business intelligence and analytics: from big data to big impact

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Chen et al./Introduction: Business Intelligence ResearchPapers in this Special IssueThe idea for this special issue began in May 2009, whenDetmar Straub, the Editor-in-Chief of MIS Quarterly, solicitedsuggestions for special issues from the editorial boardmembers. We submitted the special issue proposal on BusinessIntelligence Research in August 2009, with the call-forpapersapproved and distributed at the 30 th Annual InternationalConference on Information Systems (ICIS) in Decemberof that year. Submissions to this special issue needed torelate to MIS Quarterly’s mission with strong managerial,organizational, and societal relevance and impact. In additionto the Design Science approach (Hevner et al. 2004; March &Storey 2008), rigorous and relevant BI-related research usingmanagement science (modeling, optimization), informationeconomics, and organizational and behavioral methodologies(case studies, surveys) was also welcomed. A total of 62manuscripts was received by October 2010. In the following20 months, six of the manuscripts went through three or fourreview rounds and were then accepted for this issue.The six papers address various aspects of the BI&A researchframework presented in this introduction paper (see Table 7).All six papers are based on BI&A 1.0, with three also basedon BI&A 2.0. The first three papers by Chau and Xu, Park etal., and Lau et al. focus on BI&A 2.0 with applications on e-commerce and market intelligence using text, web, and networkanalytics. In the next two papers, both Hu et al. andAbbasi et al. work in the category of BI&A 1.0 with a focuson security, but Hu et al. use network analytics whereasAbbasi et al. emphasize security analysis and data analytics.Finally, Sahoo et al. also work in BI&A 1.0, with direct applicationto e-commerce and market intelligence using web anddata analytics.In “Business Intelligence in Blogs: Understanding ConsumerInteractions and Communities,” Michael Chau and JenniferXu recognized the potential “gold mine” of blog content forbusiness intelligence and developed a framework forgathering business intelligence by automatically collectingand analyzing blog content and bloggers’ interaction networks.A system developed using this framework wasapplied to two case studies, which revealed novel patterns inblogger interactions and communities.Sung-Hyuk Park, Soon-Young Huh, Wonseok Oh, and SangPil Han in their paper, “A Social Network-Based InferenceModel for Validating Customer Profile Data,” argue that businessintelligence systems are of limited value when they dealwith inaccurate and unreliable data. The authors proposed asocial network-driven inference framework to determine theaccuracy and reliability of self-reported customer profiles.The framework utilizes the individuals’ social circles andcommunication patterns within their circles. To construct thespecific inference and validation model, a combination ofmethods was used, including query processing, statisticalinference, social network analysis, and user profiling. Theauthors analyzed over 20 million actual mobile call transactionsand their proposed social network-based inferencemodel consistently outperformed the alternative approaches.In “Web 2.0 Environmental Scanning and Adaptive DecisionSupport for Business Mergers and Acquisitions,” RaymondLau, Stephen Liao, K. F. Wong, and Dickson Chiu analyzedcompany mergers and acquisitions (M&A). Online environmentalscanning with Web 2.0 provides top executives withopportunities to tap into collective web intelligence to developbetter insights about the socio-cultural and political-economicfactors that cross-border M&As face. Grounded on Porter’sfive forces model, this research designed a due diligencescorecard model that leverages collective web intelligence toenhance M&A decision making. The authors also developedan adaptive business intelligence (BI) 2.0 system, which theyapplied to Chinese companies’ cross-border M&A activities.In their paper, “Network-Based Modeling and Analysis ofSystemic Risk in Banking Systems,” Daning Hu, J. LeonZhao, Zhimin Hua, and Michael Wong analyzed systemic riskin banking systems by treating banks as a network linked withfinancial relationships, leading to a network approach to riskmanagement (NARM). NARM analyzes systemic risk attributedto each individual bank via simulation based on realworlddata from the Federal Deposit Insurance Corporation.NARM offers a new means for predicting contagious bankfailures and determining capital injection priorities at theindividual bank level in the wake of financial crisis. A simulationstudy showed that, under significant market shocks, theinterbank payment links became more influential than thecorrelated bank portfolio links in determining an individualbank’s survival.In their paper, “MetaFraud: A Meta-Learning Framework forDetecting Financial Fraud,” Ahmed Abbasi, Conan Albrecht,Anthony Vance, and James Hansen employed a designscience approach to develop MetaFraud, a meta-learningframework for enhanced financial fraud detection. A seriesof experiments was conducted on thousands of legitimate andfraudulent firms to demonstrate the effectiveness of the frameworkover existing benchmark methods. The research resultshave implications for compliance officers, investors, auditfirms, and regulators.The paper by Nachiketa Sahoo, Param Vir Singh, and TridasMukhopadhyay, “A Hidden Markov Model for CollaborativeFiltering,” reports on the analysis of making personalizedrecommendations when user preferences are changing. The20 MIS Quarterly Vol. 36No. 4/December 2012
Chen et al./Introduction: Business Intelligence ResearchTable 7. Summary of Special Issue Papers Within the BI&A Research FrameworkAuthors and Titles Evolutions Applications Data Analytics/ Research ImpactsChau and Xu, “BusinessIntelligence in Blogs: UnderstandingConsumer Interactionsand Communities”Park et al., “A SocialNetwork-Based InferenceModel for ValidatingCustomer Profile Data”Lau et al., “Web 2.0Environmental Scanning andAdaptive Decision Supportfor Business Mergers andAcquisitions”Hu et al., “Network-BasedModeling and Analysis ofSystemic Risk in BankingSystems”Abbasi et al., “MetaFraud: AMeta-Learning Frameworkfor Detecting FinancialFraud”Sahoo et al., “A HiddenMarkov Model for CollaborativeFiltering”BI&A 2.0 onsocial media& networkanalyticsBI&A 1.0 &2.0 on socialnetworkanalysis andstatisticalanalysisBI&A 1.0 and2.0 onscorecardsand webanalyticsBI&A 1.0 onstatisticalanalysisBI&A 1.0 ondata miningand metalearningBI&A 1.0 onstatisticalanalysisMarket intelligenceon consumers andcommunitiesMarket intelligencein predicting customers’profilesMarket intelligenceon environmentalscanningSystemic riskanalysis andmanagement inbanking systemsFraud detectionRecommender systemswith changinguser preferencesUser-generatedcontent extractedfrom blogsSelf-reported userprofiles and mobilecall recordsBusiness informationextracted fromInternet andproprietary financialinformationU.S. banking informationextracted fromFDIC and FederalReserve WireNetworkFinancial ratios, andorganizational andindustrial-level contextfeaturesBlog reading data,Netflix prize data set,and Last.fm data• Text and networkanalytics• Community detection• Network visualization• Network analytics• Anomaly detection• Predictive analytics• Text and web analytics• Sentiment and affectanalysis• Relation mining• Network and dataanalytics• Descriptive andpredictive modeling• Discrete event simulation• Data analytics• Classification &generalization• Adaptive learning• Data and web analytics• Statistical dynamic model• Collaborative filteringIncreased salesand customersatisfactionPersonalizedrecommendationand increasedcustomersatisfactionStrategic decisionmaking inmergers andacquisitionsMonitoring andmitigating ofcontagious bankfailuresFinancial frauddetectionPersonalizedrecommendationsauthors proposed a hidden Markov model based on collaborativefiltering to predict user preferences and make the mostappropriate personalized recommendations for the predictedpreference. The authors employed real world data sets andsimulations to show that, when user preferences are changing,there is an advantage to using the proposed algorithm overexisting ones.Summary and ConclusionsThrough BI&A 1.0 initiatives, businesses and organizationsfrom all sectors began to gain critical insights from thestructured data collected through various enterprise systemsand analyzed by commercial relational database managementsystems. Over the past several years, web intelligence, webanalytics, web 2.0, and the ability to mine unstructured usergeneratedcontents have ushered in a new and exciting era ofBI&A 2.0 research, leading to unprecedented intelligence onconsumer opinion, customer needs, and recognizing newbusiness opportunities. Now, in this era of Big Data, evenwhile BI&A 2.0 is still maturing, we find ourselves poised atthe brink of BI&A 3.0, with all the attendant uncertainty thatnew and potentially revolutionary technologies bring.This MIS Quarterly Special Issue on Business IntelligenceResearch is intended to serve, in part, as a platform andconversation guide for examining how the IS discipline canbetter serve the needs of business decision makers in light ofmaturing and emerging BI&A technologies, ubiquitous BigData, and the predicted shortages of data-savvy managers andof business professionals with deep analytical skills. Howcan academic IS programs continue to meet the needs of theirtraditional students, while also reaching the working ITprofessional in need of new analytical skills? A new visionfor IS may be needed to address this and other questions.By highlighting several applications such as e-commerce,market intelligence, e-government, healthcare, and security,and by mapping important facets of the current BI&Aknowledge landscape, we hope to contribute to future sourcesof knowledge and to augment current discussions on theimportance of (relevant) academic research.MIS Quarterly Vol. 36 No. 4/December 2012 21
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