Abstract
Data Warehousing systems have been used to support Business Intelligence applications by ingesting operational data and providing analytical data. As data volume, variety, and velocity increased in Big Data contexts, this data architecture needed to be modernised, and Big Data Warehouses emerged as scalable, high-performance, and highly flexible processing systems capable of handling ever-increasing volumes of data. These monolithic techniques, however, create major challenges to data engineering teams in terms of design, development, management, and evolution. Data Mesh emerged as a novel and disruptive concept aimed at data-driven businesses. The research detailed in this paper seeks to characterise Data Mesh readiness by examining the elements that influence the adoption choice using the technology-organization- environment (TOE) paradigm. A survey and a set of interviews were used in a multi-case and multi-method approach. Researchers and data triangulation were implemented to ensure rigour and arrive at a comprehensive understanding of Data Mesh adoption. The obtained results demonstrate the successful adoption of Data Mesh once its benefits are well understood, with increased teams’ creativity, data accuracy, data security, data governance and interoperability.
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The interview guide is available here.
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Acknowledgements
This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. We acknowledge the valuable contributions of Delivery Hero, Saxo Bank, Thoughtworks, Brian Leonard, Francisco Sanchez, Kristian Frederiksen, Paul Makkar, Pavel Rabaev, Rasmus Aagaard, and Sean Gustafson.
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Ramos, I., Santos, M.Y., Joshi, D., Pratik, S. (2024). Data Mesh Adoption: A Multi-case and Multi-method Readiness Approach. In: Papadaki, M., Themistocleous, M., Al Marri, K., Al Zarouni, M. (eds) Information Systems. EMCIS 2023. Lecture Notes in Business Information Processing, vol 502. Springer, Cham. https://doi.org/10.1007/978-3-031-56481-9_2
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