datapraxis

Datapraxis refers to the practical application of data-driven reasoning to real-world problems, with emphasis on social value, responsibility, and accountability. It situates data analysis within the context of decision-making processes, policy design, and community outcomes, rather than as an end in itself.

The term blends data science with the concept of praxis, highlighting a workflow that moves from data collection and analysis to action, evaluation, and refinement. Proponents emphasize participatory methods: involving stakeholders in framing questions, choosing indicators, and interpreting results.

Key practices include ethical data governance, privacy-preserving methods, transparent methodologies, open communication of uncertainty, and efforts to improve data literacy among nonexpert users. Datapraxis also stresses reproducibility and auditing of analyses to support accountability for decisions derived from data.

Applications are common in the public sector, civil society, and organizations working on urban planning, public health, education, and social services. Examples include community data collaboratives, open-data dashboards used to monitor service delivery, and evaluation frameworks that link metrics to policy changes.

Critiques note tensions between openness and privacy, potential technocratic bias, and the risk of privileging quantifiable indicators over qualitative understanding. Proponents respond by advocating inclusive design, ongoing governance, and mechanisms to ensure data products reflect diverse perspectives and local knowledge.