Frank Jacobs is publishing Strange Maps next month. Here is a sample of the data goodness inside: From anywhere in America it is Only 145 Miles to the nearest MacDonalds Restaurant
In unconnected, but related news I discovered the recently acquired Mint.com has been creating data visualizations as well. Surprisingly some topics are kind of fun.
Jeff Clark of Neoformix.com has posted a cool data visualization of Twitter traffic originating in Toronto for the last two weeks of January. Check out the whole set.
This week’s On The Media points to a story at MotherJones.com by David Goodman about the US military’s recruitment practices. Apparently George Bush’s 2001 ‘No Child Left Behind’ legislation mandates that all high schools send students’ personal information — including address, cell phone number, GPA, and social security number — to the military for data mining. It turns out the Pentagon — not as I would have thought MySpace of Facebook — has the largest repository of personal information on 16-24 year olds in the United States. Apparently it is illegal to recruit high school students into the military so the data mining allows the military to queue up potential converts the moment they graduate.
Let the algorithm manage 80% of the inventory. Let the exception reporting manage the rest. That was the mantra of a whole office of consultants and wiz kids working on a retail supply-chain project from a few years ago. The mantra was grand but the formulas were junk. The algorithms were inherited from other business sectors. When applied to a product that didn’t sell in predictable ways all they did was suppress over-ordering. And exceptions weren’t exceptions at all because they were void of actual information.
The failure in that case wasn’t in the algorithm and it is wasn’t with the consultants and wiz kids. The failure was in the business. No one in the company — quite possibly the whole industry — new good data from bad data. It wasn’t anything anyone thought about.
Thinking back it would have be better to take a human-centred data approach by asking the inventory managers what decisions they were likely to make and what data was likely to change their mind about those decisions. Now that would have been a worthhile starting point. A little data innovation would have went along way. In fact it would have been revolutionary.
As Chris Dixon points out in a recent blog post “breakthroughs come from identifying or creating new sources of data, not inventing new algorithms.”
I wonder how many data-innovation teams are out there in the business world. Probably not many that are off wall street.