The game has been dominated for a long time by the folks over in CS. But the value of many recent startups is either based on, or can be magnified by, good data analysis. Here are a few startups that are based on data/data analysis:
- The Climate Corporation -analyzes climate data to sell farmers weather insurance.
- Flightcaster - uses public data to predict flight delays
- Quid - uses data on startups to predict success, among other things.
- 100plus - personalized health prediction startup, predicting health based on public data
- Hipmunk - The main advantage of this site for travel is better data visualization and an algorithm to show you which flights have the worst “agony”.
To launch a startup you need just a couple of things: (1) a good, valuable source of data (there are lots of these on the web) and (2) a good idea about how to analyze them to create something useful. The second step is obviously harder than the first, but the companies above prove you can do it. Then, once it is built, you can outsource/partner with developers - web and otherwise - to implement your idea. If you can build it in R, someone can make it an app.
These are just a few of the startups whose value is entirely derived from data analysis. But companies from LinkedIn, to Bitly, to Amazon, to Walmart are trying to mine the data they are generating to increase value. Data is now being generated at unprecedented scale by computers, cell phones, even thremostats! With this onslaught of data, the need for people with analysis skills is becoming incredibly acute.
Statisticians, like computer scientists before them, are poised to launch, and make major contributions to, the next generation of startups.