Data Quality Auditor

When to Use

• Before building analytics or dashboards on a new data source
• When data issues cause downstream report errors
• During data migration or system integration
• When establishing data quality monitoring rules

Procedure

Step 1 — Scope & Profiling

Identify the dataset and context:

• Table/file name, row count, column count
• Business purpose: what decisions does this data support?
• Data owner: who is accountable?

Profile the data:

• Column types (string, numeric, date, boolean, null)
• Null rates per column
• Distinct value counts
• Min/Max/Mean for numerics
• Date range coverage

Step 2 — Six-Dimension Assessment

Score each dimension 1–5:

Dimension	Question	Score Method
Completeness	Are all required fields populated?	% non-null for required fields
Accuracy	Do values represent reality?	Sample validation against source
Consistency	Do related fields agree?	Cross-field rule checks
Timeliness	Is data current enough for its purpose?	Max staleness vs requirement
Validity	Do values conform to allowed ranges/formats?	Format + range validation
Uniqueness	Are there unwanted duplicates?	Duplicate rate on key columns

Step 3 — Issue Log

For each issue found:

#	Dimension	Column(s)	Issue Description	Severity	Records Affected	Example
1	Completeness	email	12% null in required field	High	1,200	row 45: null

Severity: Critical (blocks use) / High (degrades quality) / Medium (cosmetic) / Low (nice-to-fix)

Step 4 — Remediation Plan

For each Critical/High issue:

Issue	Root Cause	Fix	Owner	Deadline
Missing emails	Optional in old form	Backfill from CRM	Data Eng	Sprint 4

Step 5 — Monitoring Rules

Define ongoing quality checks:

• Automated checks to run on each data load
• Alerting thresholds (if quality drops below X%, alert owner)
• Review cadence (weekly, monthly)

Inputs

Input	Required	Format
Dataset or table	Yes	Name + access method
Business context	Yes	What this data is used for
Quality rules / expectations	Recommended	Any existing rules or SLAs
Sample size	Recommended	Full scan or % sample

Output

## Data Quality Report — [Dataset Name]

### Summary
- **Dataset:** orders_2024
- **Rows:** 145,000 | **Columns:** 23
- **Overall Quality Score:** 3.8 / 5.0

### Dimension Scores

| Dimension | Score | Key Finding |
|-----------|-------|-------------|
| Completeness | 4/5 | 2 columns with >5% nulls |
| Accuracy | 3/5 | 3% of prices don't match catalog |
| Consistency | 4/5 | state vs zip mismatch in 1.2% |
| Timeliness | 5/5 | Data refreshed within SLA |
| Validity | 3/5 | 8% of emails fail format check |
| Uniqueness | 4/5 | 0.5% duplicate order IDs |

### Critical Issues
[Issue log table]

### Remediation Plan
[Fix plan with owners and deadlines]

### Monitoring Rules
[Automated checks to add]

Definition of Done

• All six dimensions assessed and scored
• Issue log with severity and affected records
• Remediation plan for Critical/High issues
• Monitoring rules defined for ongoing quality

Quality Criteria

• Every finding is tied to a specific evidence source (log, test, metric)
• Pass/fail criteria are binary and measurable — no subjective judgments
• Severity levels are assigned with clear thresholds
• Remediation steps are provided for all critical and high findings

Verification (4C)

Check	Question
Correctness	Are all pass/fail criteria applied against the correct standard or rule?
Completeness	Were all required dimensions or checklist items evaluated?
Context-fit	Does the verification scope match the actual risk level of the deliverable?
Consequence	If this passed verification but had a hidden flaw, what is the worst-case impact?

Edge Cases

• Incomplete data for full assessment — Document which checks were limited and flag for re-verification when data becomes available.
• Ambiguous pass/fail criteria — Request clarification from the standard owner before scoring. Mark as 'Needs Review'.
• Multiple overlapping standards — Identify the governing standard and note where others diverge.

Changelog

• v1.0.0 — Initial release