Guest Editors’ Introduction

Roger H.L. Chiang, Zhe Shan, Harry Jiannan Wang


Business intelligence and analytics (BI&A) have been studied increasingly in the past 10 years by almost every business discipline. BI&A provide historical, current, and predictive views of business operations based on advanced data collection, extraction, integration, and analysis of large data sets to improve decision making. Web 2.0 has created an abundance of user-generated content (UGC) from online social media such as forums, online groups, web blogs, social networking sites, social multimedia sites, and even virtual worlds. “Big Data” and “Big Data Analytics” have been used to describe the data sets and analytical techniques in applications that are so large (from terabytes to exabytes) and complex (from sensor to unstructured social media data) that they require advanced and unique data storage, management, analysis, and visualization technologies.

Information system (IS) discipline, traditionally, has studied how to provide the right information, to the right person, at the right time, and in the right format to support decision making. With advanced BI&A research and systems that study and implement (big) data analytics, IS discipline can achieve a higher goal in meeting the information needs in businesses. BI&A have already analyzed data integrated from multiple sources in a variety of formats (e.g., structured and unstructured) to provide the right information. With the increasing use of mobile devices such as smart phones and tablets, BI&A research should study how to support mobile-oriented, location-aware, person-centered, and context and culture-relevant information. Although academic research on mobile BI is still in its earlier stage, BI&A 3.0 systems, which are mobile and sensor-based, are foreseen in the near future not only to provide the right information, but also to the right person, at the right time, and to the right location/device. In addition, BI&A research on the interface design of mobile devices and data visualization should meet ‘in the right format’ requirement to empower decision makers’ visual sensemaking and thinking.

Obviously, social media and user-generated content associated with big data era create many data management and analytics challenges; however, they also provide a unique opportunity to the IS community. In facing these challenges, we believe IS researchers have a unique foundation in conducting BI&A research.

After PACIS 2014 held in Chengdu, China, Professor Ting-Peng Liang, Editor-in-Chief of Pacific Asia Journal of the Association for Information Systems (PAJAIS), invited us to edit a special issue on Business Intelligence and Analytics research by soliciting papers from PACIS 2014 Business Intelligence and Big Data Analytics track. We identified and invited four papers for this special issue. After the review and revision, two papers were accepted for this special issue. The first paper entitled “The Identification of Noteworthy Hotel Reviews for Hotel Management,” by Hwang et al., studies how to identify features that are relevant to the noteworthiness of hotel reviews and propose a method to automatically identifying noteworthy reviews for hotel managers. It is BI&A research in analyzing user-generated content (i.e., online customer reviews in this work) with text mining and sentiment analysis techniques. The second paper entitled “Combining Online News Articles and Web Search to Predict the Fluctuation of Real Estate Market in Big Data Context,” by Sun et al., proposes and validates an integrated method on the prediction of real estate price in China by integrating the sentiment series extracted from both news data and search engine query data into the forecasting model. It is BI&A research by integrating data from multiple sources for the real estate price prediction. We hope that the insights of these two papers help IS researchers better understand how to apply BI&A research and technology in related industry areas.

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