Define metrics, model source data, validate joins, show drivers and uncertainty, and publish an analytics dashboard that supports defensible decisions.
Leaders, analysts, and operating teams that need consistent performance analysis across customers, products, channels, and time.
- A metric catalogue with calculations and owners
- A reviewed dataset at a clear grain
- Charts that expose drivers instead of hiding them
State who will use the dashboard, what decision they face, and the time horizon. A growth review, a margin investigation, and a service-quality review require different metrics and grains. The decision sets the scope.
For every metric record the formula, unit, numerator, denominator, time window, attribution rule, exclusions, owner, and source tables. Distinguish event date from processing date and choose the business time zone.
Choose whether each row represents a day, customer, order, subscription, or event. Test keys before joining and reconcile totals to a trusted source. Check missing values, duplicates, late-arriving data, and segment coverage.
A current value needs a target, prior period, forecast, or normal range. Let users break a movement down by meaningful dimensions such as segment, product, or channel. Avoid implying causation from a simple correlation or a small sample.
Use lines for trends, bars for category comparison, scatter plots for relationships, and tables when precise lookup matters. Label units and denominators. Start axes honestly and make group names visible. Decoration should not compete with the data.
Show refresh time, coverage, known exclusions, and material caveats. Monitor pipeline freshness and reconciliation checks. When a source is partial, mark the affected metric instead of silently carrying the last good value forward.
An operational dashboard helps someone act on current records. An analytics dashboard compares patterns and drivers across time or segments to support a decision.
Reconcile totals, test join keys and grain, sample underlying rows, compare edge cases, review metric definitions, and confirm filters behave as labelled.
Use only the charts needed to answer the page’s decision question. A clear sequence of four to eight views is often more useful than a dense wall of charts.
Show refresh time, source coverage, definitions, significant exclusions, sample-size limits, and known pipeline issues that could change interpretation.