This article just reinforces that management thinks that data scientists spend most of their time building models. In reality, data scientists spend the majority of their time munging and wrangling disparate data types (that are typically a total mess), understanding the data well enough to fix problems, translating business requirements to code, and converting model outputs to human understandable presentations. Building models is a trivial amount of the effort for most projects, typically 10% or less of the time. Most organizations will eventually move over to AutoML type solutions, but then will be shocked when they fail to achieve any significant gains in productivity because they're optimizing one of the shortest steps in the whole process.