Data Mining

DATA MINING

• Data means raw facts and figures. Mining means collecting valuable data, formatting it correctly, processing it and extracting valuable insights.

• Data mining is a process used to extract usable data from a larger set of any raw data.

Scope

• Businesses use the data generated by data mining to increase sales, understand the risks associated with investments, strengthen client relationships, and other purposes. A key component of effective business analytics in enterprises is data mining. We can evaluate both real- time and historical data with its tools. It helps forecast trends for the future and enables proactive business practices.

Behavior and trend prediction: Data mining is useful for automatically predicting information from vast databases. It is more accurate and faster than the conventional method of analysis. Data mining is helpful, for instance, in targeted marketing. It forecasts the intended client and utilizes that information for marketing correspondence.

Unknown pattern discovery: Data mining methods are useful for automatically predicting previously unidentified patterns. They search through the datasets and find hidden patterns that were previously unknown. An example of a pattern discovery analysis of retail sales data is finding unrelated products that are bought in addition to the product.

Benefits

• It helps in gathering information and reliable data for the companies.

• It is helpful in predicting the trends and behaviors

automatically.

• It is also helpful in automatically predicting the hidden unknown patterns.

• It helps in risk management and fraud detection.

• It is a cost-effective and efficient solution.

Limitations

• This complexity of data mining is one of its greatest disadvantages. Data analytics often requires technical skill sets and certain software tools.

• Data mining doesn’t always guarantee results.

• There is also a cost component to data mining.