Wrong assumptions lead to bad decisions

11/08/2011 at 18:59 Leave a comment

Human Capital is the ‘hard’ side of Human Resources. It is about making strategic decisions, measuring the impact and basing decisions upon ‘objective’ and measureable data. One of the most important things for a Human Capital Manager is therefore to get quality data. Without that you cannot make good decisions.

Daryl Morey writes on HBR’s blog that ‘Success Comes From Better Data, Not Better Analysis’. He argues that the right data is more important that hiring good analysts, who can interpret this data. He writes “Raw numbers, not the people and programs that attempt to make sense of them. Many organizations have spent the last few years hiring top analysts based on the belief that they create differentiation….But…real advantage comes from unique data that no one else has”.

I only partly agree with Daryl. It is definitely true that most HR managers are basing their decisions on poor and questionable data and even more questionable reasons for using that data. Poorly formulated satisfaction surveys are often the only data upon which expensive programs are initiated. Most HR departments, which I speak with, will benefit greatly from having better data.

On the other hand I believe that some of the underlying assumptions on which many HR managers base their decisions are also poor.  That many mistake correlations with causality and therefore don’t understand what drives what. The connection between Job Satisfaction and Productivity is a case in point. Better data will not help you with that. You will just have better data to make the same mistakes. The shift to Human Capital Management should also be about questioning some of the underlying assumptions in Human Resource Management.

Human Capital Management is about making better HR based upon strategic and measurable initiatives. This requires much more data of significant higher quality AND to challenge existing underlying assumptions behind how they should be interpreted.

Entry filed under: Measurement. Tags: , , , , , , , , .

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