Many human resource people are bad at math. But, even if you regard yourself as one of these people, a great event is ahead you. It’s helpful to place HR in the context of other strategic business pillars – those major units inside large firms that have their own professions or their own Vice-Presidents, such as accounting, production, and marketing. In the recent history of business, computers made it easy for each of the pillars to apply math to their data.
Finance and accounting got good at this in the 1970s and 80s. Engineers changed production lines using data throughout the 90s. Marketing and customer service saw big data happen from the millennium onward. These fields achieved great business success applying new computer tools to fresh data. In the meantime, human resources twiddled its collective thumbs for fear of the data itself.
I like to think of data as deposits of gold in a mine. And, as data miners, it is up to us to use new tools to bring this treasure to the surface. Although many mountains of data have been already mined, and have produced lots of gold, data miners have started hitting a lot of rock. The yield just isn’t what it used to be. Beginning around 2010, someone found a large, new, and un-touched mountain of gold. To many this mountain appeared to have nothing inside it. That is incorrect, though. This mountain is filled with gold, but you just need to know how to drill for it.
This gold is human resources metrics. Decades ago the finance function insisted that we automate payroll, create a line-of-sight into spending, spend the correct amount of money, and minimize risk by obeying all laws. A fancy system of rules was created to regulate salaries including collective agreements, pay policies, rules-based pension plans, and human rights laws about fair pay.
Deep inside this alleged payroll data exists a large and accurate dataset about age, sex, length of service, union, rate of pay, and job code. And the other pillars can’t get into this data because they don’t know enough about the people. Some classic examples would be that there may appear to be a large number of women quitting, or a modest number of accident claims, or a growing bureaucracy. By lining up the apples-to-apples comparisons and creating better ratios, things may look different. You might find that women’s rate of turnover is average, that one unit’s accident rate is high, or that the size of the bureaucracy is modest. Then, people can make better choices about where to devote their efforts.
By starting small, building basic skills, cleaning the data, and creating a steady growth of analysis tables, there are ways to make gold. Strangely, you can only do this the human resources way, full of stories and feelings and a sense of fairness and the motivation to strive. The data just takes you there; because this is still human resources. So start digging. There are treasures to be found.