The ongoing process of digital transformation of organizational operations resulting from the incorporation of new technologies by businesses and consumers has resulted in the creation of vast data resources. These data can provide vital strategic value to firms across industry sectors by providing actionable information for decision support or Business Intelligence (BI) through the application of analytics techniques. These analytics can range from the more basic BI methods of report generation, on-line analytical processing (OLAP), and dashboards to the more sophisticated, quantitative approaches of data mining.
The true value of these analytic endeavors do not lie solely in the production of numeric or visual results, but the application of analytics results to solve business problems. In this special issue, we asked researchers to submit works that concentrate on conducting analytics to address business initiatives. The papers in this issue represent various analytical methods applied in a case study approach to illustrate the value of analytics to producing information to enhance organizational processes and strategies.
Our first paper “ Detection of financial rumors using big data analytics: the case of the Bombay Stock Exchange” illustrates how natural language processing, naive Bayes, and support vector machine techniques can be used to leverage “big data resources” (e.g. social media based) in the financial industry to identify rumor events that can influence stock prices. The paper describes the various stages that must be addressed in order to produce reliable results. These stages for this analytics endeavor include the data selection, pre-processing, transformation, data mining, and interpretation levels which was applied to the Bombay Stock Exchange.
Our second paper “Applications of the information bottleneck method to discover user profiles in a web store” deals with the problem of discovering groups of web users with similar behavioral patterns in e-commerce sites. Using sessions, they applied unsupervised classifications based on session attributes related to user click behavior. This procedure involves the agglomerative information bottleneck algorithm using log data for a real online store efficiency of the approach, one that differentiates between buying and non-buying sessions. This approach allows for the discovery of hidden knowledge about the common characteristics of various user profiles and its ability to support managerial decisions.
Our third paper “Building an active-sematic data warehouse for precision dairy farming” deals with the development of a data warehouse technology, which improves the productivity of animal well-being in dairy farming. The data warehousing enhances the effective and efficient data management of a dairy farm. The large volumes of generated sensor data in precision dairy farming required careful decision-making to concentrate on the appropriate level of data in the dairy stores.
Our fourth paper “Comparing consumer-produced product reviews across multiple websites with sentiment classification” produces an insightful analytic approach as it leverages both the science and art of data science when applying text mining and sentiment analysis to multiple data sources that addresses customer reviews of products. By analyzing produced review of the same product from different sources (e.g. review from different retailers), the authors conclude that consumers should be wary of simply relying on product reviews from one source when making purchasing decisions given that reviews from different sources can involve differing feedback.
Our fifth paper “From an information consumer to an information author: a new approach to business intelligence” takes on a distinctly different topic and delves into the less widely mentioned “self-service” BI space. This refers to the process of decision-makers utilizing self-service BI technologies that facilitate access to data and the ability of decision-makers to create customized reports. The study leverages off of the extensive research produced in the SST market (e.g. Self-service technology) and draws comparisons to that sector in describing the evolving SSBI space.
Additional information
Notes on contributors
Jerry Fjermestad
Jerry Fjermestad is a Professor in the School of Management at the New Jersey Institute of Technology. He received his B.A. in chemistry from Pacific Lutheran University, an MS in operations research from Polytechnic-NYU, and an M.B.A. and Ph.D. from Rutgers University in management information systems. His current research deals with business intelligence and data warehousing, computer-mediated communications and leadership, electronic customer relationship management, and information personalization, privacy, and security. He has co-edited 11 special issues of various information systems journals including the International Journal of Electronic Commerce, Electronic Markets, and Group Decision and Negotiation. Dr. Fjermestad’s publications include an edited book on CRM and over one-hundred scholarly papers in journals such as Communications of the ACM, Decision Support Systems, Journal of Management Information Systems, and Information & Management and in various proceedings. He serves on the editorial boards of several journals, including the Journal of Information Science and Technology, International Journal of Electronic Collaboration, International Journal of Information Security and Privacy, and Journal of Organization Computing and Electronic Commerce.
Stephan Kudyba is an Associate Professor of Analytics and MIS in the School of Management at the New Jersey Institute of Technology. Dr. Kudyba teaches business courses addressing data, information and knowledge management, market research, and digital marketing. He has published numerous books and articles, has been interviewed by prominent magazines, and speaks at corporate and academic events addressing data mining, business intelligence, and strategic initiatives regarding information and systems management. Dr. Kudyba’s journal publications include analytics- and MIS-related subject matter in Information Systems Research, MIT Sloan Management Review, Harvard Business Review, Communications of the ACM, Knowledge and Process Management, and Futures, among others. He is a member of several information-management-based societies, including the Society of Information Management and the International Institute of Analytics and maintains relations with organizations in a variety of industries addressing strategic initiatives. He has over 15 years of experience in prominent organizations.
Kenneth D. Lawrence is a Professor in Management Science and Business Analytics at the Tuchman School of Management, New Jersey Institute of Technology. He is also a full member of the Graduate Doctoral Faculty of Management at Rutgers in the Department of Management Science and Information Systems. Dr. Lawrence’s professional employment includes over 20 years of technical management experience with AT&T as Director of Decision Support Systems and Marketing Demand Analysis, plus Hoffmann-La Roche, Inc., Prudential Insurance, and the U.S. Army in forecasting, marketing, planning and research, statistical analysis, and operations research. Dr. Lawrence’s research is in the areas of management science, business analytics, time series forecasting, econometric forecasting, data mining, data envelopment analysis, multi-criteria decision-making, and supply chain modeling. His publications have appeared in such journals as the European Journal of Operational Research, Computers and Operations Research, Operational Research Quarterly, and International Journal of Forecasting and Technometrics. Dr. Lawrence has published 44 books, including Fundamentals of Forecasting Using Excel (Industrial Press, 2009).