Data Quality Management

Using ADM for Policy Creation

Workflow Best Practices

Before Starting

• Review the table schema.

• Understand applicable business rules.

• Identify critical data elements.

• Define acceptable thresholds.

• Plan your notification and escalation strategy.

During Creation

• Use clear, descriptive language.

• Provide examples where possible.

• Review AI-generated rules carefully.

• Test rules before deployment.

• Add a business explanation for each rule.

After Creation

• Verify that the policy appears correctly in ADOC.

• Run an initial execution to validate behavior.

• Review results and investigate anomalies.

• Adjust thresholds or logic if needed.

• Document the purpose and owner of the policy.

Example Policy Creation Workflow

Step 1: Initial Request

“Create a data quality policy for the customer_email table to ensure:

• Email addresses are valid

• No duplicates exist

• All records contain email values

• Email domain is from an approved list”

Step 2: Review the Generated Policy

• Validate each rule’s logic.

• Adjust thresholds (e.g., 95% → 98%).

• Add custom validation logic if required.

• Include a business rationale for transparency.

Step 3: Test and Deploy

• Save the policy configuration.

• Run the policy manually.

• Review the first execution results.

• Fine-tune rules and thresholds as necessary.

Policy Validation

Testing Generated Policies

Before Deployment

• Review the logic behind each rule.

• Validate threshold appropriateness.

• Check generated SQL syntax.

• Test the policy on sample data.

• Confirm notification and alert configurations.

Validation Checklist

• Rule names are descriptive

• Business explanations are clear

• Thresholds reflect requirements

• SQL syntax is valid (if applicable)

• Assets are correctly mapped

• Scheduling is configured properly

• Notifications and severity levels are set

Common Issues to Check

Threshold Problems

Rule Logic Issues

Performance Considerations

• Avoid complex joins in validation SQL.

• Use indexed columns where possible.

• Apply sampling for large datasets.

• Set realistic timeouts for long-running validations.

Understanding Policy Scores

What Is a Policy Score?

A policy score measures the overall health of data quality.

Formula:

Policy Score = (Passed Rules ÷ Total Rules) × 100

Score Ranges

Interpreting Scores

High Scores (95–100%)

• Data meets established standards.

• Minor deviations are acceptable.

• Continue monitoring and review thresholds periodically.

Medium Scores (85–94%)

• Some rules fail regularly.

• Investigate root causes and pipeline logic.

• Adjust thresholds or improve upstream data quality.

Low Scores (< 85%)

• Significant quality degradation.

• Requires immediate investigation and remediation.

• Review pipelines, validation logic, and data sources.

Example Trend Analysis

Trend Insights

• A declining score indicates new issues emerging.

• Sudden drops suggest pipeline or ingestion failures.

• Gradual declines point to schema drift or process regression.

• Stable trends confirm data reliability.

Example ADM Interaction

You: “Why did the customer policy score drop to 85%?” ADM: Analysis of Decline:

1. Email validation rule pass rate dropped (15% → 8%)

   • Cause: New data source with inconsistent format.

2. Completeness check fell (100% → 92%)

   • Cause: Missing phone numbers in latest records.

3. Uniqueness rule remained stable (100%).

Recommendations:

• Add email normalization to ETL.

• Enforce phone number collection or adjust rule priority.

• Monitor for three days to confirm stability.

Troubleshooting Policy Issues

1. Policy Not Executing

Symptoms

• No execution history

• Status shows “Not Scheduled”

• No output results

Solutions

• Confirm the schedule is enabled.

• Check execution permissions.

• Verify notification group configuration.

• Confirm access to the target asset.

ADM Query:

“Why hasn’t the customer_validation policy executed today?”

2. All Rules Failing

Symptoms

• All rules show 0% pass rate.

• Policy score = 0%.

• Error messages in execution logs.

Solutions

• Check database connectivity.

• Verify table and column references.

• Review SQL syntax in custom rules.

• Confirm user or service permissions.

ADM Query:

“Analyze the failure pattern for customer_validation policy and identify common causes.”

3. False Positives

Symptoms

• Rules fail though data looks correct.

• Inconsistent results between runs.

• Thresholds appear too strict.

Solutions

• Revisit rule logic and thresholds.

• Add exception handling conditions.

• Refine validation criteria for special cases.

ADM Query:

“Show me records failing the email validation rule to verify whether they’re actually invalid.”

4. Performance Issues

Symptoms

• Policy execution takes excessive time.

• Timeout or resource exhaustion errors.

Solutions

• Optimize SQL and reduce joins.

• Use sample-based validation for large tables.

• Add indexes to frequently validated columns.

• Break the policy into smaller, targeted ones.

ADM Query:

“Suggest optimizations for the customer_validation policy that’s taking over two hours to execute.”