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. This catchword article aims to demystify and contrast the three interrelated concepts and to integrate them into an overarching framework. Further, we propose a research agenda highlighting open questions for the Business and Information Systems Engineering community to address the underlying challenges of scaling data and analytics in enterprises.