Supportsoft Glossary

Data Mining Techniques for Actionable Business Intelligence

Data mining refers to the extraction of patterns, trends, or correlations between disparate sources of information. By extracting useful insights from large amounts of data, organisations can use these insights to improve operations, make informed business decisions, and create opportunities for growth.

Companies and businesses create a significant amount of data through their transaction systems, customer interactions, and computer systems. These large amounts of data can be analysed using a variety of data mining methods, such as classification, grouping, regression analysis, and rules-based data analysis, to discover previously unknown patterns, identify unusual occurrences, and forecast future events.

Data mining is used in business intelligence to help businesses identify their customers' behaviours, uncover sales opportunities, minimise risk, and optimise processes. For instance, data mining can help businesses identify potential fraud by seeing when customers have purchased more than normal or where supervisors have inefficiently processed applications by showing them where they wasted time.

In IT and ITES companies, data mining is used for service optimisation, performance monitoring, and incident analysis, allowing managers to take actions based on data that anticipates changes before they occur.

For data mining to be successful, it is important to have clean data, appropriate tools, and industry knowledge. Combining mined data with visualisation and analytic tools allows businesses to better understand their data and take appropriate action based on that data.

Mined data is converted from the raw data to useful functional data that can assist organisations with making informed, strategic decisions and establishing a competitive advantage.