Many nonprofit leaders express discomfort with attempts to translate their work into a statistic. For them, the notion of reducing a potentially life-changing experience to a number doesn’t just feel confusing, it’s kind of insulting. Meanwhile, fundraisers making their case to individual donors, advocates, and policymakers often find that a powerful story can work wonders where facts and figures fall flat.
It’s easy to see why most people prefer stories to data. A story is rich, full of detail and shape. Data is flat. Put another way, data is mined from the common ground between various stories, which means that in order for it to work, for it to be converted into the language of numbers, you have to exclude extraneous information. Even if that “extraneous” information happens to be really interesting and cool and sums up exactly why we do what we do!
The reason stories work for us as human beings is because they are few in number. We can spend two hours watching a documentary, or a week reading a history book, and get a really deep qualitative understanding of what was going on in a specific situation or in a specific case. The problem is that we can only truly comprehend so many stories at once. We don’t have the mental bandwidth to process the experiences of even hundreds, much less thousands or millions of subjects or occurrences. To make sense of those kinds of numbers, we need ways of simplifying and reducing the amount of information we store in each case. So what we do is we take all of those stories and we flatten them: we dry out all of the rich shape and detail that makes up their original form and we package them instead in a kind of mold: collecting a specific and limited set of attributes about each so that we can apply analysis techniques to them in batch. In a very real sense, data = mass-produced stories.
It sounds horrible when I put it like that, right? But it’s an essential process because without it, we can’t be assured that we’re looking at the whole picture. Especially when we’re dealing with a large number of potential cases or examples, if we just concentrate on those that are nearest to us, whether that proximity is measured by geography or social/professional circle or similarity to our own situation, there is a very real risk that we will draw inappropriate conclusions about examples that are a little farther afield. Either random statistical noise (especially in the case of small sample sizes) or a bias that skews the kinds of examples we seek out can contribute to this lack of precision about our conclusions.
So we gain something very significant when we flatten stories into data. At a minimum, if we’re doing it right, we gain the confidence that comes with looking at the whole picture rather than only a piece of it. At its very best, we gain the opportunity to formulate stories out of data — such as in the case of Steve Sheppard’s work on MASS MoCA and the revitalization of North Adams, MA. But we lose something too. We lose the ability to cross-reference obscure details about one of our examples with obscure details about another, and sometimes those obscure details turn out to be pretty important. We lose some of the context for understanding why data points might look the way they do, and depending on how well we’ve constructed our data, that may or may not change the conclusions we draw.
But make no mistake: stories are never incompatible with data. When you or someone you know has an indelible experience at a spiritual retreat, or when a child’s life is saved through involvement with your nonprofit, or when people are brought together who wouldn’t otherwise meet because of an event you organized, those are all great stories — and they’re also data. It’s entirely possible to identify what’s special or salient about these experiences, compare them to the experiences of others, and transform them into numbers that are bursting with emotional significance. I’m not saying it’s easy to do that, but it can be done, and done meaningfully and with integrity. I think we need to challenge ourselves as a sector to be more creative about how we articulate and measure the ways in which our organizations improve lives. The answers that we’re looking for might be closer within our reach than we thought.
Many nonprofit leaders express discomfort with attempts to translate their work into a statistic. For them, the notion of reducing a potentially life-changing experience to a number doesn’t just feel confusing, it’s kind of insulting. Meanwhile, fundraisers making their case to individual donors, advocates, and policymakers often find that a powerful story can work wonders where facts and figures fall flat.
It’s easy to see why most people prefer stories to data. A story is rich, full of detail and shape. Data is flat. Put another way, data is mined from the common ground between various stories, which means that in order for it to work, for it to be converted into the language of numbers, you have to exclude extraneous information. Even if that “extraneous” information happens to be really interesting and cool and sums up exactly why we do what we do!
The reason stories work for us as human beings is because they are few in number. We can spend two hours watching a documentary, or a week reading a history book, and get a really deep qualitative understanding of what was going on in a specific situation or in a specific case. The problem is that we can only truly comprehend so many stories at once. We don’t have the mental bandwidth to process the experiences of even hundreds, much less thousands or millions of subjects or occurrences. To make sense of those kinds of numbers, we need ways of simplifying and reducing the amount of information we store in each case. So what we do is we take all of those stories and we flatten them: we dry out all of the rich shape and detail that makes up their original form and we package them instead in a kind of mold: collecting a specific and limited set of attributes about each so that we can apply analysis techniques to them in batch. In a very real sense, data = mass-produced stories.
It sounds horrible when I put it like that, right? But it’s an essential process because without it, we can’t be assured that we’re looking at the whole picture. Especially when we’re dealing with a large number of potential cases or examples, if we just concentrate on those that are nearest to us, whether that proximity is measured by geography or social/professional circle or similarity to our own situation, there is a very real risk that we will draw inappropriate conclusions about examples that are a little farther afield. Either random statistical noise (especially in the case of small sample sizes) or a bias that skews the kinds of examples we seek out can contribute to this lack of precision about our conclusions.
So we gain something very significant when we flatten stories into data. At a minimum, if we’re doing it right, we gain the confidence that comes with looking at the whole picture rather than only a piece of it. At its very best, we gain the opportunity to formulate stories out of data — such as in the case of Steve Sheppard’s work on MASS MoCA and the revitalization of North Adams, MA. But we lose something too. We lose the ability to cross-reference obscure details about one of our examples with obscure details about another, and sometimes those obscure details turn out to be pretty important. We lose some of the context for understanding why data points might look the way they do, and depending on how well we’ve constructed our data, that may or may not change the conclusions we draw.
But make no mistake: stories are never incompatible with data. When you or someone you know has an indelible experience at a spiritual retreat, or when a child’s life is saved through involvement with your nonprofit, or when people are brought together who wouldn’t otherwise meet because of an event you organized, those are all great stories — and they’re also data. It’s entirely possible to identify what’s special or salient about these experiences, compare them to the experiences of others, and transform them into numbers that are bursting with emotional significance. I’m not saying it’s easy to do that, but it can be done, and done meaningfully and with integrity. I think we need to challenge ourselves as a sector to be more creative about how we articulate and measure the ways in which our organizations improve lives. The answers that we’re looking for might be closer within our reach than we thought.