How Data Mining Works – Examples and Techniques

Data mining is a process that entails the understanding of data sets through the combination of different fields like statistics, artificial intelligence, and machine learning. Patterns, relationships, useful findings, and even anomalies can be gleaned from these data sets using these fields. Moreover, new models that offer insights, solutions, and advantages can also emanate from well-mined data.

Businesses often utilize software to extract patterns in large data sets. These patterns aid businesses in understanding their customers better and use insights to create better marketing strategies, increase profits, and reduce expenditure. It may also be used for many purposes, including database marketing, asset quality management, fraud detection, junk mail screening, and even determining user perception.

Data mining is extremely dependent on good data extraction, warehousing and computer processing. In addition, it often includes multiple data projects. It is usually confused by non-experts with data analytics, data governance, and other data processes.

This data mining guide covers how the data mining process works and the different techniques used today. 

How Data Mining Works

The data mining process involves some crucial steps. There are five major steps:

  1. Data Collection: Organizations first collect data and load it into their data warehouses. 
  2. Data storage and management: This occurs in data warehouses. It is done either on in-house servers or external cloud services. 
  3. Data Organization: Business analysts, management teams and data scientists work together to access the data and devise ways to organize it.
  4. Data Sorting: In this step, application software sorts the data based on the user’s results and characteristics.
  5. Data Presentation: The data is presented in an easy-to-share format such as graphs or tables.

Data mining describes patterns, predicts trends, and identifies outliers using three main models:

  1. Descriptive Model: This finds patterns and relationships in current data
  2. Predictive Model: This model is used to predict future trends 
  3. Outlier Analysis: Some outliers do not fit into a regular pattern in every data set. The outlier analysis model helps to identify such anomalies.

Today, many organizations that utilize data mining start collecting data from records, logs, application data, sales data, and site visitor data. The current industry standard used in the data mining process is the Cross-Industry Standard Process for Data Mining (CRISP-DM). This standard entails six major phases:

1. Business Understanding

Business stakeholders try to identify a problem or question that is solvable through data mining. The objectives and scope of the data mining project are clearly defined in this phase.

2. Data Understanding

Once the problem and objectives of the data mining project are clearly defined and understood, the collection of relevant data from the right sources begins. Data is obtained from different sources, including structured and unstructured data. In addition, exploratory analysis is often done during this phase to identify preliminary patterns. Finally, the subset of data relevant for analysis and modeling is selected at the end of this phase.

3. Data Preparation

This phase starts with a lot of hard effort. First, data preparation entails putting together the final data set, which contains all of the pertinent information needed to provide an answer to the business question. Next, stakeholders will determine which dimensions and variables to investigate and organize the final data set to create a model.

4. Modeling

In this phase, stakeholders and data analysts select the modeling techniques used to analyze the data sets. Popular data modeling techniques include clustering, classification, and estimation. More than one modeling technique can be combined to obtain the best results. Instances arise in this phase where a return to the preparation phase might be necessary if the modeling technique requires other variables or sources.

5. Evaluation

You must test and assess the models’ success in addressing the question established in the first phase once they have been created. You may need to update the model or the query if it answers aspects of things that aren’t accounted for by the model. In this phase, a progress assessment is done to ensure that you are on track to fulfill your business objectives. If it isn’t, it may be necessary to go back to a prior stage before a project is ready for deployment.

6. Deployment

Deployment is the last phase. The deployment of a model could be within an organization or shared with loyal customers. To prove the model’s reliability, reports can also be generated for company stakeholders. The job isn’t done when the final line of code is written; deployment involves meticulous planning, a roll-out strategy, and a mechanism to ensure that the relevant people are informed. The audience’s knowledge of the project is the responsibility of the data mining team.

Data Mining Techniques

Different data mining techniques can solve problems or make business recommendations. The two most popular types of data mining techniques are:


Classification is a data mining technique that groups variables into appropriate data categories. For example, the division of a variable based on ‘occupation level’ could translate into senior, associate, and entry-level categories. Other variable classifiers that you might use include sex, age, and education level. With these data fields, you can program your data model to successfully predict the occupation level of each person in the data set.

You might enter an entry for a recent graduate, and the data model would identify that individual as an ‘entry-level’ employee. Institutions in finance and insurance use classification via their algorithms to detect fraud and monitor claims.


Another prominent approach is clustering, which involves grouping records, observations, or instances based on their similarities. In contrast to categorization, there will be no target variable. Clustering essentially involves data collection division into subgroups. For example, using this strategy, users’ records can be grouped by geographic location or age group. Clustering data into groupings is a common method of preparing data for the study. The subgroups are used as inputs for another approach.

The data mining techniques highlighted above are just two of the many used data mining techniques today. Organizations can obtain advantages over their competitors by leveraging data mining and collection and searching for relevant patterns. The adoption of data mining by organizations will only grow as data collection grows with the increasing adoption of technology and improved data collection and extraction methods. 

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