Intersector Briefing: Data Collaboratives

BlogImage_Social_Network_Analysis_VisualizationAccess to data can have an enormous impact on solving complex problems, from helping to define the scope of the problem to aiding in the assessment of why certain solutions have failed. But data relevant to complex problems are often spread across government agencies, private companies, and non-profit organizations.

Data collaboratives, the spotlight of a new GovLab initiative, focus on overcoming silos to create greater transfer of data in the public interest. According to The GovLab’s Stefaan Verhulst and David Sangokoya, data collaboratives are “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.” The GovLab works to strengthen the ability of individuals and institutions to work more openly, collaboratively, effectively, and legitimately to solve public problems.

As interest in this topic grows, this month’s Intersector Briefing includes articles, resources, and more, to get a sense of what data collaboratives can look like, why they’re important, and what challenges individuals and organizations often face in implementing them.

Designing a Data Collaborative
This resource from The GovLab’s new site walks practitioners through the steps of designing a data collaborative, including defining the problem to be solved, identifying incentives to get the most promising data providers to participate, assessing major risks, and defining a baseline against which to measure progress. Each step includes discussion, questions and considerations, and resources, and some include examples. Many of the steps will be familiar to practitioners of any type of cross-sector collaboration (establishing a governance structure, for example), while others are specific to data collaboratives (such as defining the ideal type of data collaborative based on supply and demand).

Data Collaboratives Explorer
In another section of The GovLab’s website, visitors will find the Data Collaboratives Explorer, a collection of case studies sortable by data collaborative type, issue area, data type, and region. Each case study details the data involved in the collaboration, how the data were shared, and the purpose of the shared data. For example, Feeding America and Map the Meal Gap is a data collaborative between Feeding America and Nielsen to collect and map data about food insecurity in the United States in an attempt to aid research and advocacy efforts. “Most significantly the study found that 26.4 million food-insecure people live in counties where food costs are higher than the national average of $2.79. This provides important insights into the cause and possible solutions to food insecurity in America,” the case study explains.

Uber is finally releasing a data trove that officials say will make driving better for everyone
In January, Uber announced it would release some of its data on a website called “Uber Movement,” report Elizabeth Dwoskin and Faiz Siddiqui in this article from The Washington Post. This data could help cities look at how out-of-the-ordinary events, like a subway closure or large convention, affect traffic congestion. Uber holds a great deal of data that could benefit the public if shared with transportation and other government agencies. This article discusses some of the challenges the public sector has had in trying to get this data from Uber. “The challenge for the public interest is that many technology companies will share data only on their terms,” said Allan Fromberg, Deputy Commissioner for Public Affairs for the New York City Taxi and Limousine Commission. But some argue that cities have a legitimate claim to some of the company’s data, as it is in the public interest: “Just thinking about ‘how can we better manage traffic in the 21st century?’ Absolutely this data is necessary and should be provided,” said Linda Bailey, Executive Director of the National Association of City Transportation Officials. Interestingly, as Uber establishes more formal partnerships with cities, data may become easier to obtain: “Uber and similar companies may become more transparent as they realize how crucial open data sharing can be to establishing public-private partnerships in their quest for profitability,” the authors write, sharing the thoughts of Gabe Klein, who headed Washington, D.C.’s Department of Transportation.

Confronting the challenges of getting timely opioid abuse data
This section from Route Fifty’s Special Report “The Health Data Equation” (available for free download here) details the difficulties local governments face in receiving timely data on opioid abuse. While part of the issue is a delay in the release of national data, the challenge is also that information is siloed between intergovernmental and cross-sector stakeholders, from 9-1-1, to the fire department, to hospitals. To add to the difficulty of tracking comprehensive data on the opioid problem in West Virginia, the area that the article focuses on, a non-fatal overdose is not required to be reported by hospitals. Scott Lemley, who is a criminal intelligence analyst for the Huntington, West Virginia, police department and who is profiled in the article, relies on his existing relationships with all the relevant stakeholders to obtain this information. “But very few places have someone like him to scour for that information,” the article explains. “It’s no surprise therefore that the statistics for these types of survived overdoses are vastly undercounted.”

Sharing data is a form of corporate philanthropy
This 2014 piece from Harvard Business Review provides commentary on the potential for private companies to share data for the public good, framing it as a form of philanthropy. The author presents two possibilities for data philanthropy — helping to develop responsive cities, and benefiting academic researchers who typically can’t afford access to companies’ data. “Companies shaping this data-driven world can contribute to the public good by working directly with public institutions and social organizations to bring their expertise and information assets to bear on shared challenges,” the author writes. In this view, sharing data is a form of corporate social responsibility (CSR), one that, in certain contexts, may surpass the social impact of a purely monetary donation, he explains. We’ve written before about the potential for cross-sector collaboration and shared decision-making processes to increase the effectiveness of CSR activities — This holds true for data philanthropy as well. If companies move beyond simple data “donations” to involve all partners in decision making around the data, they can help better inform and refine their CSR objectives and nurture strong, long-term relationships with partners that may be beneficial to future efforts.

Moving the needle on a county-wide problem: insights from Orange County’s Homelessness Cost Study
In February, we profiled Orange County’s homelessness cost study, an alliance of cross-sector stakeholders that aims to quantify the costs of homelessness to the Orange County community as a first step to advancing solutions. A key component of being able to access data for the study is “having the right people around the table,” Carla Vargas, Chief Operating Officer at OC United Way told us. Cross-sector member organizations of Orange County’s Continuum of Care for homelessness shared data from their federally-mandated surveys of the local homeless population with the United Way and the University of California, Irvine’s (UCI) Department of Sociology. Crucially, partners were also open to improving and expanding upon the data by supporting a more comprehensive survey process. The OC Homelessness Cost Study partners recognize that data collection is only a preliminary step in a robust cross-sector effort to engage the public, elected officials, and other key decision makers, and to drive change forward. “We realize that the presentation of data, in and of itself, is never sufficient to move the dial when it comes to policy implementation,” said Dr. David Snow, Distinguished Professor in the Department of Sociology at UCI. “Much of it depends on how the data is framed, the kind of stories that are told with it, the life experiences of the folks on the streets or in different kinds of housing configurations. It’s a combination of those kinds of factors that I think will make a difference.”