Academic knowledge building has progressed for the past few centuries using small data studies characterized by sampled data generated to answer specific questions. It is a strategy that has been remarkably successful, enabling the sciences, social sciences and humanities to advance in leaps and bounds. This approach is presently being challenged by the development of big data. Small data studies will however, we argue, continue to be popular and valuable in the future because of their utility in answering targeted queries. Importantly, however, small data will increasingly be made more big datalike through the development of new data infrastructures that pool, scale and link small data in order to create larger datasets, encourage sharing and reuse, and open them up to combination with big data and analysis using big data analytics. This paper examines the logic and value of small data studies, their relationship to emerging big data and data science, and the implications of scaling small data into data infrastructures, with a focus on spatial data examples.