Mar 30, 2009

Thoughts on Careers and Predictive Analytics

Prasad blogs about how being a fast track career growth can result in a lack of learning essential to growth and development. Could this be the reason why the Peter Principle takes place?

I moved to a global/corporate role in the Learning and Organization Effectiveness (L&OE) domain without spending time in a role that involves handling complete operational responsibility for the L&OE) function/team at the business unit/country level. At this point, I don't really know what exactly have I missed because of this sublimation. While I have tried to find this out by speaking to people who have handled such jobs, I do feel that there could be significant gaps in my understanding!
This brings us to the problem of 'unknown unknowns' -a key side effect of sublimation - which can create problems for both the individual and for the organization. Usually, 'unknown unknowns' are more dangerous than 'known unknowns'. Based on our discussion above, it can be seen that the 'sublimated individuals' can create serious risks for the organization. 

Another post is by Abhijit Bhaduri who interviews a Predictive Analytics expert on how PA can help HR be more effective and touches on other issues like employee data privacy as well. Personally I think Analytics can be a huge help for an informed HR manager, however the statistics must not become the only thing to rely on but used along with both an understanding of the business and Organizational Behavior principles. In a fast changing business scenario I daresay that relying heavily on Analytics is not going to be helpful. Here's an example of how PA can help in case of attrition, according to the expert.

Just knowing that 10% of the employees leave does not make that data actionable. You need to know which 10% of the employees leave and why do they leave? For starters, it may help to classify people into different groups, where each group is defined by a specific set of characteristics with respect to attrition – Predictive Analytics can do this segmentation for you, using the data and knowledge already residing in your organization. It can tell you which group has the greatest probability of leaving, which group has the next highest probability, etc.  A process of predictive Root Cause Analysis can then identify, quantify, and rank the primary drivers responsible for attrition among each ‘at risk’ group. That’s much more actionable than a blanket statement such as 10% of employees will leave every year. You can similarly use PA to predict top performers, aggressive behavior in the workplace, etc.