My personal feeling is that this will really take off if you can start linking performance information to the more objective factual data within the various systems. How does the performance of interim staff vary and is that linked to which agency they come through, their employment history, the length of their assignment or other factors? We’ve probably all had experience of working with interim staff who were brilliant; and with others who weren’t worth a fraction of their day rate. So you can imagine some really powerful analysis that might give a strong steer into how you best choose, structure and manage your contingent workforce – and maybe even take that into the permanent staff world!
Totally agree! Now we just need to get hold of comprehensive and reliable performance data…
Truly some awesome stuff here, including the link below on writing an R package from scratch. I should definitely do that for the utility functions I use over and over.
This tutorial is not about making a beautiful, perfect R package. This tutorial is about creating a bare-minimum R package so that you don’t have to keep thinking to yourself, “I really should just make an R package with these functions so I don’t have to keep copy/pasting them like a goddamn luddite.” Seriously, it doesn’t have to be about sharing your code (although that is an added benefit!). It is about saving yourself time. (n.b. this is my attitude about all reproducibility.)
People are searching for products on Amazon, rather than using Google. The only reason search makes money for Google is that people use it to search for products they would like to buy on the internet, and Google shows ads for those products. Increasingly, however, people are going straight to Amazon to search for products. Desktop search queries on Amazon increased 47% between September 2013 and September 2014, according to ComScore.
Jeff: I think it takes more time to analyze something like that. Again, one of my jobs is to encourage people to be bold. It’s incredibly hard. Experiments are, by their very nature, prone to failure. A few big successes compensate for dozens and dozens of things that didn’t work. Bold bets — Amazon Web Services, Kindle, Amazon Prime, our third-party seller business — all of those things are examples of bold bets that did work, and they pay for a lot of experiments.
What really matters is, companies that don’t continue to experiment, companies that don’t embrace failure, they eventually get in a desperate position where the only thing they can do is a Hail Mary bet at the very end of their corporate existence. Whereas companies that are making bets all along, even big bets, but not bet-the-company bets, prevail. I don’t believe in bet-the-company bets. That’s when you’re desperate. That’s the last thing you can do.
“The dirty secret is that a significant majority of big-data projects aren’t producing any valuable, actionable results,” said Michael Walker, a partner at Rose Business Technologies, which helps enterprises build big-data systems. According to a recent report from the research firm Gartner Inc., “through 2017, 60% of big-data projects will fail to go beyond piloting and experimentation and will be abandoned.”