Data products, data mesh, and data fabric

Turning data into action has been a driving theme for decades (Legner et al. 2020;Chen et al. 2012).However, despite the growing awareness that data-driven innovation is key to corporate success, enterprises still struggle to put data to effective use (Desai et al. 2022).Neither the technological advancements (Abbasi et al. 2016) nor improved data management practices (Legner et al. 2020) have resolved the fundamental issues and barriers in leveraging data and analytics at the enterprise scale.First, firms have traditionally focused on building data warehouses and business intelligence tools, which are strongly governed, to provide curated, high-quality data to end-users (Watson 2002;Negash 2004).With data lakes and advanced analytics, this approach has failed to scale and meet the high demand for data from a growing number of analytics use cases.Not only have data lakes often turned into ''swamps,'' but data often resides in silos and analysts waste time finding and accessing relevant data sources (Giebler et al. 2009).Second, most organizations have assigned data responsibilities to a few experts in central groups who are in charge of providing data and supporting business users.These centralized data teams have become bottlenecks to scaling data-driven innovation across the whole enterprise (Someh et al. 2023).While they are highly specialized, they often lack business domain knowledge, which makes it difficult to embed data and analytics in all parts of the organization.To overcome these ''failure modes of data management'' (Dehghani 2020), three concepts for using data more effectively and efficiently have recently emerged: data product, data mesh, and data fabric.These concepts are hotly debated as a paradigm shift in data and analytics practice.By defining socio-technical principles beyond the underlying technology stack, they aim to bring scale and standardization to meet the informational needs of an increasing number of internal or external data consumers.While each of the three concepts emphasizes specific aspects, they also share common themes such as providing an enterprise-wide focus on data and analytics, a focus on decentralized and agile data teams, as well as the effective usage of data.However, from an academic perspective, we do not really know whether and how these concepts differ from each other and whether they really constitute a fundamental paradigm shift in data and analytics or just reflect an evolution of existing concepts.