Mike Kreider, CIO of DHL Supply Chain North America, says his organization has institutionalized this mindset. “A data product is a standardized dataset from one or more systems, formatted for easy reuse,” he says. Shipment data products, for example, support operations, logistics, and business development. They also power gen AI tools such as DHL’s proposal generator. “If the data product doesn’t exist or isn’t clean, the tool won’t work,” he adds.
Mike Kreider, CIO, DHL Supply Chain North America
DHL
Kreider emphasizes that defining a data product isn’t just a technical task, it’s about business alignment. Each product has an identified business owner and a lifecycle plan, including how it’ll be updated and retired. “We don’t want orphaned data products no one feels responsible for,” he says. That sense of ownership is what ensures the product remains current and reliable for AI applications.
IBM also builds AI-readiness around data products. Dinesh Nirmal, SVP of IBM Software, points to the need for self-service. “If teams can’t easily find and trust the right dataset, they can’t innovate at speed,” he says, adding that IBM’s catalogued, governed data products make trusted datasets available to AI engineers enterprise-wide, enabling them to focus on building solutions instead of searching for inputs.
