  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, decentralized, distributed concept for enterprise data
management. 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, implementation
strategies, 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 organizations 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 comprehension of the
overall concept. In our work, we derive multiple implementation strategies and
suggest organizations introduce a cross-domain steering unit, 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 implementation strategies 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 preliminary guidelines for the successful adoption of data
mesh.
