The exploration of meaningful patterns in big chunks of data to achieve established goals and knowledge discovery. This discovered knowledge can be used to improve actionable strategies. Traditionally, decision-making is based on ground information. However, various data mining techniques can be applied to develop a knowledge-rich decision-making environment.
Data mining in contrast to traditional data analysis is driven by the discovery of patterns and knowledge automatically. Online analytical processing is a way to process a big volume of data in multidimensional capacity. For successful strategies, it is important for decision-makers to leverage the strides of data by strategic data warehousing. With the widespread use of information systems across various verticals, making use of this data has become a problem. Traditional data analysis has become insufficient and computer-assisted analysis indispensable.
Application of data mining to solve real-world problems
It is difficult to find efficient ways to summarize and visualize data to give useful insights for decision-making. Data mining is used to construct models to solve such real-world problems. Knowledge discovery with a robust data mining application delivers measurable benefits to businesses. Industries in which such businesses have benefitted are retail, banking, telecom, and healthcare.
Sentiment analysis is a common application of mining texts from social media to gain an understanding of the sentiments of a group on a topic. Furthermore, natural language processing is used to discover the underlining meaning of the phrases used in social media.
A popular approach to predictive modelling involves partitioning of the data into segments. The partitioning is based on domain knowledge, heuristics, and clustering. Data visualization is used as an integral part of the process to give a general view of the findings.
Recommendation systems based on predictive consumer buying behavior is widely used by e-tailers to push consumers to make a purchase. These models are prepared by a thorough understanding of human behavior and triggering events that result in a transaction.
Deployment of data mining techniques can be useful in predicting a borrower’s ability to pay back debts. Based on demographic profile and personal information interest rates can be automatically calculated.