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Adapting data collection and utilisation to a Covid-19 reality: monitoring, evaluation and learning approaches for adaptive management

Briefing/policy papers

Hero image description: A stallholder and customer wearing face masks in a market in Kenya Image credit:Sambrian Mbaabu / World Bank Image license:CC BY-NC-ND 2.0

This briefing note focuses on the remote collection and use of data for adaptive management during the Covid-19 pandemic, setting out key considerations to help practitioners think through a transition from more ‘traditional’ monitoring, evaluation and learning (MEL) to MEL for adaptive management (MEL4AM) that reflects the unique data collection challenges presented by Covid-19. The brief provides an overview of some key considerations in remote data collection, when this is required, and identifies other sources that address these issues in more detail. It concludes with a discussion of how to bring the information resulting from remote monitoring into decision-making to enable adaptive management. 

Key messages

  • When planning for remote data collection during the Covid-19 pandemic, first determine what information is still necessary, because data needs may have changed, e.g. if programming has pivoted or needs to pivot due to Covid-19. Then identify how the programme’s information needs align with existing data sources and what gaps remain, which will guide the need for remote data collection.
  • Also consider what data is ‘good enough’ for current decision-making needs in order to provide sufficient information to the right people at the right time to an acceptable standard of rigour.
  • There may be pragmatic reasons to reduce the number or scope of MEL activities, such as logistical constraints or ethical considerations introduced by the pandemic.
  • MEL activities should be accompanied by frequent feedback loops and pause points to reflect on emergent needs and challenges, information needs that have been met, and contextual changes that may affect MEL.
  • Be clear with decision-makers about the assumptions and gaps in the data, including proxies used and their limitations, sampling changes, and how these changes and assumptions may affect the decisions/options being discussed.

For feedback or questions about this briefing note please contact Stephanie Buell ([email protected]).

A stallholder and customer wearing face masks in a market in Kenya
Jessica Ziegler and Paige Mason