Business and Market Intelligence 2.0

to the “skills, technologies, applications, and practices used to support decision making” (http:// en.wikipedia.org/wiki/Business_intelligence). On the basis of a survey of 1,400 CEOs, the Gartner Group projected BI revenue to reach $3 billion in 2009.1 Through BI initiatives, businesses are gaining insights from the growing volumes of transaction, product, inventory, customer, competitor, and industry data generated by enterprise-wide applications such as enterprise resource planning (ERP), customer relationship management (CRM), supply-chain management (SCM), knowledge management, collaborative computing, Web analytics, and so on. The same Gartner survey also showed that BI surpassed security as the top business IT priority in 2006.1 BI has been used as an umbrella term to describe concepts and methods for improving business decision making by using fact-based support systems. BI also includes the underlying architectures, tools, databases, applications, and methodologies. BI’s major objectives are to enable interactive and easy access to diverse data, enable manipulation and transformation of these data, and give business managers and analysts the ability to conduct appropriate analyses and then act.2 BI is now widely adopted in the world of IT practice and has also become popular in information systems curricula.3 Successful BI initiatives have been reported for major industries—from healthcare and airlines to major IT and telecommunications fi rms.2 As a data-centered approach, BI relies heavily on various advanced data collection, extraction, and analysis technologies.2,3 Data warehousing is often considered the foundation of BI. Design of data marts and tools for extraction, transformation, and load (ETL) are essential for converting and integrating enterprise-specifi c data. Organizations often next adopt database query, online analytical processing (OLAP), and advanced reporting tools to explore important data characteristics. Business performance management (BPM) using scorecards and dashboards allow analysis and visualization of various employee performance metrics. In addition to these well-established business analytics functions, organizations can adopt advanced knowledge discovery using data and text mining for association rule mining, database segmentation and clustering, anomaly detection, and predictive modeling in various information systems and human resources, accounting, fi nance, and marketing applications. Since about 2004, Web intelligence, Web analytics, Web 2.0, and user-generated content have begun to usher in a new and exciting era of business research, which we could call Business Intelligence 2.0. An immense amount of company, industry, product, and customer information can be gathered from the Web and organized and visualized through various knowledge-mapping, Web portal, and multilingual retrieval techniques.4 By analyzing customer clickstream data logs, Web analytics tools such as Google Analytics provide a trail of the user’s online activities and reveal the user’s browsing and purchasing patterns. Web site design, product placement optimization, customer transaction analysis, and product recommendations can Business Intelligence (BI), a term coined in 1989, has gained much traction in the IT