The quintessential Southern Californian, Wes Wu is a key example of how to convert blogging into deep industry influence. The soft spoken Wu is widely understood to be the rational voice of technology issues in the HR Enterprise Software (HRIS / HRIT) universe. Progressively deep experience in enterprise integration gives Wes an authoritative set of experiences. His blog, SystematicHR, provides a constant reinforcing drumbeat of level headed analysis. Recently, Wes joined Knowledge Infusion.
It's not just the blogging and it's not just the career experience that are the foundation of Wes' credibility. The combination of 15 years of hands-on HRIS / HRIT projects and five years of very routing punditry make SystematiHR a must read for the Technologists in HR. Wu works diligently to keep his views balanced and humble.
The people who occupy the heart of the HR technology world are not big picture HR theorists. They are IT specialists and systems designers. Without real clarity and consistency from them, the big picture can never be quite understood.
That's where Wes excels. These days, he's focused on data governance.
Data governance is a critical yet arcane part of making the information collected by your systems intelligible. Carefully naming and describing the value and requirements of each element in a database creates a level of value and trust in the data that is unavailable any other way. Data Governance is an issue whose importance increases with the scale of the operation and its data.
Data governance is a set of processes that ensures that important data assets are formally managed throughout the enterprise. Data governance ensures that data can be trusted and that people can be made accountable for any adverse event that happens because of low data quality. It is about putting people in charge of fixing and preventing issues with data so that the enterprise can become more efficient. Data governance also describes an evolutionary process for a company, altering the company’s way of thinking and setting up the processes to handle information so that it may be utilized by the entire organization. It’s about using technology when necessary in many forms to help aid the process. When companies desire, or are required, to gain control of their data, they empower their people, set up processes and get help from technology to do it. - Sarsfield, Steve (2009). "The Data Governance Imperative", IT Governance.
Wes offers a simple example. "Take the data field 'First Name.' In the US, my first name is Wes. In China, it's Wu. The solution is simple, you ask for Given Name and Family Name. If you don't have data labeling and definitions that consider the full spectrum of globalization, you end up with data entry (increasingly globalized itself) that causes more problems than it solves. Data is the source of much of the value in our organizations. Getting it perfect is the key to realizing the value.
"The 'First Name - Given Name' conundrum is just a tiny piece. From country to country, the definition of who is or isn't an employee varies. Most of my global clients can't tell you how many employees they have or where they are. Not being able to articulate the fundamentals of headcount is a symptom of poor data governance.
"The detail is mind numbing for most people. When you find a team of data governance specialists who love their work, there is an immediate consequence in productivity and control. It takes the willingness to invest resources at the managerial level and passion at the implementation level for IT systems to really deliver on their potential."
Here's Wes perspective on global data for human resources:
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 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 things 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.
Translating HR Data Elements globally
Keep you ears open for Wes Wu's name. He's emerging from the formative part of his career and starting to join the big time. He'll be one of the industry giants.
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