What Is Data Mining?

Date First Published: 2nd January 2024

Topic: Computer Systems

Subtopic: Computer Software

Article Type: Computer Terms & Definitions

Difficulty: Medium

Difficulty Level: 5/10

CONTENTS

Data Mining Techniques
Data Mining Process
Advantages and Disadvantages Of Data Mining

Learn about what data mining is in this article.

Data mining is the process of searching and analysing large amounts of data to discover patterns, trends, and other valuable information. Data mining software is used by businesses to gather information about their customers and predict future trends. They can use it to make more informed business decisions, reduce costs, increase sales, and develop more successful campaigns. For example, Google analyses search queries to discover common searches for certain areas and moves those to the top of the autocomplete list.

Data Mining Techniques

Various data mining techniques can be used to convert large amounts of data into useful output. Examples of data mining techniques include:

Association rules - Association rules are if-then statements that identify relationships between data elements.
Classification - Elements are assigned to different categories defined as part of the data mining process.
Clustering - Data elements that share particular characteristics are grouped together into clusters as part of data mining applications.
Decision trees - Used to classify or predict an outcome based on a set list of criteria or decisions.
Neutral networks - Process data using nodes. These nodes consist of inputs, weights, and an output.
Regression - Used to find relationships in data by calculating predicted data values based on a set of variables.

Data Mining Process

The data mining process consists of:

Gathering the data - Relevant data is identified and assembled. This includes both internal and external sources of data. At first, the goals, objectives, and problems it wants to solve need to be determined.
Preparing the data - Steps are taken to get the data ready to be mined. It begins with data exploration, profiling and pre-processing, followed by data cleansing work to fix errors and other data quality issues. The more organised data is, the easier it is to mine for useful information.
Mining the data - Once the data is prepared, the appropriate data mining technique is chosen and one or more algorithms are implemented to carry out the mining.
Analysing and interpreting the data - The data mining results are used to create analytical models that can help make more informed decisions.

Advantages and Disadvantages Of Data Mining

The advantages of data mining are:

It helps the decision-making processes of businesses. Data mining helps marketers understand consumer behaviour and preferences, which enables them to create targeted marketing and advertising campaigns. The results can be used to boost conversion rates and sell additional products.
Reduced costs. Data mining helps reduce costs through operational efficiencies in business processes and more efficient production. Compared to other statistical data applications, data mining is more cost-efficient.
It is often a quick process that makes it easy to get useful information from data within a short period of time. Data mining can use machine learning and AI to automate the processes and analyse data that was previously too difficult to understand due to the large amount of data or type of information.

The disadvantages of data mining are:

Data can be complex. Data analytics often requires technical skill sets and certain software tools to mine the data. This can be a complex process and require training in order for people to use the tools.
There are concerns over mined data being sold to third parties for another use or data being leaked. Some people might not feel comfortable knowing that organisations can track certain information about them across different platforms and services.
Results are not guaranteed. Data may not always be reliable and data mining can only guide decisions and not ensure outcomes.