Have you heard about Data Market? It is one of the largest (and free) curated repository of public data. Benefits: It has internal search that doesn’t suck, so you can find what you’re looking for and get out. It offers the ability to preview the data in tables and charts before you export. It offers the ability to export in popular formats. It’s freemium. (API and LIVE data have a cost). Why am I excited about this? These data sets are very clean, and some of the data has direct uses for analysts in their social-professional lives. They’re there, and you should register and check them out.

The goal of a forecast is to make an accurate prediction about the future state of a system based on the best available evidence. The goal of target setting is to make a statement about a desired future state – with or without a forecast. Targets are political artifacts. You can read all about such dynamics in public policy here. Forecasts, ideally, are scientific artifacts. The interplay between forecasts and targets is particularly interesting. Those who produce sophisticated forecasts should understand that the motivation of those probing models is to assess whether or not a future state is possible, or, in certain situations, just how probable a given scenario could be. Don’t become trapped into the mindset that a trend[…]

There are at least five types of error related to analytics. These are instrumentation error, algorithm error, transposition error, statistical error, interpretation error. 1. Instrumentation Error When the instrument is measuring a phenomenon incorrectly. This is not to be confused with a human mistaking what an instrument really actually measures. Rather, this is when the instrument itself is only recording half of something. Or not measuring something at all. It’s akin to saying that thermometer is broken. Instrumentation has varying degrees of accuracy. For instance, the unique http cookie is subjected to fault as a result of a deteriorating cookie retention curve. The instrument continues to work just fine – it’s just that user behavior has changed, affecting its accuracy.[…]

An excellent analysis done by Allan Engelhardt, back in 2006 I suppose, talks about the 3/2 rule of employee productivity. The Coles notes version is that when you triple the number of employees, you cut their productivity in half. Check out the diagram below. Pretty scary right? Naturally, the story is much more complex than portrayed. Some sectors have mild slopes, like technology companies. Arguably, they’re using technology to flatten out the productivity slope. But it’s still slightly negative. Naturally, larger companies scale, so they still make more profit overall. Small companies are very good at doing many things. They become less good as they become large. And then ultimately, they stop being really, really good at anything at all.[…]

Konrad von Finckenstein, chairman of the CRTC, went before committee yesterday and made the remark: “The vast majority of Internet users should not be asked to subsidize a small minority of heavy users.” I take issue with Finckenstein’s statement. For one, the vast majority of Internet users subsidize a number minority. Urban customers, who are comparatively cheaper to connect with bandwidth, pay more to subsidize rural customers, who are comparatively more expense to connect. Didn’t the CRTC pass fees last year, forcing the vast majority of us to subsidize the viewing habits of the small minority of people who watch the CBC, Flashpoint, and DeGrassi? Isn’t the CRTC mandating the subsidization of something called “Canadian New Media”? Seems to me[…]