At Sunlight we spend a lot of time exploring ways to open up data sets and make them more accessible. The idea is that data enables us to act collectively, making better informed decisions and building a more effective public sector. When we talk about transparency the focus is often on the possibilities that data offers. But this discussion sometimes ignores the fact that translating data into action is hard.

There's a reason for this: data alone doesn't provide answers.

Coming up with solutions to real life problems -- like designing an effective and fair tax code or improving health care -- requires an understanding of how real life works. Unfortunately, more often than not real life is messy and complicated. In order to make sense of this complexity we need models -- approximations of the world that define fundamental mechanics of a given process and reduce it to understandable and meaningful terms.

As Joshua Epstein writes in a clever essay on scientific inquiry, every time we use data to draw a conclusion we also use a model. Sometimes explicitly: when a meteorologist makes a prediction about the weather they use a rigorously designed framework for translating observational data into a forecast. Sometimes not: when I look at the sky and make a prediction I'm using an implicit model based on a mix of past experience and a rather poor understanding of atmospheric processes. Both of us are using models to interpret data and both are based on assumptions about how weather works. I'm just not sure I could explain how mine functions, nor do I have any sense of how well it works.

Having access to good observational data is incredibly important to arriving at useful answers. But well designed and transparent models are equally important. In fact, having a good model is often a prerequisite to determining what to observe and how. If I want to predict the weather should I measure the temperature? Pressure? Wind direction? Where and how frequently? Without a solid theoretical framework it's often impossible to know where to begin and it's even harder to know when I've made a wrong turn.

When we use a model we embed its assumptions into the results. If key assumptions are incorrect, good data turns into supporting evidence for a potentially misguided answer. Or a bad model might drive the collection of useless data.