Intelligent data analysis

Data mining is a computerized technology used in analytics to process and explore large datasets. Using data mining tools and techniques, organizations can identify patterns and relationships hidden in data. Data mining transforms raw data into actionable knowledge. Companies use insights to solve problems, analyze the future impact of business decisions, and improve profits.

What does the term “data mining” stand for?

Data mining is a misnomer because the goal of data mining is not to extract or analyze the data itself. In fact, there is already a large amount of data from which you need to extract meaning or valuable insights. A typical process of data collection, storage, analysis and data mining is described below.

• Data collection is the gathering of data from various sources such as customer feedback, payments, and purchase orders.
• Data storage is the process of storing this data in a large database or data warehouse.
• Data analytics is the further processing, storing and analyzing of data using sophisticated software and algorithms.
• Data mining is a branch of data analytics or an analytical strategy used to find hidden or previously unknown patterns in data.

Why is data mining important?

Data mining is an important part of any successful analytics initiative. Companies can use the process of knowledge discovery to increase customer confidence, find new revenue streams, and attract new customers. Effective data mining helps in various aspects of business planning and operations management. Below are a few examples of how different industries are utilizing data mining.