The data science hierarchy of needs relates to the necessary steps of increasing data complexity and insight. This version of the data science hierarchy of needs is inspired by others before it and borrows from the Gartner's analytic maturity model.

1. The Data Science
Hierarchy of Needs
Real World
Data Collection + Raw Data Storage
Data Cleaning + Structured Data Storage
Descriptive Analytics
Diagnostic Analytics
Predictive Modeling
Prescriptive
Optimization
What happened?
standard reports, KPIs,
score cards, ad-hoc reports
Why did it happen?
self-service analytics, segments,
aggregates, strategy, A/B Tests
What will happen?
modeling, experimentation,
predictions
What is the best thing to happen?
machine learning, deep learning,
automated systems