Motivation or Importance of Data Mining

Data Mining | Motivation/Importance: In this tutorial, we are going to learn how data mining motivated industries to use it in their venture? By IncludeHelp Last updated : April 17, 2023

Data mining is the area in which large quantities of knowledge are obtained and analyzed to retrieve any valuable information, i.e. structured information. As time goes, its desires increased. Everyone needs the succinct and accurate knowledge that is possible through it since it is not an easy job, but through a set of processes and technology, it becomes possible.

Major Sources of Abundant data

  • Business – Web, E-commerce, Transactions, Stocks
  • Science – Remote Sensing, Bioinformatics, Scientific Simulation
  • Society and Everyone – News, Digital Cameras, YouTube
  • In Industries – To know the ratings of individuals and people's likes

Data Mining Motivation

The Following areas in which data mining uses extensively are demonstrating data mining motivation:

1. Market Analysis

The best way to get a more holistic view of your clients is data mining and market analysis. We can learn more about customer tastes with data take a look at purchase histories, collect demographics, gender, place, other profile information, and much more. We can then have more customized customer experiences with this mining research, update your marketing strategy, retain a rigorous analysis process, and pitch goods to which customers are more likely to react well.

For example, email marketers, use data mining to provide users with more personalized content. They will learn things like gender, place, weather conditions, and more with the aid of a CRM or another big data collection tool. Then the information can be used by email marketers to classify lists to include more specific content.

By gathering gender knowledge about clients, Adidas does. Then, to give their new men's apparel collection to men and their new women's apparel collection to women, they segment their email lists and data sets.

2. Fraud Detection

"Usage of one's career for personal reasons enrichment by the malicious misuse or execution of the wealth or properties of the recruiting company" in technological systems have dishonest processes, This has happened in many aspects of everyday life, such as Network Telecommunications, Mobile Communications, E-commerce and internet banking. Detection of fraud includes detecting fraud as rapidly as Once it is perpetrated, as possible.

Methods for identifying theft are increasingly being built to protect offenders by responding to their tactics. New strategies for detecting fraud are being developed. More complicated owing to the extreme constraint of the exchange of views in the identification of fraud now, fraud a variety of approaches have been introduced to detect data processing, statistics, and artificial intelligence, for instance. Fraud is uncovered from data and trend irregularities

Type of Fraud - The types of frauds maybe credit card frauds, telecommunication frauds, and computer intrusion.

3. Customer Retention

The retention of customers applies to a business or product's ability to maintain its customers for a given period. High retention of customers means that buyers of the product or company prefer to return, continue to shop or otherwise not defect to another product or company or not to use it altogether.

4. Production Control

Power over output is a rich source of possible applications for data mining. The collecting and cleaning of data are reasonably simple. Organizations have their input records, but there are virtually no regulatory and privacy challenges. Since companies have a long history of setting up operating procedures to maximize production processes, cost justification and return on investment forecasts are simple to do.

5. Scientific Exploration

Data discovery is a method close to initial data analysis, whereby a data scientist uses visual exploration rather than conventional data processing systems to explain what is in a dataset and the functionality of the data.

Such features can include data size or quantity, data completeness, data consistency, potential interactions between data elements or data files/tables. Usually, data exploration is done using a mixture of automatic and manual operations.

To give the analyst an initial view of the data and an interpretation of main aspects, automated tasks may include data profiling, data visualization or tabular reports.

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