The inefficiency on the way to data science

Data Science teams today requires a lot of back-on-forth communication effort to answer a single data question from business requirements.

ㅤ	Business Analysts (NT)	Data Analysts (Somewhat NT)	Data Scientists	Machine Learning/Data Engineer
(1) Collect data E.g: Connect from db source, crawl from public, import from CSV	Heavily depends on MLE for engineering	Somewhat depends on MLE for engineering	Somewhat depends on MLE for engineering	DIY
(2) Preprocess data E.g: Remove outliers, Remove stopwords	Heavily depends on MLE for engineering	Somewhat depends on MLE/DS for engineering	DIY	Somewhat depends on BA/DS for business requirements
(3.1) Extract low level insights using low-order stats E.g: mean, avg	Can DIY using excel Somewhat depends on MLE for engineering	DIY	DIY	DIY
(3.2) Extract high-level insights using high-order stats E.g: sentiment, clustering	Heavily depends on MLE/DS for engineering/ML models	Somewhat depends on requirements from BA	Somewhat depends on requirements from DA/BA	Heavily depends on requirements from DA/BA
(4) Report E.g: Charts, interactive visualization	DIY using presentation or BI tools Heavily depends on DS/MLE for complex & interactive chart	DIY using presentation or BI tools Somewhat depends on DS/MLE for complex & interactive chart	DIY everything Somewhat depends on requirements from DA/BA	Not necessary

What do you think? Is there anything else that produce these types of friction:

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