Over the past 10 years, there has been a huge shift in how philanthropists use data. Funders have switched from simply collecting transactional information about their grants to relying on data to assess the outcomes of those investments and chart progress toward achieving their mission.
A new report from GuideStar and Exponent Partners provides a closer look at this evolution and where the sector is today. In it, Tipping Point’s Nick Arevalo and Ashley Sigmon share a few lessons from our Impact + Learning work. Here are the highlights:
1. As funders, we should foot the bill for grantee databases.
“It’s a no-brainer to fund technology for grantees if you’re focused on outcomes,” Arevalo said. “If we ask groups to use general operating funds for a database, we are taking away from the inherent flexibility that support is intended to offer them! We see technology as an essential tool for direct-service organizations’ ability to work better, smarter and more efficiently in helping people to get out of poverty. If an additional technology grant and technical assistance on top of general operating support will cut down the time it takes an organization to get the system it needs, then we’re willing to make that investment.”
2. Don’t ask for extraneous data.
“If you’re going to ask for data, make sure you know what you’re using it for,” Arevalo advised. “We want to be sure that when we’re asking grantees to go work with a client, input the data, collect, analyze and give it back to us, that we’re going to utilize that information. We understand it’s a heavy lift and that they have other things they could be doing with that precious time. It’s also critical to articulate your goal back to the grantee, so they know it’s not just going to sit there after all the work. With the structure we have in place now, we’re having conversations based on grantees’ data on a weekly basis.”
3. Adjust metrics to fit the outcome, not the grant report deadline.
“If a group works with a client in fiscal year 2015, we understand that they won’t necessarily know if that client has obtained the outcome that they’re driving towards, even if that client is out of program. Many of the outcomes we care about take a really long time to attain,” Arevalo said. “One group in our portfolio helps low-income first-generation college students. The program has to wait six years to see if a student actually graduates from high school and college, so we built our reporting structure to focus on cohorts. We ask grantees to tell us how many clients started in the program that year, how many continued on in the program, how many completed the program.”
4. Good comparative data is important.
“For the first-generation low-income college group, we know that looking at our grantees’ enrollment or persistence or graduation rates is not enough,” Arevalo continued. “You have to put it against counterfactuals. We look at general graduation rates, rates for similar populations (e.g. first-generation or Pell Grant recipients), and rates for low-income students that aren’t necessarily first-generation. We try to figure out how our groups perform at this stage against those figures, so we can help them both articulate the impact they’re having and figure out what benchmarks they ought to strive toward.”
5. Well-managed data can detect patterns and predict challenges.
“When a grantee calls [Tipping Point] and says this just happened, or this person left, or they have a grant that they’re thinking about pursuing, or they have this new need, it’s all stored in Salesforce,” Sigmon said. The goal is to create a 360-degree view of what support Tipping Point provides each grantee, the outcomes of that support, and how it compares to like organizations. “We’ll see clearly if grantees are moving forward or if the support we provide is not working. And we’ll be able to analyze and understand better what sequence we should be using in how we support organizations.”
To learn more, check out the full white paper, Data-Driven Funders: In Search of Insights.
