Paper Number
ECIS2026-1452
Paper Type
CRP
Abstract
This paper explores the alignment between enterprise architecture (EA) and data’s growing role in organizations, through AI, data platforms, and ecosystems. EA is traditionally application-centric, neglecting data and analytics architecture and data products as components. To identify AI use cases and manage data and analytics strategically, managers need insights into data products available in their organization. In response, we propose an integration framework for data products in EA and explore a distinct data and analytics architecture layer as conceptual framing. We present a list of requirements derived from literature, a metamodel, and a set of attributes for representing data products within EA. We evaluate the proposed framework based on qualitative feedback from expert interviews. In conclusion, our findings and validations provide an exploratory, practice-oriented, perceived plausible and useful foundation for integrating data products into enterprise architecture.
Recommended Citation
Mangione, Angelo and Pufahl, Luise, "Exploring Data Products As An Element For Enterprise Architecture" (2026). ECIS 2026 Proceedings. 1.
https://aisel.aisnet.org/ecis2026/entmodel/entmodel/1
Exploring Data Products As An Element For Enterprise Architecture
This paper explores the alignment between enterprise architecture (EA) and data’s growing role in organizations, through AI, data platforms, and ecosystems. EA is traditionally application-centric, neglecting data and analytics architecture and data products as components. To identify AI use cases and manage data and analytics strategically, managers need insights into data products available in their organization. In response, we propose an integration framework for data products in EA and explore a distinct data and analytics architecture layer as conceptual framing. We present a list of requirements derived from literature, a metamodel, and a set of attributes for representing data products within EA. We evaluate the proposed framework based on qualitative feedback from expert interviews. In conclusion, our findings and validations provide an exploratory, practice-oriented, perceived plausible and useful foundation for integrating data products into enterprise architecture.
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