(Photo by Julie Zeglen)
It’s easy to say that your social program had impact. It can be hard to know how to say it with data.
On the second Wednesday evening of the month, we hosted our second programmed Impact Solutions meetup, this time focusing on measuring success.
Josh Glickenhaus, Aeris Insight
Glickenhaus is an associate with Aeris Insight, a data services nonprofit organization and CDFI rating agency. CDFIs are community development financial institutions, such as Reinvestment Fund, which invest in projects benefitting low-income, low-wealth communities.
There aren’t any generally accepted impact assessments, Glickenhaus said — no central authority telling organizations what data to track. It can be difficult to prove that a CDFI is achieving its mission.
“Impact tends to rely on narrative,” he said.
That’s why Aeris is working with CDFIs and their funders — often impact investors — to “standardize” metrics. The work is still in its early stages. But the framework is a matrix comparing social impact (housing, economic security, health and food access, etc.) to disadvantaged demographic groups being served (low- to moderate-income households, people of color, etc.).
Glickenhaus hopes Aeris’s work can be extrapolated to impact investing or philanthropy more generally.
Morgan Findley, Greater Philadelphia Cultural Alliance
Findley is a research analyst with GPCA, which supports 400 members and also works in community engagement, policy and advocacy, grantmaking, audience development and research.
Its research arm has published Philadelphia-focused arts data for several years, but last year was the first it collected data for 10 other metros across the country with the help of Data Arts (formerly known as the Cultural Data Project).
The published result was a “snapshot” of the “cultural ecosystem” including 5,502 organizations with a total of $13 billion in annual spending serving 210 million people.
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Findley shared a few takeaways from the experience.
- Know your limits. She could have built her own database, she said, but because the amount of data was so huge, hiring an experienced consultant to do so made the process go faster.
- Respect the data. GPCA’s strength is its local insight, so it could notice irregularities or possible typos in Philadelphia organizations’ data — for instance, if an organization had five employees last year and noted 500 employees this year. For less familiar cities, GPCA set equations to track outliers for further investigation.
- Stick with what you know. The resulting report is beautiful, Findley said, but by expanding to include other cities, GCPA increased its work hundredfold. This was an effort that one organization might not be capable of doing on its own.
GCPA’s goal is that organizations in other metros will want to take on such a project analyzing their own data.
Kevin Moran, Fairmount Community Development Corporation
Moran is the executive director of Fairmount CDC, which serves both the Fairmount Avenue and West Girard Avenue commercial corridors. Because many CDCs have scarce resources, Moran is “trying to make research for small CDCs much more accessible” by testing and sharing a methodology that can be replicated.
Since November, Moran has been using publicly available data to determine how many and what type of people live, work and shop in the neighborhood as well as what types of businesses are thriving. This information will help shape the corridors as they develop to make them as sustainable as they can possibly be.
It can also help to attract new businesses. If, for instance, it’s found that more people are coming into the neighborhood to work during the day than leaving, a cafe focusing on lunch may succeed. Moran can then recruit such a business to open in the area.
The work is a low-cost, low-risk investment, Moran said, so it can be used anywhere. He hopes other CDCs will use Fairmount as a model and analyze the strength of their own commercial corridors.
The next Impact Solutions meetup will be on Wednesday, Feb. 10, and will focus on diversity and inclusion.-30-
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