R. Jacobus, Principal, Street Level Advisors: Who Will Pay for Data?

Mar 2016
Global, March, 17 2016 - The measurement of social impact has become a widely shared objective within the social sector, and few people in the sector doubt the potential of impact data to transform their work. Yet among nonprofit groups, social enterprises, and other social-purpose organizations, the use of impact data remains surprisingly limited.

In 2014, I led a yearlong research effort that sought to make sense of this discrepancy. CoMetrics and the F. B. Heron Foundation commissioned the study, which focused on identifying scalable business models for the collection, analysis, and distribution of social impact data. For the study, I profiled a variety of existing data projects—from grant reporting tools to certification systems, from peer benchmarking efforts to data sharing initiatives—and analyzed them to see whether they had established a reliable approach to paying for their operations.

The project profiles I created tell a remarkably consistent story. It explains why the social sector hasn’t made more progress in building an infrastructure for gathering and sharing impact data. The problem, I found, results from a fundamental misunderstanding both of the value that social impact data can provide and of the parties that are most likely to benefit from (and ultimately to pay for) access to those data.

It’s Not About Funders

The leaders of social purpose organizations constantly hear from their funders that measuring impact is important. So it seems reasonable to assume that funders—individual donors, foundations, and impact investors—are the natural customers for social impact data. Reinforcing that assumption is the fact that these funders are all, in some sense, “investors.” Traditional investors have come to rely heavily on financial data to inform their decisions, and their hunger for such data has driven the rapid growth of highly profitable businesses like Bloomberg LP and Morningstar, Inc. By the same logic, shouldn’t social sector funders be eager to pay for access to a robust data infrastructure? Shouldn’t their hunger for data have led to the creation of a Bloomberg-like entity that tracks social impact? 

Indeed, organizations like GuideStar USA and Charity Navigator are seeking to fill that niche, and they have borrowed their business model from the financial sector. They collect public and voluntarily submitted data on nonprofit organizations and social enterprises and then sell access to the resulting dataset to grantmakers. But they have found it hard to make this model work. GuideStar, for example, has steadily increased its revenue from user fees—yet that revenue still isn’t enough to cover all or even most of its costs. Bloomberg charges $24,000 annually for a single terminal. (The Bloomberg system requires customers to lease computer hardware through which they access the company’s proprietary data.) GuideStar, in contrast, charges only $1,500 for an annual subscription to its database. Ironically, GuideStar makes up for its earned revenue shortfall with grants that often come from foundations that apparently don’t value its service highly enough to pay (or to pay a higher rate) for subscriptions. 

The same pattern is evident in other data projects as well. The Workforce Benchmarking Network, for example, has collected detailed information on the performance of more than 200 employment-training programs. The network uses these data to provide organizations with confidential reports that benchmark their programs against similar efforts. It also publishes reports that highlight overarching lessons for the employment-training field. In short, the network appears to provide exactly the kind of data that funders in that field would value. Yet it has struggled to find a sustainable source of revenue. The foundations that supported the creation of the project haven’t been willing to provide funding to sustain it over time. 

The experience of projects like GuideStar and the Workforce Benchmarking Network suggests that although funders want to support the collection of useful impact data, they don’t see themselves as the true customers for such data. 

It’s About Funded Organizations 

If funders aren’t the ultimate customer for information about social outcomes, then who is? In my research on impact data projects, I discovered that the most reliable customers for impact data are likely to be the very organizations—nonprofits and social enterprises—whose work is being measured. Although a given set of data may be of interest to both funders and funded organizations, the latter group simply has a keener need for such data. Here’s why: The challenges that nonprofits face in planning their work and securing financial support for it are far bigger than the challenge that foundations face in choosing which nonprofits to fund. 

Social purpose organizations, in other words, can use impact data to unlock value in two areas that are of limited relevance to most funders: organizational differentiation and program design. 

Organizational differentiation | Social sector leaders often view social impact data as a resource that they can use to distinguish their organization from competitors: To set their organization apart, they draw on published impact data and highlight those data in their funding appeals. An organization that can demonstrate even a small advantage over competing organizations can derive real value from using data in this way. 

Take the example of LEED certification, a project of the US Green Building Council (USGBC). LEED collects data about the environmental performance of “green buildings,” and real estate investors and corporate tenants then use that information to decide which properties to buy or lease. LEED certification costs more than $30,000 per building. But USGBC doesn’t expect investors or tenants to pay that fee. Instead, it charges property developers (along with architects and other professionals) for the certification service. Developers presumably already know that their buildings are green, but they pay for LEED certification because they see it as a way to differentiate their properties in the eyes of investors and tenants. 

An organization called Beyond 12 follows a similar model. Beyond 12 collects data about college completion among young people who have graduated from various college readiness programs. These data enable funders to understand which readiness programs help kids not only to get into college but also to stay in school and graduate. But Beyond 12 doesn’t support this work by charging funders for access to data. Instead, the organization collects fees from charter schools and other groups that offer readiness programs. Although data on college completion may be relevant to funders, it is truly valuable to these frontline organizations. 

Program design | Impact data can also provide value to social purpose organizations by informing the way that they design specific interventions. 

The Chapin Hall Collaborative for Youth Development Programs illustrates this approach. Chapin Hall, a policy research center at the University of Chicago, joined with five nonprofit youth organizations in Chicago to form a project that aims to evaluate their respective after-school programs. Each group contributes data on the young people whom it serves, and Chapin Hall matches that information with confidential data provided by the Chicago public school system and by the city’s juvenile justice and child welfare agencies. In this way, the nonprofit youth groups are able to track outcomes for individual participants. But the collaborative also enables those groups to analyze comparison sets that include young people who are not participating in a given program. Nonprofit leaders can use those comparative data to gauge the extent to which outcomes are attributable to the program—and they can then adjust the program in response to those findings. At this point, funding for the collaborative comes entirely from the five nonprofits, each of which pays about $25,000 annually to support the project. 

The HomeKeeper project, which I helped to develop, offers another example of using data to guide program design decisions. HomeKeeper is a shared data platform used by organizations that manage affordable homeownership programs. Like the Workforce Benchmarking Network, the HomeKeeper project provides both confidential peer benchmarking reports and publicly available field-wide social impact reports. 

Through peer benchmarking, for example, the leaders of participating organizations can find out if the foreclosure rate in a given program is higher than average. (Although foreclosures are rare in the affordable housing field, they do occur.) Leaders can then make program adjustments to solve that problem. Because housing organizations truly value this kind of insight, HomeKeeper is able to fund its operations primarily through user fees paid by those organizations. 

And It’s About Business Models 

Of course, even when funded organizations write the checks that support impact data systems, it is funders that ultimately foot the bill. But the choice of payment structure is critically important. Consider an example from the business sector: Zappos, the online shoe retailer, has a “free shipping” policy that is central to its business model. Even though customers indirectly pay the cost of shipping, the use of this pricing structure has enabled Zappos to overcome a crucial barrier to the adoption of online shoe shopping. 

Similarly, it’s unlikely that real estate investors and corporate tenants would have invested directly in a certification system for green buildings. Yet those groups have indirectly—that is, through higher development costs and higher rents—paid hundreds of millions of dollars in LEED certification fees. In the same way, all of the funding for the Chapin Hall collaborative is likely to come from grants or donations of one kind or another. But because participating organizations must build that cost into their program budgets, the project has proven to be more scalable than it would be if a single donor were funding it directly. 

Business models matter. If we want to build a data culture for the social sector, we need to channel the limited resources available within the sector toward models that have staying power. Instead of investing in projects designed to cater to funders, we should work to create structures that will encourage nonprofits and social enterprises to embrace their role as customers for impact data.


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