Paper Number
ECIS2026-2681
Paper Type
CRP
Abstract
This paper presents the Competence-Aware Decision Support Architecture (CADSA), a design science research artifact developed to rethink how digital technologies can improve competence management and human oversight in data-driven organizations. CADSA integrates the Competence-Aware Requirement Engineering (CARE) and Competency-Aware Human-in-the-Loop (CA-HIL) frameworks with a taxonomy of automation tools. This integration aligns decision support with validated competence structures, project risks, and automation capabilities. The CADSA prototype was developed as a web-based application, and its effectiveness was assessed through four iterative cycles involving 20 expert interviews from both academic and industry sectors. The findings indicate enhanced usability, transparency, and elevated learning value, thereby demonstrating the potential of digital decision support environments to facilitate the management of data science projects and the advancement of data science teams.
Recommended Citation
Holtkemper, Maike and Beecks, Christian, "Designing The Competence-Aware Decision Support Architecture (Cadsa): Reimagining Human-Automation Collaboration In Data Science Projects" (2026). ECIS 2026 Proceedings. 13.
https://aisel.aisnet.org/ecis2026/comp_mgmt/comp_mgmt/13
Designing The Competence-Aware Decision Support Architecture (Cadsa): Reimagining Human-Automation Collaboration In Data Science Projects
This paper presents the Competence-Aware Decision Support Architecture (CADSA), a design science research artifact developed to rethink how digital technologies can improve competence management and human oversight in data-driven organizations. CADSA integrates the Competence-Aware Requirement Engineering (CARE) and Competency-Aware Human-in-the-Loop (CA-HIL) frameworks with a taxonomy of automation tools. This integration aligns decision support with validated competence structures, project risks, and automation capabilities. The CADSA prototype was developed as a web-based application, and its effectiveness was assessed through four iterative cycles involving 20 expert interviews from both academic and industry sectors. The findings indicate enhanced usability, transparency, and elevated learning value, thereby demonstrating the potential of digital decision support environments to facilitate the management of data science projects and the advancement of data science teams.
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