What Is Data Mining: Definition, Benefits, Applications, Top Techniques, and More

We are living in an information-rich, data-driven world. While it’s comforting to know there’s a plethora of readily available knowledge, the sheer volume creates challenges. The more information available, the longer it can find the useful insights you need.

That’s why today we’re discussing data mining. We’ll be exploring all aspects of data mining, including what it means, its stages, data mining techniques, the benefits it offers, data mining tools, and more. Let’s kick things off with a data mining definition, then tackle data mining concepts and techniques.

We will now begin by understanding what is data mining.

What is Data Mining?

Typically, when someone talks about “mining,” it involves people wearing helmets with lamps attached to them, digging underground for natural resources. And while it could be funny picturing guys in tunnels mining for batches of zeroes and ones, that doesn't exactly answer “what is data mining.”

Data mining is the process of analyzing enormous amounts of information and datasets, extracting (or “mining”) useful intelligence to help organizations solve problems, predict trends, mitigate risks, and find new opportunities. Data mining is like actual mining because, in both cases, the miners are sifting through mountains of material to find valuable resources and elements.

Data mining also includes establishing relationships and finding patterns, anomalies, and correlations to tackle issues, creating actionable information in the process. Data mining is a wide-ranging and varied process that includes many different components, some of which are even confused for data mining itself. For instance, statistics is a portion of the overall data mining process, as explained in this data mining vs. statistics article.

Additionally, both data mining and machine learning fall under the general heading of data science, and though they have some similarities, each process works with data in a different way. If you want to know more about their relationship, read up on data mining vs. machine learning.

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Data mining is sometimes called Knowledge Discovery in Data, or KDD.

Now that we have learned what is data mining, we will now look at the data mining steps.

Data Mining Steps

When asking “what is data mining,” let’s break it down into the steps data scientists and analysts take when tackling a data mining project.

  1. Understand Business 

    What is the company’s current situation, the project’s objectives, and what defines success?
  2. Understand the Data

    Figure out what kind of data is needed to solve the issue, and then collect it from the proper sources.
  3. Prepare the Data

    Resolve data quality problems like duplicate, missing, or corrupted data, then prepare the data in a format suitable to resolve the business problem.
  4. Model the Data

    Employ algorithms to ascertain data patterns. Data scientists create, test, and evaluate the model.
  5. Evaluate the Data

    Decide whether and how effective the results delivered by a particular model will help meet the business goal or remedy the problem. Sometimes there’s an iterative phase for finding the best algorithm, especially if the data scientists don’t get it quite right the first time. There may be some data mining algorithms shopping around.
  6. Deploy the Solution

    Give the results of the project to the people in charge of making decisions.

To extend our learning on what data mining is, we will next look at the benefits.

What Are the Benefits of Data Mining?

Since we live and work in a data-centric world, it’s essential to get as many advantages as possible. Data mining provides us with the means of resolving problems and issues in this challenging information age. Data mining benefits include:

  • It helps companies gather reliable information
  • It’s an efficient, cost-effective solution compared to other data applications
  • It helps businesses make profitable production and operational adjustments
  • Data mining uses both new and legacy systems
  • It helps businesses make informed decisions
  • It helps detect credit risks and fraud
  • It helps data scientists easily analyze enormous amounts of data quickly
  • Data scientists can use the information to detect fraud, build risk models, and improve product safety
  • It helps data scientists quickly initiate automated predictions of behaviors and trends and discover hidden patterns

After having learned what is data mining, let us look into the drawbacks.

Are There Any Drawbacks to Data Mining?

Nothing’s perfect, including data mining. These are the major issues in data mining:

  • Many data analytics software tools are complex and challenging to use. Data scientists need the right training to use the tools effectively.
  • Speaking of the tools, different ones work with varying types of data mining, depending on the algorithms they employ. Thus, data analysts must be sure to choose the correct tools.
  • Data mining techniques are not infallible, so there’s always the risk that the information isn’t entirely accurate. This obstacle is especially relevant if there’s a lack of diversity in the dataset.
  • Companies can potentially sell the customer data they have gleaned to other businesses and organizations, raising privacy concerns.
  • Data mining requires large databases, making the process hard to manage.

After going through what is data mining, let us look into the various kinds.

What Kinds of Data Mining Tools Are Out There?

As engineers are fond of saying, “Use the right tool for the right job.” Here is a selection of tools and techniques that provide data analysts with diverse data mining functionalities.

  • Artificial Intelligence

    AI systems perform analytical functions that mimic human intelligence, such as learning, planning, problem-solving, and reasoning.
  • Association Rule Learning

    This toolset, also called market basket analysis, searches for relationships among dataset variables. For example, association rule learning can determine which products are frequently purchased together (e.g., a smartphone and a protective case).
  • Clustering

    This process partitions datasets into a set of meaningful sub-classes, known as clusters. The process helps users understand the natural structure or grouping within the data.
  • Classification

    This technique assigns particular items in a dataset to different target categories or classes. The goal is to develop accurate predictions within the target class for each case in the data.
  • Data Analytics

    The data analytics process enables professionals to evaluate digital information and turn it into useful business intelligence.
  • Data Cleansing and Preparation

    This technique transforms the data into a form optimal for further analysis and processing. Preparation includes activities such as identifying and removing errors and missing or duplicate data.
  • Data Warehousing

    Data warehousing consists of an extensive collection of business data that businesses use to help them make decisions. Warehousing is a fundamental and necessary component of most large-scale data mining efforts.
  • Machine Learning

    Related to the AI technique mentioned earlier, machine learning is a computer programming technique that employs statistical probabilities to provide computers with the ability to learn without human intervention or being manually programmed.
  • Regression

    The regression technique predicts a range of numeric values in categories such as sales, stock prices, or even temperature. The ranges are based on the information found in a particular data set.

Two specific tools need mentioning.

  • R. This language is an open-source tool used for graphics and statistical computing. It provides analysts with a wide selection of statistical tests, classification and graphical techniques, and time-series analysis.
  • Oracle Data Mining (ODM). This tool is a module of the Oracle Advanced Analytics Database. It helps data analysts make predictions and generate detailed insights. Analysts use ODM to predict customer behavior, develop customer profiles, and identify cross-selling opportunities.

In our learning about what is data mining, let us now look into the applications.

Data Mining Applications

Data mining is a useful and versatile tool for today’s competitive businesses. Here are some data mining examples, showing a broad range of applications.

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Banks

Data mining helps banks work with credit ratings and anti-fraud systems, analyzing customer financial data, purchasing transactions, and card transactions. Data mining also helps banks better understand their customers’ online habits and preferences, which helps when designing a new marketing campaign.

Healthcare

Data mining helps doctors create more accurate diagnoses by bringing together every patient’s medical history, physical examination results, medications, and treatment patterns. Mining also helps fight fraud and waste and bring about a more cost-effective health resource management strategy.

Marketing

If there was ever an application that benefitted from data mining, it’s marketing! After all, marketing’s heart and soul is all about targeting customers effectively for maximum results. Of course, the best way to target your audience is to know as much about them as possible. Data mining helps bring together data on age, gender, tastes, income level, location, and spending habits to create more effective personalized loyalty campaigns. Data marketing can even predict which customers will more likely unsubscribe to a mailing list or other related service. Armed with that information, companies can take steps to retain those customers before they get the chance to leave!

Retail

The world of retail and marketing go hand-in-hand, but the former still warrants its separate listing. Retail stores and supermarkets can use purchasing patterns to narrow down product associations and determine which items should be stocked in the store and where they should go. Data mining also pinpoints which campaigns get the most response.

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Do You Want to Study Data Analytics?

There’s a lot of data generated every day, and consequently, there is a correspondingly great demand for professionals to analyze that information using techniques like data mining. Simplilearn’s Post Graduate Program in Data Analytics is the perfect data analytics certification course for anyone on a data scientist career path.

This program, held in partnership with Purdue University and collaboration with IBM, gives you broad exposure to key technologies and skills currently used in data analytics and data science. You will learn statistics, Python, R, Tableau, SQL, and Power BI. Once you complete this comprehensive data analytics course, you will be ready to take on a professional data analytics role.

According to Indeed, data scientists can earn an annual average of USD 122,875. Additionally, there is an ever-growing, healthy demand for data scientists. Let Simplilearn help you find that new career. Check out the courses today and get a start on your rewarding data-driven future!

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