What is Data Mining | Pro's, Cons, & Applications

What is Data Mining | Pro's, Cons, & Applications

Data Mining is a process of extracting useful information or important patterns from a large dataset. Data mining is beneficial for organizations to understand their customer's needs. Now let us see what exactly data mining is, the data mining process, what are its advantages and disadvantages, and the applications of data mining.

What is Data Mining?

Data Mining is also known as Knowledge discovery in databases. Data mining was introduced in the 1990s. Data Mining is considered very significant to organizations. Organizations receive a substantial amount of data from customers, sensors, databases, and many other data sources. This data is very difficult to manage and it is very hard to find or uncover useful information from this data. So, To uncover useful information we use data mining. Now with the help of useful information, Many Organizations can decide how to run their business. These decisions are very helpful for an organization to have a competitive edge over its competitors or rival organizations.

Different data sources that can be mined

Data Mining can be applied to any kind of data, but it is only applicable as long as the data contains meaningful information. We can apply data mining to various sources:-

  1. Relational Database.
  2. Data Warehouse.
  3. WWW.
  4. HyperText and Multimedia.
  5. Time Series Database.

Data Mining Process:-

 firstly before applying data mining, we need to understand the problem and identify the goal. 7 key steps in data mining steps:-

1) Data Cleaning:- In this step, we remove noisy and inconsistent data from the data source.
2) Data Integration:- In this step, Multiple data sources are combined.
3) Data Selection:-  In this step, only relevant data is retrieved from the database.
4) Data Transformation:- In this step, we transform the data so that it is appropriate for mining.
5) Data Mining:- In this step, we use various methods to extract data patterns.
6) Pattern Evaluation:- In this step, we uncover interesting patterns so that we can get insights.
7) Knowledge Presentation:-  In this step, we visualization and knowledge representation techniques for presenting mined results.

Advantages of Data Mining

  • It collects reliable information- This information is very helpful for organizations in decision-making. This provides an organization to have a competitive edge over other organizations.
  • Analyze large amounts of data quickly- Data mining helps us to analyze large amounts of data very quickly and help in decision making.
  • It detects risks and fraud- Data mining helps us to detect risks and fraud which are usually harder to find through other means. 
  • It is an efficient and cost-effective method.
  • It helps data scientists to discover underlying patterns which are helpful in decision making

Disadvantages of Data Mining

  • There are enormous data mining tools available, one needs to have the experience or training to use the tools. As every tool has its own task and tools are also complex. 
  • Data mining requires a large database to work on it. If the database is of small size then it would be difficult for decision making.
  • There are many privacy concerns with data mining, as organizations may sell their customer data to other third-party organizations.
  • If there is no adequate data, then data mining does not produce the best results.

Applications of Data Mining

      Data Mining has various applications, a few of them are:-
      • Financial Data Analysis- like Money laundering detection, loan payment prediction, etc.
      • Intrusion Detection
      • Retail Sector- like Product Recommendation, Analysis of sales and customers.
      • Telecommunication Industry- like Fraud Pattern Analysis, Telecommunication Data Analysis.
      • Research- like Web Mining, Classification of unknown data.
      • Farming- to predict the yield of crops harvested.
      • Healthcare and Insurance- like Medical Claims Analysis, Identify successful therapy for different health problems.

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