In order to understand the reality on the ground, this resource provides three mini-briefs based on initiatives in developing countries that seek to reach and provide highly localized digital products and services for digitally marginalized populations through a human-centered approach to data governance. 

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Harnessing the power of analytics — using data to inform and proactively guide institutional actions and decisions — is no longer an optional “extra.” A strong analytics capability has become a necessity that allows colleges and universities to provide the services that students expect and to predict and respond to change.

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Well-managed data can support social impact organizations (SIOs) as they conduct lifesaving health research, reduce environmental threats, and produce societal value for individuals and communities. But these benefits are often overshadowed by harms, as current practices in data collection, storage, sharing, and use have led to high-profile misuses of personal data, data breaches, and sharing scandals. This report provides a practical framework and shares case studies to demonstrate how citizens can participate in shaping and improving ways data is collected and used. 

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The Open Commons Consortium (OCC) manages and operates cloud computing, data commons, and data ecosystems to advance scientific, medical, health care, and environmental research for human and societal impact. This resource provides the agreements, operating principles, and a typical list of working groups. 

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To become a truly data-driven organization, leadership must work to create a culture that encourages and empowers the entire team to be data-driven. This is even more important in an impact-focused organization, where many team members may not have the training or work experience to understand the importance of data collection and analysis. This resource shares 5 steps you can take to build a data-driven culture at your organization.

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The Gen3 platform consists of open-source software services that support the emergence of healthy data ecosystems by enabling the interoperation and creation of cloud-based data resources, including data commons and analysis workspaces. 

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“A data commons is a system of stewardship through which data resources are managed involving processes of sustainable and ethical production, use, re-use, and redistribution — and governed through collaboration among stakeholding users and/or data producers.

Despite a wide range of possible organizational structures, it is possible to identify some core principles that characterize healthy commons — in order to guide the stakeholders of a data commons through the process of designing and adapting their institutional arrangements accordingly.”

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Data commons collocate data with cloud computing infrastructure and software services, applications, and tools to create powerful resources for the large-scale management, analysis, harmonization, and sharing of data.  Unlike data warehouses, data lakes, and other systems that support an organization’s business analytics, a data commons is focused on providing a resource for a community or collaboration, or in support of multiple communities. 

The term data commons derives from the more general concept of a “commons,” a term that was popularized by Elinor Ostrom, who defined it as a natural, cultural, or digital resource accessible to all members of a community, or, more broadly, of a society. An example is a pasture for animals to graze in a village. These resources are held in common, through a partnership, a not-for-profit, or another entity, but not owned privately for commercial gain [1].

An example of a data commons is the UK Biobank; a large-scale biomedical database with 500,000 participants who consent for their data to be used for the specific purpose of bonafide research on the diagnosis, prevention, and/or treatment of serious and life-threatening illnesses. Evidently, data commons can be powerful catalysts to innovation and transformation of the social impact sector especially when collaborative and synergistic action is required in the efforts to strengthen epidemic preparedness and response, financial inclusion, and enabling access to opportunity.

Data commons can be very challenging to set up and more so to maintain successfully. In this guide, we will share more examples of working data commons and three essential steps to maximize chances of success.

In this Guide

• Get inspired: explore examples of existing data commons
• Define your why and how: Understand the importance of governance in data commons
• Bootstrap: Investigate technology platforms for data commons
• Plan for the long term: Consider issues of data ingest and harmonization

Data Commons in Practice

Data Commons have been developed to address a diverse set of data sharing and research needs.  The following are some examples of data commons:

• NCI Genomic Data Commons – a data commons for cancer genomics data
• European Open Science Cloud – a European partnership supporting open science
• Australian BioCommons – a data commons supporting Australian bioscience
• datacommons.org – a data commons operated by Google

Gen3 – a general-purpose open-source platform supporting ad hoc analysis 

Terra – a platform for biomedical data

Figshare – a general data platform for storing, sharing, and discovering research data

Dataverse – open-source research data repository software

The FAIR data principles

The CARE Principles for Indigenous Data Governance

Research Data Alliance

Conclusion

Data commons support a community’s management, analysis, and sharing of data.  For this reason, data commons require a governance framework that supports the community’s values and goals.  Furthermore, these frameworks are key to ensuring resources are used for the specific purposes for which they were made available. This, in turn, enables more resources to be safely mobilized for the benefit of the community. Successful data commons tend to carefully curate and harmonize the data they contain, which reduces the time and effort required for users to analyze the data, especially when the data comes from multiple sources. This is especially helpful for emergency research and innovation. 

Please feel free to suggest any other guides you found helpful by contacting us and we may incorporate them.

[1] Elinor Ostrom. Governing the commons: The evolution of institutions for collective action. Cambridge university press, 1990.

Grateful for the contribution of Robert Grossman, Ph.D., Frederick H. Rawson Distinguished Service Professor of Medicine and Computer Science, and the Jim and Karen Frank Director of the Center for Translational Data Science (CTDS) at the University of Chicago.

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Data governance provides the process and procedures to manage data in organizations effectively. Data governance comes in a variety of shapes and sizes. Often it means different things to different organizations, and many organizations struggle to get their data governance initiatives up and running. This workbook is a starter kit designed to help nonprofit organizations practice safe, ethical, and responsible data governance that reflects their values and supports their mission.

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Data governance defines roles, responsibilities, and processes for ensuring accountability for and ownership of data assets across an organization. It encompasses the people, processes, and technologies required to manage and protect data assets.

The Data Governance Institute defines it as “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.”

This resource provides an introduction to data governance for social impact organizations, and offers a sample framework, goals, and benefits.

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It seems like every week there are new data breaches, concerns about our privacy, and stories about data science gone wrong. We hear about banks giving credit to customers they know can’t afford it, casinos using data science and behavioral economics to keep customers coming back for more and driving them into bankruptcy, and on and on…

These kinds of behaviors aren’t limited to private sector actors in search of greater profits. Nonprofits can also be irresponsible with their data, or fall victim to unintended consequences. Blackbaud suffered a data breach and ended up paying ransom to hackers that were in their system for months. The United Nations shared data about Rohingya refugees without their consent, possibly increasing their vulnerability in returning to Myanmar.

In this Guide

• Simple, practical tools and principles to help your organization use data responsibly. 
• Checklist created by the DrivenData team for data scientists to assess the risk of a project at the outset.
• A tool developed by IDEO to support ethical design with data.
• How Salesforce developed the first office of Ethical & Humane Use.
• Help an organization develop a mindset for collecting and using data responsibly.

Ethical and Humane Use Leadership

Salesforce developed an Office of Ethical & Humane Use to develop principles and practices for ensuring that their technology is being used in ways that align with the company’s values. It has enabled employees to report unethical uses by customers, developed a toolkit to help teams build with intention, and pushed other technology companies to develop similar offices. 

Conclusion

The resources above will help you implement responsible data use for your organizations. 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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Data governance isn’t easy, and getting a skilled team in place matters. Appointing the wrong people to key roles can cause the wheels to come off any well-thought-out initiative. Getting the basics right and the most effective and suitable team from the outset will improve chances for a successful data governance implementation. This resource provides guidance for organizations to identify data governance roles and responsibilities.

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