Abstract

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.

Recommended Citation

Ribeiro, V., Barata, J. & Rupino Da Cunha, P. (2024). A Maturity Model for Data Governance in Decentralized Business Operations: Architecture and Assessment Archetypes. In B. Marcinkowski, A. Przybylek, A. Jarzębowicz, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Harnessing Opportunities: Reshaping ISD in the post-COVID-19 and Generative AI Era (ISD2024 Proceedings). Gdańsk, Poland: University of Gdańsk. ISBN: 978-83-972632-0-8. https://doi.org/10.62036/ISD.2024.5

Paper Type

Full Paper

DOI

10.62036/ISD.2024.5

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A Maturity Model for Data Governance in Decentralized Business Operations: Architecture and Assessment Archetypes

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.