The #Data4COVID19 Review : Assessing the Use of Non-Traditional Data During A Pandemic Crisis

The COVID-19 pandemic has had a catastrophic impact on the world—inflicting enormous (and often uneven) health, economic, political, and cultural costs. Leaders from around the world have increasingly sought to mitigate these complex problems through the use of data, including data from non-traditional sources. In this report, we refer to that as “non-traditional data” and we define it as data that is “digitally captured [...], mediated [...] or observed,” using new instrumentation mechanisms.1 As documented elsewhere, nontraditional data (NTD) has the potential to expand a decision maker’s toolbox to respond to not only pandemics but all kinds of dynamic crises, from climate disasters to energy emergencies. However, the use of non-traditional data for public good purposes remains nascent and there is much to be learned about how to utilize it ethically and effectively. With the support of the Knight Foundation, The GovLab’s #Data4COVID19 Review assesses if and how NTD was used during the different waves of the COVID-19 pandemic and provides guidance for how future data systems may be more effectively employed in future dynamic crises. The Review does this with four briefings that document and evaluate the most prominent uses of NTD: health, mobility, economic, and sentiment analysis. These four uses were synthesized from an assessment of The GovLab’s #Data4COVID19 Data Collaborative Repository–a crowdsourced list of almost 300 data collaboratives, competitions, and data-driven efforts that aimed to address the pandemic response. Based on these briefings and our review of the current literature, we identified the following findings and recommendations for how decision-makers might better use NTD in future crisis management efforts