Insights from the CGMA Data Competencies Model: The Role of Data Culture to the Value Creation Process
Keywords:
Business Intelligence, Big Data, Data Competencies, Value Creation,Abstract
With the emergence of the big data phenomena, the business intelligence maturity approach tends to be limiting and lacks the capability to capture and engage with the relevant variables and develop into a theoretical framework to explain the big data economy. The concept of data competencies proposed by Chartered Global Management Accountants (CGMA) was thought to be a more comprehensive alternative framework to explore the phenomena. The four types of data competencies, namely, data culture, data management, data analytics and value creation were used to construct the conceptual framework to understand and explain the big data initiative implementation process. It was found that data culture tends to moderate the data management-data analytics relationship. In addition, data analytics appears to partially mediate the impact of data management on value creation. The implications of these findings confirm that data culture is the essential foundation to the value creation process.References
Damjanovic, V. & Behrendt, W. “UNDERSTANDER: Business Intelligence Seeker – User Agent”, 37th Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1491 – 1496, 2014.
Yoon, T. S., Ghosh, B. & Jeong, B. K.. “User Acceptance of Business Intelligence (BI) Application: Technology, Individual Difference, Social Influence, and Situational Constraints”. 47th Hawaii International Conference on System Sciences (HICSS), pp. 3758 – 3766, 2014.
Harpham, A. The APM Group's assessment model for portfolio, program and project management, its PRINCE2 maturity model and their benefits to organizations, Available from :. [Retrieved: 27 December 2009].
Paulk, M. C., Curtis,B., Chrissis, M. B. & Weber, C. “Capability Maturity Model for Software, Version 12”, Software Engineering Institute/Carnegie Mellon University, 2006.
Rajterič, I. H. “Overview of Business Intelligence Maturity Models”, International Journal of Human Science. Vol. 15, No. 1, pp 47-67, 2010.
Gartner Research, IT Score Overview for Business Intelligence and Performance Management. Available from :. [Retrieved: 11 November 2010].
Wong, K. L., Chuah, M. H. & Ong S. F. “Are Malaysian companies ready for the big data economy? A business intelligence approach”, .International Conference on Accounting Studies (ICAS) 2015, 17-20 August 2015, Johor Bahru, Johor, Malaysia.
Sekaran, U. & Bougie, R. Research Methods for Business: A Skill Building Approach, 5th Ed. John Wiley & Sons: West Sussex, UK, 2009.
CGMA Report, CGMA briefing - Big data: Readying business for the big data revolution, 2014.
McKinsey Report, Views from the front lines of the data revolution, McKinsey & Co, 2014.
Chuah, M. H and Wong, K. L., “A framework for accessing an Enterprise Business Intelligence Maturity Model: Delphi study approach”, African Journal of Business Management, Vol.6 (23), pp 6880-6889, 2012.
Judd, C. M., Yzerbyt, V. Y., & Muller, D., “Mediation and moderation”, Handbook of research methods in social and personality psychology, Vol. 2, pp. 653-676, 2014.
Dawson, J. F., “Moderation in management research: What, why, when, and how”, Journal of Business Psychology, 29: 1-19, 2014.
Andrews, J. C., Goes, P. B., & Gupta, A. “Understanding adolescent intentions to smoke: an examination of relationships among social influences, prior trial behaviors, and antitobacco campaign advertising”, Journal of Marketing, 68,110-123, 2004.
Nunnally, J., Psychology Theory. New York: McGraw-Hill, 1978.
Barclay, D. W., Thomson, R., Higgins, C., “The partial least squares (PLS) approach to causal modelling: personal computer adoption and use an illustration”, Technology Studies, 2 (2), 285-309, 1995.
Fornell, C., & Larcker, D. F., “Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, 19, 39-50, 1981.
Chin, W. W., Marcolin, B. L., & Newsted, P. R., “A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and voice mail emotion/adoption study”, 17th International Conference on Information Systems, 16-18 December 1996, Cleveland, Ohio, 1996.
Chen, J. S. & Tsou, H. T., “Performance effects of IT capability, service process innovation, and the mediating role of customer service”, Journal of Engineering and Technology Management, 29: 71-94, 2012.
Downloads
Published
How to Cite
Issue
Section
License
TRANSFER OF COPYRIGHT AGREEMENT
The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):
- All proprietary right other than copyright, such as patent rights
- The right to make further copies of all or part of the published article for my use in classroom teaching
- The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
- The right to make copies of the published work for internal distribution within the institution that employs me
I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.