100 Examples of Non-Traditional Data Reuse for the Public Interest

Non-traditional data has become an increasingly important source of evidence for public-interest work. Data generated through digital platforms, connected devices, commercial transactions, satellites, environmental sensors, and online communities is helping researchers and practitioners answer questions that conventional sources such as surveys, censuses, and administrative records often cannot answer quickly or in sufficient detail. 

To help document how these data sources are being used in practice, we have published The Re-Use of  Non-Traditional Data  for Public Interest Purposes:  A Curated Compilation of 100 Use Cases (2024-2026), compiling recent use cases drawn from academic research, operational deployments, pilot projects, and institutional initiatives from around the world. 

Although interest in non-traditional data has grown rapidly, the field remains highly fragmented. The term encompasses many fundamentally different data sources, from satellite imagery and wastewater signals to mobile phone records, loyalty card transactions, online job postings, and biodiversity observations. Each comes with its own methods of collection, governance arrangements, privacy considerations, technical challenges, and pathways to public value. As a result, it can be difficult to understand where these data types are being used most effectively and what broader lessons are beginning to emerge. 

The 100 examples span public health, humanitarian response, climate and environmental monitoring, mobility, labor markets, economic measurement, digital governance, urban planning, and many other fields. They include applications such as:

• detecting emerging disease outbreaks through aircraft wastewater;

• assessing disaster damage using satellite imagery and AI;

• analyzing evacuation behavior with mobile network and geolocation data;

• identifying emerging workforce needs using online job postings and professional networking data; and

• supporting biodiversity conservation and disaster preparedness through citizen-generated maps and observations.

To make recurring patterns easier to identify, we organized the compilation by the underlying source of the data. The examples are grouped into seven broad domains—digital communication and online interaction; mobility and geolocation; health, wellness, and biomedical; financial, commercial, and consumption; work, education, and skill; in-home devices and Internet of Things; and environmental, geospatial, and infrastructure data—and then into more specific data types. 

Looking across the examples, we can draw several lessons:

• Non-traditional data is most valuable when combined with traditional sources of evidence. 

• Certain data types are already tied to clear public-interest applications—for example, satellite imagery for environmental monitoring, wastewater for pathogen surveillance, and mobile network data for mobility analysis. 

• Although much of the field remains research- or mission-driven, many applications now support operational decision-making.

• AI is expanding what can be learned from large and complex datasets, while increasing the importance of validation and human oversight. 

• Questions of access, governance, stewardship, privacy, representation, and trust continue to shape whether these data types can be reused responsibly and at scale. 

We hope this compilation serves as a practical resource for researchers, policymakers, practitioners, and anyone interested in understanding how non-traditional data is being applied today. It provides a snapshot of a rapidly evolving field and highlights the institutional, technical, and governance work still needed to translate promising applications into trusted public-interest practice. 

Read the full compilation here: https://docs.google.com/document/d/19ojoRQgzRZk5WNxG6m62ZHEGgelbH0pqSZaxKY5pLAU/edit?pli=1&tab=t.0#heading=h.sgf0byledgc 

Back to the Blog