Towards Avoiding the Data Mess: Industry Insights from Data Mesh Implementations

• DOI: 10.48550/arxiv.2302.01713
• Publication Date: 2023-01-01
• Authors: Jan Bode; Niklas Kühl; Dominik Kreuzberger; Sebastian Hirschl
• Keywords: Data governance; Implementation; Scope (computer science); Sensemaking; Business Intelligence; Archetype

With the increasing importance of data and artificial intelligence, organizations strive to become more data-driven. However, current architectures are not necessarily designed keep up with scale scope analytics use cases. In fact, existing often fail deliver promised value associated them. Data mesh is a socio-technical, decentralized, distributed concept for enterprise management. As still novel, it lacks empirical insights from field. Specifically, an understanding motivational factors introducing mesh, challenges, implementation strategies, its business impact, potential archetypes missing. To address this gap, we conduct 15 semi-structured interviews industry experts. Our results show, among other insights, that have difficulties transition toward federated governance concept, shift responsibility development, provision, maintenance products, comprehension overall concept. our work, derive multiple strategies suggest introduce cross-domain steering unit, observe product usage, create quick wins in early phases, favor small dedicated teams prioritize products. While acknowledge need apply according their individual needs, also deduct two provide suggestions detail. findings synthesize experts researchers professionals preliminary guidelines successful adoption mesh.