Data Re-Use and Collaboration for Development

By Stefaan Verhulst

This article was originally published in Data & Policy, the peer-reviewed, open-access venue dedicated to the potential of data science to address important policy challenges.

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Markus Spiske/Unsplash

The OECD recently released its Development Co-operation Report 2021. Stefaan G. Verhulst (Co-Founder of GovLab & one of the Editors-in-Chief of Data & Policy) contributed a chapter “Reusing data responsibly to achieve development goals,” which explores ways in which data can be harnessed for development, with a particular emphasis on the potential of data reuse and data collaboratives. In the blog below he seeks to summarize the key points toward operationalizing governance structures and frameworks for data collaboration to unlock the potential of digital data for development.

I. Data Asymmetries are holding back the Use of Data for Development

It is often pointed out that we live in an era of unprecedented data, and that data holds great promise for development. Yet equally often overlooked is the fact that, as in so many domains, there exist tremendous inequalities and asymmetries in where this data is generated, and how it is accessed. The gap that separates high-income from low-income countries is among the most important (or at least most persistent) of these asymmetries.

Image: Adobe Stock

II. Data collaboratives offer a model for responsible reuse of data

Data collaboratives are an emerging form of public-private partnership that, when designed responsibly, can offer a potentially innovative solution to this problem. Data collaboratives offer at least three key benefits for developing countries:

1. Cost Efficiencies: Data and data analytic capacity are often hugely expensive and beyond the limited capacities of many low-income countries. Data reuse, facilitated by data collaboratives, can bring down the cost of data initiatives for development projects.

2. Fresh insights for better policy: Combining data from various sources by breaking down silos has the potential to lead to new and innovative insights that can help policy makers make better decisions. Digital data can also be triangulated with existing, more traditional sources of information (e.g., census data) to generate new insights and help verify the accuracy of information.

3. Overcoming inequalities and asymmetries: Social and economic inequalities, both within and among countries, are often mapped onto data inequalities. Data collaboratives can help ease some of these inequalities and asymmetries, for example by allowing costs and analytical tools and techniques to be pooled. Cloud computing, which allows information and technical tools to be easily shared and accessed, are an important example. They can play a vital role in enabling the transfer of skills and technologies between low-income and high-income countries.

III. Concerns over governance and misuse of data stymie greater collaboration

Despite their clear potential and growing use around the world, data collaboratives remain relatively rare in low- and middle-income countries. Several obstacles to their greater dissemination exist, most related to concerns over weak regulation, potential misuses of shared data, and a limited evidence base to make the case for data reuse.

Broadly, the challenges to greater data collaboration fall into three main categories:

1. Finding the right governance model: Designing regulatory and institutional frameworks that can unleash the positive potential of data while limiting their potential for harm remains a tremendous challenge. To the extent such frameworks exist, they often suffer from regulatory capture, political pressures, and insufficient knowledge or skills on the part of policymakers. These problems are often particularly acute in low-income countries, where regulatory capacity and independence tend to be weaker.

2. Addressing concerns about misuse: Concerns over data misuse and privacy — often very valid — remain one of the most significant obstacles to greater data collaboration. A multi-pronged strategy to address such concerns is needed, one that would focus on raising awareness and establishing effective institutional and legal frameworks to ensure accountability and responsible data reuse.

3. Building and sharing evidence from data reuse: Despite mounting evidence about the potential of data reuse and collaboration for development, much remains unknown. A more systematized knowledge base, consisting of key examples and lessons learned, could help reduce duplication of effort and inform more successful initiatives.

IV. Maximising the positive potential of data reuse

In order to overcome obstacles to data collaboration, we require a new governance framework. Such a framework would help maximize the benefits of data reuse and collaboration, while limiting potential harms. Although still incipient, the shape of a responsible data reuse governance framework is beginning to come into view. Among its three most important components:

1. Replace outdated data governance mechanisms and structures: Existing models and policies to protect privacy are largely outdated, and too often predicated on a risk reduction rather than a rewards maximisation approach. Policymakers need new ways of balancing risk and reward, reinvigorated institutional models and forms, and fresh ways of ensuring accountability. Some of the measures to be considered include innovative risk assessment and mitigation methods to better balance risk and reward; data responsibility by design approaches to ensure in-built privacy and other protections; a global governance framework to smooths cross-border data flows; and greater public engagement through citizen assemblies, awareness-raising campaigns and educational strategies.

2. Improve decision-making with a new science of questions: Currently, data sharing is largely a reactive process, driven less by public need than by what data happen to be available or shared. Data collaboration can have more impact if it is driven by demand rather than supply. This entails asking the right questions to identify priorities and share data accordingly. Indeed, we propose a new science of questions that would help identify priorities and needs, and provide a more systematic and unbiased way to allocate often scarce resources (a particular concern in low-income countries).

3. Increased human capacity: Although there exist a variety of technical means to help strengthen a framework for data reuse, data governance ultimately relies on people. Low-income countries in particular need support to bolster their human capacity to oversee responsible and systemic data sharing. This goal can be achieved through a number of steps, including: better training and education; more targeted capacity building that addresses a wider segment of the population (e.g., journalists and business leaders in addition to policymakers); and the creation of new institutional positions within organizations so as to better ensure accountability and oversight of data and data sharing initiatives.

V. Conclusion: Data governance frameworks to support sustainable development

Our research strongly suggests that data can lead to more informed and better targeted policies, and that reusing data in collaborative partnerships can be a particularly cost-effective way to generate new insights and decisions on development. We find that both the promise and the challenges posed by data access and data reuse are heightened in low-income countries, where limited human and financial resources can undermine data governance, fail to protect privacy and prevent data misuse, and miss opportunities to improve the well-being of their citizens.

In conclusion, we therefore strongly argue for establishing and operationalising a framework for responsible, systemic and sustainable data reuse. Updated and innovative governance mechanisms to manage data can proactively address risks and maximise the positive potential of data in development initiatives. Importantly, these governance mechanisms need to be developed in a strategic and collaborative way, recognising the role that citizens and experts have to play in shaping policy frameworks as well as establishing trust and buy-in within the broader data ecology. Only with such trust can low-income countries succeed in maximising the potential of data in pursuit of development goals.

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Data & Policy is a peer-reviewed open access journal published by Cambridge University Press in association with the Data for Policy Conference. Read the latest articles, find us on Twitter @data_and_policy and sign-up for content alerts.

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