I’ve been interested in visualisation and prediction markets for a while now, so this post from Google is right up my alley combining two areas of interest. It’s a fascinating read if you have the time, particular the paper which Bo Cowgill, the author of the post, co-authored.
I’ll leave the blog for you to read, but I will pull out the key thing I took away and found fascinating.
1. The Power of Visualisation
There is a great heat map of a Google Office, showing how profitable trades cluster together, demonstrating the way groups of employees share knowledge. While this is not all there is to say, it is a powerful graphic that sums up much of the article. I’ve seen this kind of geographic heat map used in marketing type applications, layering results on to suburb level or post code level type information, but this micro-geographic use is very powerful.
Link to the graphic (for some reason I can’t insert the image here directly properly…
In my experience employee surveys (as one example) typically break responses down by group or business unit. While this undoubtably represents some value, what might the responses reveal if there was a heat map cluster of good and bad sentiment? This is a powerful tool for correlating location which I suspect is an equally important part of the picture.
2. Prediction Markets are going to grow
One I’ve been looking at for a while now and really like is Inkling Markets. My own experience of trying to evangalise a prediction market had limited success last year, but the more knowledge that enters the world from serious players like Google, the more others will want to pick these up, take them seriously and want to have a look too.
In my opinion, prediction markets are yet another tool in the rich set of collaborative tools like blogs and wikis that promote a new way of knowledge sharing; and like blogs and wikis, it will no doubt take mainstream companies a while to get their heads around it all. Solid research like this from Google can only help ease the path for others in this regard.
