The concept of Dataspaces was proposed in a 2005 paper by Franklin, Halevy and Maier as a new data management paradigm to help unify the diverse efforts on flexible data models, data cleaning, and data integration being pursued by the database community. The key idea is to expand the scope of database technology by moving away from the schema-first nature of traditional data integration techniques and instead, support a spectrum of degrees of structure, schema conformance, entity resolution, quality and so on. Dataspaces offer an initial, lightweight set of useful services over a group of data sources, allowing users, administrators, or programs to improve the semantic relationships between the data in an incremental and iterative manner. This “pay-as-you-go” philosophy is at the heart of the Dataspaces approach. In this talk, I will revisit the motivation behind Dataspaces, outline the evolution of thinking on the topic since the 2005 proposal, and describe our current work focused on developing a metrics-driven framework for Dataspace systems.