The Puzzling World of HR Technology

by Ron Hanscome, VP HCM Systems Strategy Consulting

Well, it’s been a long time since my last post. The last six months at HRchitect have been incredibly busy with project work. Now that the dust has settled a bit, I’ll be sharing my perspective on what has happened in HCM technology in the first half of 2011 over the next few weeks.

One continuing trend over the past couple of years has been a renewed focus on workforce information management (WIM, aka HR Analytics, Workforce Analytics, etc.). Organizations are struggling to pull data from disparate systems and derive useful insights to drive the HR function and (hopefully) improved business results. While there are many challenges that HR  needs to resolve in order to execute WIM successfully (e.g., data cleansing/integrity procedures, enabling technologies, change management,  internal marketing), I want to focus your attention on perhaps the most  important critical success factor – those in HR responsible for WIM must have the  right mindset.

Two recent events have driven this to the forefront over the past two weeks. The  first was my wife picking up a copy of “Freakonomics,” at a local used book sale. This seminal 2005 work by Steven D. Levitt and Stephen J. Dubner is well  worth a re-read (or an initial read if you missed it the first time), as it is  a fascinating application of data analysis to such diverse topics as incentives  and cheating (by school teachers and sumo wrestlers, no less), the power of  information (using the Ku Klux Klan and real estate agents as comparators),  crime, and parenting.  The central point  of the book is this – knowing what to measure, and how to measure it, is the  key to modern life. The book also advocates a healthy skepticism of “experts” who emerge in a given situation who tend to bend facts to fit their version of  reality (thus helping sell their product, make themselves indispensible, etc.). The subjects addressed also illustrate the extent to which “conventional  wisdom” is faulty and must be challenged. Finally, Levitt and Dubner deliver a great primer on the complexity of analysis needed to move from merely identifying correlation (two elements are related to each other in some way) versus causality (one element drives another element or outcome); often this requires a significant set of accurate, clean data that has been accumulated over time.

The second event occurred during the recent SuccessConnection meeting in Chicago on Thursday July 21, delivered by SuccessFactors. In the midst of a number of interesting speakers, I particularly enjoyed the presentation by Peter Howes of SuccessFactors’ Inform division, which focuses on delivering analytics projects to the client base. To me, the most interesting of Peter’s client stories involved an analysis of the effectiveness of a specific employee referral program. Of course,  conventional wisdom in the recruiting function states that referrals are the source of the best quality candidates. In this case, the “first cut” review of average data revealed mediocre results for referrals, but more granular analysis indicated that all referrals are not created equal; those given by top performers were of much higher quality than those of medium or low performers.By correlating the performance ratings of the referring employee to the performance of the referred candidate, the organization was able to focus on the best referral sources. Peter used this example to illustrate the dangers of relying on averages (especially when benchmarking with other organizations) and the need to regularly dive to a more granular level of analysis in order to gain real insights from HR data.

How are these two disparate events “correlated?”  I believe that the fundamental principles outlined in “Freakonomics” – knowing what and how to measure, healthy skepticism of experts, challenging of conventional wisdom, and increasing the sophistication of analytical tools / techniques– need to be adopted by the HR function in order to improve the effectiveness of HR programs. In the end, the best starting point for making the needed changes is embodied in the book’s closing pages (206):

The most likely result of having read this book is a simple one; you may find yourself asking a lot of questions. Many of them will lead to nothing. But some will produce answers that are interesting, even surprising.

My challenge to you is twofold – first, give Freakonomics a fresh look. It’s a quick read and manages to deliver a lot of learning in a very entertaining manner. Second, become an HR “Data Freak” by asking questions – lots of questions – and let the data lead you to the answers and insights that are valuable to your organization. Don’t accept conventional wisdom at face value – dig deeper.  Remember that data cited by others can be manipulated, so always push for context and assumptions and be alert for hidden agendas.  View high-level aggregate benchmarks with suspicion; instead, get your HR data to a place where you can “go granular” to gain greater insights. Not only will your job become a lot more interesting, but you are likely to dramatically increase the value of HR information to your organization (and your own value as well). In my opinion, HR desperately needs more “Data Freaks” – will you be one of them?