dashboards within business software, which aggregates metrics and key performance indicators and displays them with simple-to-understand visuals.

  • Optimal product/service pricing: Using data mining to analyze the interplay of pricing variables, such as demand, elasticity, distribution and brand perception, can help a business set prices that maximize profit.

  • Better marketing: Data mining can help a company get more value out of their marketing campaigns by segmenting customers with different behaviors, optimizing engagement by segment or providing insight to aid development of personalized ad creative. The results of ad campaigns can often be demonstrated in sales dashboards.

  • Heightened employee productivity: Analyzing employee behavior patterns and viewing KPIs in HR dashboards can lead to strategies for boosting employee engagement and productivity.

  • Improved customer retention: Understanding customer behavior can improve customer relations, reducing churn.

  • Increased cost efficiency: Manufacturing costs, for example, could be lowered through many different data mining analyses, from insights into supplier pricing behavior to better understanding customer buying patterns.

  • Higher product/service quality: Finding and fixing areas where quality falters can decrease product returns.

Privacy Concerns

No organization should begin a data mining initiative involving customer and employee information without careful consideration of the potential privacy issues involved and the ethical questions that may arise. Data mining algorithms can find patterns and relationships that may lead to identifying people even when care is taken during the data collection process to protect their privacy. Therefore, any organization planning to use data mining where people are involved should include privacy and ethics experts to help guide their work from the very beginning of the project.

Data Mining Process

Data mining is an iterative process that normally begins with a stated business goal, such as improving sales, customer retention or marketing efficiency. The process works by gathering data, developing a goal and applying data mining techniques. The selected tactics may vary depending on the goal, but the empirical process for data mining is the same.

  1. Define goal: Do you want to learn more about your customers? Do you want to cut manufacturing costs? Do you want to increase revenue? Do you want to detect fraud? Clearly identify the desired outcome of data mining implementation to get started.

  2. Gather the data: Data mining can answer all those questions, but each one requires a different set of data. Often the data comes from multiple databases, for example, customers and orders.

  3. Cleanse the data: Once selected, the data usually needs to be cleansed, reformatted and validated.

  4. Get to know the data: Become familiar with the data by running basic statistical analyses and building visual graphs and charts. This is where analysts identify variables they believe to be most important to the goal and begin to form hypotheses that lead to a model.

  5. Build a model: Model building is where the data mining process is most iterative. Analysts choose one or more of the technology approaches discussed in the next section and apply one or more to the data being mined. The possible approaches are better suited to different questions. The outcome of this step is to find the data mining technology approach that produces the most useful results. This may require a reiteration of step three because some models require data to be formatted in specific ways.

  6. Validate the results: Whichever techniques are used, examine the results to validate that the findings are accurate. If not, go back to step No. 5 — rebuild the model.

  7. Implement the model: Use the discoveries to fulfill your original business goal.

Data Mining Technology

Much of data mining uses well-known algorithms that cluster, segment, associate and classify data. Each technique builds a model which is then used to describe current data or predict outcomes for new data cases.

  • Classification: Assigns data to multiple categories or classes. For example, a loan applicant can be assigned to a low, medium or high-risk category. Usually, the categories for the model are predefined based on previous analysis of the data.

  • Anomaly detection: A form of classification that uses machine learning to detect data that does not fit a class. For example, anomaly detection is used to find fraudulent credit card charges.

  • Clustering: Identifies groups of similar data. For example, clustering can be used to find customers with similar buying habits.

  • Association: Generates a probability of multiple events occurring together. One application is “market basket analysis,” which discovers when two or more items are frequently bought together.

  • Regression: Using a data set where values are known, regression techniques attempt to predict a value based on multiple attributes. For example, regression could predict sales based on the advertising dollars, month, website visits and other financial attributes.

  • Neural networks: A form of artificial intelligence that mimics the human brain to find relationships in data. Neural networks have multiple applications, for example, in predicting customer behavior.

Data Mining Use Cases and Examples

As individual organizations collect larger volumes of data, more public data sets are made available and data mining technologies become easier to use and less expensive, the potential applications of data mining are expanding. Examples of data mining improving processes and delivering benefits can be found in multiple business segments. And it’s easy to extrapolate from these uses to imagine how your organization could deploy data mining. Here are only a few of the countless ways data mining is already in use.

  • Banking: Data mining is used to predict successful loan applicants as well as to detect fraud in credit cards.

  • Retail: Create effective advertisements based on past responses.

  • Insurance: Predict probability and costs for future disasters, based on past hurricanes or tornadoes.

  • Grocery stores: Analyze market baskets to find products usually bought together. Running a sales promotion on one item can improve sales of the other item at its normal price.

  • Manufacturing: Implement just-in-time fulfillment by predicting when new supplies should be ordered or when equipment is likely to fail.

  • Customer relationship management: Identify characteristics of customers who move to competitors, then offer special deals to retain other customers with those same characteristics.

  • Security: Intrusion detection techniques use data mining to identify anomalies that could be network break-ins.

History and Evolution of Data Mining

People have been manually analyzing data to find patterns for centuries. The rise of digital information technology and databases beginning in the 1950s was, of course, a game changer for such analyses. The term “data mining” came into use around 1990 as research into the technologies and techniques described above was put to practical use in the computer database community. Data mining has grown in popularity, mainly because of its demonstrated value to companies.

Today, large data warehouses with information collected from multiple sources in varying formats, combined with larger storage capacities and faster computers, allow even small companies to reap the benefits of data mining. Data mining algorithms have also grown in sophistication. For example, relatively new machine learning techniques can infer relationships not found by previous algorithms.

Future of Data Mining

The fundamental technologies underlying data mining — computing, databases, data warehouses, neural networks, machine learning and artificial intelligence — continue to become more powerful, less expensive and easier to use. Therefore, they are becoming more accessible to many more — and smaller — businesses. So, the overall arc of data mining’s future is that it will be put to increasing use by many more, and more diverse, kinds of businesses.

Meanwhile, more data about the world we live in is becoming available, opening up the potential for future data mining techniques to evolve specifically for analysis of what we now consider nontraditional data. This includes video, audio and images; geographical and spatial data; and mobile phone data, and it’s often stored in what’s known as a data lake. Similar to a data warehouse, data lakes are repositories for information, but the data does not have to be structured and is stored in its natural or raw format.

The foreseeable future for data mining includes its potential use in everything from the mundane — think finding the best airfares at the moment or the best prices for portable generators in Long Island, N.Y. — to the profound, like new medical treatments or discoveries about the nature of the universe.

Data Mining Software & Tools

In the past, data scientists had to use programming languages such as R and Python in data mining applications. However, there are now tools that facilitate data mining and software can perform many of the necessary tasks and help identify rules and other insights from your data. Graphics capabilities are usually included in these tools for visualizing the results in pre-configured and customizable business intelligence dashboards.

More recently, cloud-based data warehouse software has become available for companies that wouldn’t otherwise be able to afford data mining or have the IT infrastructure necessary to support it. These tools represent a significant simplification of what it takes for an organization to pursue data mining. They can house a business’s own data in the same repository as external data and can include structured as well as semi-structured data. They also represent a step up in computational power, which means that data mining analyses can occur faster than before.

By combining all of an organization’s data in a single warehouse, a business can get a more comprehensive and holistic view of its operations. And by including externally acquired data and mining it together with internal data, a business can discover new opportunities.


Data mining opens opportunities for companies to improve their bottom lines by finding patterns and relationships in data they already collect. It has proven benefits in every industry. Meanwhile, the technologies required to perform data mining are becoming more automated, easier to use and less expensive, making them more broadly available to smaller organizations. The future opportunities for data mining are limited only by a company’s imagination.

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Data Mining FAQs

What do you mean by data mining?

Data mining combines statistics, artificial intelligence and machine learning to find patterns, relationships and anomalies in large data sets. From this knowledge, a business can discover current behavior and predict future trends.

What is data mining used for?

The knowledge gained through data mining can be used in almost unlimited ways — limited only by the availability of data and the imagination of an organization to use it. A few ways data mining is used today include to improve marketing, predict buying trends, detect fraud, filter emails, manage risk, increase sales and improve customer relations.

What skills are required for data mining?

Data scientists have developed complex data mining algorithms that are now implemented in software, enabling companies without special knowledge to mine their data. But data mining still requires analysts who understand the nature of the business, as well as the data the business generates or acquires from external sources.

What is data mining and its types?

Data mining can be used to describe current patterns and relationships in data, predict future trends or detect anomalies or outlier data. It does this using three primary models, or types: the descriptive model, which finds patterns and relationships in current data; the predictive model, which is used to predict future outcomes; and outlier analysis, which finds anomalies — data that doesn’t fit neatly into a pattern.