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?

I've got some suggestions for your screenplay

Not really, and unless you are up to something on the side, you probably don't even have a screenplay (or a short story or a book for that matter). But what you might have, still, is that problem of folks in the HR and even IT game have been lamenting just about forever - no 'real' business people take you all that seriously.

For whatever reason the people in the organization that get to decide the 'what' of what people do are more important and 'strategic' than the people that (largely) are responsible for finding and hiring those people in the first place (HR), and identifying, procuring, deploying, and maintaining all the technologies that the people rely on every day (IT). That is probably true in most organizations and it's also true that it's unlikely to change unless HR and IT start to think a little differently about the problem.

I was thinking about this over the weekend when I read this piece in the New York Times, Solving the Equation of a Hit Film Script, With Data, about a new method or process where Hollywood film scripts are evaluated, and suggestions for improvement given, based on data-driven analysis. How does the process work? From the NYT piece:

Netflix tells customers what to rent based on algorithms that analyze previous selections, Pandora does the same with music, and studios have started using Facebook “likes” and online trailer views to mold advertising and even films.

Now, the slicing and dicing is seeping into one of the last corners of Hollywood where creativity and old-fashioned instinct still hold sway: the screenplay

A chain-smoking former statistics professor named Vinny Bruzzese — “the reigning mad scientist of Hollywood,” in the words of one studio customer — has started to aggressively pitch a service he calls script evaluation. For as much as $20,000 per script, Mr. Bruzzese and a team of analysts compare the story structure and genre of a draft script with those of released movies, looking for clues to box-office success. His company, Worldwide Motion Picture Group, also digs into an extensive database of focus group results for similar films and surveys 1,500 potential moviegoers. What do you like? What should be changed?

Pretty interesting and still in this age of data trumping everything kind of unusual. Although even as I recently wrote about here, data and algorithms and machine learning approaches encroaching on formerly 'creative' endeavors are starting to pop up more and more.

Applying intelligence, Big Data, and more powerful technologies for improving movie screenplays does more than just fix up the dramatic scene in Act III, it allows a guy like Vinny Bruzzese, who as far as we can tell had no 'real' movie experience, to become an influential participant in the movie-making process.

His data, team of analysts, and statistically-backed conclusions and suggestions, now put him more and more 'at the table' (sorry), where formerly only writers and movie producers used to meet. It doesn't really matter that he didn't go to film school or he didn't spend the 80s directing episodes of Full House, his data-driven solutions make him a Hollywood player.

Influence in business seems to be becoming more about who can gather, assess, and make data actionable, than who has the 'right' degree or experience. And the background of the people who can do that might be a lot different than who normally used to have that kind of influence. 

Follow-up: Big Data for HR - what do you make of this?

Yesterday I took about 550 words to say essentially this : Once your CEO decides that 'Big Data' is the next big thing to upskill your organization's talent level it is on you as an HR or Talent pro to make that happen. (It was in the Times you know).

One of the ways, besides the more obvious ones like 'Invest in some new technology' or 'Take a statistics course' is to challenge yourself to starting thinking differently about information and data (and not the typical data you might be used to considering) and what it might or might not mean for your organization and your talent game.

Here's an example of what I mean pulled from a recent Business Insider piece on some data around student loan debt load and default rates by State, (and let's assume for the purposes of this exercise that college recruiting and hiring is an important part of your workforce planning).

Chart 1 - Average Student Loan Debt

Dang, that doesn't look good anywhwere, but student debt loads seem particularly high in certain states and regions. Let's take this one more step.

Chart 2 - Average Student Loan Delinquency Rates

Interesting - not perfect alignment between the states with the highest average student loan levels and the highest default rates. But nevertheless, there are some pretty large sections of the country with average default rates at 15% or more. So the exercise is this - what, if anything would or should you do with data like this, (incomplete as it is, bear with me, it's just an example to make us think).

What if you are recruiting college grads or soon-to-be grads in the parts of the country with the highest debt loads and default rates?

Would that change your approach at all to things like signing bonuses or retention schemes that have an element of student loan repayment built in?

Would you formulate a plan for more strategic counter-offers for your younger talent that is likely to be much more receptive to make a jump to a competitor for even a small bump in salary?

Would you consider overpaying in the first few years for the best college grads knowing that some or even most of them have pretty significant financial worries outside of work?

Would you make access to a financial planner or accountant part of your signing package?

Or would you do nothing at all?

The point to all this is not really the student loan data, but rather to raise just one possibility of the potential and challenge that big data holds for you as a Talent pro, and to try and illustrate that using data to your advantage is likely going to require not just technical skills, but the ability to think differently about what drives your business.

And like we established yesterday, since it hit the Times, you can't pretend it doesn't matter for much longer.

Big Data for hiring - now everyone knows, (including the CEO)

While it can be cool to say and think that old or traditional media is dead or at least dying, (witness CNN's Keystone Cops-like coverage of the Boston Bombings and their wall-to-wall coverage of the Carnival 'poop cruise', interesting only to the people on the actual ship), it is still pretty remarkable to witness, at least in our little HR and HR Tech corner of the world, the sheer power to drive conversation the big, mainstream outlets still wield.

The latest example? The NY Times piece over the weekend titled How Big Data is Playing Recruiter for Specialized Workers, that did admittedly a fine job of covering some HR Tech startups like Gild, TalentBin, and Entelo, and how data, algorithms, and smart machine learning are combining with traditional sourcing methods in attempts to help organizations make better hires faster, and less expensively than in the past.Robert Rauschenberg, Yoicks 1953

It is a good piece and I recommend you checking it out, if you follow this space at all chances are you have already read the article, as it seemed to me over the weekend everyone Tweeted out the link (I did too). Even though these solutions have been out for some time - I just wrote about Gild myself here - once news like this hits the mainstream, you can bet you'll have some explaining to do back in your office about how you and the HR organization plans to leverage this kind of data in hiring and talent management decisions. Let's face it - even though people like me have written about these new technologies, and some of them have been featured at the HR Tech Conference, it's still the rare CEO or COO that has heard about them.  

But drop a feature about these cool new technologies in the Times, on the weekend no less, when Mr. or Ms. CEO is kicking back over brunch with their iPad and has a few minutes to read and think about a piece like this - well some of you are getting an email (maybe it already arrived, 'sent from my iPad') from the CEO with the link and a question along the lines of 'What can we do with this? or 'Are we using Big Data in hiring?'

It is pretty fun to stay on top of the latest trends and catch demos or webinars from the coolest new technologies. It is also fun to be sort of 'in the know', to be the only one in your office or in your local HR community to have some insight and savvy about the latest solutions and tools. You (mostly) get respect and cred from just knowing about them. 

But that position of 'person who knows all about the technology' will only take you so far once everyone else starts catching on too - especially the C-suite types that really only take notice of something until it hits the Times of the Wall St. Journal.  And that is happening my friends.

We're coming up fast to the point where 'awareness' is simply the ante that lets you play in the game. Your bluff is about to be called, by a CEO in a fancy suit, and iPad, and a link to the Times article.

Stay thirsty my friends...