Here's one of the main differences between data engineering and data science: ETL (Extract / Load / Transform) is for data engineers, or sometimes data architects or DBA's.
DAD (Discover / Access / Distill) is for data scientists. Sometimes data engineers do DAD, sometimes data scientists do ETL, but it's rather rare, and when they do it, it's purely internal (the data engineer doing a bit of statistical analysis to optimize some database processes, the data scientist doing a bit of database management to manage a small, local, private database of summarized info (not used in production mode usually, though there are exceptions).
What DAD means:
The last step might or might nor require: statistical modeling (many predictors are now model-independent), presenting results to management (less important if the purpose is to design a machine-to-machine communication system, instead a proof-of-concept or prototype might be required first), or integrating results in some automated process. Documenting is always part of all these steps.