This is a guest post by Kyle Reis. After twenty-five years at the Ford Foundation, Kyle has just joined the nonprofit social enterprise TechSoup Global as Senior Director of Global Data Services and East Coast Representative. When not busy raising three daughters or reading Emily Dickinson poems, he spends his time thinking about the intersection of data, design, and social change.
Data are not sexy.
There, I’ve said it. The sentence proves the point. Data are. Data is. Data, hmm. Personally, I love data. But we all know what invariably happens when the ‘D’ word comes up in conversation other than at a hackathon or Google staff party. Our eyes glaze over, we nod that, yes, this is indeed the era of Big Data, and then excuse ourselves to freshen our drink.
But let me clarify. It’s not that I love data per se. The data point, ‘New Jersey,’ does not, thankfully, excite me. What I do love, however, is what can come of data, particularly when it gets big and varied. Often, a surprising thing happens: the data get interesting. Really interesting. Even more importantly, the data become meaningful. Individual data points begin morphing into larger concepts like, say, The Law of Large Numbers. Now we’re talking sexy. I would even go so far as to call the Law of Large Numbers awesome! See for yourself:
OK. So, formulas aren’t sexy either. But, in layman’s terms, what this tells us is that, as we get more and more data (e.g., rolls of a die, Google “flu” searches, grants approved by foundations), the data become more predictable and informative. Data points bond together and in so doing undergo a kind of metamorphosis in our perception. They begin to reveal previously hidden truths, to show surprising patterns and correlations, and to surface anomalies. If you’ve read Malcolm Gladwell’s Outliers or studied the concept of positive deviance, you know how much we can learn from that which deviates from the norm. Here’s a quote from the Positive Deviance Initiative that touches on this topic, and sounds a lot like something that would be of interest to foundations:
“Positive Deviance is based on the observation that in every community there are certain individuals or groups whose uncommon behaviors and strategies enable them to find better solutions to problems than their peers, while having access to the same resources and facing similar or worse challenges.”
When cultivated and well-presented, this “data metamorphosis” takes on a new resonance for one simple reason: it begins to reveal stories.
We all love stories. Stories resonate. Stories delight. Stories move us. For foundations, stories are vital to our work. Stories of need or injustice, of action or inaction. Not only can stories help us decide who to fund, but also what results from our funding. However, for philanthropy’s stories to move others to action, they must have data.
Why data, you might ask. Isn’t the folksy anecdote that moves us to action good enough? In the past, the answer might have been yes because supporting an organization that’s doing good is better than not supporting one. But this is no longer sufficient. The needs are too big for us to be funding all but the best organizations. So how do we find these organizations? Using data. Data matter. Data help us unearth facts that, in turn, help us learn about organizations and the impact their work has on the communities they serve.
And that’s why initiatives like the Reporting Commitment are so important. Though it might seem small that sixteen foundations – including some of the largest in the country – have begun publishing their grants data in an open, accessible fashion – the truth is this is big news. Here’s what these records look like:
In just nine months these 16 foundations have made available to the public more than 10,000 grants totaling $9 billion. Now imagine what this data set could look like in two or three years’ time, with several hundred foundations contributing tens of thousands of grants totaling tens of billions of dollars. Then imagine these same foundations and others working to improve people’s lives downloading this data and mashing it up with Census, World Bank, or other information. Or using word clouds and other visual tools to reveal beautiful patterns. Or mapping the geographic-area-served data to see if funding is reaching the places of greatest need. And then, if you will, imagine this data being used in ways we can’t yet dream. This metamorphosis of data could be spectacular, and the impact of what we do with this knowledge would be tangible.
So here is my call to all funders: join the Reporting Commitment. Send in your data so that you and others can use it to tell the stories that are out there waiting for the data to find them. Do this and you, too, will come to love data as I do.
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Kris Putnam-Walkerly is a philanthropy expert and consultant. If you found this blog post useful, please subscribe. On Twitter? Follow me @Philanthropy411.
© Kris Putnam-Walkerly and Philanthropy411, 2013.
