On the new premises of Antwerp Management School, seven leadership development experts gathered to dive into the world of measures, with an open mind and a degree of healthy skepticism. We were off to a strong start when one of them said: “As HR professionals we need numbers to distinguish between leadership programs that are ‘nice to have’ and ‘must have’. But is it really possible to unequivocally link interventions to outcomes?”. Dr. Fabrice de Zanet (HEC Liège) was going to guide us through those questions. He called himself a data enthusiast and as long as he can remember, he’s always been part of that tribe.

3 stories to reveal potential and traps

Surprisingly enough, he didn’t start the workshop with numbers, nor graphs. Instead he kicked off with three stories to reveal the potential and traps of data. HR analytics provide organizations with the opportunity to test hypotheses on a small scale before implementing interventions to the whole company. All too often it turns out that large human capital investments do not result in the expected outcomes. Is it because bad decisions are made? Without data, nobody knows. Objective data also help us to challenge our reasoning. The human brain is prone to cognitive biases, and the Dunning-Kruger effect shows us that experience unfortunately doesn’t help in this regard. On the contrary, because of an increase in confidence it is harder for experts to assess the extent to which one is right. We need data to counterbalance this effect.

DunninKrugerEffect
 

Sources of data

But where to look for data? These can come from stakeholders, professionals, scientific research or the own organization. The latter two sources are often used by Google to make decisions in terms of their HR practices. In the so-called Oxygen project, for instance, the Google analytics team used organizational data to come up with an algorithm of ‘what makes a good team at Google’. To their surprise it was not about having superstars. The difference is made by the team’s climate – is it safe enough to interact constructively. Does this mean that we all have to become computer geeks? Zanet assures us that what distinguishes good quantitative thinkers is not their skill with pure mathematics.

“HR-analytic projects don’t start with data, they start with the right questions.” 

Frame the business challenge

And that’s where HR or leadership experts can make a difference. We do not have to be ‘statisticians’. We need to be good at helping our internal clients to frame and crystalize the business challenge that needs to be addressed.

According to Zanet’s HR analytics roadmap it is important to understand what you want the data to do for you, rather than to manipulate the data. Next, you want to give thought to the method of data collection. Finally, it is important to build a model – What do we already know? What are our hypotheses? The goal of the model is to determine which variables should be included. It is only after all these questions are addressed that the data collection and number crunching starts.

We were invited to apply the above questions to our own context. Looking at a real challenge, two teams tried to come up with a strategy map outlining strategic objectives and hypothetical causal links for the issue at hand. While doing that we learned to define the ‘why’ or the importance & relevance for the business, as well as the ‘how’ to get there. 

Strong to see that, using Zanet’s roadmap, we all can bring academic rigor into practice for key HR or leadership challenges. Leading with data, not just opinions, nor the latest fads.

This Leadership Lab is part of the Connect program of our Expertise Center Leadership. Want to know more?

Contact Karen Wouters

 

Topics: Leadership

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