A review and future direction of agile, business intelligence, analytics and data science

We provide review of agile methodologies, business intelligence and data science.We explain the current trends and interplay between agile methodologies, business intelligence and data science.We develop lifecycle approaches for BI, analytics and data science.We discuss challenges and future directions for agile methodologies, business intelligence and data science. Agile methodologies were introduced in 2001. Since this time, practitioners have applied Agile methodologies to many delivery disciplines. This article explores the application of Agile methodologies and principles to business intelligence delivery and how Agile has changed with the evolution of business intelligence. Business intelligence has evolved because the amount of data generated through the internet and smart devices has grown exponentially altering how organizations and individuals use information. The practice of business intelligence delivery with an Agile methodology has matured; however, business intelligence has evolved altering the use of Agile principles and practices. The Big Data phenomenon, the volume, variety, and velocity of data, has impacted business intelligence and the use of information. New trends such as fast analytics and data science have emerged as part of business intelligence. This paper addresses how Agile principles and practices have evolved with business intelligence, as well as its challenges and future directions.

[1]  V. Mayer-Schönberger.  [Big data: a revolution that will transform our lives]. , 2015, Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz.

[2]  Scott W. Ambler,et al.  The Disciplined Agile Process Decision Framework , 2016, SWQD.

[3]  Chin-Yun Hsieh,et al.  Patterns for Continuous Integration Builds in Cross-Platform Agile Software Development , 2015, J. Inf. Sci. Eng..

[4]  Xiaofeng Wang,et al.  Where agile research goes: starting from a 7-year retrospective (report on agile research workshop at XP2009) , 2009, SOEN.

[5]  F. Burstein,et al.  Handbook on Decision Support Systems 1 , 2008 .

[6]  Neda Kaleshovska,et al.  Contribution of scrum in managing successful software development projects , 2015 .

[7]  Darius Hedgebeth.  Data‐driven decision making for the enterprise: an overview of business intelligence applications , 2007 .

[8]  Ralph Hughes.  Agile Data Warehousing Project Management: Business Intelligence Systems Using Scrum , 2012 .

[9]  Rachel Schutt,et al.  Doing Data Science , 2013 .

[10]  Julie E. Kendall,et al.  AGILE METHODOLOGIES AND THE LONE SYSTEMS ANALYST: WHEN INDIVIDUAL CREATIVITY AND ORGANIZATIONAL GOALS COLLIDE IN THE GLOBAL IT ENVIRONMENT , 2004 .

[11]  Mihaela Muntean,et al.  Agile BI - the Future of BI , 2013 .

[12]  William Yeoh,et al.  Critical Success Factors for Business Intelligence Systems , 2010, J. Comput. Inf. Syst..

[13]  G. Parra,et al.  Mayer Schönberger, Viktor; Cukier, Kenneth. Big Data: A Revolution That Will Transform How We Live, Work and Think. London: John Murray, 2013 , 2015 .

[14]  Scott Ambler,et al.  Agile Database Techniques: Effective Strategies for the Agile Software Developer , 2003 .

[15]  Tore Dybå,et al.  Empirical studies of agile software development: A systematic review , 2008, Inf. Softw. Technol..

[16]  Soumendra Mohanty,et al.  Big Data Imperatives: Enterprise Big Data Warehouse, BI Implementations and Analytics , 2013 .

[17]  Efraim Turban,et al.  Business Intelligence: Second European Summer School, eBISS 2012, Brussels, Belgium, July 15-21, 2012, Tutorial Lectures , 2013 .

[18]  Muthu Ramachandran,et al.  Cloud Computing Adoption Framework – a security framework for business clouds , 2015 .

[19]  Jeff Sutherland,et al.  Manifesto for Agile Software Development , 2013 .

[20]  James Y. L. Thong,et al.  Acceptance of Agile Methodologies: A Critical Review and Conceptual Framework , 2009, Decis. Support Syst..

[21]  Ram S. Sriram.  Business Intelligence - in the Context of Global Business Environment , 2008 .

[22]  Pekka Abrahamsson,et al.  Agile Software Development Methods: Review and Analysis , 2017, ArXiv.

[23]  Markus Grünwald,et al.  Business Intelligence , 2009, Informatik-Spektrum.

[24]  Alistair Cockburn,et al.  Using Both Incremental and Iterative Development , 2008 .

[25]  Victor I. Chang,et al.  The Business Intelligence as a Service in the Cloud , 2014, Future Gener. Comput. Syst..

[26]  Victor I. Chang,et al.  NEURAL COMPUTING IN NEXT GENERATION VIRTUAL REALITY TECHNOLOGY An overview , examples , and impacts offered by Emerging Services and Analytics in Cloud Computing virtual reality , 2017 .

[27]  Daniel A. Keim,et al.  Mastering the Information Age - Solving Problems with Visual Analytics , 2010 .

[28]  Muthu Ramachandran,et al.  Towards Achieving Data Security with the Cloud Computing Adoption Framework , 2016, IEEE Transactions on Services Computing.

[29]  Victor I. Chang,et al.  Model and experimental development for Business Data Science , 2016, Int. J. Inf. Manag..

[30]  Thomas H. Davenport,et al.  Big Data at Work: Dispelling the Myths, Uncovering the Opportunities , 2014 .

[31]  Bernard Marr,et al.  Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance , 2015 .

[32]  Victor I. Chang,et al.  Towards a Big Data system disaster recovery in a Private Cloud , 2015, Ad Hoc Networks.

[33]  Victor I. Chang,et al.  A model to compare cloud and non-cloud storage of Big Data , 2016, Future Gener. Comput. Syst..