Systematic web data mining with business architecture to enhance business assessment services

Abstract

Among the numerous challenges faced by enterprises today, two recent trends have significantly impacted the manner businesses are run and decisions are made. First, many enterprises have adopted Business Architecture concepts to structure, define, plan, measure and optimize their operations. Second, enterprises have leveraged the vast wealth of dynamic and unstructured web information to identify competitive advantages, recognize social media sentiment patterns or anomalies, and assuage potentially damaging client perceptions. These tasks are commonly performed independently of each other, thus missing several synergetic opportunities. By establishing a systematic relationship between these seemingly disjoint trends, enterprises and consulting service companies gain competitive, operational advantages, and recurring benefits. This paper describes a systematic approach whereby results from text mining analysis are integrated with the Business Architecture to empower business users to leverage social media data for increased decision making efficiencies. Applying design science, the proposed approach is explained by providing a conceptual model and a two-phased integration method. It is then buttressed by a sample scenario derived from the banking industry. We discuss the potential operational and competitive gains realized by adopting our approach, and the directions for future work. © 2011 IEEE.