## Metadata - Author: [[dbt-labs|Dbt Labs]] - Full Title:: 2024 State of Analytics Engineering - Category:: #🗞️Articles - URL:: https://www.getdbt.com/resources/reports/state-of-analytics-engineering-2024?ref=blef.fr - Finished date:: [[2024-04-14]] ## Highlights > **It starts with well-organized data**:‌ Unsurprisingly, like last year, most respondents spend most of their time organizing data sets for analysis (over 50% selected it as their #1 task). This critical task is the bedrock that enables all downstream analytics to be done with accuracy ([View Highlight](https://read.readwise.io/read/01hvehncjg598s4dt96gz9zh6y)) [[Data team roles|Data team roles]] >  ([View Highlight](https://read.readwise.io/read/01hvehr518tyj9eddg44rp8x8g)) > Work is distributed among data teams in a variety of ways—by function (data engineering, data science, etc.), by business area (marketing, sales, finance, etc.), by project, or by a hybrid approach. > 1. Hybrid: both business area and function 36% > 2. Function (e.g. data engineering, data science, business intelligence) 26% > 3. Project-based 17% ([View Highlight](https://read.readwise.io/read/01hvehsyfq94w48v16st7p9raw)) >  ([View Highlight](https://read.readwise.io/read/01hvehtptdjm52kbsabn81wjz1)) >  ([View Highlight](https://read.readwise.io/read/01hvehtwm6532ebngjvgvr0jsd))