GMM-BI: A methodological guide to improve organizacional maturity in Business Intelligence

Keywords: Business Intelligence, BI maturity models, enterprise intelligence, methodological guide in business intelligence


Maturity models in Business Intelligence (BI) put forth a baseline for measuring the value of initiatives in this area, helping organizations to understand where they are and what improvements are needed. In this context, the main problem for organizations that are aware of their current level of BI maturity and want to implement improvements is to know how to make them. Currently, there are no studies guiding organizations to make BI maturity improvements. This paper presents a framework called GMM-BI to measure, analyze, plan, and implement BI maturity improvements in an organization for a given key process area (KPA). In general, the framework is instanced in KPA knowledge for which three procedures are defi ned so that organizations can perform the activities defi ned for a given KPA. In addition, the proposed guide considers a methodological path to implement improvements in the current maturity state of the KPA involved. This methodological path describes the different phases, activities, and tasks to be performed by an organization to implement these improvements. The result of applying this methodological guide is a qualitative description of the current BI maturity level of the organization and a quantitative characterization of the maturity improvement of the processes making up the KPA involved. In addition, this methodological guide is applied in three case studies.

= 190 veces | PDF
= 191 veces|


Download data is not yet available.

Author Biographies

Roberto David Prieto-Morales, Universidad Católica del Norte

Departamento de Ingeniería de Sistemas y Computación

Claudio Juvenal Meneses-Villegas, Universidad Católica del Norte

Departamento de Ingeniería de Sistemas y Computación

Vianca Rosa Vega-Zepeda, Universidad Católica del Norte

Departamento de Ingeniería de Sistemas y Computación


Gartner, Inc. Business Intelligence, Mobile and Cloud Top the Technology Priority List for CIOs in Asia: Gartner Executive Programs Survey. 2012. Available on: Accessed: July 26, 2013.

Gartner, Inc. Executive Program Survey of More Than 2,000 CIOs Shows Digital Technologies Are Top Priorities in 2013. 2013. Available on: Accessed: July 26, 2013.

I. Rajterič. “Overview of Business Intelligence Maturity Models”. Management. Vol. 15. 2010. pp. 47-67.

J. Huffman, L. Whitman. Developing a Capability Maturity Model for Enterprise Intelligence. Proceedings of the 18th World Congress of the International Federation of Automatic Control (IFAC). Milano, Italy. 2011. pp. 13086-13091.

R. Prieto, C. Meneses, V. Vega. “Análisis comparativo de modelos de madurez en inteligencia de negócios”. Ingeniare. Vol. 23. 2015. pp. 361-371.

D. Wells. Business Analytics — Getting the Point. 2008. Available on: Accessed: May 16, 2012.

Software Engineering Institute (SEI), Carnegie Mellon University. Capability Maturity Model Integration (CMMI) Version 1.1. Technical report CMU/SEI-2002-TR-029. Carnegie Mellon University. Pittsburgh, USA. 2002. pp. 81-91.

R. Prieto. Guía para mejorar la madurez en inteligencia de negocios (GMM-BI). Master’s Thesis, Catholic University of the North. Antofagasta, Chile. 2014. pp. 86-147.

T. Davenport. “Some principles of knowledge management”. CIO Journal. Vol. 1. 1996. pp. 12-18.

A. Krishnan. Knowledge bases, Ontologies and Key- Value Stores. Available on: Accessed: July 26, 2013.

M. Darling. Getting Better at Getting Better—How the After Action Review Really Works. Proceedings of the 15th Annual Pegasus Conference. San Francisco, USA. 2005.

C. O’Dell, C. Grayson. If only we knew what we know: identification and transfer of internal best practice. California Management Review. Vol. 40. 1998. pp. 154- 174.

P. Peña. To know or not to be. Conocimiento, el oro gris de las organizaciones. 1st ed. Ed. Fundación DINTEL. Madrid, Spain. 2001. pp. 1-47.

I. Nonaka, H. Takeuchi. The knowledge-creating company: how Japanese companies create the dynamics of innovation. 1st ed. Ed. Oxford University Press. New York, USA. 1995. pp. 1-284.

J. Han, M. Kamber, J. Pei. Data Mining. Concepts and Techniques. 2nd ed. Ed. Morgan Kaufmann. San Francisco, USA. 2012. pp. 1-42.

J. Giraldo, J. Jiménez. “Caracterización del proceso de obtención de conocimiento y algunas metodologías para crear proyectos de minería de datos”. Revista Latinoamericana de Ingeniería de Software. Vol. 1. 2013. pp. 42-44.

Y. Marcano, R. Talavera. “Minería de datos como soporte a la toma de decisiones empresariales”. Opción. Vol. 23. 2007. pp. 104-118.

T. Koulopoulos, C. Frappaolo. Lo fundamental y lo más efectivo acerca de la gerencia del conocimiento. 1st ed. Ed. McGraw Hill Interamericana. Bogotá, Colombia. 2000. pp. 1-204.

E. Turban, J. Aronson. Decision support systems and intelligent systems. 1st ed. Ed. Prentice Hall. New Jersey, USA. 2001. pp. 1-865.

How to Cite
Prieto-Morales R. D., Meneses-Villegas C. J., & Vega-Zepeda V. R. (2015). GMM-BI: A methodological guide to improve organizacional maturity in Business Intelligence. Revista Facultad De Ingeniería Universidad De Antioquia, (76), 7-18.