Here are five key takeaways about data mining:

    1. A good data mining process involves five stages: understanding your goals, understanding your data sources, preparing the data, conducting data analysis, and reviewing results.
    2. The technique that's right for you depends on your specific BI goals.
    3. A strong data integration platform is essential for effective data mining.
    4. Essential data mining techniques include classification analysis, clustering, neural networks, and regression analysis.
    5. Use cases for data mining include understanding customer satisfaction, credit risk assessment, and medical diagnosis and patient risk assessment.

    Data mining techniques draw from a wide range of subjects, from database management to machine learning and everything in between. In this article, updated for 2022, we discuss the most important data mining techniques and how to employ them to maximize your data investments.

    Table of Contents

    Data Mining Techniques in Action

    In 2022, businesses have access to more raw data than ever before. (Experts predict that the world will produce and then consume 94 zettabytes of data this year alone.) All of this data can unearth patterns that are useful for business intelligence, and the process of discovering these patterns in data is called data mining. Data mining techniques, when applied correctly, can drive business success. Before we get to the techniques, though, let's first understand the process of data mining.

    A good data mining process involves five stages:

    1. Understanding the Goals of Your Data Mining Project 

    The first stage of data mining defines how the process will support your business goals. For example, what areas of business do you want to improve through data mining? Do you want to make your product recommendation systems better the way Netflix does? Do you want to understand your customers better through personas and segmentation?

    After codifying your data mining goals, you can develop a project timeline, key actions, and assign roles for completing the project. 

    2. Understanding Where Your Data Comes From

    Next, you need to assess your data sources. Data visualization tools like Google Data Studio or Chartio let you explore the properties of your data to decide which information will be useful to achieve your goals. Understanding your data also helps you determine which data mining strategies will produce the insights you want. You can also improve data quality, data mining, and different types of data.

    3. Preparing the DataBefore data can be analyzed, it needs to be integrated into a single system. Data preparation happens through multiple processes, including:

    • ETL (extract, transform, load) extracts data from its source, transforms it to the correct format for data analytics, and loads it to a data warehouse or lake. From here, you can analyze data.

    • ELT (extract, load, transform) extracts data, loads to a data warehouse or lake, and then transforms it to the correct format for analytics.
    • Reverse ETL uses a data warehouse as the data source, not the destination. It extracts data from a warehouse, transforms it, and loads it to an operational system such as a SaaS tool.
    • Change Data Capture (CDC) identifies and tracks changes made to data within a database and makes sure data in multiple systems always stays in sync.

     All of the above techniques can also ensure data integration complies with data governance and data collection frameworks like GDPR, CCPA, and HIPAA. Organizations like yours can receive hefty fines for not complying with these frameworks.

    You can use an automated solution like to gather e-commerce data and other information from different business applications, relational databases, SaaS platforms, and external sources via ETL, ELT, or CDC. You can then transform the information and optimize it for high-speed analysis. Ultimately, cleanses the data, addresses missing information, and makes sure your data mining applications can analyze the information as a whole. Email to learn about a 7-day pilot or demo.



    4. Analyzing, Mining, and Modeling the Data

    The prepared data is then fed into business intelligence (BI) tools—like Tableau Server, Looker, InsightSquared, Amazon QuickSight, or Microsoft Power BI. These tools use different machine learning algorithms for data mining to unearth patterns and forecast future trends. For example, you can identify trends in e-commerce data to learn more about your most popular products or high-value customers.

    Related Reading: Top 7 ETL Tools for 2022

    5. Reviewing and Sharing the Findings Across the Organization

    The last stage of the data mining process is to review the results and answer key questions, such as:

    • Whether the findings are accurate
    • If they support your goals
    • How to act on them
    • How to share the findings with your team
    • How you can improve data management in the future
    • Whether your chosen methods supported large volumes of data
    • How you will improve your use of data going forward

    Most enterprise-level BI platforms allow you to efficiently distribute key findings from data mining across an organization.

    Essential Data Mining Techniques

    Techniques for data mining can encompass the entire gamut of data science, right from classification methods to complex machine learning algorithms. Here are some of the most widely used data mining techniques for business intelligence.

    Classification Analysis

    One of the most fundamental data mining techniques, it classifies data into different categories. The goal of classification analysis is to be able to predict behavior or answer a key business question. For example, take the case of a credit card company. The company is trying to determine which users in its database should get a credit card offer. By analyzing information such as purchase history and annual income, it can categorize users into “'low risk,” “medium risk,” and “high risk.”


    Association Rule Learning

    This is a popular algorithm for market researchers. Association learning looks for interesting relationships between variables in a massive set of data to reveal events that frequently occur together.

    For example, the system might discover that women aged 30 to 40 like to buy products with a specific shade of red. This would tell product designers to include that color in a new product line.

    Related Reading: 17 BI Tools Pricing and Capabilities 

    Regression Analysis

    Primarily used for forecasting, regression analysis is used for identifying the relationship between variables in a dataset. More specifically, it is used to predict continuous values based on other variables present in a dataset. For instance, you might use regression analysis to predict the future price of a product based on demand, availability, and other factors. 

    Although there are different kinds of regression techniques, two of the most common are linear regression and logistic regression

    Linear Regression: This algorithm predicts the value of an unknown variable by analyzing other variables. For example, you could train a linear regression model with data pertaining to recently sold businesses (using data that includes business type, location, size, sale price, sale date, etc.). The linear regression model could then forecast the market value of another business based on location, sector, or future sale date. This technique can improve decision-making and solve business problems. thumbnail image

    Logistic Regression: This algorithm is valuable for predicting whether a variable supports or does not support a specific result. For instance, logistic regression could analyze a dataset to answer the following yes-or-no questions:

    • Does the number of cigarettes you smoke per day influence your chances of getting lung cancer (yes or no)?
    • Does heart attack risk increase with age (yes or no)?

    For logistic regression to work, the variable needs to be “dichotomous.” In other words, you must be studying how the presence or non-presence of a variable affects a “yes-or-no” answer.


    This data mining technique organizes similar and dissimilar items together. Clustering identifies relationships between objects in an unstructured dataset to provide a meaningful, searchable, and analyzable structure. For example, if you use clustering to identify "look-alike" audiences in your dataset, you might learn that 25% of your customers are aged 45 to 50, female, and enjoy red wine.

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    Outlier Detection

    Anomalies in data can provide actionable business intelligence. An anomaly, or an outlier, is a value or a set of values that deviate considerably from expected patterns. Outlier detection as a data mining technique is particularly useful for fraud detection, intrusion monitoring, and performance monitoring of systems.

    Time Series Forecasting

    These machine learning models are used to predict the best timing for specific actions. They do so by using historical data and identifying patterns in historical data through artificial intelligence. For example, a vehicle manufacturer could analyze past data with a time series model to predict when it's necessary to restock inventories. Similarly, a retailer could use time forecasting to schedule the release of a new product. Decision Trees

    These are predictive modeling techniques that forecast outcomes based on a set of binary rules. By following the rules, a decision tree algorithm produces the same result with the same input. Decision trees are used for building classification models and regression analysis. There are various decision tree algorithms, with the most notable ones as follows:

    • Classification and Regression Tree
    • C4.5
    • Iterative Dichotomiser 3 (ID3)

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    Neural Networks

    Modeled after the human brain, neural networks learn through repetition over time. Neural network models are useful when machine learning systems require fast, rapid-fire responses. For instance, in driverless vehicle technology. 


    A crucial part of data mining, visualization is a powerful tool for unearthing data mining insights. Most modern data visualization tools use dashboards to quickly organize large datasets. Some common data visualization methods are tree-maps, charts, heat maps, and histograms.

    Sequential Pattern Mining

    Similar to the time series data mining technique, sequential pattern mining identifies events that happen in sequence. Mostly applied to transactional datasets, it can be useful for understanding customer behavior. Sequential pattern data mining can inform product recommendations and up-sell opportunities. is a new ETL and data integration platform with deep e-commerce capabilities that moves data to a centralized location for real-time analytics. You can move big data from one location to another with the platform’s pre-built no-code integrations, even if you have no data engineering experience. Email to learn about a 7-day pilot or demo. 

    Related Reading: 3 Data Mining and Business Intelligence Case Studies

    Use Cases of Data Mining Techniques

    Modern organizations use data mining to inform their business decisions in the following areas:

    Understanding Customer Satisfaction and Public Sentiment

    Companies analyze data from social media platforms through “text mining” to reveal how the public views their products and offerings. Text mining uses natural language processing (NLP) and statistical pattern recognition to understand overall feelings and sentiments based on what people are saying online. Once you understand the public sentiment, you can steer your marketing campaigns, PR, and product development to improve knowledge discovery and boost your reputation.

    Targeted Ads, Marketing, and Improved Recommendations

    Data mining helps advertisers identify look-alike customers so that they can target them with tailored ads and promotions. Companies like Amazon and Netflix use these techniques to offer purchase recommendations based on customer browsing, viewing, and spending habits. 

    Medical Diagnosis and Patient Risk Assessment

    Data mining also helps healthcare organizations and medical researchers improve patient diagnosis and treatment. The statistical models from data mining medical records have allowed doctors to create risk factor warnings and lifestyle recommendations for better preventative care.

    Insurance Industry Optimization

    Predictive analytics through data mining can help insurance companies understand their customers and the risks related to accidents, bodily injury, medical conditions, surgical outcomes, and property damage. By comparing one customer’s claim history to thousands, machine learning can find potential cases of fraud.

    Credit Risk Assessment

    Banks now mine data related to customer credit histories, credit scores, and demographics information—and then apply machine learning algorithms to the information to automatically approve or deny loans and calculate more strategic interest rates.

    Financial Fraud and White-Collar Crime Prevention

    Financial institutions use data mining to red-flag potentially fraudulent transactions, which they pause while requesting customer verification by text or email. These machine learning models monitor customer spending habits to identify transactions that fall outside the norm. Fueling Your Data Mining

    Data mining can be incredibly powerful. It is now an integral part of operations for businesses around the world to help them improve user experiences and build better products. However, it can only be as powerful as the information it works on. Your data mining tools need to be supplied with clean, organized data that is ready for analysis. That's where can help. Our new automated, cloud-based data integration platform makes data integration a breeze via techniques such as ETL, ELT, Reverse ETL, and super-fast CDC. Our platform also has deep e-commerce capabilities. Schedule a demo and see for yourself how is helping businesses get rid of data integration bottlenecks.