Description: Business Intelligence Data Mining is the process of discovering patterns and insights from large amounts of data. This approach combines data analysis techniques, statistics, and machine learning to extract valuable information that can be used for strategic decision-making in organizations. Through data mining, companies can identify trends, consumer behaviors, and market opportunities, allowing them to optimize their operations and enhance their competitiveness. The main characteristics of this process include the ability to handle large volumes of data, the identification of hidden patterns, and the generation of predictive models. The relevance of data mining in the field of business intelligence lies in its potential to transform data into useful information, thus facilitating the creation of evidence-based strategies and the continuous improvement of business processes. In a world where information is a key resource, data mining becomes an essential tool for organizations looking to adapt and thrive in a dynamic and competitive environment.
History: Data mining as a discipline began to take shape in the 1990s when organizations started to recognize the value of the data generated by their operations. With advancements in technology and increased storage capacity, tools and techniques were developed to analyze large volumes of data. In 1996, the term ‘data mining’ became popular in the academic and business community, and since then it has evolved with the incorporation of machine learning techniques and predictive analytics.
Uses: Data mining is used in various areas, including marketing, finance, healthcare, and manufacturing. In marketing, it allows for customer segmentation and personalized offers. In finance, it helps detect fraud and assess risks. In the healthcare sector, it is used to predict disease outbreaks and improve patient care. In manufacturing, it optimizes processes and reduces costs.
Examples: An example of data mining in action is the use of recommendation algorithms on digital platforms, which analyze user behavior to suggest relevant content. Another case is real-time transaction analysis by financial institutions to identify fraud patterns.
