Oriental Journal of Computer Science and Technology

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An Evaluation of Big Data Analytics Projects and The Project Predictive Analytics Approach


Affiliations
1 Atlantic International University 900 Fort Street Mall 40 Honolulu, Hawaii 96813, United States
 

Big Data is the process of managing large volumes of data obtained from several heterogeneous data types e.g. internal, external, structured and unstructured that can be used for collecting and analyzing enterprise data. The purpose of the paper is to conduct an evaluation of Big Data Analytics Projects which discusses why the projects fail and explain why and how the Project Predictive Analytics (PPA) approach may make a difference with respect to the future methods based on data mining, machine learning, and artificial intelligence. A qualitative research methodology was used. The research design was discourse analysis supported by document analysis. Laclau and Mouffe’s discourse theory was the most thoroughly poststructuralist approach.

Keywords

Assignment Decisions, Big Data, Communication Methodology, Project Manager.
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  • Ajah, I.A. and Nweke, H.F. (2019). Big Data and Business Analytics: Trends, Platforms, Success Factors and Applications, Big Data and Cognitive Computing, 3(32), 1-30.

  • Ali, A., Qadir, J., Rasool, R., Sathiaseelan, A., Zwitter, A., and Crowcroft, J. (2016). Big Data for Development: Applications and Techniques, Big Data Analytics, 1(2), 1-24.

  • Armstrong, M.B., Landers, R.N., and Collmus, A.B. (2016). Gamifying Recruitment, Selection, Training, and Performance Management: Game-thinking in Human Resource Management, In Gangadharbatla, H., and Davis, D.Z. (Eds), Emerging Research and Trends in Gamification, 140-165, Hershey: IGI Global.

  • Axr yd, S. (2019). Why 85% of Big Data Projects Fail, www.digitalnewsasia.com/insights/why-85-big-data-projects-fail [Accessed 10 September 2019]

  • Bartlett, C.A. and Ghoshal, S. (2011). Building Competitive Advantage through People, MIT Sloan Management Review, 84(2), 34-45.

  • Beringer, C., Jonas, D. and Kock, A. (2013).Behaviour of Internal Stakeholders in Project Por tfolio Management and its Impact on Success, International Journal of Project Management, 31(6), 830-846.

  • Ber kun, S. (2005). The Ar t of Project Management, New York: O’Reilly.

  • ClickUp (2019) https://clickup.com/ 9. Cristobal, J.R.S. (2017). Complexity in Project Management, CENTERIS Inter national Conference on Project Management, 8-10 November 2017, Barcelona, Spain.

  • Demirkan, H. and Dal, B. (2014). Big Data, Big Opportunities, Big Decisions, Harvard Business Review Turkish Edition, March 2014, 28-30.

  • Dollan, K. 2010. Address Project Failure Through PRINCE2, London: Best Management Practice, White Paper, Stationery Office.

  • EpicFlow (2019) How AI Will Change the Future of Project Management https://www.epicflow.com/blog/how-ai-will-change-the-future-of-project-management/

  • Garavan, T.N., Carbery, R., and Rock, A. (2012). Mapping Talent Development: Definition, Scope and Architecture, European Journal of Training and Development, 36(2012): 5–24.

  • Han, J., Kamber, M., and Pei, J. (2012). Data Mining: Concepts and Techniques, Third Edition, Waltham, MA: Morgan Kaufmann, and Imprint of Elsevier.

  • Hanson, E. (2011). Talent Reviews and High-Potential Identification: Overcoming five Common Challenges, Pittsburgh; Development Dimensions International.

  • Hemlata, C. and Gulia, P. (2016). Big Data Analytics, Research Journal of Computer and Information Technology Sciences, 4(2), 1-4.

  • Henderson, P. (2006). Why Large IT Projects Fail, Southampton: University of Southampton, School of Electronics and Computer Science.

  • Hussain, M. and Manhas, J. (2016). Artificial Intelligence for Big Data: Potential and Relevance, International Academy of Engineering and Medical Research, 1(1), 1-5.

  • Jorgensen M, Phillips LJ (2002). “Discourse Analysis as Theory and Method”, SAGE Publications, London • Thousand Oaks • New Delhi.

  • Kerzner, H.R. (2013). Project Management: A Systems Approach to Planning, Scheduling, and Controlling, 11th Edition, Hoboken: Jon Wiley and Sons.

  • Liu, Y., Han, H. DeBello, J.E. (2018). The Challenges of Business Analytics: Successes and Failures, Proceedings of the 51st Hawaii International Conference on System Sciences.

  • Mahmud, N.B. (2009). A Study of Effectiveness of Project Management Consultancy (PMC) Services, University of Malaysia Pahang, A Report Submitted in Partial Fulfilment of the Requirements for the Award of the Degree of Bachelor of Civil Engineering.

  • Makuia, A., Zadeh, P.M., Bagherpour, M., and Jabbarzadeh, A. (2018). A Structural Equation Modelling Approach to Examine the Relationship between Complexity Factors of a Project and the Merits of Project Manager, Journal of Project Management, 3(2018), 1-12.

  • Mazur, A.K. and Pisarski, A. (2015). Major Project Managers’ Internal and External Stakeholder Relationships: The Development and Validation of Measurement Scales, International Journal of Project Management, 33(2015), 1680-1691.

  • Mpingajira, M. (2013). E-government Project Failure in Africa: Lessons for Reducing Risk, African Journal of Business Management, 7(32), 3196-3201.

  • Mutula, S. and Mostert, J. (2010). Challenges and Opportunities of E-government in South Africa, Electron Lib.28(1), 38-53.

  • National Audit Office. (2006). National Programme for IT in the NHS, London: Report by the Comptroller and Auditor General.

  • Nikolaou, I. and Foti, K. (2017). Personnel Selection and Personality, In Zeigler-Hill, V. and Shackelford, T. (Eds), The SAGE Handbook of Personality and Individual Differences, New York; Sage, 1-37.

  • Nikumbh, A.R. and Pimplikar, S.S. (2014). Role of Project Management Consultancy in Construction Project, IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), 10(6), 14-19.

  • Patanakul, P. (2015). Project Manager Assignment: A Strategic Perspective, Open Economics and Management Jour nal, 2(Suppl 1: M4), 21-28.

  • Patanakul, P., Milosevic, D., and Anderson, T. (2003). Assigning Projects to Project Managers in a Multiple-Project Management Environment: A Pilot Study of a Decision Support Model, Management of Engineering and Technology, 2003. PICMET 03. Technology Management for Reshaping the World, Portland International Conference, 20-24 July 2003.

  • Patanakul, P., Milosevic, D., and Anderson, T. (2007). A Decision Support Model for Project Manager Assignments, Engineering and Technology Management Faculty Publications and Presentations, Paper 22.

  • Power, T. (2013). What are examples of Database Project Failures? Available on www.dssresources.com [Accessed 4 September 2019].

  • Pospieszny P. (2017) Application of Data Mining Techniques in Project Management – an Overview Research Gate https://www.researchgate.net/publication/317973749

  • Project Management Institute. (2013). A Guide to the Project Management Body of Knowledge, 5th Edition, Newton Square: Pennsylvania; Publications Division.

  • Qassim, A. (2012). Why Information Systems Projects Fail: Guidelines for Successful Projects, Oman: State Audit Institution.

  • Riahi, Y. (2017). Identifying, Analysing, Executing, Planning, and Monitoring Project Stakeholders: Analysis and Management Processes, SSRG Inter national Jour nal of Economics and Management Studies, 4(3), 37-42.

  • Richardson, T.M., Marion, J. and Onu, S. (2015). Insights from Global Project Managers: Career Advice for Successful International Assignments,International Journal of Business and Management, 10(10), 9-18.

  • Salgado, J.F., Moscoso, S., and Alonso, P. (2013). The Sub-Dimensional Structure of the Hogan Personality Inventory, International Jour nal of Selection and Assessment, 21(2), 277-285.

  • Shaw, K. (2012). Lessons from a Billion Dollar Project Failure, Available on www.itbusinessedge.com [Accessed 5 September 2019].

  • Shibani, A. and Sukumar, D. (2015). The Role of the Project Manager in Construction Projects in India, Chinese Business Review, 14(6), 298-324.

  • Singh, S., Firdaus, T., and Sharma, A.K. (2015). Sur vey on Big Data Using Data Mining, International Journal of Engineering Development and Research,3(4), 135-143.

  • Su, J. (2013). Enabling Risk Analytics for Project Success, Ontario: Deloitte and Touche.

  • Sun, Z. (2017). Big Data Analytics and Artificial Intelligence, PNG University of Technology, UNITECH Research Committee Seminar, 2 May 2017.

  • Swailes, S. (2013). The Ethics of Talent Management, Business Ethics: A European Review 22 2013, 32-46.

  • Torfi, F. and Rashidi, A. (2011). Selection of Project Managers in Construction Firms Using Analytic Hierarchy Process (AHP) and Fuzzy Topsis: A Case Study, Journal of Construction in Developing Countries, 16(1), 69-89.

  • Vargas, R. V. (2010). Using the analytic hierarchy process (ahp) to select and prioritize projects in a portfolio. Paper presented at PMI® Global Congress 2010—North America, Washington, DC. Newtown Square, PA: Project Management Institute

  • Watson, H.J. (2014). Tutorial: Big Data Analytics: Concepts, Technologies, and Applications, Communications of the Association for Information Systems, 34(65), 1247-1268.

  • Weibl, J. and Hess, T. (2018). Success or Failure of Big Data: Insights of Managerial Challenges from a Technology Assimilation Perspective, Luneburg: Institute for Information Systems and New Media.

  • Windman, J. (2005). IT Biggest Project Failures and What we can Learn from them, Available on www.computerwor ld.com [Accessed 5 September 2019].


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  • An Evaluation of Big Data Analytics Projects and The Project Predictive Analytics Approach

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Authors

Gabriel Kabanda
Atlantic International University 900 Fort Street Mall 40 Honolulu, Hawaii 96813, United States

Abstract


Big Data is the process of managing large volumes of data obtained from several heterogeneous data types e.g. internal, external, structured and unstructured that can be used for collecting and analyzing enterprise data. The purpose of the paper is to conduct an evaluation of Big Data Analytics Projects which discusses why the projects fail and explain why and how the Project Predictive Analytics (PPA) approach may make a difference with respect to the future methods based on data mining, machine learning, and artificial intelligence. A qualitative research methodology was used. The research design was discourse analysis supported by document analysis. Laclau and Mouffe’s discourse theory was the most thoroughly poststructuralist approach.

Keywords


Assignment Decisions, Big Data, Communication Methodology, Project Manager.

References