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What is data mining and why is it important?

What is data mining?

Data mining is the process of sifting through large sets of data to find relevant information that can be used for a specific purpose. Essential for both data science and business intelligence, data mining is essentially all about patterns.

Once data has been harvested and stored, the next step is to make sense of it — otherwise, it's meaningless. Data analysis is carried out in various ways, including using concepts like machine learning, where complex adaptive algorithms are used to artificially analyze the data.

More traditional data mining methods involve data scientists — experts trained specifically to make sense of complex information — producing reports for management teams to act on.

How does data mining work?

Data mining involves examining and analyzing large volumes of information to find meaningful patterns and trends. 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. A typical data mining process might look like this:

Define your goal: For example, do you want to learn more about customer behavior? Do you want to cut costs or increase revenue? Do you want to identify fraud? It’s important to define a clear objective at the start of the data mining process.

Gather your data: The data you gather will depend upon your objective. Organizations typically have data stored in multiple databases – for example, from information that customers have submitted through transactions, and so on.

Cleanse the data: Once selected, the data will usually need to be cleansed, reformatted, and validated.

Interrogate the data: At this point, analysts become familiar with the data by running statistical analyses and building visual graphs and charts. The aim is to identify variables which are important to the data mining goal, and to form initial hypotheses that lead to a model.

Build a model: There are different techniques for data mining – see below – and at this stage, the aim is to find a data mining approach that will produce the most useful results. Analysts may choose to use one or more of the approaches summarised in the next section, depending on their goal. Model building is an iterative process and may require data formatting to be repeated, as some models require data to be formatted in specific ways.

Validate the results: At this stage, analysts will examine the results to check that the findings are accurate. If they are not, it’s a case of rebuilding the model and trying again.

Implement the model: The insights that have been uncovered can be used to fulfil the goal defined at the start of the process.

Types of data mining

There are a variety of data mining techniques and the one you use will depend on your overall objective. There are different data models and each of those models relies on different data mining techniques. The main data models are called descriptive, predictive, and prescriptive:

Descriptive modeling

This uncovers similarities or groupings within historical data to understand reasons behind success or failure, such as categorizing customers by product preferences or sentiment. Sample techniques include:

  • Association rules: This is also known as market basket analysis. This type of data mining searches for relationships between variables. For example, association rules might review a company’s sales history to see which products are most often bought together. The company can use this information for planning, promotion, and forecasting.
  • Clustering analysis: Clustering aims to identify similarities within a data set, separating data points that share common traits into subsets. Clustering is useful for defining traits within a data set, such as the segmentation of customers based on purchase behavior, need state, life stage, or preferences in marketing communication.
  • Outlier analysis: This model is used to identify 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.

Predictive modelling

This modeling goes deeper to classify events in the future or estimate unknown outcomes – for example, using credit scoring to determine an individual's likelihood of repaying a loan. Sample techniques include:

  • Decision trees: These are used to classify or predict an outcome based on a set list of criteria. A decision tree is used to ask for input of a series of cascading questions that sort the dataset based on given responses. Sometimes shown as a tree-like visual, a decision tree allows for specific direction and user input when drilling deeper into the data.
  • Neural networks: These process data through the use of nodes. These nodes are comprised of inputs, weights, and an output. Similar to how the human brain is interconnected, data is mapped through supervised learning. This model can be fit to give threshold values to determine a model's accuracy.
  • Regression analysis: Regression analysis aims to understand the most important factors within a data set, which factors can be ignored, and how these factors interact.
  • Classification: This involves data points being assigned to groups, or classes, based on a specific question or challenge to address. For example, if a retailer wanted to optimize its discount strategy for a specific product, it might look at sales data, inventory levels, coupon redemption rates, and consumer behavioral data to guide their decisions.

Prescriptive modelling

With the growth in unstructured data from the internet, email, comment fields, books, PDFs, and other text sources, the adoption of text mining as a related discipline to data mining has also grown significantly. Data analysts need the ability to parse, filter and transform unstructured data to include it within predictive models for improved prediction accuracy.

Types of data in data mining

Types of data that can be mined include:

  • Data stored in a database or data warehouse
  • Transactional data – for example, flight bookings, website clicks, store purchases etc
  • Engineering design data
  • Sequence data
  • Graph data
  • Spatial data
  • Multimedia data
An image of a server room

Why is data mining important?

Most organizations are becoming more digital. As a result, many companies find they are sitting on vast amounts of data which, if analyzed properly, has the potential to be as valuable as their core products and services.

Data mining gives businesses a competitive advantage by helping to find insights in the data from digital transactions. By understanding customer behavior in greater depth, companies can create new products, services, or marketing techniques. Here are some of the advantages that data mining can bring to a business:

Optimize pricing:

By using data mining to analyze different pricing variables, such as demand, elasticity, distribution and brand perception, businesses can set prices at a level that maximizes profit.

Optimize marketing:

Data mining allows businesses to segment their customers by behavior and need. In turn, this allows them to deliver personalized ads which perform better and are more relevant to customers.

Greater productivity:

Analyzing employee behavior patterns can feed into HR initiatives to improve employee engagement and productivity.

Greater efficiency:

From customer buying patterns to supplier pricing behavior, businesses can use data mining and data analysis to improve efficiencies and reduce costs.

Increased customer retention:

Dating mining can uncover insights which help you understand your customers in greater depth. In turn, this can improve your interactions with customers, increasing retention.

Improved products and services:

Using data mining to locate and fix any areas where quality falls short can decrease product returns.

Use of data mining

Data mining is used for many purposes, depending on the organization and its needs. Here are some possible uses:


Data mining can help drive sales. For example, consider a point-of-sale register at a high street store. For every sale, the retailer records time of purchase, what products were sold together, and what products are most popular. The retailer can use this information to optimize its product line.


Businesses can use data mining to improve their marketing activity. For example, insights from data mining can be used to understand where prospects see ads, what demographics to target, where to place digital ads, and what marketing strategies work best with customers.


For companies which produce their own goods, data mining can be used to analyze the cost of raw materials, whether materials are being used most efficiently, how time is spent along the manufacturing process, and what barriers impact the process. Data mining can be used to support just-in-time fulfilment by predicting when new supplies should be ordered or when equipment needs to be replaced.

Fraud detection

The purpose of data mining is to find patterns, trends, and correlations that link data points together. An organization can use data mining to identify outliers or correlations that should not exist. For example, a business may analyze its cash flow and find reoccurring payments to an unknown account. If this is unexpected, the company may wish to investigate to check for potential fraud.

Human resources

HR departments often have a wide range of data available for processing, including data on staff retention, promotions, salary ranges, company benefits and how those benefits are used, and employee satisfaction surveys. Data mining can correlate this data to get a better understanding of why employees leave and what motivates recruits to join.

Customer service

Customer satisfaction is shaped by a variety of factors. Take, for example, a retailer that ships goods. A customer may become dissatisfied with the delivery time, delivery quality, or communication on delivery expectations. That same customer may become frustrated by slow email responses or long telephone wait times. Data mining gathers operational information about customer interactions and summarizes findings to determine weak points as well as areas where the company is performing well.

Customer retention

Companies may use data mining to identify characteristics of customers who move to competitors, and then offer special deals to retain other customers with those same characteristics.


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


Streaming services carry out data mining to analyze what users are watching or listening to and to make personalized recommendations based on their habits.


Data mining helps doctors diagnose medical conditions, treat patients, and analyze X-rays and other medical imaging results. Medical research also depends heavily on data mining, machine learning and other forms of analytics.

The future of data mining

Cloud computing technologies have had a significant impact on the growth of data mining. Notwithstanding cloud security issues and challenges, cloud technologies are suited to the high speed, vast quantities of semi-structured and unstructured data that many organizations now collect. The cloud’s elastic resources scale to meet these big data demands. Consequently, because the cloud can hold more data in various formats, it requires more tools for data mining to turn that data into insight. In addition, advanced forms of data mining like AI and machine learning are offered as services in the cloud.

Future developments in cloud computing will probably continue to fuel the need for more effective data mining tools. AI and machine learning are growing, and so too is the amount of data. The cloud is increasingly used to store and process data for business value. It seems likely that data mining approaches will become increasingly reliant on the cloud.

FAQs about data mining

Frequently asked questions about database mining, how data mining works, and data mining importance include:

Where is data mining used?

Data mining is used to explore large data volumes to find patterns and insights that can be used for specific purposes. These purposes might include improving sales and marketing, optimizing manufacturing, detecting fraud, and enhancing security. Data mining is used across a wide range of industry sectors, such as banking, insurance, healthcare, retail, gaming, customer service, science and engineering and many more.

How does data mining work?

Data analysts generally follow a certain flow of tasks along the data mining process. A typical dating mining process might begin by defining the goal of the data analysis, then work on understanding where the data is stored, how it will be gathered and what analysis is required. The next steps are to prepare the data for analysis, build the model, evaluate the findings of the model and then implement change and monitor outcomes.

Why is data mining used?

Data mining is used to identify organizational challenges and opportunities. It might be used to optimize product pricing, improve productivity, drive efficiencies, enhance customer service and retention, and aid product development. Data mining gives businesses a competitive advantage by helping to find insights in the data from digital transactions.

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What is data mining and why is it important?

Data mining is the process of analyzing data to find patterns, trends and insights. Learn about data mining techniques and uses.
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