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Today’s enterprises have accumulated vast amount of data and keep exploding by business activities. These datasets may contain potential undiscovered business strategies as a key basis of competition; underpin new waves of productivity growth, innovation, and consumer surplus. Data analysis is crucial in making managerial decisions. Although there are many Business Intelligence (BI) software of commercial and open source, but serving statistical purpose as exuberant as GNU-R (R) is rare. R is a highly extensible language and environment for providing a variety of statistical and graphical features. In enterprise environment, the source data are stored in various forms such as files, database, and streaming data. Currently analysts conduct data analysis in offline mode using statistical software. The offline mode means analysts 1) extract the desired data; 2) store extracted data into files; 3) manipulate software; 4) draw analytical results; 5) generate the inferences. Automating the statistical procedures by directly pulling source data will make critical decision sooner and less costly. It is a common practice that enterprise adopts Service-Oriented Architecture (SOA) to achieve its operation excellence. Since the business applications populate the source data during the operation processes, pulling the source data directly under SOA is the most effective way of data analysis. This paper demonstrates how service-oriented statistics engine was developed and how such a system benefits the business decision-making

A Service Oriented Analytics Framework for Multi-Level Marketing Business