business intelligence and analytics: from big data to big impact

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Chen et al./Introduction: Business Intelligence Researchsurpassed Netflix’s own algorithm for predicting ratings by10.06 percent. However, the publicity associated with thecompetition also raised major unintended customer privacyconcerns.Much BI&A-related e-commerce research and developmentinformation is appearing in academic IS and CS papers aswell as in popular IT magazines.E-Government and Politics 2.0The advent of Web 2.0 has generated much excitement forreinventing governments. The 2008 U.S. House, Senate, andpresidential elections provided the first signs of success foronline campaigning and political participation. Dubbed“politics 2.0,” politicians use the highly participatory andmultimedia web platforms for successful policy discussions,campaign advertising, voter mobilization, event announcements,and online donations. As government and politicalprocesses become more transparent, participatory, online, andmultimedia-rich, there is a great opportunity for adoptingBI&A research in e-government and politics 2.0 applications.Selected opinion mining, social network analysis, and socialmedia analytics techniques can be used to support onlinepolitical participation, e-democracy, political blogs andforums analysis, e-government service delivery, and processtransparency and accountability (Chen 2009; Chen et al.2007). For e-government applications, semantic informationdirectory and ontological development (as exemplified below)can also be developed to better serve their target citizens.Despite the significant transformational potential for BI&A ine-government research, there has been less academic researchthan, for example, e-commerce-related BI&A research. E-government research often involves researchers from politicalscience and public policy. For example, Karpf (2009) analyzedthe growth of the political blogosphere in the UnitedStates and found significant innovation of existing politicalinstitutions in adopting blogging platforms into their Webofferings. In his research, 2D blogspace mapping with compositerankings helped reveal the partisan makeup of theAmerican political blogsphere. Yang and Callan (2009)demonstrated the value for ontology development for governmentservices through their development of the OntoCopsystem, which works interactively with a user to organize andsummarize online public comments from citizens.Science and TechnologyMany areas of science and technology (S&T) are reaping thebenefits of high-throughput sensors and instruments, fromastrophysics and oceanography, to genomics and environmentalresearch. To facilitate information sharing and dataanalytics, the National Science Foundation (NSF) recentlymandated that every project is required to provide a datamanagement plan. Cyber-infrastructure, in particular, hasbecome critical for supporting such data-sharing initiatives.The 2012 NSF BIGDATA 3 program solicitation is an obviousexample of the U.S. government funding agency’s concertedefforts to promote big data analytics. The programaims to advance the core scientific and technologicalmeans of managing, analyzing, visualizing, and extractinguseful information from large, diverse, distributedand heterogeneous data sets so as to acceleratethe progress of scientific discovery and innovation;lead to new fields of inquiry that would nototherwise be possible; encourage the development ofnew data analytic tools and algorithms; facilitatescalable, accessible, and sustainable data infrastructure;increase understanding of human and socialprocesses and interactions; and promote economicgrowth and improved health and quality of life.Several S&T disciplines have already begun their journeytoward big data analytics. For example, in biology, the NSFfunded iPlant Collaborative 4 is using cyberinfrastructure tosupport a community of researchers, educators, and studentsworking in plant sciences. iPlant is intended to foster a newgeneration of biologists equipped to harness rapidly expandingcomputational techniques and growing data sets toaddress the grand challenges of plant biology. The iPlant dataset is diverse and includes canonical or reference data,experimental data, simulation and model data, observationaldata, and other derived data. It also offers various opensource data processing and analytics tools.In astronomy, the Sloan Digital Sky Survey (SDSS) 5 showshow computational methods and big data can support andfacilitate sense making and decision making at both themacroscopic and the microscopic level in a rapidly growingand globalized research field. The SDSS is one of the mostambitious and influential surveys in the history of astronomy.3 “Core Techniques and Technologies for Advancing Big Data Science &Engineering (BIGDATA),” Program Solicitation NSF 12-499 (http://www.nsf.gov/pubs/2012/nsf12499/nsf12499.htm; accessed August 2, 2012).4 iPlant Collaborative (http://www.iplantcollaborative.org/about; accessedAugust 2, 2012).5 “Sloan Digital Sky Survey: Mapping the Universe” (http://www.sdss.org/;accessed August 2, 2012).6 MIS Quarterly Vol. 36No. 4/December 2012
Chen et al./Introduction: Business Intelligence ResearchOver its eight years of operation, it has obtained deep, multicolorimages covering more than a quarter of the sky andcreated three-dimensional maps containing more than 930,000galaxies and over 120,000 quasars. Continuing to gather dataat a rate of 200 gigabytes per night, SDSS has amassed morethan 140 terabytes of data. The international Large HadronCollider (LHC) effort for high-energy physics is anotherexample of big data, producing about 13 petabytes of data ina year (Brumfiel 2011).Smart Health and WellbeingMuch like the big data opportunities facing the e-commerceand S&T communities, the health community is facing atsunami of health- and healthcare-related content generatedfrom numerous patient care points of contact, sophisticatedmedical instruments, and web-based health communities.Two main sources of health big data are genomics-driven bigdata (genotyping, gene expression, sequencing data) andpayer–provider big data (electronic health records, insurancerecords, pharmacy prescription, patient feedback andresponses) (Miller 2012a). The expected raw sequencing datafrom each person is approximately four terabytes. From thepayer–provider side, a data matrix might have hundreds ofthousands of patients with many records and parameters(demographics, medications, outcomes) collected over a longperiod of time. Extracting knowledge from health big dataposes significant research and practical challenges, especiallyconsidering the HIPAA (Health Insurance Portability andAccountability Act) and IRB (Institutional Review Board)requirements for building a privacy-preserving and trustworthyhealth infrastructure and conducting ethical healthrelatedresearch (Gelfand 2011/2012). Health big data analytics,in general, lags behind e-commerce BI&A applicationsbecause it has rarely taken advantage of scalable analyticalmethods or computational platforms (Miller 2012a).Over the past decade, electronic health records (EHR) havebeen widely adopted in hospitals and clinics worldwide.Significant clinical knowledge and a deeper understanding ofpatient disease patterns can be gleanded from such collections(Hanauer et al. 2009; Hanauer et al. 2011; Lin et al. 2011).Hanauer et al. (2011), for example, used large-scale, longitudinalEHR to research associations in medical diagnosesand consider temporal relations between events to betterelucidate patterns of disease progression. Lin et al. (2011)used symptom–disease–treatment (SDT) association rulemining on a comprehensive EHR of approximately 2.1million records from a major hospital. Based on selectedInternational Classification of Diseases (ICD-9) codes, theywere able to identify clinically relevant and accurate SDTassociations from patient records in seven distinct diseases,ranging from cancers to chronic and infectious diseases.In addition to EHR, health social media sites such as DailyStrength and PatientsLikeMe provide unique research opportunitiesin healthcare decision support and patient empowerment(Miller 2012b), especially for chronic diseases such asdiabetes, Parkinson’s, Alzheimer’s, and cancer. Associationrule mining and clustering, health social media monitoringand analysis, health text analytics, health ontologies, patientnetwork analysis, and adverse drug side-effect analysis arepromising areas of research in health-related BI&A. Due tothe importance of HIPAA regulations, privacy-preservinghealth data mining is also gaining attention (Gelfand 2011/2012).Partially funded by the National Institutes of Health (NIH),the NSF BIGDATA program solicitation includes commoninterests in big data across NSF and NIH. Clinical decisionmaking, patient-centered therapy, and knowledge bases forhealth, disease, genome, and environment are some of theareas in which BI&A techniques can contribute (Chen 2011b;Wactlar et al. 2011). Another recent, major NSF initiativerelated to health big data analytics is the NSF Smart Healthand Wellbeing (SHB) 6 program, which seeks to addressfundamental technical and scientific issues that would supporta much-needed transformation of healthcare from reactive andhospital-centered to preventive, proactive, evidence-based,person-centered, and focused on wellbeing rather than diseasecontrol. The SHB research topics include sensor technology,networking, information and machine learning technology,modeling cognitive processes, system and process modeling,and social and economic issues (Wactlar et al. 2011), most ofwhich are relevant to healthcare BI&A.Security and Public SafetySince the tragic events of September 11, 2001, securityresearch has gained much attention, especially given theincreasing dependency of business and our global society ondigital enablement. Researchers in computational science,information systems, social sciences, engineering, medicine,and many other fields have been called upon to help enhanceour ability to fight violence, terrorism, cyber crimes, and othercyber security concerns. Critical mission areas have beenidentified where information technology can contribute, assuggested in the U.S. Office of Homeland Security’s report“National Strategy for Homeland Security,” released in 2002,including intelligence and warning, border and transportation6 “Smart Health and Wellbeing (SBH),” Program Solicitation NSF 12-512(http://www.nsf.gov/pubs/2012/nsf12512/nsf12512.htm; accessed August 2,2012).MIS Quarterly Vol. 36 No. 4/December 2012 7
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