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Using Public Data to Advance Philanthropy

Monday, August 12, 2019

By Shira Hodges and Michael Johnston

For the past year and a half, I have sat down with many philanthropists in the region. As we begin to chat about their work and the challenges faced, we almost always end up discussing data.  Of the over 2,000 foundations in the area, just under 5 percent have paid staff, meaning their capacity to dive into quantitative research is limited. Most of their emphasis, rightly so, has been placed on qualitative factors and relationship building with grantee partners. 

The sum of these expressed frustrations led me to realize that Philanthropy Network could foster a solution to what should be a simple issue - user friendly access to several datasets already available*.  The missing link was the technology and data expertise to make it happen. Just as I started in on a plan, an old friend and colleague, Michael Johnston, reached out. A Data Science consultant and self-proclaimed data geek, his excitement for data only seems to be exceeded by his passion for social impact.  He could help the idea become a reality.

Starting with this post, we will be releasing a blog series that will explore stories easily told through data. Specifically, how philanthropists of any size should be able to use their data to produce effective change. 

The problems philanthropists are attempting to solve are complex, multifaceted problems.  We’ll have an article on how open data is important in helping understand the scope and scale of the problems we’re tackling. However, many times smaller philanthropic entities only have anecdotal evidence and aren’t able to quickly or easily pull together data to prove their strategy before and after implementation.  Nonetheless, we’ll show how this data can help even smaller organizations understand their roles, and how collaborations can help us all move the needle. 

Then we'll be diving in deep.  We'll look in-detail at how some of our local philanthropists, and their grantees, are requesting reporting of data (and even anecdotes, which is unstructured qualitative data).  This will help us understand the specific issue the grantor is reviewing. This will also allow us the opportunity to thoughtfully share and align information, so we can come to better understand our overall impact.  What does it all add up to?

You may also have heard about the electronic open 990 data from the IRS.  Over the past two years, the IRS released all electronic tax filings publicly, and various attempts have been made to expand the use of that data - now much easier to access, more detailed, and less costly to use.  We'll look at a number of uses of that data, from understanding challenges in grantmaker-grantee relationships, operational limitations of current grantmaking processes that affect our ability to understand our impact, and more.

With the greater accessibility of all this data, we'll be exploring tools and capacity building that can enable organizations with specific objectives to leverage this data themselves, fitting the data to their unique context.  And we hope these explorations will also set the stage for collaboration. When key stakeholders on an issue agree on how we define major parts of our work, we can overcome some of the biggest barriers to practical and impactful data use, and make a bigger difference, together.

Throughout the series we look forward to your feedback and contributions. Look for our next blog post in September 2019.

Shira Hodges is Vice President, Strategy and Learning at Philanthropy Network Greater Philadelphia.  Michael Johnston is Data Scientist in Residence at Philanthropy Network.  He has spent the last decade working as a management consultant, data analyst, and technologist, seeking ways to use technology and process improvement to help others make a bigger impact. 

 

*Datasets: (1) Nonprofit data: Open 990s, NCES business masterfile and website data, enriched with geocoding, clustering, and diversity APIs; (2) Public data related to populations and issues: Census data, education data from the city and state, public health indicators, the Area Deprivation Index; a few topic-specific datasets relevant to international funders; (3) Survey data: surveys to ask key question not covered elsewhere; (4) Funder-specific data: grant application and report data from individual funders, to analyze and tie into the above

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