Data-driven organizations have a comprehensive data strategy that links key initiatives and data, ensuring they inform goals, objectives, and mission. Building and maintaining a data strategy requires both strategic and financial commitment from the entire organization. Therefore, before anyone builds their data strategy, they will need to secure support from leadership and their IT department, who will advocate and help orchestrate critical changes to scale the framework across the enterprise. 

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In taking on urgent global challenges, organizations are faced with a critical hurdle: a lack of necessary data. There are many ongoing efforts to fill this data gap, including developing new technologies, sources, and collaborations. But promising data-sharing partnerships and data collaboratives (particularly between the private and public sectors) can struggle to overcome institutional and legal barriers.

This resource provides both an analytical framework and an online library of legal clauses that capture the key elements of sharing agreements for data collaboration, complete with links to several detailed guides. This resource addresses the lack of clarity around data sharing and data action issues that often result in high transaction costs and significant delays during the negotiation process.

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Once data is collected, it can be released in various ways, from closed networks within an organization to platform-dependent sharing between peer organizations, publishing with closed licenses, and publishing with fully open licenses. It’s often tempting to think that making data available to the widest possible audience is the best way to maximize that data’s impact. Still, without careful consideration, there can be a myriad of unintended consequences.

This guide introduces the benefits, limitations, and recommendations for internal and external data sharing, including open data.

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The GovLab has developed a Periodic Table of Open Data Elements detailing the enabling conditions and disabling factors that often determine the impact of open data initiatives. This resource shows that the elements included in five central issue categories — Problem and Demand Definition, Capacity and Culture, Partnerships, Risks, Governance — are likely to either enable or disrupt the success of open data projects when replicated across countries.

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The term “data collaborative” refers to a new form of collaboration beyond the public-private partnership model, in which participants from different sectors — including private companies, research institutions, and government agencies — can exchange data to help solve public problems. This resource outlines eight phases for designing and implementing a data collaborative (partnership) at an institutional level with examples, enablers, tools, and resources for each phase.

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The volume of data collected by multiple devices, such as mobile phones, sensors, or satellites, is growing at an exponential rate. Accessing and aggregating different sources of data, including data outside the public domain, has the potential to provide insights into many societal challenges. This catalyzes new forms of partnerships between public, private, and nonprofits aimed at leveraging different sources of data for positive societal impact and the public good.

The following resources are intended to help you navigate and build successful data partnerships so that you can start reaping the rewards of external collaboration.  

In this Guide

• Basic principles of managing data partnerships.
• Resources on seeking technical assistance from other organizations.
• Data sharing agreements and ethical data sharing best practices.

Conclusion

The resources above will help you start partnering with others on your data projects and initiatives. We also suggest reaching out to peer organizations, whose challenges and learning may provide direct and current examples of ways forward. Please feel free to suggest any other guides you found helpful by contacting us and we may incorporate them.

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The DataKind Playbook is a globally-accessible, living knowledge base that enables users to design, implement, and manage a Data Science and AI for Good project following DataKind’s proven approach. Created by DataKind’s staff and volunteers, the Playbook provides direction on how to approach a Data Science and AI for Good project and to do it well. It also codifies the lessons learned from failures and successes for use on future projects.

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The Responsible Data for Children (RD4C) Data Ecosystem Mapping Tool from GovLab intends to help users to identify the systems generating data about children and the key components of those systems. Actors who seek to ensure responsible data for children in their context often need to make decisions without a detailed understanding of the current situation.

This resource provides an understanding of the breadth of data generated and hold about children; assesses data systems’ redundancies or gaps; identifies opportunities for responsible data use.

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The Data Responsibility Journey for Data Collaboratives is an assessment tool that outlines the opportunities and risks to consider at each stage of the data lifecycle when implementing a data collaborative. This resource provides a series of questions regarding the data collaboration project and based on the user’s answers, the tool will deliver a report with customized recommendations and useful resources.

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The RD4C Decision Provenance Mapping methodology provides a way for actors to design or assess data investments for children to identify key decision points and determine which internal and external parties influence those decision points. This distillation can help users to pinpoint any gaps and develop strategies for improving decision-making processes and advancing more professionally accountable data practices.

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Humanitarian organizations regularly partner with the private sector on data and technology-related initiatives. Such public-private partnerships offer humanitarians access to areas of expertise that have the potential to improve response efficiency. For the private sector, humanitarian partnerships offer access to new markets and may contribute toward corporate social responsibility objectives, among other benefits. Alongside these expected benefits, however, these partnerships present a range of risks related to data management.

This guide details best practices for data resonsiblity in public-private partnerships, drawing on work in the humanitarian sector.

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Data collaboration encompasses sharing information, insights, strategies, and resources across projects, organizations, and sectors, increasing efficiency and impact. People working in digital development have a shared vision to create a better world, and collaboration is essential to making this vision a reality. No single initiative or organization can make it happen alone. By collaborating, those working in digital development and beyond can pool their resources and expertise to benefit each initiative and strengthen the global community. Collaborating does not just happen accidentally; it requires time, planning, and dedicating resources to look for and develop opportunities. This guide details how organizations can collaborate with internal and external stakeholders throughout the project lifecycle.

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