Business Intelligence and Analytics: From Big Data to Big Impact

Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.

[1]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[2]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[3]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[4]  C StoreyVeda,et al.  Design science in the information systems discipline , 2008 .

[5]  Kevin Humphreys,et al.  New Directions in Question Answering , 2006, Information Retrieval.

[6]  Sachchidanand Singh,et al.  Big Data analytics , 2012 .

[7]  D. Spence,et al.  Heterogeneous treatment effects of integrated soil fertility management on crop productivity: Evidence from Nigeria , 2011 .

[8]  L. Mosley,et al.  The World Is Flat: A Brief History of the Twenty-First Century , 2005 .

[9]  Albert-László Barabási,et al.  Linked - how everything is connected to everything else and what it means for business, science, and everyday life , 2003 .

[10]  J. Hirsch.  An index to quantify an individual's scientific output , 2005 .

[11]  Hsinchun Chen.  Dark Web: Exploring and Mining the Dark Side of the Web , 2012, ICFCA.

[12]  ubir Sachde,et al.  Phase Transition , 2019, Encyclopedia of Social Network Analysis and Mining.

[13]  Hinrich Schütze,et al.  Book Reviews: Foundations of Statistical Natural Language Processing , 1999, CL.

[14]  Michael Stonebraker,et al.  MapReduce and parallel DBMSs: friends or foes? , 2010, CACM.

[15]  Hsinchun Chen,et al.  Editorial: Design science, grand challenges, and societal impacts , 2011, TMIS.

[16]  William G. Vanderbok.  The World is Flat: A Brief History of the Twenty-First Century , 2005 .

[17]  Jon M. Kleinberg,et al.  The link-prediction problem for social networks , 2007, J. Assoc. Inf. Sci. Technol..

[18]  Sophia Ananiadou,et al.  Smart Health and Wellbeing , 2013, TMIS.

[19]  Vladimir Batagelj,et al.  Pajek - Program for Large Network Analysis , 1999 .

[20]  David A. Hanauer,et al.  Exploring Clinical Associations Using ‘-Omics’ Based Enrichment Analyses , 2009, PloS one.

[21]  Daniel Lewis,et al.  What is web 2.0? , 2006, CROS.

[22]  Hsinchun Chen.  Dark Web: Exploring and Data Mining the Dark Side of the Web , 2011 .

[23]  Wil M.P. van der Aalst.  Process Mining: Overview and Opportunities , 2012, TMIS.

[24]  T. Davenport.  Competing on analytics. , 2006, Harvard business review.

[25]  D. Murphey,et al.  The World Is Flat: A Brief History of the Twenty-First Century , 2006 .

[26]  Garry Robins,et al.  An introduction to exponential random graph (p*) models for social networks , 2007, Soc. Networks.

[27]  Alan W. Brown,et al.  Current State , 2019, ECE Energy Series.

[28]  Martina Morris,et al.  ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks. , 2008, Journal of statistical software.

[29]  L. Bettencourt,et al.  The power of a good idea: Quantitative modeling of the spread of ideas from epidemiological models , 2005, physics/0502067.

[30]  Hsinchun Chen,et al.  AI, E-government, and Politics 2.0 , 2009, IEEE Intelligent Systems.

[31]  J. E. Hirsch,et al.  An index to quantify an individual's scientific research output , 2005, Proc. Natl. Acad. Sci. USA.

[32]  Barbara Wixom,et al.  The Current State of Business Intelligence , 2007, Computer.

[33]  David M. Blei,et al.  Probabilistic topic models , 2012, Commun. ACM.

[34]  Yubo Chen,et al.  The Phase Transition of Markets and Organizations: The New Intelligence and Entrepreneurial Frontier , 2010 .

[35]  Melnned M. Kantardzic.  Big Data Analytics , 2013, Lecture Notes in Computer Science.

[36]  Hsinchun Chen,et al.  Digital Government : E-Government Research , Case Studies , and Implementation , 2009 .

[37]  Ian Witten,et al.  Data Mining , 2000 .

[38]  Alon Y. Halevy,et al.  Crowdsourcing systems on the World-Wide Web , 2011, Commun. ACM.

[39]  Paulo B. Góes,et al.  Business Intelligence and Analytics Education, and Program Development: A Unique Opportunity for the Information Systems Discipline , 2012, TMIS.

[40]  Surajit Chaudhuri,et al.  An overview of business intelligence technology , 2011, Commun. ACM.

[41]  Jamie Callan,et al.  OntoCop: Constructing Ontologies for Public Comments , 2009 .

[42]  David Karpf,et al.  Blogosphere Research: A Mixed-Methods Approach to Rapidly Changing Systems , 2009 .

[43]  Ove Frank,et al.  http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained , 2007 .

[44]  G. Brumfiel.  High-energy physics: Down the petabyte highway , 2011, Nature.

[45]  Duncan J. Watts,et al.  Six Degrees: The Science of a Connected Age , 2003 .

[46]  David A. Patterson,et al.  Technical perspective: the data center is the computer , 2008, CACM.

[47]  Hsinchun Chen,et al.  Intelligence and Security Informatics for International Security: Information Sharing and Data Mining (Integrated Series in Information Systems) , 2006 .

[48]  Alan R. Hevner,et al.  Design Science in Information Systems Research , 2004, MIS Q..

[49]  Hsinchun Chen,et al.  Terrorism Informatics: Knowledge Management and Data Mining for Homeland Security , 2008 .

[50]  Gerald Salton,et al.  Automatic text processing , 1988 .

[51]  A. Hanauer,et al.  Opportunities and Challenges in Association and Episode Discovery from Electronic Health Records , 2022 .

[52]  S. Chatterjee,et al.  Design Science Research in Information Systems , 2010 .

[53]  J. Manyika.  Big data: The next frontier for innovation, competition, and productivity , 2011 .

[54]  Andy Cowper,et al.  All too much , 2004 .

[55]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[56]  Tim O'Reilly,et al.  What is Web 2.0: Design Patterns and Business Models for the Next Generation of Software , 2007 .

[57]  Patricia L. Brantingham,et al.  Computational Criminology , 2011, 2011 European Intelligence and Security Informatics Conference.

[58]  J. Avery,et al.  The long tail. , 1995, Journal of the Tennessee Medical Association.

[59]  Bill Hostmann,et al.  Magic Quadrant for Business Intelligence Platforms , 2012 .

[60]  Daniel Choquet,et al.  The data deluge , 2012, Nature Cell Biology.

[61]  Veda C. Storey,et al.  Design science in the information systems discipline: an introduction to the special issue on design science research , 2008 .

[62]  Hsinchun Chen,et al.  Dark Web: Exploring and Mining the Dark Side of the Web , 2012, 2011 European Intelligence and Security Informatics Conference.

[63]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.