As many of you have heard, Google’s search engine data can give advance warning of where a flu epidemic is located. They can track the percentage of the local population who are entering queries related to flu symptoms – once these hit a certain level, there is a high likelihood that a spike in flu cases will be reported through normal channels.

The same also applies to video games. A certain fraction of the players use “player assistance tools” (like a scrabble helper or a list of tips). Demand for these tools is a direct function of player activity and tends to be relatively constant over the life of the game.

Want to see how the demand and usage looks for a video game? Watch the cheaters.

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Linkedin rolled out their one-click endorsement feature this past month. As I’ve traded clicks with friends and colleagues, I’ve been trying to figure out what their real goal is. On the surface, this feature feels redundant with their existing “recommendation” feature. Given their aggressive efforts to promote this new feature, the data they are gathering is clearly important to the development of the algorithms behind their services – but how?

Linkedin has been fairly quiet about the inner workings of their search engine. This is likely to prevent people from manipulating the results, since there is significant value in being on the first page of a Linkedin search for a lucrative professional skill. They share some basic pointers about how to “be visible” on their help page. Key points from their page:

• There is no single rank for Linkedin Search – results are unique to each user/query
• The profile keywords of both parties (searcher, results) play a significant role
• Rankings are adjusted based on how prior searchers have reacted to your profile

While the above metrics are fine for identifying which candidates are relevant to a search, they don’t rate candidate quality: who actually knows their stuff? What’s missing here is a broader assessment of “page trust” (SEO concept) that candidates possess the skills that they reference on a profile. For example, Google’s search algorithm incorporates an evaluation of the credibility of a site using link patterns, brand signals, and social activity. With these new features, it looks like Linkedin may be trying to adapt Google’s Pagerank algorithm (or something similar) to ranking candidates for specific skillsets.

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In which an experienced analytics guy advises the younger generation to leave the walled garden of enterprise analytics tools and learn how to write code using a real programming language. Specifically advocating the use of R and Python for data analysis and related programming. But hey, I’m flexible on that point…

The use of COBOL cripples the mind; its teaching should, therefore, be regarded as a criminal offense.

- Dijkstra

I was taught a long time ago in some Management 101 course to sandwich constructive criticism between two compliments. So I’ll open with this statement:

SAS and the other BI vendors have done a nice job of bringing statistical computing techniques within the reach of the typical college graduate.

Now pull up a chair and grab yourself some popcorn, since I’m going to bite the hand that fed me for the first half of my career. I spent the first seven years of my career in roles involving significant usage of SAS and a variety of drag & drop query tools. The COBOL of the analytics world.

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There’s been a lot of hand-waving in the SEO blogosphere over the past year about how the most recent Google algorithm changes (Panda, Penguin, brand signals) are “ruining SEO”. I’ve heard comments about how organic search doesn’t work and that marketers should abandon SEO for PPC, social, and other traffic generation options.

I’m sorry, this kind of talk is moronic. If you ignore the static and look at the fundamental shifts in the search engine ranking algorithms, there has actually never been a better time to learn basic search engine optimization.  

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I sat quietly in the chair, number six or seven on George’s agenda of young hopefuls. The on campus recruiting process was a brutal cattle call. On the positive side, we had a large number of great companies coming to visit. Unfortunately, the vast majority of my 1500 classmates were tipped off about their arrival. Moo!

His eyes skimmed down my resume – and locked onto a phrase halfway down the page.

“Combinatorics Problems? What the heck is that?”

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