> Growth marketers wanted more autonomy to manage their data, but were not necessarily willing to put the extra effort into: (i) understanding the structure of

> Growth marketers wanted more autonomy to manage their data, but were not necessarily willing to put the extra effort into: (i) understanding the structure of a database (dozens of tables), (ii) practice lightweight data modeling. This highlights the primary issue for most tools in the data analytics space: poor data modelling, leading to the exposed data being "too difficult to use in Business Tools". I've gathered some thoughts [1][2], but it boils down to the following: What users experience as the problem, is probably not the cause of the problem. Most builders respond to the user's poor experience at the interface as sufficient signal to start building solutions that improve that interface, while the underlying cause is deeper within the system (and the primary method for improving that underlying, currently, is data modelling). Here [3] is a snippet towards an example of Facebook building a solution that addresses the complexity issue. Also common for ex-FAANG engineers to their surprise, discover that none of this type of infra exists outside of FAANG. [1] https://news.ycombinator.com/item?id=33804938 https://news.ycombinator.com/item?id=33804938 [2] https://twitter.com/rdrn_/status/1604528546784870402 https://twitter.com/rdrn_/status/1604528546784870402 [3] https://seattledataguy.substack.com/p/a-zero-etl-future#:~:text=Integrating%20Data%20%2D%20One,the%20analytical%20side https://seattledataguy.substack.com/p/a-zero-etl-future#:~:t....