To raise your analytics game to the stars, you need to first look at your foundations.
I live in a part of North Vancouver that is seeing a lot of construction. The multi-storey condo blocks are going in thick and fast. The most fascinating part of this process is how far they have to dig down in order to build successfully to the heights that they want.
I am often contacted by human resources groups and analysts looking to take their work to the next level and discover the next great insight. Often they are seeking some holy grail or mystic equation that will simply answer the complex questions that human systems create. This is a worthy and powerful quest and one which is moving human resources groups and the organizations they serve into a better and more productive position.
Unfortunately, I am not the keeper of the mystic equation. But I am often impressed by the creativity and insight of the folks who are looking. However, as with most quests, the searchers start by looking in the wrong place for their answers. For many, the answers seem to lie in ever more complex equations or clever factors which level or balance the variability that comes with any group of independent agents.
Conversely, my view is that to enhance your overall analytical work and enhance your contribution, the first place you need to look is at your foundational data.
The improvements in HR technology and HR information systems (HRIS), and the proliferation of electronic payroll systems has done lots to increase the quantity of data about people that is out there. However, three factors need to change to really unleash the potential in people data.
First of all, the systems need to be designed with analytics in mind. Most were designed with finance and process in mind, so the structure of the data or the process for pulling out the data is challenging. This makes it hard to get the data you need in the format you need.
Secondly, they need to standardize data elements into a common approach. Many organizations would get a different full-time equivalent (FTE) count from their payroll database than from their HRIS. Often this is due to a conflicting view of what an FTE is. Agreeing on a common standard, and aligning all systems—in the way that finance does—would be a huge help.
Thirdly, more human analytical people need to be involved in system design and system output. For us to get beyond turnover, absence, vacancy, etc., we need to begin being able to integrate qualitative and quantitative data within the overall data set. For example, does your performance data match your finance data and match any employee satisfaction, engagement or exit data? Often these items have different time frames and therefore cannot be used to build integrated quantitative and qualitative models.
Once this third foundational element is in place, I believe we will start to make real progress toward the deeper insight people are looking for. To get there we need to look to our foundations, with the knowledge that it will help us reach the stars.
Next month, we will share an example of this quantitative-qualitative integration and explain why this is a crucial next step for HR analytics.
Ian J. Cook
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