Following on from data relativity, the second thing that grabbed me at #ukgc11 (UKGovCamp 2011) happened during Will Perrin’s introduction to “Making a Difference with Data”, and followed up in conversation with Helen Jeffrey (@imhelenj) on community-led data. Will talked of comparing his local authorities’ data on lamp-post repair time to his own count for how long a lamp-post had been broken for (encouraging crime). Helen talked about data that a group of volunteers had collected themselves, and then turned into a report to feed into government decision-making.
In both cases, what struck me was that people don’t have such an aversion or fear of data that is often assumed - if they start by generating it themselves.
To go back to data relativity, the most confusing and scary part of data is figuring out the thought processes and assumptions that have gone into a dataset, as well as figuring out what the hell’s important (often around 0.01%-0.5% of the data) and what’s “noise” - to an individual. Self-generated data doesn’t suffer from this, because all the scary background and assumption bits are part of the citizen’s/volunteer’s mindset and experience. Voila, understanding data comes from experience. And as such, successful engagement with data is about creation as well as consumption.
OK, it’s clearly a little more complicated than that, but it’s a principle that’s often far too implicit for some datasets (crowdsourced maps, etc), and far too often forgotten about for others (most centrally-gathered stats). There are also a whole bunch of stereotypes about what “people” “want” from “data”, and often these stereotypes do little except re-establish the status quo. When data comes up against real-world users (yes, even geeks), and the magic “fails to happen”, we’re left wondering if natural engagement is such a given after all.
It’s difficult to get excited about thousands of datasets when you have no idea where to start or where they can be relevant to you. It’s much, much easier to get excited about data that is relevant to to you, that you understand, and that you can see how it will benefit you. I think that’s why I love the idea of Mappiness or Christian Nold’s maps - both involve getting people to create data they find interesting, and through this “personal” data, the need and relevance for other data suddenly becomes instant and appreciated. (For example, if I’m feeling most happy on street X, what other properties does that street have, and how do they relate to me - house prices? Pollution? Streetworks?)
This seems to be an issue that is bubbling along - everyone knows that organisations collect data, for instance, so an open data system worth its salt will take that into account. But there are still assumptions about the scale and complexity of that data, I would argue - whereas really, data can be as simple as counting, and everyone can count.