Data deprivations, data gaps and digital divides: Lessons from the COVID-19 pandemic

This paper draws lessons from the COVID-19 pandemic for the relationship between data-driven decision making and global development. The lessons are that (i) users should keep in mind the shifting value of data during a crisis, and the pitfalls its use can create; (ii) predictions carry costs in terms of inertia, overreaction and herding behaviour; (iii) data can be devalued by digital and data deluges; (iv) lack of interoperability and difficulty reusing data will limit value from data; (v) data deprivation, digital gaps and digital divides are not just a by-product of unequal global development, but will magnify the unequal impacts of a global crisis, and will be magnified in turn by global crises; (vi) having more data and even better data analytical techniques, such as artificial intelligence, does not guarantee that development outcomes will improve; (vii) decentralised data gathering and use can help to build trust - particularly important for coordination of behaviour.

Automated summary

This paper reflects on lessons learned from the COVID-19 pandemic regarding the relationship between data-driven decision making and global development, highlighting the shifting value of data, costs associated with predictions, limitations of data interoperability, impact of digital and data deluges, and magnifying effects of digital divides on global crises. It also emphasizes that having more data and better analytical techniques does not necessarily lead to improved development outcomes, and decentralization of data gathering and use can help build trust for coordinating behavior.