Many of the problems in delivering data science are the result of waste. Sometimes, identifying waste is obvious, including wasted time waiting for data and provisioning of systems or wasted effort cleaning data. However, sometimes waste is not obvious to see. Such instances are squandering expensive, hard-to-recruit talent, or working on the wrong things for the wrong stakeholders.
Waste also exists in manufacturing operations, supply chains, and product development. These fields have successfully adopted a philosophy, Lean thinking , to eliminate as much waste as possible, improve quality, and increase speed ...
