Data analytics transforms raw data into actionable insights through systematic computational analysis of datasets, enabling organizations to make evidence-based decisions rather than relying on intuition. It encompasses a spectrum from descriptive (what happened) to diagnostic (why it happened), predictive (what might happen), and prescriptive (what should be done) approaches. At its core, data analytics balances statistical rigor with practical business context—understanding not just correlation but causation, not just averages but distributions, and recognizing that the most sophisticated analysis is worthless without clear communication to stakeholders who will act on the findings.
What This Cheat Sheet Covers
This topic spans 24 focused tables and 190 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: Analytics Types
Every analytics question falls somewhere on a ladder of ambition — from simply describing what happened, to diagnosing why, predicting what comes next, and ultimately prescribing what to do about it. Knowing which rung you're on sets the expectations for the data, the methods, and the value you can deliver.
| Type | Example | Description |
|---|---|---|
Total sales = $500KAvg order value = $75 | • Summarizes what happened using historical data • reports past performance through aggregations, trends, and KPIs. | |
Sales dropped due tocompetitor launch | Explains why something happened by drilling into data to identify root causes, correlations, and anomalies. | |
Forecast: 15% growthnext quarter | Estimates what might happen using statistical models, machine learning, and historical patterns to forecast future outcomes. |