In preparation for my upcoming trip to Las Vegas (no, it’s not what you think; I’m going to AU) I was checking into what’s new in transportation alternatives and perhaps avoid taking a taxi. As such, something I have always wanted to try is Uber, an interesting online service you can request using a smartphone. Check out their site for more details. BTW, unfortunately the Inforbix “garage” is outside the current Uber coverage zone in Boston. Oh, well…
Checking Uber out, however, reminded me of an article from Uber’s blog I had a chance to read a couple of weeks ago, Uberdata: how prostitution and alcohol made uber better. Yes, I know, it’s quite an unusual topic, at least for this blog. The keyword that caught my attention on the post was “data”. If you want to know more about an unusual use of data, then this is a “must read” article for you. Uber folks are running some very interesting analysis of data related to uber services in multiple cities and coming to some fascinating conclusions.
This finding is a perfect example of the fascinating insights you can get when you combine big, seemingly disparate datasets. By trying to figure out how to predict where to position our cars, we got a peek at the ebb and flow of the life and crimes of San Francisco. Expect more of these kinds of posts in the next couple of weeks.
Conclusion. I want to make a conclusion relating to “combining data sets”. This is an interesting thing we like to do. Inforbix is not focusing on crime. However, the ineffective way many manufacturing companies use data is very close to a crime. At inforbix we are trying to help people improve their access to product data. We are going to come up with some useful examples of how Inforbix can combine multiple data sets together. Keep your eyes on this space. For now, I’d like to wish my US-based readers an enjoyable long weekend.