Idealistics is shutting down. This blog will continue to be updated at fullcontactphilanthropy.com.
Data as hero

The hype over data has become deafening. Small non-profits are obsessed with leveraging big data, and foundations are on the hunt to find the one evaluative metric to rule them all.

We need to take a breath.

Data offers some exciting opportunities in our sector. Predictive algorithms are really good at helping Amazon.com figure out when to sell me things I do not need. So too can we use predictive analysis for social good, like trying to predict the likelihood that a low-income teen will become homeless.

But data is not the end all be all. It is not our savior, and machines do not (and should not) make decisions.

Data, regression, machine learning, etc. are all tools that we can (and should) be using. And while these tools can help illuminate the path forward, they are not the path forward.

The debate around data has become unnecessarily dichotomous and fractious. There should be no debate between people versus data, man versus machine. There is no Skynet.

Those who argue that data is all that matters are equally as wrong as those who say it is all about the people. In the social sector, it ought to be all about achieving social outcomes, and toward that end you need good people and good data.

Importantly, we need to do a better job of teaching our people the promise and pitfalls of data. As we look to incorporate more data into social sector decision making, our organizations need to be better integrated than simply having the so-called data-nerds in one room and social sector specialists in the other.

If we are to become savvy users of data, it is essential that we raise the level of data-literacy for all organizational decision makers.

As things stand now, with so much data-hype, it is far too easy for anyone with a spreadsheet to win an argument, even if they are wrong. Indeed, I often worry that my customers do not question my own analysis enough, or that they assume causation where there is none.

Data is no hero. But people can be. We are more likely to be heroes if we work with the data, but data alone will not save us from anything. Used incorrectly, it has the potential to make things worse.