A dashboard is useful when it helps someone decide what to do. Start with those decisions, then choose the measures and charts. Beginning with a large collection of visualisations often creates more interpretation work.
Give every metric a definition
For each measure, record its business meaning, data source, calculation, exclusions and owner. Define the reporting period and timezone. Decide how refunds, duplicates and late-arriving records affect historical results.
For example, a registered account, an active visitor and a paying customer describe different events. They should not be combined under a generic “users” label without explanation.
Specify the operating requirements
State who will use the dashboard and which records each role can access. Describe the required refresh frequency and the maximum tolerable delay. A daily management report and an operational exception queue have different needs.
Include an obvious data-freshness indicator. If the source fails, the dashboard should show the failure rather than presenting old numbers as current.
Agree on acceptance examples
Choose a small set of source records and calculate the expected totals independently. Ask the developer to reconcile the dashboard against that set. Test filters, empty results, timezone boundaries and permission differences.
Require a metric dictionary and a handover explaining how to add a measure without changing existing definitions. Name the person responsible for approving future changes.
For website reporting, Google's explanation of Analytics key events helps distinguish meaningful events from general traffic. A data analytics project should make that distinction explicit from the first brief.