With the increasing importance of data and artificial intelligence, organizations strive to become more data-driven. However, current data architectures are not necessarily designed to keep up with the scale and scope of data and analytics use cases. In fact, existing architectures often fail to deliver the promised value associated with them. Data mesh is a socio-technical concept that includes architectural aspects to promote data democratization and enables organizations to become truly data-driven. As the concept of data mesh is still novel, it lacks empirical insights from the field. Specifically, an understanding of the motivational factors for introducing data mesh, the associated challenges, best practices, its business impact, and potential archetypes, is missing. To address this gap, we conduct 15 semi-structured interviews with industry experts. Our results show, among other insights, that industry experts have difficulties with the transition toward federated governance associated with the data mesh concept, the shift of responsibility for the development, provision, and maintenance of data products, and the concept of a data product model. In our work, we derive multiple best practices and suggest organizations embrace elements of data fabric, observe the data product usage, create quick wins in the early phases, and favor small dedicated teams that prioritize data products. While we acknowledge that organizations need to apply best practices according to their individual needs, we also deduct two archetypes that provide suggestions in more detail. Our findings synthesize insights from industry experts and provide researchers and professionals with guidelines for the successful adoption of data mesh.
翻译:随着数据和人工智能日益重要,组织努力成为更注重数据的机构。然而,当前的数据架构并不一定旨在跟上数据和分析用例的规模和范围。事实上,现有的架构经常无法提供与之相关的承诺价值。数据网格(Data mesh)是一个包括建筑方面的社会技术概念,旨在促进数据民主化,使组织真正成为注重数据的机构。由于数据网格的概念仍然很新,缺乏实证洞见。具体而言,缺乏了解引入数据网格的动机因素、相关挑战、最佳实践、其商业影响和潜在原型等方面的信息。为解决这个问题,我们对15位行业专家进行了半结构化访谈。我们的结果显示,行业专家在转向与数据网格概念相关的联邦治理、开发、提供和维护数据产品的责任转移以及数据产品模型的概念方面存在困难。在我们的工作中,我们推导出多个最佳实践,并建议组织采用数据织物元素、观察数据产品使用、在早期阶段获得快速成功,并优先考虑优先考虑小而专业的团队,重视数据产品。虽然我们承认,组织需要根据自己的个性化需求应用最佳实践,但我们还是归纳出了两个提供更详细建议的原型。我们的发现综合了行业专家的洞见,并为研究人员和专业人士提供了成功采用数据网格的指南。