Oct 11, 2010
Forget for a moment that I’m talking about a mathematical theory. At the core of this post is how we apply some quantitative reasoning to our ability to look at data and predict the future. Personally, I don’t think we do enough to extend and create meaning from our data. If we looked at data with an increasing view of the quantitative sciences, we would have a proportional increase in our ability to apply “art” to our interpretation.
We predict things intuitively every day. Driving down the road, we watch other cars, measure their velocity, and predict if we are going to collide or not – and based on those predictions we often change our course. In Human Resources, we should be predicting the direction and velocity that our workforce is travelling in a number of ways. Not only do we watch the size of the workforce, but productivity, engagement, competency growth, and any number of other factors.
A dynamical system is a mathematical construct that predicts what the state of a particular object will be at a given point in time in the future. Dynamical systems are useful in a limited way – through them we can predict future state outcomes in a limited number of variables. But the point is that we can predict vector and velocity – in other words, if the workforce is travelling in the right direction and at the right speed.
First off, I should absolutely admit that HR is not comprised of a bunch of mathematicians. This should be no surprise to anybody. However, our organizations that make up the analytical arms of the HR organization, those who generate analytics and create meaning out of data, should have some ability to quantitatively view the data and understand trending, directionality, velocity, and the general parameters of the vector. I hate taking us all back to high school calculus, but we remember the first derivative of the curve is the slope of the curve. This is general trend of what our workforce metric is doing. However, the second derivative of a curve is the determination of whether the curve’s direction is slowly shifting. In other words, we might know that our overall turnover rate is dropping, and that is a positive sign. However, do we know if the turnover rate is dropping more slowly than it was last quarter, even if the rate is better than it was before?
While most of HR is not in the mathematics field, and most of us don’t want to be anywhere near it, we should be applying some of these theories to our analytics. If we look at any of our programs, we can see acceleration of desired outcomes after specific events, and deceleration of outcome after we have not done any change management or communications for a while. We should be looking at competencies not as a growth metric, but as an acceleration curve over time. Growth is good, don’t get me wrong, but acceleration is better.
I’d like to think that all HR data analysts out there are taking a serious look at the data they are presenting, trying to create and extend meaning beyond what is being requested, but today’s reality is quite mundane, even with all the cool business intelligence and dashboard technologies out there.