Whether doing scientific or pragmatic research, the difficult part is bringing meaning to your data. It’s not easy to extract something meaningful, something innovative, or the essence from a vast body of data. I’ve seen quite a few times how data gets collected and is only used to conjure up some vague notions and general ideas, simply because people do not manage to get past the wall of data. One of the issues there might be the digital tools the world todays offers to aid the process of turning research data into information. To give an example, for the qualitative research in my PhD I used nVivo, and – to be honest – I didn’t really manage to get anywhere after coding my transcripts until I started to use Post-Its and print-outs to make the codes more tangible in the space around me. I tried to do the same in nVivo but even on two 1080p monitors I did not get to the point where it became easy to move through data and make connections. Personally, I don’t even see how a Minority Report interface (like the one they seem to be building a the MIT) would be better at this than a pack of Post-Its. So when I read John Kolko’s article (link below) on turning research into innovation, the point of inertia after data collection just sounded all too familiar. It describes 1) how laptops can make it impossible to see and feel connections between data, and 2) how important it is to draw models when finding connections instead of just talking about them. Well, I guess that’s enough good advice to get a post at this newsfeed.