Case in point: my life, the other day. I had two retail experiences that were total opposites, one that I liked, and one that I hated. I wish you could have been there as I ran my errands. 

First stop: a Best Buy store, where I asked the clerk to print missing receipts for three gizmos purchased at three stores over the past three months. Two minutes later, they had reviewed every purchase from that last, oh, forever, and printed exactly what I wanted. Happy Karl. 

Next stop: NAPA Auto Parts, with a defective alternator from a year ago but no receipt. No paper receipt, they were powerless. Their data system appeared to be one step above using clay tablets and manual typewriters. My expectations were high, and they failed. Note: they lost my business for the future. Quality of product was fine; quality of data is the modern deal breaker.

As a benefits consultant, I still am helping organizations automate some basic processes. Why is any open enrollment process for an organization of more than 1,000 employees paper-based?  For that matter, how can the Federal Insurance Marketplaces be effective, responsive and paper-based? Don't laugh. They are paper-based, for the first year at least. Argh. I sense a train wreck on the horizon.

One of the better sessions I attended at this year's Society for Human Resource Management conference in Chicago was on data analytics. Those who know me would find it normal that I would be geeked up on data and how to analyze it. Also, with the "perfect storm" of the ACA and new data technologies, this is one of the few areas of hope for guiding wellbeing programs and bending the cost curve. For example, the advent of reference-based benefits will require a ton of data. 

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 our 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 of the use of data analytics could be seen in the recent Brad Pitt movie, "Moneyball." She said it was 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 Oakland Athletics assembled a competitive team for one-fourth the cost of a normal team. A data-driven victory.

Key point: "With data analytics, you can understand people and what their strengths and weaknesses are, and end up with 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 using the data to look 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 percent accurate. This enabled the organization to target the high performers who were predicted to leave early in the process, and the organization was able to retain a high percentage of them.

OK, you get the point. Where will this be going? I predict there will be two significant areas of emphasis in 2014 – the arrival of the PPACA in everyone's lives will drive data analytics to the smartphone (for an example, check out vendors like www.castlighthealth.com) and the arrival of high horsepower data analytics in the benefits manager's office.

I am working on implementing benefit costs transparency tools, and on 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. 

Pay attention to the data in your life, and let's get ahead of this very important curve. We don't want the ACA to fail on our watch because we didn't handle our data well.