Using Data Responsibly During the COVID-19 Crisis
Over the past decade, monitoring, evaluation, research and learning (MERL) practices have become increasingly digitalized. The COVID-19 pandemic has caused that the process of digitalization to happen with even greater speed and urgency, due to travel restrictions, quarantine, and social distancing orders from governments who are desperate to slow the spread of the virus and lessen its impact.
MERL Tech and CLEAR-Anglophone Africa are working together to develop a framework and guidance on responsible data management for MERL in the Anglophone African context. As part of this effort, we held three virtual events in early June during CLEAR’s gLOCAL Evaluation Week.
At our June 2 event, Korstiaan Wapenaar, Genesis Analytics, Jerusha Govender, Data Innovator, and Teki Akkueteh, Africa Digital Rights Hub, shared tips on how to be more responsible with data.
Data is a necessary and critical part of COVID-19 prevention and response efforts to understand where the virus might appear next, who is most at risk, and where resources should be directed for prevention and response. However we need to be sure that we are not putting people at risk of privacy violations or misuse of personal data and to ensure that we are managing that data responsibly so that we don’t unnecessarily create fear or panic.
Watch the video below:
Listen to the audio from the session here:
Session summary:
- MERL Practitioners have clear responsibilities when sharing, presenting, consuming and interpreting data. Individuals and institutions may use data to gain prestige, and this can allow bias to creep in or to justify government decisions. Data quality is critical for informing decisions, and information gaps create the risk of misinformation and flawed understanding. We need to embrace uncertainty and the limitations of the science, provide context and definitions so that our sources are clear, and ensure transparency around the numbers and the assumptions that are underpin our work.
- MERL Practitioners should provide contextual information and guidance on how to interpret the data so that people can make sense of it in the right way. We should avoid cherry picking data to prove a point, and we should be aware that data visualization carries power to sway opinions and decisions. It can also influence behavior change in individuals, so we need to take responsibility for that. We also need to find ways to visualize data for lay people and non-technical sectors.
- Critical data is needed, yet it might be used in negative or harmful ways, for example, COVID-related stigmatization that can affect human dignity. We must not override ethical and legal principles in our rush to collect data. Transparency around data collection processes and use are also needed, as well as data minimization. Some might be taking advantage of the situation to amass large amounts of data for alternative purposes, which is unethical. Large amounts of data also bring increased risk of data breaches. When people are scared, such as in COVID times, they will be willing to hand over data. We need to ensure that we are providing oversight and keeping watch over government entities, health facilities, and third-party data processors to ensure data is protected and not misused.
- MERL Practitioners are seeking more guidance and support on: aspects of consent and confidentiality; bias and interference in data collection by governments and community leaders; overcollection of data leading to fatigue; misuse of sensitive data such as location data; potential for re-identification of individuals; data integrity issues; lack of encryption; and some capacity issues.
- Good practices and recommendations include ethical clearance of data and data assurance structures; rigorous methods to reduce bias; third party audits of data and data protection processes; localization and contextualization of data processes and interpretation; and “do no harm” framing.
Download reports:
Read about the other gLOCAL Evaluation 2020 events from CLEAR-AA and MERL Tech: