Commentary: Bringing employees along for the data ride

Eric Johnson, Research Director and IT Editor of American Shipper, said when it comes to modernizing supply chains with data and technology, you have to bring everyone – suppliers, customers, and even your own workforce – along for the ride.    I recently watched the first few episodes of an absurd show about a down-on-his-luck rodeo clown called Baskets. Now, this may not seem to relate in any way to logistics technology, but stay with me here.
   One of the side characters on the show works as a claims adjuster for the insurance arm of a major retailer. A couple of episodes in, she is not-so-gently encouraged to sell executive memberships to the retailer. Fail to sell a membership and she’ll be let go, despite the fact that she’s good at her primary job. The problem is that her mild-mannered demeanor just doesn’t jive with her new side responsibilities. (Remember, she’s supposed to be a claims adjuster, not a salesperson).
   A day after watching the show, I attended eyefortransport’s CIO Forum in Austin, where Mel Kirk, senior vice president and chief information officer of the trucking and logistics company Ryder System, talked about the data transformation at his company that started in 2012 and continues today.
   The corporate goal was to get all of Ryder’s disparate systems connected through a single version of data. He said buy-in from employees wasn’t really an issue, since they would often find multiple systems telling them different things when trying to solve a problem.
   But that employee buy-in doesn’t mean that implementing Ryder’s new model for the use of data and analytics across the organization was accomplished uniformly, or quickly.
   One of the key points Kirk made is that data and technology initiatives are too often developed without thinking about the outcomes for end customers. What’s more, they are often deployed with little to no consideration of the ways in which existing operations personnel will use them.
   Kirk put it this way: most Ryder employees gravitated toward their job at the company to get away from data. They’ve based their career around relationships, experience and the technical expertise involved with doing their jobs. They don’t want their processes interrupted.
   I immediately thought back to the character on Baskets, being told to do a job she didn’t want to do, was never trained to do, and for which she had little natural aptitude.
   Herein lies the tricky part of modernizing supply chains with data and technology. You have to bring everyone – suppliers, customers, and even your own workforce – along for the ride.
   For Kirk, that meant not only incorporating feedback from operations employees about what problems they had serving customers, but also ensuring they were properly motivated to use the tools now at their disposal.
   This is a key distinction. Ryder wanted a single version of truth across its systems so that it could allow employees to make decisions based on fact, not emotion. But if an employee doesn’t feel compelled to use the data, or doesn’t know how to use the data, then you’ve created data that isn’t being monetized. When you’re talking about a company the size of Ryder, these initiatives cost real money. There are shareholders and customers to satisfy, and investment must be turned into revenue.
   Kirk called it the “trap” of data investment. It’s easier than ever to get C-level buy-in for initiatives that generate and harmonize supply chain data, because big data is used in virtually every management article known to man these days.
   But the trap comes when those same C-level execs come later asking about the return on their investment.
   The trick, according to Kirk, is getting that buy-in from the people least likely to gravitate towards using data. You may have a knockout data platform, with metrics that show employees using data to make decisions make better decisions, but those employees that are naturally inclined to run from data and trust their instincts aren’t going to just change their ways overnight.
   Ryder turned to money as a carrot. Employees that used data and analytics were rewarded with a higher percentage of a discretionary bonus pool, and those that didn’t received a smaller bonus than they ordinarily would have. The goal was to reward the early adopters and compel others to follow suit.
   The equation seemed simple for Kirk: the company invested in data, and employees using data to make decisions are helping improve the company’s bottom line, so they should be rewarded.
   It’s certainly easier said than done though. Out of Ryder’s roughly 33,000 employees, Kirk said around 800 are “tightly coupled” with Ryder’s business intelligence tools, trying to extract value from them.
   “But that leaves a lot who aren’t,” he said. “You can’t just focus on the folks that live in the system. It’s too big an investment to not make sure those others are using the data as well.”
   As another speaker at the event put it, the “Field of Dreams” model of technology deployment is not very effective. You can’t expect people to naturally gravitate toward something they’re not accustomed to doing.
   Data initiatives are not just valuable, they are becoming mandatory, especially in large shipper and LSP organizations that have been slowly collecting different systems over the years, along with all the isolated data associated with those systems.
   The lesson is not to lose sight of how the data initiative will fundamentally improve the customer experience, and how you will ensure that your employees actually use the data you’ve spent time and money to create. Both drive the bottom line benefits that the C-level will expect to get for their investment.
   You can’t expect a claims adjuster to suddenly become a salesperson any more than you can expect someone who’s spent a career avoiding technology to become a data rockstar with the flip of a switch. It takes time, incentives, and an upfront strategy not only to create the data, but to properly leverage it as well.