What is Data Mining?
- Data Mining is the process of searching, analysing, identifying patterns and extracting information from a large batch of information.
- It can be used by corporations for learning customer interests, trends as well as detecting any potential fraud claims.
- In addition, social media companies use data mining processes to commodify their users to help generate profit.
- The main criticism of data mining is that users are often unaware of what happens with their personal information.
Four Main Steps Involved in Data Mining
- The collected data is implemented into a data warehouse system on site or on a cloud service.
- Business analysts, management teams, and information technology professionals access the data and determine how they want to organise it.
- Custom applications software sorts and organises the data.
- The end users present the data in an easy to share format, such as a graph or a table.
Why do Companies Choose to Use Data Mining Softwares?
- Companies or organisations choose to utilise data mining softwares to improve decision-making processes through acquiring insightful data analyses.
- There are two main aims of companies using data mining softwares, firstly to describe the target dataset and secondly, to predict outcomes through the use of machine learning algorithms.
- The reason why data mining is valuable to companies is due to the fact that it can generate new market opportunities by giving businesses the insight they require to detect and predict the behaviour of consumers which allows companies to meet customers’ needs and expectations.
Examples of Data Mining
Retail: The purpose of data mining methods in the retail sector is to analyse and understand customers purchase history as well as identifying customers patterns and trends.
Financial Services: The main aim would be to detect fraudulent claims or transactions by identifying suspicious activities which helps to prevent financial fraud.
Marketing and Customer Relationship Management: The utilisation of data mining in this situation would be to assist in personalised marketing campaigns and targeting customer relationships.
The Advantages and Disadvantages of Data Mining
Advantages
- One of the advantages of data mining is instant pattern discovery which helps predict future behaviour.
- A second advantage of companies using data mining programs is to assist in understanding analysing trends as well as ensuring that the organisation is up-to-date with current developments in the market and sector.
- The third advantage involves dating mining techniques that help in fraud detection by discovering anomalies in datasets which are used to further detect credit card purchases and insurance claims where fraudulent activity is a possibility.
- A final advantage is that data mining methods can be used to model financial markets and predict likely outcomes.
Disadvantages
- A disadvantage of using data mining softwares involves training employers and employees about the various tools as it is a hard and complex process.
- The second disadvantage is that data mining programs require high-quality data to produce correct and accurate outcomes, therefore the lower data quality could lead to inaccurate results and decisions.
- One of the main concerns of data mining processes is the topic of privacy issues and therefore companies, organisations and individuals must comply with data privacy regulations when collecting and analysing any data information.
- The final disadvantage is that data mining and other machine learning programs is the lack of human intervention due to the over-reliance on technology.
References
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