The document outlines the data science hierarchy of needs, starting from real-world data collection and raw data storage, advancing through data cleaning and structured storage. It emphasizes various analytics stages—descriptive, diagnostic, predictive, and prescriptive—highlighting their respective functions such as reporting and modeling. The hierarchy culminates in advanced methodologies like machine learning and automated systems for optimized decision-making.

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