What is Data Mining and Techniques involved









Data mining is the process of analysing data collected from different sources into useful information. It can be used to increase business profits and keep a track on the company’s performance. Data mining software helps users to analyse data from different angles. The data is categorized, summarized and correlated with large relational databases. The main aim is to get a view of the company’s performance. With the help of data mining, business owners can get a broad view on various trends. It can show the areas where there is more profit and where there is loss. It can reveal the ways how to control the unnecessary expenditure and increase the total income.

How does data mining work?

Data mining provides the link between transaction and analytical systems. On the basis of user queries, data mining software analyses relationships in stored transaction data. A retailer can determine when customers visit and what products they buy. The data items are grouped according to consumer preferences. Date is mined to analyse customer behaviour and market trends. The major elements in data mining comprises of extraction and loading transaction data onto the data warehouse, store the data in a database system, providing data access to analysts, application of data mining software and presenting the data in a more useful format. The technology has opened many ways to the companies to take its advantage and analyse their data efficiently.

Data mining techniques

Data mining is a sort of prediction that can help analyse data based on the market trends, establish relationship between variables and applying the findings in favour of the business. It involves three stages i.e. initial exploration, model building with validation and deployment.


  1. Initial exploration: In this stage, data preparation starts involving data cleaning, data transformation and preliminary feature selection operations. Based on the statistical methods, the numbers of variables are brought to a manageable range. The process involves a wide variety of statistical methods to identify the most relevant variables.
  2. Model building: In this stage, the best models are chosen based on their predictive performance. A variety of techniques are developed to achieve this goal. It involves an elaborate process and there is competitive evaluation of models. Different models are applied on the same data, which is then compared on the basis of their performance. The main aim is to get the best model of the data set.   
  3. Deployment: This is the final stage of data mining. The model selected in the previous stage is applied to the new data in order to generate predictions. The outcome can tell about the company’s financial status more accurately.


The concept of data mining is becoming more popular as an important business management tool. It focuses on providing an effective business solution that can generate valid predictions.