## Metadata - Author: [[brittany-krauth|Brittany Krauth]] - Full Title:: Analysts Make the Best Analytics Engineers - Category:: #🗞️Articles - URL:: https://docs.getdbt.com/blog/analysts-make-the-best-aes#loop-1-reacting-to-findings-in-the-raw-data - Finished date:: [[2023-07-28]] ## Highlights > You’ve got three steps that stand between you and your finished curated dataset. *If you don’t have an analytics engineer*, then the work may be split up like this: ([View Highlight](https://read.readwise.io/read/01h6cgqh88wyhcf119kw0rtdk0)) ![[Pasted image 20230814071429.png|400]] ^d82b4b This becomes even worse if the docs and validation are done by some other people >  ([View Highlight](https://read.readwise.io/read/01h6cgrhz840ktbf8z7m8cphcm)) ^bb8e95 > An analyst will have to use their experience to know when the dataset is “good enough” for the stakeholder and their question since 100% accuracy might not be the goal. And if we're being honest, sometimes being directionally correct is all that’s needed to make a business decision. ([View Highlight](https://read.readwise.io/read/01h6cgy6bhfb5rxz574zgbd2ng)) > dbt makes it very quick to add [data quality tests](https://docs.getdbt.com/docs/build/tests). In fact, it’s so quick, that it’ll take an analyst longer to write up what tests they want than it would take for an analyst to completely finish coding them ([View Highlight](https://read.readwise.io/read/01h6cgz6t1xbpt2ax4ej2m03ab)) > we want to know *why* a certain logic was built into that specific model, then that’s where we’d turn to the documentation. ([View Highlight](https://read.readwise.io/read/01h6ch3c25a86ncajdr2dxp79w))