How are companies making use of their data for managing supply chains and logistics? Simon Vandemoortele, chief executive officer with GeoPostcodes, details the challenges and opportunities that they face.

The state of data maturity in supply chain and logistics is “very disparate,” Vandemoortele says. It ranges from companies with “very ad hoc solutions” to those with processes that are “completely scalable.”

Many companies know they have an issue with data quality, but don’t know how to approach it. Leadership isn’t aligned on the initiative, and the problem ends up being dumped on IT, which lacks the necessary skills to proceed. The situation is especially difficult for companies with a worldwide footprint, where local teams might be trying to solve problems at their level, rather than scaling the effort to address data quality worldwide. 

One might assume that artificial intelligence can help, but in the short term it could be more of a hindrance to data-quality efforts. Companies need to figure out how to incorporate AI into their existing initiatives. And they shouldn’t be stopping data-quality projects in favor of embracing AI. 

“AI is the next step,” Vandemoortele says.  “It’s important to take them in order. Don’t jump into AI projects if you don’t have your data quality in order.”

It’s important to start with a vision that defines the precise problem to be solved. Then devote the necessary time and budget to implementing the solution, Vandemoortele says. He advises the creation of a dedicated data team, preferably one that’s global in scale, and can address issues of data quality and governance across the organization.

Data quality becomes more difficult to achieve outside the U.S. and a handful of other developed countries. The challenge of normalizing and cleaning data “increases exponentially,” Vandemoortele says.