Holistic Model of Advanced Analytics to Optimize Organizational Decision-Making

In increasingly data-intensive organizational environments, decision-making processes require analytical frameworks capable of integrating data governance, advanced analytics, and strategic interpretation under a unified structure. This study proposes a holistic advanced analytics model designed to optimize organizational decision-making through the integration of data quality, data integration, analytical capabilities, and data-driven storytelling within a continuous decision-support lifecycle. The proposed model was developed using the Design Science Research (DSR) methodology and structured according to the intelligence, design, and choice phases of the classical decision-making process. The framework incorporates internationally recognized standards and methodologies, including ISO/IEC 25012, ISO/IEC 11179, CRISP-DM, DataOps, and analytics value chain principles, enabling methodological interoperability and adaptive analytical governance. The resulting artifact was conceptually validated through expert judgment involving seven specialists in analytics, business intelligence, and organizational decision-making. The evaluation produced average scores ranging from 3.29 to 4.57 on a five-point Likert scale, with agreement levels reaching 85.71% in the highest-rated dimension. The results indicate favorable perceptions regarding the model’s consistency, interpretability, usefulness, and organizational applicability. The proposed model contributes an integrative and adaptive framework that bridges fragmented analytical practices and supports more informed, scalable, and context-aware organizational decisions.

@article{Morales-Arevalo2026,
  title     = {Holistic Model of Advanced Analytics to Optimize Organizational Decision-Making},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Juan Carlos Morales-Arevalo and Ciro Rodríguez},
  doi       = {10.14569/IJACSA.2026.0170654},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170654}
}