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March 9, 2010

On Monday, March 1, the Center hosted a group of 19 representatives from foundations, tech companies, and nonprofits at the Pew DC Conference Center to discuss issues raised by “Disrupting Philanthropy: Technology and the Future of the Social Sector,” by Lucy Bernholz with Edward Skloot and Barry Varela.
One of the participants in the discussion, Geoff Livingston of Zoeticaliveblogged the event. For another take on the conversation, I present my own notes from the meeting, in their raw form:

Introduction, by Lucy Bernholz:
The major themes to be explored during the meeting are:
Most people are both donors and doers.
Information exchange is at the root of the market and at the root of the commons.
There are four big ideas:
  1. Data are the new platform for change.
  2. There are new business/corporate models (social enterprises, B Corps, L3Cs) to support social change.
  3. New governance models are needed, from the consolidated to the decentralized.
  4. There are new tensions between market and nonmarket solutions.
Morning session:
Regarding “Data are the new platform for change”: emphasis should be less on the data and more on the shared protocols; that is, on the information architecture. How information is shared rather than data themselves.
Example of Lex Mundi Pro Bono Foundation was offered.
Another example: One of the metrics at FasterCures is how well grantees share knowledge; this metric causes change in the behavior of organizations.
A comment on how the National Science Foundation is debating how to make research public led into a brief discussion of the so-called semantic web; which led into a discussion of how foundation grants are coded: “What constitutes ‘social justice,’ when trying to classify grants?”
Data integrity is important, but there has to some sort of cost-benefit analysis applied: what are we using the data for, and what does it cost to aggregate them?
There’s a limit to the utility of data aggregation.
Market solutions (philanthropic capital markets, for example) require data: standardization, taxonomy, and so on. B-corps and L3Cs are “code-level changes”—literally representing changes in the tax code.
Regular for-profit corporations, too, participate in the “networked ecosphere” of actors for social change. Such networked ecospheres—among corporations, B Corps, nonprofits, government, and the public, for example—are more fluid, less vertical than previous arrangements.
The way nonprofits have been funded will push up against B Corps, L3Cs, social entrepreneurship; lawmakers will ask why fund nonprofits? Why grant tax preference? Nonprofit will fight to preserve themselves even if they support diversity of form.
Hasn’t the “networked ecosphere” always existed, but now we can see it more clearly because the filter has been removed?
Is the danger of removing the filter that there will be too much data to make sense of?
On the other hand, data make the role of each actor (funders, nonprofits, corporations, governments) clearer.
Regarding different kinds of actors in the social sector: Many “organizations” aren’t even organizations—for example, there are groups of people working to bring technology to the developing world that have given up on foundation funding.
Example of Ushahidi, which had difficulty as a startup getting funding because of its lack of formal structure.
Another example: crisis camps for Haiti that arose spontaneously.
There’s the issue of governance of “anti-organizations”: Who’s in charge?
There’s discomfort among foundation staff with “anti-organizations.”
Consequently, there’s a lack of venture funding. Foundations don’t like to give money to organizations in the larval stage. Creative Commons was an exception.
More of examples of “guerilla” organizations: Alex’s Lemonade StandBlame Drew’s Cancer, the Lance Armstrong Foundation, all may be cutting into the American Cancer Society’s market share.
However, the natural pull for any group over time is toward institutional structure.
Does that pull tend to inhibit innovation? Many large corporations lose their ability to innovate. They go out of business.
However: In nonprofits, there’s an irrational impulse to carry on regardless. In contrast to for-profit companies, nonprofits rarely go out of business. Example of the March of Dimes, which solved the problem it set out to solve (polio) 50 years ago yet is still with us.
Will form of governance begin to reflect the values of the organization?
Example: PatientsLikeMe enables its constituents to form disease communities to share experiences.
The historical perspective: What happened to governance structures after the invention of the telegraph, radio, etc.?
The commons can be understood as a value-generating paradigm; there are now lower barriers to entry for the formation of organizations.
Nevertheless, capital becomes necessary as groups become larger.
Question: Where have we found data that have forced change in the way institutions operate? The example of climate change is that the data haven’t made much headway in changing behaviors.
Metacomment on the conversation: there seem to be two separate strands: one about how networked technologies may or may not improve efficiency, and one about how data aggregation may or may not change behaviors.
Break for lunch.
After lunch, Marcus Peacock of the Pew Charitable Trusts presented Pew’s Subsidyscope project, a web-based interactive tool that collects data on federal subsidies—where they go, to whom, etc.
Afternoon session:
Citation of the newly launched Panton Principles, which make scientific data sets open to the public.
The presentation of data is still crude. To make the case for social justice, foundations need to make better pictures. The New York Times VizLab and Good magazine are examples of media that present data effectively.
Example of effective use of data: the Portland Red Cross used fire department maps to figure out that the school districts where they were doing their training were not the ones where most fires were.
Data by themselves aren’t sufficient: there’s a need for marketing, storytelling, sharing protocols.
Example of climate change again: needs better messaging.
The dichotomy between big media and the people is false. It’s not either/or, it’s both.
To the “Five P’s” of foundations, a fourth should be added: Principles, Platforms, Pedagogy, Partnerships, and now Public Engagement.
The old way of thinking was that foundations’ resource was money; now it’s more obvious that information is important.
But what kind of expectation will be placed on nonprofits to gather/provide data? Will the burden be excessive?
Back to governance structures:  Governance structures will sort themselves out according to what delivers value. Ones that are ineffective will wither away.
However, many nonprofits survive because they’re good at persuading donors, not because they deliver value.
Thanks to GlobalGiving and others (i.e., the long tail of giving), we can see how individuals give—another massive data set.
Session wrapup: final comments and observations:
Data can help grantees find donors as well as donors find grantees.
There’s unresolved tension between centralized control and letting go.
Should foundations be expected to spell out why they chose the grantees they chose?
Or better, announce what their expectations for the grant are, so there can be accountability?
What of return on investment? For some nonprofits, it may not be worth it to invest in technology.
What new governance structures are out there?
Policy concerns: privacy? Use of data from the crowd? Security? Transparency? Ownership and control? Intellectual property rights?
The library and journalism communities are having conversations that are very similar to the one we’re having.
It may be worthwhile to collect success stories and make them available in a format similar to the Center’s Case Study Database.
Barry Varela