What can we prove?

Over the weekend I went all 'Back in the day' with my 'Generation X movies, ranked' post, but something I heard today made me compelled to fire up the way back machine once again. 

The backstory....

Sitting on a (delayed) plane waiting to get clearance to take off last night and I could not help but overhear the dude next to me carry on a 'You were supposed to turn off your cell 10 minutes ago but obviously you are too important to follow the rules' conversation with what I think must have been his colleague at whatever monkey business they were up to.

My pal in seat 4A kept repeating the following questions to the person on the other end of the conversation, (who I have to think was probably praying for merciful death, or a fire drill):

"Do we know that for sure? Can we prove it?"

So to tie this back to the 'In the day' reference at the top, the (interminable) conversation reminded me of one of my favorite films that I probably could have included on the 'Gen X' list, 'And the Band Played On', an HBO film from 1993 about the discovery of the AIDS virus and the political and medical flights that were hallmarks of the earliest efforts to combat the disease. 

In the film, the doctors and the medical researchers of the CDC are featured prominently - the agency was at the time at the forefront for governmental efforts towards the identification of the virus, understanding its effects, and finally, attempting to identify the best approaches to keeping the virus from spreading. Throughout the film, the CDC researchers and doctors would develop theories about the disease and make (educated) guesses as to what the government and public health officials should be doing to try and stem the danger to the public.

But every time one of the doctors shared his or her theories about what was happening the head of the CDC would respond with the following series of questions, or challenges:

What do we think?

What do we know?

What can we prove?

The motivation behind the CDC head's questions was that the suits in charge would not authorize additional funding for testing and research unless the doctors had a way to prove that their theories about how the disease was being spread and the needed actions to take were accurate. 

Bottom line: It doesn't matter what we think. It even doesn't matter what we know. It only matters what we can prove.

And I think these three simple questions are good ones to keep in mind for HR/Talent pros who are seeking to adopt more data-driven approaches and analyses to their practices of recruiting, development, retention, and succession planning, (and maybe more). 

It is a good reminder because like the CDC head in the movie, the execs that control the budget and the strategic direction for all HR programs are more likely to back ones that are more about what can be proved, and less about ones that are about what some HR person thinks.

What do we think?

What do we know?

What can we prove?

A solid set of questions to use as you frame up your data driven HR projects.

The analytics takeover won't always be pretty

Seems like it has been some time since I dropped a solid 8 Man Rotation contribution here on the blog, so to remedy that, please first take a look at this recent piece on ESPN.com, 'Fears that stats trump hoops acumen', a look at the tensions that are building inside NBA front offices and among team executives.

In case you didn't click over and read the piece, the gist is this: With the increased importance and weight that a new generation of NBA team owners are placing on data-driven decision making and analytical skills, that the traditional people that have been the talent pool for NBA team management and executive roles, (former NBA players), are under threat from a new kind of candidate - ones that have deep math, statistics, and data backgrounds and, importantly, not careers as actual basketball players.

Check this excerpt from the ESPN piece to get a feel for how this change in talent management and sourcing strategies is being interpreted by long time (and anonymously quoted) NBA executives:

Basketball guys who participated in the game through years of rigorous training and practice, decades of observation work through film and field participation work feel under-utilized and under-appreciated and are quite insulted because their PhDs in basketball have been downgraded," the former executive, who chose to remain anonymous, told ESPN NBA Insider Chris Broussard.

One longtime executive, who also chose to remain anonymous, postulated that one reason why so many jobs are going to people with greater analytical backgrounds is because newer and younger owners may better identify with them.

"Generally speaking, neither the [newer generation of] owners nor the analytic guys have basketball in their background," the longtime executive told Broussard. "This fact makes it easy for both parties to dismiss the importance of having experience in and knowledge of the game.

The piece goes on to say that since many newer NBA owners have business and financial industry backgrounds, (and didn't inherit their teams as part of the 'family business'), that they would naturally look for their team executives to share the kinds of educational and work experience profiles of the business executives with which they are accustomed to working with, and have been successful with.

The former players, typically, do not have these kinds of skills, they have spent just about all their adult lives (and most of their childhoods), actually playing basketball. A set of experiences, it is turning out, no longer seems to provide the best training or preparation for running or managing a basketball team. 

But the more interesting point from all this, and the one that might have resonance beyond basketball, is the idea that the change in hiring philosophy is coming right from the top - from a new generation of team owners that have a different set of criteria upon which they are assessing and evaluating talent.

Left to tradition, hiring and promotion decisions would have probably only slowly begun to modernize. But a new generation of owners/leaders in the NBA are changing the talent profile for the next generation of leaders.

The same thing is likely to play out in your organization. Eventually, if it has not happened yet, you are going to go to a meeting with your new CHRO who didn't rise through the HR ranks and maybe is coming into the role from finance, operations, or manufacturing. In that meeting your 19 years of experience in employee relations might be a great asset to brag on. Or it might not be.

And you might find out only when you are introduced to your new boss, who has spent her last 5 years crunching numbers and developing stats models.

Have a great week!

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!

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!