What Is Data Mining? Meaning, Techniques, Examples & Tools

Illustration of a pick axe to demonstrate data mining.
Illustration of a pick axe to demonstrate data mining.

Big data makes the world go ‘round, right? It’s at the heart of business decisions, marketing strategies, and growth initiatives. But big data by itself isn’t enough to tell you what you need to know. That’s where the data mining process comes in.

In traditional mines, precious gemstones are embedded in rock and ore. With enough digging and chipping away at the rock, miners can extract the more valuable stones and use them for various purposes. Data mining applies the same concept – get rid of the “extra” and focus on what’s most valuable.

Here’s a closer look at data mining, including data mining meaning, techniques, examples, and tools to support your data analytics processes.

Table of Contents:

Definition: What Is Data Mining?

Let’s start with the meaning of data mining – what is it, exactly?

We define data mining as the process of uncovering valuable information from large sets of data.

This might take the form of patterns, anomalies, hidden connections, or similar information. Sometimes referred to as knowledge discovery in data, data mining helps companies transform raw data into useful knowledge.

Why Data Mining Matters

Data mining.

One glance at your collection of data should tell you exactly why data mining matters.

Think of a huge pond or lake filled with fish, and you’re looking for one specific fish. You don’t want to catch every fish until you find yours. But if you had predictive data, you could narrow your options so you don't have to scour the entire pond.

We’re doubling the amount of data in the world every couple of years. As data keeps growing, it becomes harder to find specific information or make sense of all the data we collect.

Data mining puts this challenge to rest by providing an easy, effective way to sift through the massive volumes of information. Companies can identify what’s relevant and put their data to work. This helps to streamline data-driven decision-making and improve their outcomes.

How Does Data Mining Work?

Data mining is a process, not a one-and-done activity. It includes steps for data collection, data preparation, data visualization, and data extraction.

These steps are usually performed by a data scientist, who works through data sets to identify and describe patterns and correlations. They also help to classify data and identify outliers for specific use cases, such as fraud detection.

Data mining work flow.

Here’s a simplified look at the steps involved in data mining:

Set Your Data Objectives

Setting objectives is often one of the biggest challenges of data mining because it usually requires the collaboration of multiple stakeholders, data scientists, and departments.

All parties should work together during this pre-processing stage to decide what data needs to be mined and set parameters for the project. This also requires parties to have enough context into the project goals to shape the direction of the process.

Prepare Your Data for Data Mining

Once you define the scope of the project, data scientists will determine which data sets will help to answer the most pressing questions.

They’ll work to set up the process to collect the relevant data, cleanse it, and remove duplicates, errors, and other noise.

Build Data Models

Data modeling turns your mined data into helpful visuals. These visual representations make it easier to understand data in context, even for stakeholders who don't have a data science background.

Data models also make it easier to see potential relationships or connections between data. They can often reveal anomalies or deviations in data that could indicate something interesting, such as potential fraud or spam.

The end-user will build rules based on historical data to explain the data and make predictions for the future. Part of this process may include the use of machine learning algorithms to classify data sets.

If the data is labeled or structured, the algorithm can categorize the data to make statements and predictions. If the data is not labeled or is unstructured, the algorithm can look for similarities between individual data points and classify them accordingly.

Evaluate the Results

After aggregating the data, data scientists will need to review the data and turn it into usable insights. The final results should be useful, accurate, and understandable.

At this point, organizations should be able to use the data to inform business decisions, improve their marketing, optimize spending, or take other appropriate actions.

What Are Data Mining Techniques?

Data mining software uses a variety of techniques and processes to turn loads of data into bite-sized insights. Here’s a closer look at some of the most common data mining techniques and methods:

Data Clustering

Various sized clusters.

Data clustering is a common machine learning technique that takes individual items and groups them by similarities. Objects in one cluster are more similar to each other than they are to items in another cluster.

Clustering helps data scientists to divide data into different subsets, where the data can be more carefully observed. One use case of clustering is to identify customers who have similar buying patterns.

It’s helpful in conducting market research, recognizing patterns, and understanding the context of images.

Association Rules

This rule-based data mining technique works to find relationships between data points. Commonly used for market basket analysis, association rules help customers understand relationships between various products. For example, it can answer the question, “What products are commonly purchased together?”

This technique helps business to improve their cross-selling strategies and product recommendations.

Neural Networks

Neural networks.

Neural networks aim to mimic the human brain by mapping several connections between data points.

A common technique in supervised machine learning, neural networks rely on nodes that are each made of inputs, weights, a threshold, and an output. If the output exceeds the threshold, the node is “fired” and data passes to the next layer of the network.

The values adjust based on the loss of function through a gradual descent. When the cost function reaches zero (or close to it), it reinforces the model’s accuracy.

Tip: Check out our in-depth blog about neural networks to learn more.

Decision Tree

Illustrated tree.

A decision tree classifies data and predicts outcomes based on a series of decisions.

Using lots of if-then statements and a tree-like visualization, decision trees break down what happens next when each decision is made.

Data Mining Examples and Use Cases

The data mining process presents an array of business use cases. It helps organizations extract business intelligence that would otherwise be impossible to discover, or at least take a very long time to uncover manually.

Some of the most popular real-world data mining examples include:

Data Mining in Sales & Marketing

Typing on a laptop.

We touched on the market basket use case already, but the uses for data analytics in sales and marketing go beyond this example. From loyalty programs to online sales to social media and email marketing, companies collect an impressive amount of data about their customers.

By diving deeper into demographics, behaviors, and interests, companies can improve their marketing campaigns and approaches. They can build stronger connections with target audiences by creating content they care about.

This real-world data mining example may help companies to become more customer-centric by learning more about customer behavior. They can enter new markets or launch new products with greater confidence. They can also find more effective up-sells and cross-sells.

All of these can contribute to a healthier bottom line and greater marketing ROI.

Data Mining for Fraud Detection & Prevention

Fraudsters continue to find new ways to exploit consumers and companies. In the past, companies have played the cat-and-mouse game by closing gaps that fraudsters have already discovered.

But machine learning and data mining are poised to help companies find potential gaps before they’re exploited – or at least put a stop to them before too much damage is done.

Machine learning can help to detect patterns that brands and companies aren’t already looking for.

Companies in banking and financial services, SaaS, e-commerce, insurance, and many other industries can benefit from improved fraud detection.

Spot fake user accounts, reduce the exploitation of coupons or special offers and make improvements to internal processes to prevent fraud from occurring in the future.

Data Mining in Higher Education

Students studying at a table.

Colleges and universities can leverage data mining to better understand their student populations.

They can uncover insights on which environments are most conducive to success, compare the dynamics of online vs in-person classes, and find areas where students may require more support.

Universities may also be able to predict student performance before they begin their coursework, allowing them to improve acceptance decisions.

Data Mining in Manufacturing

Data mining can also prove useful in optimizing internal processes and operations.

For example, manufacturers can use machine learning algorithms to predict machine wear and maintenance based on production and usage.

Reducing costs and removing bottlenecks allows companies to run more efficiently.

Data Mining in the Insurance Industry

Insurance companies have long used data mining to predict the potential impact of future disasters and should therefore always be listed as good data mining examples.

Companies can review past data from hurricanes, tornadoes, or similar disasters and detect probabilities and costs.

Predictive modeling may help insurance reduce their financial risk, adjust their pricing, and improve customer service.

Data Mining for IT/Network Security

Security breaches can pose significant threats to a company’s operations and reputation.

Data mining can help to mitigate the effects of a security breach by detecting data anomalies and addressing them as they occur.

The Best Data Mining Tools

In the past, companies relied on coding languages such as Python or R. Today, there are a number of software applications and tools that simplify many data mining tasks and help you gather insights from your data.

Here are our top picks of data mining tools to help you start gaining insight into your business’s performance:

MonkeyLearn

MonkeyLearn feedback analytics.

MonkeyLearn is a cost-effective platform powered by data mining algorithms. Its specialty is text-based mining, helping companies make sense of data such as online reviews, trending topics, and customer support notes.

This data mining tool is useful for analyzing keyword repetition and names and uncovering audience sentiment. For example, MonkeyLearn can pick up on negative customer feedback on social media or review sites, allowing leaders to address comments and make improvements.

MonkeyLearn also offers MonkeyLearn Studio, which allows you to turn your data into visuals for easier trend detection.

RapidMiner

RapidMiner visual workflow designer.

This free open-source platform has hundreds of ready-made data analysis algorithms in place. Use the pre-built predictive analytics models to create workflows across a variety of use cases, such as fraud detection or customer acquisition.

It's designed to be less time-consuming and more user-friendly than higher-end data mining tools.

Like MonkeyLearn, RapidMiner offers a studio feature that visualizes your data. This helps you spot anomalies, trends, and outliers at a glance to get more from your data mining.

It's a great entry-level data mining tool when you don't have many data resources.

Meltwater

Meltwater’s data mining advantage is that it offers real-time analytics without coding, programming, or internal data scientists.

Its comprehensive analytics and insights platform combines artificial intelligence with human expertise. Companies can collect the data that matters to them and get spelled-out useful information that matters without resorting to traditional data mining processes.

Meltwater collects and analyzes millions of conversations online in real-time, including social media, news publications, blogs, podcasts, and other sources. We not only aggregate the data and turn it into relevant insights, but also offer the context around the data. Learn more about your customers’ sentiments behind the words they use and take action with confidence.

Learn more about Meltwater’s approach to data mining when you schedule a demo.

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