A recent post about Linsanity from the Enterprise Irregulars crowd went almost unnoticed. Not a lot of retweeting. Maybe because NY basketball isn’t what most folks in big data are paying attention to these days, or maybe it’s because Lin is a perfect Black Swan. However you cut it, Jason Corsello ends his short post with a question: “why aren’t most companies analyzing their employee data to find the rising stars?” Good question.

Outcome oriented analysis

Jason argues that the issue is that companies spend too much time looking at process and not enough at outcomes. Too much time looking at Lin’s muscles and less time looking at number of assists. Starting to sound like Moneyball? There’s a good reason – it works, to a degree.

I don’t doubt that more focus on outcomes is advantageous, but painting a brush stroke that broad doesn’t satisfy my curiosity or appetite. So here are a few ideas for talent builders that builds off of Jason’s premise:

1. Identify metrics that highlight value-added contributions – what’s the equivalent of an assist in your organization and how can you quantify it? Does this look different at different levels of the organization?
2. Build in emotional intelligence competencies into your feedback system – I’ll write more about this later, but basically if you’re going to do any type of retroactive analysis, you need to have data to work with, but if the data only speaks to one side of the deliverable chain, you’re losing priceless correlation/causation data.
3. Consider changing your feedback cycle altogether – SalesForce bought Rypple for just this reason. I’m hoping the purpose isn’t to “get into the HR game” but rather to offer its customers more data points on building and growing their internal talent with more connected data. Point is, sometimes how you get feedback can impact what type of talent gets brought to the fore.
4. Build systems and processes to capture internal collaboration data – who is contributing high value content? Think about engagement scores for tweets and the like. If there’s a parallel in the world of Chatter and Yammer, capturing the data, analyzing the keywords, and seeing who’s generating the most energy around focus areas is another important set of data to get. IBM is getting good at this.