It’s called Big Data. Every transaction, every contact with a customer, every employee interaction is a data point that gets recorded, analyzed and processed. It’s going to be a boon to companies that can get a handle on how to use it. You can read a lot about how it will change business here, here and here. But you’ll notice that most people discuss Big Data as an outwardly focused phenomenon: How can it improve profits?
What you don’t see enough of, we think, is how it can improve hiring. In other words: How can it improve the teams that drive those profits in the first place?
In the human capital business, that’s a thing we think about a lot. In a recent write up about the movement they call “Talent Analytics,” Forbes explained its importance thusly:
There are around 160 million workers in the US alone, and most companys’ largest expense is payroll. In fact in most businesses payroll is 40% or more of total revenue, meaning that total US payroll expense is many billions of dollars. How well do organizations truly understand what drives performance among their workforce? The answer: not really very well.
Simply put, companies know how to measure success. That’s what the bottom line is for. What they don’t know is why people are successful.
Do we know why one sales person outperforms his peers? Do we understand why certain leaders thrive and others flame out? Can we accurately predict whether a candidate will really perform well in our organization? The answer to most of these questions is no. The vast majority of hiring, management, promotion, and rewards decisions are made on gut feel, personal experience, and corporate belief systems.
We all know that past performance is not a guarantee of future results. But everyone wants to hire the guy with the past performance because he has demonstrated results. Even if it works, the guy with the past performance is expensive. No one likes expensive. But we need results. Round and round this loop we go.
How do we get off? Talent Analytics. The concept is familiar to anyone who follows baseball or saw the Brad Pitt film “Moneyball.” (Baseball calls their analytics Sabermetrics.) Much of the old, analog world hasn’t yet caught up to Big Data so there are huge market inefficiencies to exploit.
(The business world) operates under a belief system that employees with good grades who come from highly ranked colleges will make good performers. So their recruitment, selection, and promotion process is based on these academic drivers.
Makes sense, right? So they looked at the data. You want to know three things they found did not matter when correlated with performance?
- Where the candidate went to school
- Their grades
- The quality of their references.
You need to find the traits and process of successful people, and hire people who have those traits and processes. If you focus solely on a candidate’s past results, you’re using too small a sample size that is too largely affected by chance.
Focus on process, process and process. (Billy Bean of “Moneyball” fame had the simple but brilliant observation that batting average was a terrible stat because it was dependent on the luck of where the ball landed. Why not get cheaper players who put the ball in play but had so far been unlucky or hadn’t gotten opportunity? i.e. find the players who had the best batting process, but not results? Voilà, competitive advantage.)
Here at Morgan Samuels, we love this kind of stuff. As a Lean Six Sigma company, we’re big on fine-tuning our own process as we figure out what works for us. But we’ve also found that we can make better executive placements across industries by matching skill sets, and sometimes ignoring the particulars of a candidate’s experience, as long as they have enough of it. We can find a bigger slate of great candidates that way because no one else is doing it.
What can you accomplish by getting ahead of the data curve? Read this book: “Trading Bases.” It’s an out-of-the-box example of a former securities trader turned sports-bettor who used data-heavy analytics to turn $1 million into $1.41 million in one baseball season. (He made only a 14 percent return the second year.)
How could he do that? Simple. He’s using Big Data, and the people he’s betting against are using their instincts. Or as the trader, Joe Peta, put it:
Sabermetrics in baseball allows employers to pay for skill sets and not get confused by results.
When your competitors are stuck in the past, confused by attributes that don’t matter, that’s competitive advantage.