**This blog entry orginally appeared on the website oceanspaces.org.**/p>

I can’t bear to go running without my smartphone because I’ve become so attached to the data I get from its various tracking features–distance, pace, elevation change, calories burned, etc. This information is somewhat useful, I think. Tracking my progress over weeks and months helps to motivate me, and it helps me see how my fitness is evolving (if at all!). For this benefit I’m happy to drag this iThingy along with me on an afternoon jog.

Our professional, personal, and political lives are increasingly data-driven. The proliferation of readily-available data on all manner of phenomena–sea-level rise, national health outcomes, page-views, miles per gallon–is almost synonymous with opportunity and potential. And we can see this proliferation playing out in citizen science. I could easily fill my phone with apps encouraging me to collect observations about the world around me, even if the reasons for doing this (where do the data go? who uses them?) might not always be clear.

We are obsessed with the notion that a seemingly intractable problem, with just a little more data, will become solvable. But our exuberance tends to drown out a few very important questions: what are data good for? what are they not good for? how much is enough, or even too much?

For example, would more data on my jogging make a difference? What if I could add a heart rate monitor, or improve the accuracy of the location tracking? These marginal additions would tell me a bit more, but probably wouldn’t change the benefit–primarily motivation–that I receive from the running app already. There’s a limit past which it’s not worth worrying about getting more data. It’s the difference between relevant and useful.

For some people this idea of “the quantified self” is a total turn-off, while others take it to the opposite extreme. So while it’s true that the utility of the data will always be limited, it’s also important to remember that these limitations vary across contexts: running is actually a different kind of thing to different kinds of people.

What does all of this have to do with citizen science and MPA monitoring? These questions about data–what are data good for? what are they not good for? how much is enough?–have been front and center in shaping our approach to MPA monitoring in general, and our explorations of citizen science in the Central Coast region.

We cannot know everything about the socio-ecological dynamics playing out in California’s network of MPAs. And even if we could, comprehensive data would not resolve the bigger questions on their own. They can’t tell us an appropriate definition of ecosystem health, an acceptable timeline for adaptive management decisions, or how balance competing goals of conservation and resource extraction. To paraphrase a recent article by David Brooks, “Big Data” has trouble with big problems, and it tends to obscure important values (e.g. what actually matters about the environment?).

So in our approach to MPA monitoring, and to engaging citizen science, we are not just thinking about how to get data. We are designing a constructive role for science in a complicated process that involves much more than just data. We are explicitly acknowledging that understanding and using data require time, effort, and resources. And we are seeking partnerships based on this shared goal of seeing science put to constructive use.

Citizen science programs need to make sure that they don’t get caught up in the Big Data fad without some larger sense of purpose. On the other side of that coin, citizen science should not be valued merely for its ability to create tons of data but also for the linkages it can forge. The path from data to action is neither automatic, nor easy; it must be crafted and maintained.