Resumen
The field of business analytics has improved significantly over the past few years as of August 8, 2003, giving business users better insights, particularly from operational data stored in transactional systems. Business users, while expert in their particular areas, are still unlikely to be expert in data analysis and statistics. To make decisions based on the data collected by and about their organizations, they must either rely on data analysts to extract information from the data or employ analytic applications that blend data analysis technologies with task-specific knowledge. Analytic applications incorporate not only a variety of data mining techniques but provide recommendations to business users as to how to best analyze the data and present the extracted information. Business users are expected to use it to improve performance along multiple metrics. Therefore analytic applications minimize the need for data analysts. Unfortunately, the gap between relevant analytics and users' strategic business needs is significant. The gap is characterized by several challenges like cycle time, analytic time and expertise, business goals and metrics and goals for data collection and transformations.