This has been liveblogged. Sorry for mistakes, they’ll be fixed.
The concept of data literacy is touted as the be-all-and-end-all solution to all information issues, but it’s pretty loosely defined, and may not be entirely viable for the wider public.
The ODcamp Data Literacy discussion on Saturday afternoon was challenged to define the term, and figure out all that it entails.
Does that compute?
To attain the sort of data literacy that can decode huge sets – interrogate and interpret – you require, to an extent, mastery of both a subject and computing. That second skill, the computing one, is from where most of the problems emerge. It’s not feasible to computer-skill up everyone, but an understanding of how to use data is pretty important.
Sure, data can be better designed, made more useable for the uninitiated, but literacy really comes into play when the brick wall of bad data is hit. The combination of field and computing expertise enables you to articulate what is bad data, why it’s so bad, and figuring out how to circumvent that wall.
It’s about asking the right questions, the group agreed.
The english-plumbing divide
But the extent to which “problematic” computer skills are required depends on how critically you view the whole thing. Data skills were described alternately as equivalent to both:
- learning the english language – an absolutely necessity
- learning the trade of plumbing – useful but something you’re likely to outsource.
Perhaps one to file under politics > being more engaged with the world