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Executive Summary

Each day, scanners, sensors, satellites, smartphones, and online platforms capture the details of Canadians’ digital lives. As a result, there has been an explosion of real-time, continuous, and more granular data.

This study explores the potential of these new digital datasets to build on lagged traditional data and improve policymakers’ understanding of our economy and society, as well as the world’s. Real-time, continuous, and fine-grained measures can enable leaders to spot problems as they emerge and develop more targeted, evidence-based policy responses.

This paper considers several recent experiments in which analysts leverage non-traditional data for public interest or public policy purposes. One example: during the pandemic, researchers pulled together datasets from credit card processors, financial services firms, and payroll companies to create a timely, detailed, and public data series of key economic indicators. They were then able to determine the pandemic’s impact on different income groups and the effects of different policy responses (such as stimulus payments), instead of relying on lagged and insufficiently detailed traditional datasets.

Policymakers need to understand and seize the potential these datasets present to build on existing data and improve their understanding of the world, and, in turn, better target public resources. To do so, they must confront a range of challenges, including data bias, outdated laws and frameworks, and privacy and ethical risks.