Exploring Folk Theories of Data Labor in Human Services

The nonprofit human service sector in the United States is much slower than the private sector in adopting new data technologies to track and improve services, evaluate outcomes, and communicate successes. While for‐profit companies sell data warehouses and analytic services to human service organizations, many organizations lack the resources or administrative commitment to develop data cultures and systems required to foment knowledge production and meaningful data use. Furthermore, documented tensions between key stakeholders, such as funders, managers, frontline staff, and service users, highlight important differences between industry and other sectors in the adoption of data systems. This article draws from interviews and focus groups with many stakeholders and human service organizations to highlight multiple, sometimes conflicting folk theories of data labor in human service organizations. The results demonstrate numerous competing theories for the uses of data and the work of laboring with data in human services. Drawing on these results, we propose a novel competing data values framework for reading data laborers' theories of data and data work, with a horizontal axis spanning from that categorization of poverty of data to information abundance. Our findings indicate that folk theories cluster in specific quadrants of the model, in particular, poverty and extractivism.