codexkit-data-quality-auditor

name	codexkit-data-quality-auditor
description	Audit data quality across six DAMA DMBOK dimensions — Completeness, Accuracy, Consistency, Timeliness, Validity, Uniqueness. Produces a scored data quality report with issue log and remediation plan. Use when profiling datasets, onboarding new data sources, or building data quality gates.
version	1.0.0
category	verification

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