Data Analytics
Last Updated: July 29, 2026 | By Mihail Sebastian | AI Dictionary
The practice of examining, cleaning, and visualizing data to answer business questions, from describing what happened to recommending what to do next.
What is Data Analytics?
Data analytics is the practice of examining, cleaning, and visualizing data to answer business questions and support decisions. It turns raw records such as transactions, clicks, and sensor logs into findings a person can act on.
Most analytics work looks backward: what happened, where, and why. That focus on explanation rather than prediction is what distinguishes it from data science.
Types of Data Analytics
- Descriptive analytics summarizes what happened: sales by region, traffic by week, churn by segment.
- Diagnostic analytics digs into why it happened, tracing a drop in revenue to the specific product, channel, or customer group behind it.
- Predictive analytics projects historical patterns forward to forecast what comes next, borrowing methods from predictive modeling.
- Prescriptive analytics recommends what to do about it, simulating scenarios and comparing their likely outcomes.
Data Analytics vs Data Science
The practical difference: data analytics answers business questions from existing data, while data science builds predictive systems that keep answering after the analyst walks away. Analytics ends in a finding a person acts on; data science ends in a model that acts on its own.
| Criterion | Data Analytics | Data Science |
|---|---|---|
| Core question | What happened, and why? | What will happen, and can we build a system around it? |
| Typical output | Reports, dashboards, recommendations | Predictive models, algorithms, data products |
| Main methods | SQL, statistical analysis, visualization | Machine learning, statistical modeling, software engineering |
| Time orientation | Past and present | Future-facing |
Example of Data Analytics
A retail chain pulls a year of transaction records and finds that two products sell together far more than chance predicts. The finding starts as descriptive work (which pairs co-occur) and becomes diagnostic when the team checks whether a shared shelf, season, or customer segment explains it.
The chain acts on it: the products move to nearby shelves and a bundled promotion runs for a quarter. A follow-up analysis compares sales before and after to confirm the change earned its shelf space.
Related AI terms: Data Science · Data Analyst · Exploratory Data Analysis · Data Mining · Big Data · Query Processing
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Mihail Sebastian — Writes about AI governance, regulation, and the technology behind them. Placeholder bio — replace with a real credential line. About