It sure seems that data is a big topic – in Washington DC and in our lives in HR.
One of the better sessions I attended at SHRM13 was on data analytics. Those that know me would find it normal that I would be geeked up on data and how to analyze. Also, with the “perfect storm” of the ACA and new data technologies, this is one of the few areas of hope for guiding wellness programs and bending the cost curve.
On a secondary based level, the advent of reference based benefits will require a ton of data. It’s on the horizon, and headed our way.
So, what did I learn? I was amazed at how many of my fellow attendees had no idea the level and sophistication of data analytics that are out there for use. Most of them have a broker relationship, but from the volume of “I had NO idea” responses, many of those brokers are either not sharing the data, or do not have a basic level of sophistication on data analytics.
The leader of the session, Cecile Alper-Leroux of Ultimate Software, said that one of the best examples to use of data analytics is the recent movie Moneyball. She said it is a great example of how data can be used as a game-changer (literally) and provide a competitive advantage. In the movie, by analyzing non-traditional statistics, the Athletics assembled a competitive team for one-fourth the cost of a normal team.
Key point: “With data analytics, you can understand people and what their strengths and weaknesses are, and end up some great results like lowering labor costs or raising productivity.”
The interesting part of the presentation was not the basic examination of data analytics on past info and plotting trends, but rather taking the data and looking at the future. This is a significant mindset shift from reacting to changes to “what can I do to change these predictions?”
An example was shared from retail, where a data model was built that attempted to predict which employees were likely to leave the company. The actual resignations were tracked, and the model appeared to be 90% accurate. This enabled the organization to target the high performers that were predicted to leave early in the process, and the organization was able to retain a high percentage of them.
An example I will be working on are implementing benefit costs transparency tools, and the issue of reference-based benefits. Data analytics are a key part of making it work, and I will be spending a lot more time on the subject. Let me know if you want more information on transparency tools, and I will share what I have learned.
Now, off to hustle to another session…almost done!
Wear comfortable shoes and plan for traffic jams near the escalators.
Karl Ahlrichs' Uncommon Experience 