Skip to content

ACCA Strategic Professional · Advanced Performance Management · Data science and analytics

Brelmont Logistics reports a dashboard KPI of delivery on-time rate. An analyst finds that depot managers can overwrite delivery timestamps, and 12% of records contain a delivery time earlier than the dispatch time. Which data quality dimension is most clearly failing?

Accuracy and validity are failing. Records showing delivery before dispatch are impossible values, and manual overwriting of timestamps allows errors or manipulation. The problem is not missing data, slow refresh or access, so completeness, timeliness and accessibility are not the main dimensions affected.

  1. ATimeliness, because the dashboard is refreshed too slowly
  2. BAccuracy and validity, because recorded values are wrong or impossibleCorrect
  3. CCompleteness, because some depots submit no records at all
  4. DAccessibility, because managers cannot view the dashboard

Explanation

Delivery times earlier than dispatch times are impossible values, which breaches validity and accuracy. Nothing in the scenario says data is missing (completeness), late (timeliness) or inaccessible. Allowing manual overwrites is a control weakness that causes the errors.

Did you get it right without looking?

One question tells you little. A timed set on Data science and analytics shows your real accuracy, how long you take and where you lose marks.

Practise Data science and analyticsFree diagnostic mock

More Data science and analytics questions