Autor(es): Ribeiro, Vitor ; Barata, João ; Cunha, Paulo Rupino da
Data: 2024
Identificador Persistente: https://hdl.handle.net/10316/116679
Origem: Estudo Geral - Universidade de Coimbra
Projeto/bolsa: info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB/PT; info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP/PT;
Assunto(s): Inter-Organizational Data Governance; Maturity Assessment Archetypes; Maturity Model
Organizations increasingly participate in inter-organizational partnerships that exploit business opportunities supported by shared data assets. Hence, data governance is required to establish collaborative operations between the partners, ensure accountability for shared data assets, define data ownership, identify data provenance, and comply with data-related regulations. This paper presents (1) the structure of a data governance maturity model for inter-organizational operations and (2) a set of maturity assessment archetypes for data governance. These results emerge from a research partnership with a major European technology and service provider involved in data collaboration ecosystems for digital and green logistics. Our contribution extends the state-of-the-art on distributed data governance, specifically for increasingly common business ecosystems built on shared data processing, and provides practical tools for organizations to conduct a data governance maturity assessment tailored to their role in such collaborative operations.
This research was funded by Project “Agenda Mobilizadora Sines Nexus” ref. No. 7113, supported by the Recovery and Resilience Plan (PRR) and by the European Funds Next Generation EU, following Notice No. 02/C05-i01/2022, Component 5 - Capitalization and Business Innovation - Mobilizing Agendas for Business Innovation. It was also financed through national funds by FCT - Fundação para a Ciência e a Tecnologia, I.P., in the framework of the Project UIDB/00326/2020 and UIDP/00326/2020. The first author is funded by FCT - Foundation for Science and Technology, I.P., under the Ph.D. grant 2023.00740.BD.
