Constructing MIDA5: A Design Science Approach for a User-Centered Data Analytics Methodology for Business Process Improvement

Data Analytics methodologies provide structured approaches for transforming organizational data into actionable knowledge. However, many existing methodologies emphasize technical activities while offering limited support for stakeholder participation, user-centered validation, and organizational adoption. This study presents the construction of MIDA5, a Data Analytics methodology for Business Process Improvement developed using the Design Science Research paradigm. The research combined a comparative analysis of existing Data Analytics methodologies with an empirical experimentation process conducted in an organizational environment. The experimentation involved the execution and analytical deconstruction of a previously implemented Data Analytics methodology to identify operational limitations, stakeholder-related challenges, and methodological gaps. The findings were synthesized into design requirements, methodological components, and design needs that guided the construction of MIDA5. The resulting artifact incorporates principles of Business Process Analytics, User-Centered Design, and User Engagement and Gamification Dynamics through a five-phase structure supported by activities, artifacts, and stakeholder validation procedures. The study contributes a traceable Design Science-based development process that connects empirical findings with design decisions and provides a methodological foundation for the complete methodological specification and empirical evaluation of MIDA5. Accordingly, this study focuses on artifact construction rather than on demonstrating the effectiveness of the resulting methodology.

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Published
2026-07-17
DOI
https://doi.org/10.3390/info17070697
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Big Data and Business Intelligence
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Constructing MIDA5: A Design Science Approach for a User-Centered Data Analytics Methodology for Business Process Improvement

Patricia Acosta-Vargas, José Aguilar, Boris Astudillo, Marco Santórum et al.
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Big Data and Business Intelligence
article

Constructing MIDA5: A Design Science Approach for a User-Centered Data Analytics Methodology for Business Process Improvement

Patricia Acosta-Vargas, José Aguilar, Boris Astudillo, Marco Santórum, Mayra Carrión-Toro
article en

Abstract

Data Analytics methodologies provide structured approaches for transforming organizational data into actionable knowledge. However, many existing methodologies emphasize technical activities while offering limited support for stakeholder participation, user-centered validation, and organizational adoption. This study presents the construction of MIDA5, a Data Analytics methodology for Business Process Improvement developed using the Design Science Research paradigm. The research combined a comparative analysis of existing Data Analytics methodologies with an empirical experimentation process conducted in an organizational environment. The experimentation involved the execution and analytical deconstruction of a previously implemented Data Analytics methodology to identify operational limitations, stakeholder-related challenges, and methodological gaps. The findings were synthesized into design requirements, methodological components, and design needs that guided the construction of MIDA5. The resulting artifact incorporates principles of Business Process Analytics, User-Centered Design, and User Engagement and Gamification Dynamics through a five-phase structure supported by activities, artifacts, and stakeholder validation procedures. The study contributes a traceable Design Science-based development process that connects empirical findings with design decisions and provides a methodological foundation for the complete methodological specification and empirical evaluation of MIDA5. Accordingly, this study focuses on artifact construction rather than on demonstrating the effectiveness of the resulting methodology.

InformationVol. 17(7)
Universidad de Los Andes (VE), Universidad de Las Américas (EC), National Polytechnic School (EC), Universidad EAFIT (CO)
Openalex Percentile: Top 5%
Big Data and Business Intelligence
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