scorecard

Data Quality Scorecard & Correction Workflow

An interactive scorecard to assess operational dataset quality across core dimensions, run or record automated checks, and submit correction requests with clear ownership and SLAs so teams can act and track resolution.

Interactive Tool

Data Quality Scorecard & Correction Workflow

Use this scorecard to assess operational dataset quality across four core dimensions and to request corrections when needed. Each dimension uses a 0–5 scale (0 = Unusable, 5 = Excellent). Calculate the Overall Score as the average of the four dimension scores. Automated checks help estimate scores—see the automated checks list below.

Scoring Rubric

  • 5 — Complete & reliable for intended use
  • 4 — Minor issues, reliable for most uses
  • 3 — Noticeable gaps; use with caution
  • 2 — Significant gaps; not recommended as primary source
  • 1 — Mostly unreliable
  • 0 — Unusable

Recommended Automated Checks

  • Null/empty value counts
  • Range and value validation
  • Format/regex checks
  • Duplicate key detection
  • Foreign-key integrity checks
  • Timeliness / lag checks
  • Simple anomaly or trend detection

When you submit a correction request, assign an owner and requested SLA. Submissions are stored with this item so teams can track progress and measure dataset health over time.

Name as used in dashboards, reports, or models (e.g., ops.orders_fact).
Primary owner responsible for dataset quality and follow-up.
Best way to reach the owner for clarifying questions.
What this dataset is primarily used for.
Select checks that are scheduled or available for this dataset.
How complete is the dataset for its intended purpose?
How often values reflect the true state?
Is the data available when needed?
Does the dataset follow agreed formats, types and definitions?
Average of the four dimension scores. Calculate externally or enter manually.
Example problems, known gaps, sample rows/values, affected dashboards/models, or other context.
If yes, complete the correction request fields below.
One-line description of the quality issue.
Steps to reproduce, sample rows/values, timestamps, dashboards or models impacted, and suggested fix.
Person or team to assign the fix to.
Number of calendar days to resolve the correction. Enter 0 for same-day.
Optional. Use ISO date format if available.
Link to related incident, ticket, dashboard, or dataset location (URL or ticket ID).
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