Analysis of historical data to summarize and describe what has already happened — the most common form of data analysis.
Descriptive analytics answers the question "what happened?" It uses aggregation, summarization, and visualization to turn raw data into understandable summaries: totals, averages, trends, distributions, and comparisons. Most reports, dashboards, and data visualizations are descriptive analytics.
Descriptive analytics is the foundation for more advanced analysis. Before you can predict what will happen (predictive analytics) or prescribe what to do (prescriptive analytics), you need to understand what has already happened.
Example: A city analyzes three years of 311 service request data to produce a descriptive report: total requests by category, average response time by neighbourhood, month-over-month trends, and the top 10 most common request types. This report describes past performance without making predictions or recommendations.