The field of business analytics has improved significantly over the last few years, providing business users with better insights, particularly from operational data stored in transactional systems. As an illustrative example, analysis of e-commerce data has recently come to be considered a killer-app for data mining. The data sets created by integrating clickstream records generated by web sites with demographic and other behavioral data dwarf, in size and complexity, the largest data warehouses of a few years ago, creating massive databases that require a mix of automated analysis techniques and human effort in order to provide business users with critical insight about the activity on the site and the characteristics of the site’s visitors and customers. With many millions of clickstream records being generated on a daily basis and aggregated to records with hundreds of attributes, there is a clear need for automated techniques to find patterns in the data. In this paper we discuss the technology and enterprise-adoption trends in the area of business analytics.
The key consumer of these analytics is the business user, a person whose job is not directly related to analytics per-se (e.g., a merchandiser, marketer, salesperson), but who typically must use analytical tools to improve the results of a business process along one or more dimensions (e.g., profit, time to market). Fortunately, data mining1, analytic applications, and business intelligence systems are now being better integrated with transactional systems creating a closed loop between operations and analyses that allows data to be analyzed faster and the analysis
results to be quickly reflected in business actions. Mined information is being deployed to a broader business audience, which is taking advantage of business analytics in everyday activities.
Analytics are now regularly used in multiple areas, including sales, marketing, supply chain optimization, and fraud detection.