Data Quality Assessment
Assess data quality across completeness, accuracy, consistency, and timeliness with remediation priorities.
Your team makes decisions from a dashboard that nobody trusts. 'Is this number right?' is the most common question in every meeting. Without a formal data quality assessment, you don't know which data is reliable, which is stale, and which is outright wrong.
Who it's for: data teams implementing data governance practices, analytics managers validating data pipeline outputs, BI teams building trust in their reporting infrastructure, data engineers assessing source system data quality, organizations preparing for data-driven decision-making transformations
Example
"Assess the quality of our customer database" → Quality scorecard: completeness (87% — email field 60% empty), accuracy (3 fields with validation failures), consistency (date format issues across 4 columns), timeliness (last_updated stale for 200 records), and prioritized remediation plan
New here? 3-minute setup guide → | Already set up? Copy the template below.
# Data Quality Assessment
## Your Role
You are an expert data quality analyst. Your job is to evaluate data health and create remediation plans that improve decision-making reliability.
## Core Principles
- Assess all dimensions: completeness, accuracy, consistency, timeliness, uniqueness
- Quality standards vary by field importance
- Fix root causes (input processes), not just symptoms
- Prioritize by business decision impact
- Establish ongoing monitoring, not just one-time cleanup
## Instructions
Produce: quality scorecard by dimension, issue inventory, root cause analysis, prioritized remediation plan, and monitoring recommendations.
## Commands
- "Data quality assessment" - Full evaluation
- "Quality scorecard" - Health grades by dimension
- "Top issues by impact" - Prioritized problem list
- "Remediation plan" - Fix strategy with priorities
What This Does
Evaluates data quality across multiple dimensions — completeness, accuracy, consistency, timeliness, and uniqueness — then produces a health scorecard with prioritized remediation recommendations.
Quick Start
Step 1: Download the Template
Click Download above to get the CLAUDE.md file.
Step 2: Load Your Dataset
Place the data file(s) to assess in your working directory.
Step 3: Start Using It
claude
Say: "Assess data quality in our customer database. Check for completeness, duplicates, format inconsistencies, and stale records."
Assessment Dimensions
| Dimension | What's Checked |
|---|---|
| Completeness | Missing values, empty required fields |
| Accuracy | Invalid formats, out-of-range values |
| Consistency | Same data represented differently |
| Timeliness | Stale records, outdated information |
| Uniqueness | Duplicates and near-duplicates |
| Validity | Values that don't match business rules |
Tips
- Quality scores need context: 95% complete is great for notes fields, unacceptable for emails
- Fix root causes, not symptoms: If data keeps going bad, fix the input process
- Prioritize by business impact: Which quality issues affect revenue-impacting decisions?
- Regular monitoring: One-time cleanup is useless without ongoing quality checks
Commands
"Assess data quality in this dataset"
"Generate a data quality scorecard with grades"
"Identify the top 10 data quality issues by business impact"
"Create a remediation plan with priorities"
Troubleshooting
Too many issues found Prioritize: "Focus on fields that drive business decisions — email, revenue, dates"
Can't determine accuracy Ask: "Cross-reference against a known-good source to validate"
Issues keep recurring Say: "Analyze the data entry process — where is bad data being created?"