This blog is part of a running series from the Responsible Data for Children initiative highlighting each of the RD4C principles and real-life efforts to realize them. You can learn more about this series here.

Today’s blog looks at the importance of proportionality, how Cellule d’Analyse Intégrée embedded this principle in its work, and which resources are available to you to operationalise it. 

What is Proportionality?

What does it entail, to be proportional? The principle of proportionality means that, when developing and implementing an initiative, you can consider what data—and how much—you need to achieve the intended purpose. This work can be considered from the outset of an initiative and throughout the data lifespan. In fact, because data tends to become superfluous over time, it might accumulate risks for minimal value added as the insights it provides might no longer be relevant. While the concept of proportionality is valid for any data, it is even more so when it comes to data for and about children, given the potential and actual vulnerabilities this data generates. Today’s children are the first generation to grow up amid the rapid datafication of virtually every aspect of life. Unlike adults, however, children typically do not have full agency to make decisions about their participation in programmes or services that may generate and record personal data.

How Have Others Pursued Proportionality?

One interesting UNICEF project that champions proportionality is Cellule d’Analyse Intégrée (CAI). CAI is an operational research and analytics cell created by UNICEF to provide local and national level actors, government leaders, UN staff, and associated partners with integrated and actionable evidence to respond to public health emergencies and contexts. The project came about to support the response to the tenth Ebola outbreak in the eastern part of the Democratic Republic of the Congo. CAI has both local teams who collect data in communities through mixed methods and a team of national and international researchers who support study designs, data collection and analysis and who produce insights used for public health decision-making.

The CAI team only collects data when they can be sure it will be relevant and used. The team involves the data users from the outset so that it is not overburdening communities to participate in studies that do not ultimately have operational use. CAI also ensures that it uses locally identified and trusted researchers—women working with women and men with men, but also specific members of communities—to ensure they capture all (and only) relevant data.

Though it might sound straightforward and sensible, it is not always easy to ensure proportionality.  During its work, the CAI team sometimes faced difficult ethical situations that required referral. Enumerators and community workers sometimes struggled to know how to deal with unexpected and sensitive information shared by children and their communities—such as disclosure of sexual exploitation and abuse. Frequent exchanges were therefore organized by the CAI team to remind practitioners about how to handle these incidents and what to do with the data. 

On the other hand, easy-to-implement tweaks can help operationalise proportionality at any time. Data users should have a clear process for identifying when and why data about children should be retained, when it should be archived, and when it should be destroyed. This effort could start from mapping data, identifying what needs to be disposed of and then proceed to design appropriate procedures to do so.

What Resources Can I Use For Proportionality?

The RD4C Data Ecosystem Mapping Tool can help you identify the breadth of data generated by the systems handling data about children, their key components, and their alignment with the RD4C principles by capturing: why the system exists (RD4C principles: Purpose-Driven; People-Centric; and Proportional), who is involved RD4C principles: Professionally Accountable and Participatory) and what data is held (Protective of Children’s Rights; and Prevention of Harms Across the Data Lifecycle).

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We hope this blog has been useful to you in helping you understand what it means to be proportional. Please return to our blog next week when we’ll discuss our next principle or subscribe for updates to the RD4C initiative by signing up to our mailing list here.

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