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Improving Government Programs Relies on Data Access

America Forward Oct 20, 2017
This article is deemed a must-read by one or more of our expert collaborators.
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data access
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Last year, a federal commission began studying how data that the government already collect can be used to improve government programs and policies. The Commission on Evidence-Based Policymaking was the creation of Speaker Paul Ryan (R-WI) and Senator Patty Murray (D-WA), who are long-time champions of data and the use of evidence to drive federal policymaking. After over a year of meetings and deliberations, the 15-member Commission wrapped up its work with a final report and list of recommendations focused on data access, privacy, and capacity.

 


Impact Philanthropy is a complex topic, and others found these selections from the Impact Giving archive from Giving Compass to be good resources.

  • This article is deemed a must-read by one or more of our expert collaborators.
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    Machine Learning in the Social Sector

    Giving Compass' Take: • Ben Brockman, Andrew Fraker, Jeff McManus, and Neil Buddy Shah discuss the ways in which machine learning can boost the work of the social sector to increase impact.  • How is machine learning being applied in your issue area(s)? How can your work be augmented by machine learning?  • Learn about the potential for machine learning to shape better plant conservation.  The next big thing in the social sector has officially arrived. Machine learning is now at the center of international conferences, $25 million dollar funding competitions, fellowships at prestigious universities, and Davos-launched initiatives. Yet amidst all of the hype, it can be difficult to understand which social sector problems machine learning is best positioned to solve, how organizations can practically use it to enhance their impact, and what kind of sector-wide investments can enable the ambitious use of it for social good in the future. Our work at IDinsight, a nonprofit that uses data and evidence to help leaders in the social sector combat poverty, and the work of other organizations offer some insights into these questions. Machine learning uses data (usually a lot) and statistical algorithms to predict something unknown. In the private sector, for example, ride sharing apps use traffic data to predict customer wait times. Online streaming companies use customer history to predict which videos customers will want to watch next. In the social sector, machine learning is particularly ripe for use in addressing two kinds of problems. The first is prevention problems. If an organization focused on conflict resolution can predict where violent conflict is likely to breakout, for example, it can double-down on peacebuilding interventions. If a health NGO can predict where disease is most likely to spread, it can prioritize distribution of public health aid. The second is data-void problems. The data governments and nonprofits use to target social programs is rarely granular, recent, or accurate enough to pinpoint the specific regions or communities that would benefit most, and collecting more-comprehensive data is often prohibitively expensive. As a result, many of the people who need a program the most don’t receive it, and vice versa. If, however, an NGO fighting hunger in a rural state in Ethiopia knows which villages have the highest malnutrition rates, it can focus its outreach efforts in those communities, instead of oversaturating a different region that has fewer needs. Read the full article about machine learning by Ben Brockman, Andrew Fraker, Jeff McManus, and Neil Buddy Shah at Stanford Social Innovation Review.


This Commission articulated what many in the Pay for Success (PFS) community have been uncovering through their feasibility study and project construction support work for years now. Data are at the center of our ability to generate evidence, assess programs for impact, and ultimately make decisions about what to fund and at what level with our limited resources.

As the Commission on Evidence-Based Policymaking recognized, data are important building blocks for the generation of evidence and many of the greatest gains for evidence building can be achieved by addressing the challenges that surround data access, capacity, and privacy. The support provided by the SIF PFS program is critical to surface data challenges, assess ways to address these challenges, and ultimately achieve the promise of the evidence-based movement to measurably improve the lives of individuals through more effective services.

Read the full article by Nicole Truhe about data access on americaforward.org

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