Data mining has a long history. Originally known as knowledge discovery in databases, the term “data mining was coined until the 1990s. Data mining is the process of digging through large sets of data to identify patterns to predict future trends. Data mining is used at the intersection of machine learning, statistics, and database systems.
Database information is doubling every two years developing chaotic and repetitive noise in data. More information does not necessarily mean more knowledge. Unstructured data makes up 90 percent of the digital universe. This is why data mining is important, it allows you to understand what is relevant and how you can use that information to assess likely outcomes.
Data mining consists of four main steps. These steps include setting objectives, collecting and analyzing data, applying data mining algorithms, and evaluating results.
Data is stored in data warehouses, either in-house servers or the cloud. Information technology professionals, business analysts, and management teams then access the data and decide how they want to organize it using application software. The data is then presented in easy-to-understand and share formats, such as a graph or table.
The most common algorithms and techniques used to turn data into useful information include association rules, neural networks, decision trees, and K-nearest neighbor
In sales and marketing companies collect large amounts of data about their customers and prospects. Companies can optimize their marketing efforts by observing their consumer demographics and online user behaviors to increase profits. In educational institutions, data is collected to understand student population and environmental performance for optimal success.