Global HR data is tough. Often times when we’re thinking about implementing a global core HR system, or a global data warehouse, we implement these systems according to a U.S. centric view of the world. (I’ll note here that I once worked with a U.K. based company that looked at their core HR system with an EMEA view of the world). This is rather disadvantageous since one of the ore problems with global implementations is that you usually start with skepticism and disengagement. This only increases when you propose your U.S. centric view of the world.
Translating not only data elements but any definition you are going to use is simply a starting point in translating data across global geographies, countries and business units. Before embarking on the implementation of systems, it’s truly useful to get some things straight. To do this, I’ll just give a couple of the more obvious examples.
— EEO (race) Codes: We love to report on EEO codes in the U.S. So much of our reporting is defined by these EEO categorizations, but we also know that EEO is exclusively a U.S. concept. As you travel globally, you quickly realize that race and ethnicity is not at all meaningful. If you go to Japan, they really could care less, since 99% of the population is actually Japanese, but they might care about ethnic variations in Japan. You might go to the Middle East where race and ethnicity does matter, but they also may want to know for discrimination purposes if you are Sunni or Shiite. In the end, most implementations I’ve done decide that trying to define and categorize race across the globe doesn’t actually make sense. Instead, they go after the thinks that they can collect like age and gender.
— Exempt versus non-exempt: Again, this is a U.S. centric concept that is defined by FLSA. If you went around the globe and talked about exempt employees, your audience would be bewildered. While it’s not a direct and perfect translation, most other organizations in the world can indeed relate to overtime eligible or not overtime eligible. Simply changing the data labels allows you to move forward with a global terminology that makes sense to everyone.
One of the key failure points of global implementations (there are many however) is the lack of common definitions across the globe. As soon as your global population feels that this is just another corporate initiative that U.S. stakeholders will benefit from, your international population will disengage. There are all sorts of prepatory activities you need to tackle prior to implementing, and data definitions is probably one of the first you should tackle.