Markets for Good Selected Readings: Making Sense of Data and Information in the Social Sector

Markets for Good is an effort by the Bill & Melinda Gates Foundation, the William & Flora Hewlett Foundation, and the progressive financial firm Liquidnet to improve the system for generating, sharing, and acting upon data and information in the social sector.

Our vision is of a social sector powered by information, where interventions are more effective and innovative, where capital flows efficiently to the organizations that are having the greatest impact, and where there is a dynamic culture of continuous learning and development.

Over the past several years, Markets for Good has been a forum for discussion and collaboration among online giving platforms, nonprofit information providers, nonprofit evaluators, philanthropic advisors, and other entities working to improve the global philanthropic system and social sector. This effort has included over 50 people from more than 20 organizations. The website, MarketsforGood.org, and the work that we hope follows from it, is an outgrowth of what we have learned and observed through this collaboration.

This retrospective collection of selected readings from our site includes an introduction by Jeff Raikes, CEO of the Bill & Melinda Gates Foundation, in which he highlights the "continuing wave of efforts that will push our sector to achieve even greater impact." Following Jeff's introduction, the Markets for Good Collaboration Team recaps the first 15 months of the campaign, and how they expect Markets for Good to evolve going forward. The subsequent 17 posts and authors' updates provide a range of perspectives on the most critical data-related challenges facing the social sector, and how these challenges can be addressed. Posts were chosen for their high readership, topic diversity, and thought leadership. The authors debate new and recurring hurdles in the social sector, like capacity and capital constraints; how qualitative data, including stories and beneficiary insights, can be incorporated into data-driven decision processes; and big-, medium-, and small-data management.

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