Annie Donovan, CEO of Coop Metrics discusses the difference between collecting financial and impact data, the two ends of the spectrum in impact data collection, and a model of the ideal.
The purpose of the panel was to discuss the nature of mission-driven and finance-driven data and was part of our January webinar with experts on data at work for enterprises. Watch and read more below...
Here Ms. Donovan responds to Market's for Good's Eric J. Henderson's questions regarding the convergence and divergence of financial and impact data, especially in terms of collection, reliability and reporting. CoopMetrics offers a tool that creates a common chart of accounts so that clients' financial data can be standardized, lending it to robust analysis and comparison.
"It’s relatively straightforward to create a tool like that where everybody agrees on what current assets are, or liabilities, or cash. (Actually, that’s not necessarily true – there’s a lot of subtlety to the way people define those, but in any case.) We have an infrastructure, accounting practices, that we follow that make it easier to work with financial data.
"Impact data is a whole other animal. Impact is often in the eye of the beholder. What is impact? Maybe the bottom line is impact. The first thing to do is to sort through what it is that we mean by impact investing. In our field and the social sector, we’re wrestling with this right now.
"But what we see on the measurement side is really two ends of the spectrum. On one end, a lot of what’s happening now is that data gets collected in a survey, and it’s self-reported, and it’s hard to standardize and figure out if the data is of high quality or not—let alone what does it mean when you get to the other end of it in creating reports.
"The other side of that spectrum, and the one that we like, is the model where data’s being collected, as a matter of course. In the normal course of business, data is being collected, and from that data, you can extract out what are the data points that are telling us something about impact. It’s a long way between where we are now – collecting data from surveys that are sort of hand-done and might take four hours to do – all the way over to sponging up data from the daily activity of organizations and making a meaning of it from there.
"There are a couple models out there that we really like. One of them is called HomeKeeper, and it’s a program that’s part of the Cornerstone Partnership, which is a program of Capital Impact Partners. We’ve been looking at that as a good model of how you can collect data. It’s a shared equity home ownership program that they created a data management tool for. They were trying to figure out if this intervention is effective for getting low-income people into home ownership, and if it is effective in maintaining an affordable housing stock in a city or community.
"Beyond that, once you have that basic data, you can start looking at the broader impacts. Are the kids in these units doing better in school? Are their participation rates better? And then you can think about the health impacts of people who are in a program like this. But if you’re not collecting the basic data, it’s hard to get to those bigger extractions. So we like to think about it as, what we’re doing is really about little data. Big data is great and we are all for it, but if you don’t have little data then it’s hard to get to big data, because big data is really built off of a lot of little data, right?"