Glossary
Descriptive analytics
Analytics that summarizes what has already happened in the data, without explaining why or predicting what comes next.
Also called: descriptive statistics, reporting analytics
Descriptive analytics answers "what happened." It aggregates raw data—transactions, events, survey responses—into summaries like totals, averages, rates, and trends that describe past or current performance. It is the foundation layer of the analytics maturity model: before an organization can diagnose causes or predict outcomes, it needs a reliable picture of its own history.
In practice, descriptive analytics is delivered through dashboards and reports built on business intelligence tools, tracking KPIs such as revenue, conversion rate, or churn over time. Techniques are largely arithmetic: counts, sums, averages, percentages, and simple visualizations rather than statistical modeling. A well-built dashboard also shows the definitions and time windows underlying each number so results aren't compared apples-to-oranges.
Its value is that it gives every other analytics activity a shared, trustworthy baseline; teams can't diagnose or predict what they haven't first measured consistently. The common pitfall is treating descriptive output as an explanation—flat revenue and a spike in the same chart don't say why. Descriptive analytics also degrades quietly when data quality issues creep into source systems, since a clean-looking chart can mask flawed or duplicated inputs.
Last reviewed September 19, 2026