Data-driven decision-making, American Express style- Valutrics

Creating a data-driven culture

Companies that strive to inculcate data-driven decision-making often believe that if leadership understands the value of data, it will all work out. But that is not the whole story; there needs to be buy-in from the rank and file. “We all know that this is a two-pronged approach,” Vapenik said. “The rubber hits the road from the bottom up.” 

Here are the traits he’s found are important to look for in people when building a culture that promotes data-driven decision-making:

Right and left brain input

At American Express, the leaders in Vapenik’s group were tested for right and left brain characteristics. “We had one person out on the creative end; everybody else was on the left, and I was in the middle,” he said. But in building a data-driven culture, Vapenik said leaders will need to understand and connect with groups such as marketing, which depend on right-brain talent.

His advice on building the leadership group: “Your best bet is to have a mix of people, or have people who are toward the middle of the continuum of the analytic mindset,” he said. 

Natural curiosity

“People who are really interested in how things work and how things get created, they are the folks who ask a lot of questions — the who, what, where and why. They will get deeper into understanding what the data is and, ideally, will help in coming up with new solutions,” Vapenik said.

Curious people will also gravitate to new technology and pull others along, he said, adding that he puts himself in this category. A recent curiosity-fueled foray he took was a deep dive into blockchain technology.

Seeing the forest, trees and roots

Vapenik has worked with numerous big-picture people who “are great at strategy,” but aren’t good on details; he’s also worked with others who spend too much time focused on the minutiae — the roots — and can’t see the proverbial forest or the trees. “When I think about a data-driven culture, it’s about trying to find people who can descend from the forest to the trees to the roots and back [again].”

Experience with bad or nonexistent data

Vapenik said he came out of school believing that his biggest concern was how to build a data model. But what turned out to be even more critical was dealing with bad or missing data.

“Some of my colleagues on the purely modeling side [think]: ‘I want to fix this data to make my model work,’ versus, ‘I want to fix this data to get a good answer.’ Those are sometimes not the same thing,” he said.

Enhancing the data or filling in missing information may improve the model’s performance, but the use of the information that model generates “can cause problems,” he said. He cited his team’s work on the New York City Department of Sanitation’s recycling program. The job was to ascertain the amount of garbage collected that could be recycled — with only the total tonnage to work with.

“The consultant was doing the dirty work of picking through the garbage, but we set up a stratified sample across the boroughs [on the hunch] that garbage created in Manhattan will be different from that created in Brooklyn and so on,” he said, in order to tease the data out.

People who can think through problems that have bad or missing data, “we have found to be very helpful,” Vapenik said.

Willingness to experience the data

For a project at American Express aimed at reducing disputed card charges, team members were asked to look at their own credit card statements. When Vapenik, a New Jersey resident, reviewed his statement, he saw a charge listed as “Mountainside,” the name of a borough, and not as the movie theater’s company name.

“I could see where that is a problem for the average consumer. But then I tried to think about why a merchant would do that,” he said. The company had theaters all over, so it identified theaters by the city they’re located in, not by the brand name of the chain. When people analyze the data, they first need to think about the motivation behind the data, he said.

Storytellers and influencers

Look for people who “can make the complex simple,” Vapenik said. “The easier you can make it for people to understand the data, the more success you will have.” Data presentations — the use of charts, bar graphs or pictures — should be pegged to the groups you are trying to persuade.

Another tip: People who are skilled at influencing others often have a high degree of emotional intelligence, Vapenik said. Their empathy for people makes them effective communicators who are good at translating complicated data into useful information. “There’s a connection between heart and brain,” he said.

Team players and orchestrators

Vapenik said finding people who possess all the traits he described will be difficult, if not impossible. In reality, data-driven decision-making rides on many types of intellects. “Go for a collaborative, team players,” he said. But like an orchestra, the group will need a conductor “who steps in, so all you hear is the beautiful music.”