Abstract

Citizen-generated data is playing an increasingly prominent role in shaping decision-making and policy design across a wide range of domains (UNSD Collaborative on Citizen Data). From community mapping and environmental monitoring to participatory reporting and civic technology initiatives, citizens are actively contributing data that is often timely, highly granular, and deeply embedded in local contexts. These new forms of data production expand the traditional boundaries of statistical systems, offering valuable opportunities to complement and enrich official statistics.
At the same time, the growing relevance of citizen participation in data ecosystems raises fundamental questions about how data quality should be defined, assessed, and ensured. Conventional data quality frameworks—largely developed within the context of official statistics and based on controlled production processes—may not fully capture the specific characteristics of citizen-generated data. In participatory settings, data are often produced through decentralized, heterogeneous, and sometimes informal processes, where contributors may differ in skills, motivations, and levels of engagement (see the principles of the Copenhagen Framework on Citizen Data, UNSD). As a result, traditional dimensions such as accuracy, reliability, comparability, and coherence require careful reinterpretation.
This talk argues that the rise of citizen-generated data calls for a rethinking of data quality, moving beyond a narrow compliance-based perspective toward a more inclusive and adaptive framework. Rather than viewing citizen data primarily through a deficit lens—focusing on potential biases, inconsistencies, or lack of standardization—it is important to recognize its distinctive strengths, including contextual richness, rapid availability, and the capacity to capture phenomena that may otherwise remain unobserved.
The discussion will examine how key data quality dimensions can be reframed in light of citizen participation. For instance, accuracy may need to be considered alongside notions of credibility and trust, which are shaped by transparency, community validation, and traceability of data sources. Relevance may be enhanced through the direct involvement of citizens in identifying data needs and priorities. Timeliness can benefit from real-time or near real-time contributions, while coherence and comparability may require new methodological approaches to integrate diverse and non-standardized data sources with official statistics.
Furthermore, the talk will explore the challenges associated with integrating citizen-generated data into formal statistical and administrative systems. These include issues related to data governance, standardization, metadata documentation, and the establishment of quality assurance mechanisms that are both rigorous and flexible. Particular attention will be given to the role of collaboration between statistical institutions, local communities, and technological platforms in co-producing data that meet shared quality standards.
By adopting a forward-looking perspective, the presentation highlights pathways for embedding citizen-generated data within official data ecosystems without undermining their participatory nature. Ultimately, rethinking data quality in this context is not only a technical exercise but also a cultural and institutional shift, requiring openness to new forms of knowledge production and a rebalancing of authority between traditional data producers and citizens.