What is data mining?

Data mining is a computer-assisted technique used in analytics to process and explore large data sets. With data mining tools and methods, organizations can discover hidden patterns and relationships in their data. Data mining transforms raw data into practical knowledge. Companies use this knowledge to solve problems, analyze the future impact of business decisions, and increase their profit margins.

What does the term data mining mean?

“Data mining” is a misnomer because the goal of data mining is not to extract or mine the data itself. Instead, a large amount of data is already present, and data mining extracts meaning or valuable knowledge from it. The typical process of data collection, storage, analysis, and mining is outlined below.

  • Data collection is capturing data from different sources like customer feedback, payments, and purchase orders.
  • Data warehousing is the process of storing that data in a large database or data warehouse.
  • Data analytics is further processing, storing, and analyzing the data using complex software and algorithms.
  • Data mining is a branch of data analytics or an analytics strategy used to find hidden or previously unknown patterns in data.

Why is data mining important?

Data mining is a crucial part of any successful analytics initiative. Businesses can use the knowledge discovery process to increase customer trust, find new sources of revenue, and keep customers coming back. Effective data mining aids in various aspects of business planning and operations management. Below are some examples of how different industries use data mining.

Telecom, media, and technology

High-competition verticals like telecom, media, and technology use data mining to improve customer service by finding patterns in customer behavior. For example, a company could analyze bandwidth usage patterns and provide customized service upgrades or recommendations.

Banking and insurance

Financial services can use data mining applications to solve complex fraud, compliance, risk management, and customer attrition problems. For example, insurance companies can discover optimal product pricing by comparing past product performance with competitor pricing.


Education providers can use data mining algorithms to test students, customize lessons, and gamify learning. Unified, data-driven views of student progress can help educators see what students need and support them better.


Manufacturing services can use data mining techniques to provide real-time and predictive analytics for overall equipment effectiveness, service levels, product quality, and supply chain efficiency. For example, manufacturers can use historical data to predict the wear of production machinery and anticipate maintenance. As a result, they can optimize production schedules and reduce downtime.


Retail companies have large customer databases with raw data about customer purchase behavior. Data mining can process this data to derive relevant insights for marketing campaigns and sales forecasts. Through more accurate data models, retail companies can optimize sales and logistics for increased customer satisfaction. For example, data mining can reveal popular seasonal products that can be stocked in advance to avoid last-minute shortages.

How does data mining work?

The Cross-Industry Standard Process for Data Mining (CRISP-DM) is an excellent guideline for starting the data mining process. CRISP-DM is both a methodology and a process model that is industry, tool, and application neutral.

  • As a methodology, it describes the typical phases in a data mining project, outlines the tasks involved in each stage, and explains the relationships between these tasks.
  • As a process model, CRISP-DM provides an overview of the data mining life cycle.

What are the six phases of the data mining process?

Using the flexible CRISP-DM phases, data teams can move back and forth between stages as needed. Also, software technologies can do some of these tasks or support them.

1. Business understanding

The data scientist or data miner starts by identifying project objectives and scope. They collaborate with business stakeholders to identify certain information.

  • Problems that need to be addressed
  • Project constraints or limitations
  • The business impact of potential solutions

They then use this information to define data mining goals and identify the resources required for knowledge discovery.

2. Data understanding

Once they understand the business problem, data scientists begin preliminary analysis of the data. They gather data sets from various sources, obtain access rights, and prepare a data description report. The report includes the data types, quantity, and hardware and software requirements for data processing. Once the business has approved their plan, they begin exploring and verifying the data. They manipulate the data using basic statistical techniques, assess the data quality, and choose a final data set for the next stage.

3. Data preparation

Data miners spend the most time on this phase because data mining software requires high-quality data. Business processes collect and store data for reasons other than mining, and data miners must refine it before using it for modeling. Data preparation involves the following processes.

Clean the data 

For example, handle missing data, data errors, default values, and data corrections.

Integrate the data

For example, combine two disparate data sets to get the final target data set.

Format the data

For example, convert data types or configure data for the specific mining technology being used.

4. Data modeling

Data miners input the prepared data into the data mining software and study the results. To do this, they can choose from multiple data mining techniques and tools. They must also write tests to assess the quality of data mining results. To model the data, data scientists can:

  • Train the machine learning (ML) models on smaller data sets with known outcomes
  • Use the model to analyze unknown data sets further
  • Adjust and reconfigure the data mining software until the results are satisfactory

5. Evaluation

After creating the models, data miners start measuring them against the original business goals. They share the results with business analysts and collect feedback. The model might answer the original question well or show new and previously unknown patterns. Data miners can change the model, adjust the business goal, or revisit the data, depending on the business feedback. Continual evaluation, feedback, and modification are part of the knowledge discovery process.

6. Deployment

During deployment, other stakeholders use the working model to generate business intelligence. The data scientist plans the deployment process, which includes teaching others about the model functions, continually monitoring, and maintaining the data mining application. Business analysts use the application to create reports for management, share results with customers, and improve business processes.

What are the techniques for data mining?

Data mining techniques draw from various fields of learning that overlap, including statistical analysis, machine learning (ML), and mathematics. Some examples are given below.

Association rule mining

Association rule mining is the process of finding relationships between two different, seemingly unrelated data sets. If-then statements demonstrate the probability of a relationship between two data points. Data scientists measure result accuracy using support and confidence criteria. Support measures how frequently the related elements appear in the data set, while confidence shows the number of times an if-then statement is accurate.

For example, when customers buy an item, they also often buy a second related item. Retailers can use association mining on past purchase data to identify a new customer's interest. They use data mining results to populate the recommended sections of online stores.


Classification is a complex data mining technique that trains the ML algorithm to sort data into distinct categories. It uses statistical methods like decision trees and nearest-neighbor to identify the category. In all these methods, the algorithm is preprogrammed with known data classifications to guess the type of a new data element.

For example, analysts can train the data mining software by using labeled images of apples and mangoes. With some accuracy, the software can then predict if a new picture is an apple, mango, or other fruit.


Clustering is grouping multiple data points together based on their similarities. It is different from classification because it cannot distinguish the data by specific category but can find patterns in their similarities. The data mining result is a set of clusters where each collection is distinct from other groups, but the objects in each cluster are similar in some way.

For example, cluster analysis can help with market research when working with multivariate data from surveys. Market researchers use cluster analysis to divide consumers into market segments and better understand the relationships between different groups.

Sequence and path analysis

Data mining software can also look for patterns in which a particular set of events or values leads to later ones. It can recognize some variation in data that happens at regular intervals or in the ebb and flow of data points over time.

For example, a business might use path analysis to discover that certain product sales spike just before the holidays or to notice that warmer weather brings more people to its website.

What are the types of data mining?

Depending on the data and the purpose of mining, data mining can have various branches or specializations. Let's look at some of them below.

Process Mining

Process mining is a branch of data mining that aims to discover, monitor, and improve business processes. It extracts knowledge from event logs that are available in information systems. It helps organizations see and understand what's happening in these processes from day to day.

For example, e-commerce businesses have many processes, like procurement, sales, payments, collection, and shipping. By mining their procurement data logs, they might see that their supplier delivery reliability is 54% or that 12% of suppliers are consistently delivering early. They can use this information to optimize their supplier relationships.

Text mining

Text mining or text data mining is using data mining software to read and comprehend text. Data scientists use text mining to automate knowledge discovery in written resources like websites, books, emails, reviews, and articles.

For example, a digital media company could use text mining to automatically read comments on its online videos and classify audience reviews as positive or negative.

Predictive Mining

Predictive data mining uses business intelligence to predict trends. It helps business leaders study the impact of their decisions on the company’s future and make effective choices.

For example, a company might look at past product returns data to design a warranty scheme that does not lead to losses. Using predictive mining, they will predict the potential number of returns in the coming year and create a one-year warranty plan that considers the loss when determining the product price.

How can AWS help with data mining?

Amazon SageMaker is a leading data mining software platform. It helps data miners and developers prepare, build, train, and deploy high-quality machine learning (ML) models. It includes several tools for the data mining process.

  • Amazon SageMaker Data Wrangler reduces the time to aggregate and prepare data for mining from weeks to minutes.
  • Amazon SageMaker Studio provides a single, web-based visual interface where data scientists can perform ML development steps, which improves the data science team’s productivity. SageMaker Studio gives complete access, control, and insight into each step as data scientists build, train, and deploy models.
  • Distributed training libraries use partitioning algorithms to automatically split large models and training data sets for modeling.
  • Amazon SageMaker Model Training optimizes ML models by capturing real-time training metrics, such as sending alerts when anomalies are detected. This helps to fix inaccurate model predictions immediately.

Get started with data mining by creating a free AWS account today.

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