Happiness and HR Data - Coming to a delivery truck near you

Sometimes in all the conversation in the HR/talent space about the increased use of data, Big Data, and workforce analytics by HR leaders and organizations that practical, innovative (and possibly somewhat creepy), examples of how all this data coupled with better tools to understand it all are sometimes hard to find. Or hard to understand. Or not really specific enough that they resonate with many HR and Talent pros.

Lots of the articles and analysis about data and analytics for HR end up reading more like, 'This is going to be important', or 'This is going to be extremely important and you are not ready for it', or even 'This is going to be extremely important, you are not ready for it, but I (or my company) is ready to help you sort it out.'

Fortunately for you, this is not one of those kind of articles.

Over the weekend I read a long-ish piece called Unhappy Truckers and Other Algorithmic Problems on the Nautilus site, that provides one of the most interesting and practical examples of how a better understanding of HR data, (among other things), is helping transportation companies plan routes, assign work, and execute managerial interventions, often before they are even needed.

At the core of most transportation and delivery problems is essentially a logistics challenge as the 'Traveling Salesman' problem.  Given a fixed time period, say a day or an 8-Hour shift, and set number of destinations to visit to make sales calls, how then should the traveling salesman plan his route for the maximum efficiency. 

For a salesperson making four or five stops in a day the problem is usually not that hard to solve, but for say a UPS or FedEx delivery truck driver who may have as many as 150 stops in a day - well that problem of math and logistics gets much, much more complex.  And, as the piece from Nautilus describes, the Traveling Salesman problem is not only incredibly important for transportation companies to try and solve, it becomes even more complex when we factor in the the delivery drivers are actual human beings, and not just parts of an equation on a whiteboard.

Check out this excerpt from the piece to see how one (unnamed) delivery company is taking HR and workforce data, couples with the realization that indeed, people are a key element,  and baking it in to the classic math problem of the Traveling Salesman:

People are also emotional, and it turns out an unhappy truck driver can be trouble. Modern routing models incorporate whether a truck driver is happy or not—something he may not know about himself. For example, one major trucking company that declined to be named does “predictive analysis” on when drivers are at greater risk of being involved in a crash. Not only does the company have information on how the truck is being driven—speeding, hard-braking events, rapid lane changes—but on the life of the driver. “We actually have built into the model a number of indicators that could be surrogates for dissatisfaction,” said one employee familiar with the program.

This could be a change in a driver’s take-home pay, a life event like a death in the family or divorce, or something as subtle as a driver whose morning start time has been suddenly changed. The analysis takes into account everything the company’s engineers can think of, and then teases out which factors seem correlated to accident risk. Drivers who appear to be at highest risk are flagged. Then there are programs in place to ensure the driver’s manager will talk to a flagged driver.

In other words, the traveling salesman problem grows considerably more complex when you actually have to think about the happiness of the salesman. And, not only do you have to know when he’s unhappy, you have to know if your model might make him unhappy. Warren Powell, director of the Castle Laboratory at Princeton University’s Department of Operations Research and Financial Engineering, has optimized transportation companies from Netjets to Burlington Northern. He recalls how, at Yellow Freight company, “we were doing things with drivers—they said, you just can’t do that.” There were union rules, there was industry practice. Tractors can be stored anywhere, humans like to go home at night. “I said we’re going to need a file with 2,000 rules. Trucks are simple; drivers are complicated."

Did you catch all the HR/talent/workforce data baked into the model described above?

Payroll, time and attendance, life events that likely would show up in the benefits admin system, scheduling are all mentioned, and I bet digging deeper into the model we'd find even more 'talent' elements like supervisor or location changes, time since a driver's last compensation increase, and maybe even 'softer' items like participation in company events or number of unread emails in their inbox.

The specifics of what bits of talent data aere being incorporated into the process matter less than the fact that in the example the HR data is being mashed up so to speak with the 'hard' data from the truck itself (which is another interesting story as well), and analyzed against past driver experiences to alert managers as to when and where an accident is more likely to occur.

There is even more to the problem than the technical observations from the truck itself, and the alogorithms' assessment of the HR/Talent data - things like Union rules and contracts factor into the equation as well. 

But for me, this example of taking HR data and using it not just to try and 'predict' HR events like involuntary turnover or a better or worse performance review score, and apply it to real business outcomes, (the likelihood of accidents) represents a great example of where 'Big Data for HR' is heading.

I definitely recommend taking a few minutes this week to read the entire piece on the Nautilus site, and then think about some the next time the FedEx driver turns up with a package.

Have a great week!

Three keys if you want to become a more data-driven organization

So you've bought into it -  Big Data, Moneyball for HR, workforce analytics - all of it. And whatever you call this increased reliance on data, analysis, and more objective information in your talent processes, chances are this represents a pretty significant change to the way you've always done business, how managers and leaders have made decisions, and perhaps most importantly how you evaluate and reward employees.

Of the many tough challenges you have to negotiate if indeed you are the designated numbers geek/quant in your shop, once again the world of sports offers three recent examples, (NOT AGAIN), that help to point out some key focus points or areas of concern as you hatch your nefarious plans.

One - Make sure you as the 'stats' person, knows how to translate the numbers into strategies that are likely to get buy-in from the team. From the SB Nation blog - How and why NBA coaches communicate advanced metric to players, an interesting piece on the Boston Celtics' new coach Brad Stevens and his desire to bring more data and analytics to bear in the organization:

The numbers don't always offer solutions, but they do tend to generate better options and that's all an NBA team can offer with each possession and every front office decision. That's the next step in the analytics movement. What started in blogs has been appropriated by front offices and has now trickled down to coaches. Communicating those ideas effectively to players is the final hurdle.

Two - Make sure the team members know how to and understand the importance of doing more accurate self-assessments in light of the new measurements. It is great when management and leaders make the move towards a more data-driven decision making process, but don't forget the folks on the front lines.

Here is a great example from a recent piece on the WEEI Radio site by former Major League baseball player Gabe Kapler titled STATS 101: Why it's time to re-educate players in meaningful statistics:

To take it a step further, when we discussed our numbers with our agents, it was in the form of the traditional verticals, the ones we used for decades prior. We correctly assumed that our reps were using these statistics in conversations with the general managers of our clubs. We stood in the truth that our value — our worth as baseball players — was wrapped up in these metrics.

Times have changed, but substantially less among players. While progressive front offices have altered the way they evaluate us, we have lagged far behind in the way we grade ourselves. It’s akin to unhealthy communication in a relationship.

Three - Make sure what you are measuring and holding people accountable for, is actually at least largely in their conrol or influence. This really isn't exclusive to a more data-centric approach to business, it applies everywhere. We generally can only control what we can control and penalizing the clever point guard because the slow-footed center can't convert enough of his excellent passes near the rim is not a long-term winning strategy.

More from the Kapler piece:

 If, for example, we taught pitchers about Fielding Independent Pitching — which truly spotlights what a pitcher can control (walks, strikeouts and homers) and removes balls in play, thereby eliminating a fielder’s ability to have an impact on the outcome of a play and consequently a pitcher’s line — we place the responsibility right where it belongs. If we show a hitter how well hit balls and exit velocity/speed off the bat are being examined more and more closely, then the hitter will freak out less when crushing a ball off the pitcher’s forearm and having it ricochet safely into the glove of the first baseman for an out. He may walk back to the dugout thinking, “Ka-ching!” instead of throwing a water cooler and forcing some nearby cameraman to change clothes. 

Let's do a quick review:

One -  make sure you know how to communicate the value and merit of these new statistical approaches to the team. 

Two - make sure the team starts to do their own self-assessments through the lens of these new data-driven approaches

Three - make sure you are holding people accountable for numbers that they can legitimately influence and can they can own.

What other tips or recommendations do you have to transform an organization from one that relies on gut feeling to one that counts on the data?

Trying to look better vs. trying to get better

Quick take from the world of the NBA - and no, I'm not tapping the sports world solely to try and surpass in 2013 the number of contributions I had last season towards The 8 Man Rotation - A Look at Sports and HR E-book.

So here's the take - if you are an experienced professional near the top of your game, but still have some room to grow to truly reach your ultimate goals - the big promotion, the fatty paycheck, or in the sports world, it might be the Championship title, etc. the outside advice that you seek and who you choose to engage with makes a pretty big statement about your dedication to your craft.

What do I mean by that?

Let's take a look at two recent examples from the Association:

Exhibit A - Deron Williams of the Brooklyn Nets  hires his own personal beat reporter

Here's an odd story from today's Wall Street Journal, about a new member of the Brooklyn Nets corps of beat writers. Devon Jeffreys is a credentialed reporter like all of the rest, but he's really only at Nets games to cover one player: Deron Williams. And he's there to cover Deron Williams for a website that Deron Williams himself is the owner of: DeronWilliams.com. Athletes having a personal website to trumpet their accomplishments is nothing new, but Williams's site is rare in that it features content that is written like regular news stories, save for the fact that Williams is always the central figure.

Exhibit B - Kevin Durant of the OKC Thunder hires his own personal performance analytics coach/consultant

Kevin Durant has hired his own analytics expert. He tailors workouts to remedy numerical imbalances. He harps on efficiency more than a Prius dealer. Durant sat in a leather terminal chair next to a practice court and pointed toward the 90-degree- angle at the upper-right corner of the key that represents the elbow. “See that spot,” Durant said. “I used to shoot 38, 39 percent from there off the catch coming around pin-down screens.” He paused for emphasis. “I’m up to 45, 46 percent now.”

Pretty obvious that these two 'hires', or personal development strategies represent two strikingly different approaches to performance improvement. Williams' personal beat reporter is there to make Williams look better.  Durant's analytics coach is there to help Durant get better. 

Now to be fair these examples are kind of cherry-picked - Williams might have his own analytics coach, personal trainer, dietitian, etc. to help his actual game improve. And Durant might have his own PR reps and spin doctors to help his public image. Both players have the resources necessary to have all of their professional bases covered. So it isn't completely fair to call them out in this way with imperfect or incomplete information.

But you and me?

If we are engaging with experts or taking the time to get some outside 'performance' help, we probably do have to make choices about where to invest our more limited resources, and perhaps more importantly, our limited time. I think about this a lot in the context of what people do online - maybe it's changing profile pics every other day or making sure they shoehorn in a comment on every LinkedIn group discussion that they know people in their field will see. Or perhaps it's the proliferation of personal branding or career coaches - to me that entire field only exists because people are getting a little too focused on looking better vs. actually getting better.

If you worry about looking better too much, you might end up looking a little better, sure.

If you care mostly about getting better, then the looking part takes care of itself.

The true goals of HR Big Data projects

Buried near the end of this fairly standard but still pretty interesting piece on how software giant SAP is deploying Human Capital workforce analytics solutions in their internal organization from their recently acquired SuccessFactors product suite is perhaps one of the most clear, coherent, and instructive observations about the goals (or what should be the goals), of any HR organization embarking on an analytics or (buzz work blog police look the other way) 'Big Data' project.

Here's the quote from SAP's Helen Poitevin:

We see this (the implementation of modern workforce analytics solutions) as a transformation for us first, moving from being specialists in extracting data from our systems, to being specialists in answering workforce related business questions.

I know that this seems like a kind of overly simple and somewhat of an obvious point of emphasis, but I think it is one that serves to remind those of us that like to talk, read, or prognosticate about how Big Data will have a truly transformative impact on HR professionals, workforce planning, and human capital management need to remain mindful that collecting more data, and even making the extraction and presentation of that data simpler and even more beautiful, is only the first step in the journey to realizing better business outcomes.

The goal of these analytics and Big Data projects, as the SAP article makes plain, is not just the ability to organize, describe, extract, and present workforce data (which in truth are necessary and important steps), but to leverage that data, to have the data lead to the asking of the right questions, to illuminate a path towards answering these questions, and to help the organization understand and relate the story that their human capital data wants to tell.

Again, the SAP piece makes it clear what their goals are, and what has to be the end-state for HR analytics and data projects:

(the analytics projects) represents a transformation for our business, by virtue of leveraging data-based insights and analysis about our workforce to make better, more sustainable decisions

Again, you probably already know this. Probably.

But it is a telling reminder just in case you've let your goals slip a little, or if you want to (or feel like you have to) claim victory with the initial successes in your analytics programs. 'Look we have reports!'

You're not really there, (and hardly anyone is yet), until the workforce data becomes an essential part of how your business makes decisions, and is not just a set of cool dashboards or a slick set of charts on an iPad app.

Have a great week all!

Cause, correlation, and chemistry

I am willing to be you have probably read, heard, or even repeated the following admonition in the last few weeks:

Correlation is not causation.

Here's why I think this assertion need to be retired, or at least pushed off to the side and filed away for a while with your Vanilla Ice CDs, He-Man and the Masters of the Universe figures, and Gloria Vanderbilt jeans.You have the power.

First, on the inability of correlation, i.e. lining up two sets of data that seem to track in the same direction and making a claim that one event or activity 'causes' the other, well sure, I think everyone understands that trap.

After seeing everyone at the State Fair rocking their bad tattoos, and therefore thinking that getting a bad tattoo will make one attend the State Fair is not a conclusion most rational observers would reach.

But the problem with the 'Correlation is not Causation' admonition is that it has the effect or shutting down the debate and stifling the potential discovery of useful information. A strong correlation between two related and relevant data series may not imply or prove causation, but it probably implies something. And that something might just be really important for us to understand - say the correlation between the course of study our last dozen newly promoted employees took, or the relationship between managers that successfully completed the latest leadership development program and the 12-18 month success of their team members.

In HR and Talent Management I am not sure the goal should be to try and 'prove' any one thing can actually cause another thing to happen anyway. We are dealing with people, not robots, (not yet anyway), and attempting to make sense out of interpersonal relationships, motivations, rewards, and capabilities. It is, in many ways, much harder than sitting in a chemistry lab tracking how agents react to one another. 

If I remember my high school chem lab accurately, loading up a beaker with a few solids and enough unstable acids and stopping up the top caused it to explode every time.  People, while often predicable, are not always that consistent.

The last warning I will raise is that team 'Correlation is not Causation' like to use this conclusion as a fake scientific argument against any proposals or ideas that they disagree with, or did not come up with themselves.

Here's a simple example:

'Hey, I noticed last week when his car was in the shop, Jake got a tremendous amount of work accomplished working from home - maybe we should explore letting some of the other developers do some teleworking?'

Can we know for certain that working from home was the reason for the spike in Jake's productivity?

Of course not. 

Might there have been a dozen other factors that might have been more responsible for the increase in output?

Sure.

Does your organization have the time or capacity to set up highly controlled experiments to try and figure it out - assuming that is even possible?

No way.

In science, proving causation might be the goal, the desired end state, but in Talent, we are much better served finding the correlations, using our understanding of work, people, and the world, and seizing on the ones that make sense for our business and our teams.

What do you think? Do you ever drop the 'Correlation' bomb around the office?