Data Mining Process: A Useful Guide In 5 Points


The world today operates on a global feedback loop where a majority of corporations in any business or industrial sector rely on fresh data to enhance and update their products and services continuously. 

  1. What is data mining?
  2. Data extraction as a process
  3. Data mining models
  4. Steps in the data mining process
  5. Data mining process in the data warehouse

1) What is data mining?

Data mining process is an essential process for any corporate entity to reinvent itself and stay relevant in a dynamic business environment. To tackle this phenomenon at a grassroots level, we need first to get acquainted with the concept of data mining, and the impact of data mining seen in different lines of business. We shall familiarize ourselves with the process of data mining, classification of data mining tools, and the practical uses of data mining.

2) Data extraction as a process

Data mining is the process of collecting raw, unprocessed data in order to visualize discernible patterns that reflect current market trends for the purpose of accumulating business intelligence. This becomes valuable input for business as they effectively strategize and prepare for any unprecedented variations in the market conditions.

Data extraction is the act of retrieving raw data from various sources in order to process and analyze them. Data extraction consolidates and refines data in such a way that the final output can be used for further processing or storage. To attain feasible data mining results, one must be apprised of different tools and techniques involved in the process. 

3) Data mining models

Data mining models have been historically used in various industries, namely banking and insurance, which deploy a fraud claiming model to detect fraudulent transactions and claims. Corporates prefer a revenue and profit predictive model which provides monthly forecasts based on a given estimate. Models make use of data mining algorithms, some of which are listed below.

  • Logistic regression – This technique utilizes machine learning to enable predictions based on existing models using logistic functions. It also considers multivariable effects on the outcome.
  • Naïve Bayes – The essence of this technique lies in the core assumption that the variables present in a class are largely independent and doesn’t exert any influence whatsoever.
  • Decision tree induction – This technique takes the form of a flowchart, where a test is performed on an attribute. The algorithm separates the attributes into classes based on the outcome of the test.
  • Backpropagation – This technique assigns a set of weights for class predictions arising from iterative performances in order to minimize any error.
  • Neural network – This is another technique that uses machine learning to create complex functions using dynamic variables to resolve classification problems.

4) Steps in the data mining process

The steps involved in data mining can vary depending upon the nature of the process performed. However, the procedure involves a few basic steps:

  • Data cleansing – a process that refines the raw data by deleting or replacing incomplete records and modifying existing records in an effort to improve the overall quality of the database and to increase productivity.
  • Association – It is one of the frequently used techniques. It identifies relationships in occurring trends and presents associations between data to suggest an underlying pattern of behaviour. To put it simply as an example, customers purchase warm clothing only during winters. This suggests that there is an active trend of selling out jackets during colder climates, and clothing stores can stock their goods appropriately.
  • Classification – this refers to the grouping of like variables under a subclass in order to facilitate accurate predictions for each case of data.
  • Data analysis – This is more of an umbrella term that also includes data mining. It covers a variety of processes from cleansing to the transformation of data in order to arrive at heuristic evidence.
  • Interpretation of data – The information arrived through data analysis is then presented in a user-friendly interface to communicate the end-user results.

5) Data mining process in the data warehouse

A data warehouse is a structure that enables the management decision-making process by combining data cleaning, data integration, and data consolidation. The process of data mining within a data warehouse can provide a business with results relevant to its operations. One of the main benefits of data mining boils down to forecasting profitable trends.

In a dynamic world where change governs all, it is very crucial to have the upper hand when it comes to decision making. Data mining fulfils the requirement of having enough information to formulate a game plan to wade through uncertain waters. It is also beneficial in providing relevant information that grants the interpreter a unique perception of viewing target markets and to avoid pitfalls in the course of conducting business.


In the business world, information is the currency of success. The more relevant knowledge an entity possesses, the higher are its chances to maintain sustainable growth. The ability to adapt to an ever-changing hostile environment is obtained thanks to data mining, which provides access to unlimited raw knowledge. It can only be said that data mining operations will not only prove successful but essential to an entity’s survival in the future.

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 


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