Making quality business decisions relies on the efficiency of data analysis processes. There are many existing Business Intelligence (BI) software products providing rich features to simplify the data analysis processes, but serving statistical purpose as effective and flexible as GNU-R (R) is rare. R is a highly extensible language and runtime environment providing a variety of statistical and graphical features. In enterprise environments, software records business activities in various forms such as files, database, and streaming data. Currently analysts conduct the data analysis in an offline mode using statistical software; it means that the analysts are manually (1) extracting the desired data; (2) storing extracted data into files; (3) manipulating the software; (4) drawing analytical results, and (5) making the inferences. Automating the statistical procedures-by directly extracting the data-will expedite the decision-making sooner and less costly. It is a common practice that enterprises build applications under Service-Oriented Architecture (SOA) to achieve their operation excellence. This paper proposes a serviceoriented statistics framework integrating R to improve the quality of decision-making by automatically extracting the business live data directly. It also presents how such a system was developed from a software engineering perspective to benefit business decision making.
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