An Image of the ‘How-to guide’ from Data for Children Collaborative’s website.

The Data for Children Collaborative has created a How-to-Guide designed to assist organizations working on complex social challenges using data. This guide was developed by the collaborative comprising The United Nations Children’s Fund (UNICEF), The Scottish Government, and The University of Edinburgh’s Futures Institute. It aims to offer tools and approaches for planning and executing data collaborations. While the guide focuses on issues affecting children and young people, its principles and strategies can be applicable to a broad range of challenges and sectors.

The guide includes examples from UNICEF Children’s Climate Risk Index project, which illustrates how data can be used to assess children’s vulnerabilities to climate change. The project focuses on specific climate stressors, such as heatwaves, and explores how these events may impact children. Experts from various disciplines, including social scientists, geoscientists, and data scientists, collaborated to refine the challenge question, in efforts to ensure the project addressed key issues and aligned with the required expertise.

The Data Collaboration Process: Step-by-Step

The guide outlines the Data Collaboration Process in six stages, each focusing on a key element of managing data collaborations, with examples drawn from the UNICEF Children’s Climate Risk Index project:

1. Qualifying the Problem: The first step in any data collaboration is identifying the problem that needs to be addressed. This stage involves ensuring that the problem aligns with the goals of all stakeholders and is suitable for data-driven solutions. It often requires narrowing a broad challenge into specific, actionable questions. For instance, in the case of UNICEF's Children’s Climate Risk Index, the initial exploration of climate change's impact on children was refined to focus on specific climate stressors, like heatwaves, and their effects on vulnerable populations. This process helps clarify the scope and complexity of the project, supporting better planning and resource allocation.

2. Framing the Question: Once the problem is defined, framing the challenge question clearly is essential. The question should be broad enough to engage multiple areas of expertise and focused on the desired outcomes. For example, the UNICEF project’s challenge question was refined to: 'How are children most vulnerable to the impact of climate change, both now and in the future?' This revision helped bring in input from experts across various fields, including social scientists and geoscientists.

3. Building the Right Team: A data collaboration requires a mix of expertise. The guide suggests identifying collaborators from different sectors and areas of expertise, ensuring a balance of skills and alignment with the project’s goals. It is important to ensure the team can work together effectively, with an environment that allows for constructive challenges and diverse perspectives to inform the work. For example, in the Children’s Climate Risk Index project, UNICEF worked with a team that included social scientists, climate scientists, and geographers, in efforts to ensure the necessary skills to address the challenge from a child-focused perspective.

4. Co-Creating the Project: Collaboration workshops are central to this stage, where stakeholders engage in co-creation sessions to identify deliverables and refine their approach. The guide emphasizes the importance of facilitating discussions that align roles, expectations, and methodologies, ensuring clear communication—particularly when managing large and complex datasets. For instance, in the Children’s Climate Risk Index project, workshops helped bring together experts from various fields, including social scientists and climate scientists, to refine the challenge question and ensure that the focus remained on addressing the most pressing issues.

5. Delivering the Solution: This phase involves implementing the solution based on the outputs developed through collaboration. It requires effective project management and attention to ethical considerations, particularly when handling sensitive data, such as children's information. For example, the Children’s Climate Risk Index project involved detailed planning and consideration of governance structures to ensure the solution addressed both technical and ethical requirements.

6. Measuring the Impact: To evaluate a project’s success, its impact must be regularly assessed. The guide recommends using tools like impact dashboards and frameworks such as the Theory of Change to track both outputs and outcomes. The Theory of Change framework helps map the relationships between activities, outcomes, and impacts, ensuring that a project remains aligned with its objectives and that changes are measured. For example, in the Children’s Climate Risk Index project, the projections were used to help organizations better understand the scale of children's vulnerabilities to climate change and the potential responses.

Three Takeaways for the DATA4Philanthropy Network

1. Cross-Sector Collaboration Can Address Complex Challenges: Engaging experts from various sectors provides a broader set of perspectives, which can help tackle complex problems. Bringing together diverse knowledge and skills can uncover solutions that may not be apparent within a single discipline.

2. Ethical Data Practices Are Important: In data collaborations, especially when dealing with sensitive data, it is important to adhere to ethical standards. This includes transparency, proper data protection, and ensuring that all stakeholders understand and respect the ethical implications of their work, contributing to responsible data use.

3. Strong Stakeholder Engagement Can Improve Collaboration Outcomes: Effective collaborations require clear communication and managing expectations among stakeholders. Ensuring that stakeholders are engaged throughout the project can help keep the collaboration focused and adaptable to changes.

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