Data scientists are increasingly being asked to deliver business value, and they are being held accountable for their results. Given this reality, data science teams can’t wait weeks for IT administrators to come up with the resources they need to develop proofs of concept and train artificial intelligence models. Yet too often, this is exactly what happens, leaving data scientists at odds with their IT departments.
This is a bad situation for all parties. If IT takes a traditional approach to resource provisioning — with a cost-center mindset and limits to system access — data scientists are likely to circumvent standard procurement processes and run their workloads in the cloud. That creates shadow IT that is both risky and costly.