sf-agent-framework/sf-core/checklists/data-quality-checklist.md

AI Agent Orchestration Framework for Salesforce Development - Two-phase architecture with 70% context reduction

# Data Quality Checklist

This comprehensive checklist ensures data quality standards are met across all
aspects of Salesforce data management, from initial import to ongoing
maintenance.

## Data Profiling

### Level 1: Basic Data Analysis

- [ ] Record counts documented for all objects
- [ ] Field utilization rates calculated
- [ ] Null value percentages identified
- [ ] Data type consistency verified
- [ ] Character encoding issues identified

### Level 2: Data Patterns

- [ ] Common data patterns identified
- [ ] Outliers and anomalies documented
- [ ] Format consistency analyzed
- [ ] Value distribution reviewed
- [ ] Data clustering patterns noted

### Level 3: Relationship Analysis

- [ ] Parent-child relationships validated
- [ ] Orphaned records identified
- [ ] Circular references checked
- [ ] Relationship cardinality verified
- [ ] Junction object integrity confirmed

### Level 4: Historical Analysis

- [ ] Data aging patterns reviewed
- [ ] Growth trends analyzed
- [ ] Seasonal variations identified
- [ ] Archive requirements defined
- [ ] Retention compliance verified

## Data Completeness

### Level 1: Required Fields

- [ ] All required fields populated
- [ ] Business-critical fields filled
- [ ] Default values appropriate
- [ ] Conditional requirements met
- [ ] Dependent field logic validated

### Level 2: Business Rules

- [ ] Mandatory relationships established
- [ ] Cross-object dependencies satisfied
- [ ] Business logic requirements met
- [ ] Process dependencies fulfilled
- [ ] Workflow requirements completed

### Level 3: Reference Data

- [ ] Picklist values standardized
- [ ] Lookup relationships valid
- [ ] Master data references correct
- [ ] External ID mappings complete
- [ ] Global picklist values aligned

## Data Accuracy

### Level 1: Format Validation

- [ ] Email formats validated
- [ ] Phone number formats standardized
- [ ] Date formats consistent
- [ ] Currency formats correct
- [ ] Number precision appropriate

### Level 2: Business Validation

- [ ] Address data verified
- [ ] Geographic data accurate
- [ ] Product information correct
- [ ] Pricing data validated
- [ ] Category assignments accurate

### Level 3: Calculated Fields

- [ ] Formula fields calculating correctly
- [ ] Roll-up summaries accurate
- [ ] Cross-object formulas valid
- [ ] Currency conversions correct
- [ ] Date calculations accurate

### Level 4: External Validation

- [ ] Third-party data verification completed
- [ ] Address standardization performed
- [ ] Email deliverability checked
- [ ] Phone number validation done
- [ ] Business registry verification completed

## Data Consistency

### Level 1: Standardization

- [ ] Naming conventions applied
- [ ] Abbreviations standardized
- [ ] Case consistency enforced
- [ ] Special characters handled uniformly
- [ ] Spacing normalized

### Level 2: Cross-Object Consistency

- [ ] Related data synchronized
- [ ] Denormalized data aligned
- [ ] Status values consistent
- [ ] Timestamps synchronized
- [ ] User references consistent

### Level 3: System Integration

- [ ] External system data matched
- [ ] Integration keys aligned
- [ ] Bi-directional sync verified
- [ ] Master system of record identified
- [ ] Conflict resolution rules applied

## Data Uniqueness

### Level 1: Duplicate Detection

- [ ] Duplicate rules configured
- [ ] Matching rules optimized
- [ ] Fuzzy matching implemented
- [ ] Cross-object duplicates identified
- [ ] Merge candidates identified

### Level 2: Duplicate Prevention

- [ ] Real-time duplicate blocking enabled
- [ ] Import duplicate handling configured
- [ ] API duplicate prevention active
- [ ] User training on duplicates completed
- [ ] Duplicate metrics tracked

### Level 3: Master Data Management

- [ ] Golden record strategy defined
- [ ] Survivorship rules established
- [ ] Merge procedures documented
- [ ] Data stewardship assigned
- [ ] MDM processes implemented

## Data Timeliness

### Level 1: Currency of Data

- [ ] Last modified dates reviewed
- [ ] Stale data identified
- [ ] Update frequency analyzed
- [ ] Real-time requirements met
- [ ] Batch timing optimized

### Level 2: Data Refresh

- [ ] Refresh schedules defined
- [ ] Integration latency acceptable
- [ ] Cache invalidation working
- [ ] Time-sensitive data current
- [ ] Historical snapshots maintained

### Level 3: Data Lifecycle

- [ ] Creation dates accurate
- [ ] Audit trail complete
- [ ] Version history maintained
- [ ] Archive schedule implemented
- [ ] Purge procedures defined

## Data Security & Privacy

### Level 1: Access Control

- [ ] Field-level security appropriate
- [ ] Record access verified
- [ ] Sharing rules validated
- [ ] Public group access reviewed
- [ ] Role hierarchy impact assessed

### Level 2: Data Classification

- [ ] Sensitive data identified
- [ ] PII fields marked
- [ ] Encryption requirements met
- [ ] Masking rules applied
- [ ] Classification labels assigned

### Level 3: Compliance

- [ ] GDPR requirements addressed
- [ ] Right to erasure implemented
- [ ] Consent tracking active
- [ ] Data portability enabled
- [ ] Retention policies enforced

## Data Governance

### Level 1: Ownership

- [ ] Data owners identified
- [ ] Stewardship assigned
- [ ] Accountability matrix created
- [ ] Escalation paths defined
- [ ] RACI documented

### Level 2: Standards

- [ ] Data standards documented
- [ ] Naming conventions defined
- [ ] Quality thresholds set
- [ ] Validation rules implemented
- [ ] Exception processes defined

### Level 3: Monitoring

- [ ] Quality dashboards created
- [ ] KPIs defined and tracked
- [ ] Alert thresholds configured
- [ ] Trend analysis performed
- [ ] Improvement plans active

## Migration & Import Quality

### Level 1: Pre-Migration

- [ ] Source data profiled
- [ ] Mapping document complete
- [ ] Transformation rules defined
- [ ] Test migrations performed
- [ ] Rollback plan prepared

### Level 2: Migration Execution

- [ ] Data extracted successfully
- [ ] Transformations applied correctly
- [ ] Load process validated
- [ ] Error handling implemented
- [ ] Reconciliation completed

### Level 3: Post-Migration

- [ ] Record counts matched
- [ ] Data integrity verified
- [ ] Relationships preserved
- [ ] Performance acceptable
- [ ] User acceptance obtained

## Ongoing Maintenance

### Level 1: Regular Audits

- [ ] Weekly quality checks scheduled
- [ ] Monthly deep dives planned
- [ ] Quarterly reviews conducted
- [ ] Annual assessments performed
- [ ] Continuous improvement active

### Level 2: Issue Resolution

- [ ] Data issues tracked
- [ ] Root cause analysis performed
- [ ] Corrective actions implemented
- [ ] Preventive measures added
- [ ] Process improvements made

### Level 3: User Training

- [ ] Data entry training provided
- [ ] Quality standards communicated
- [ ] Best practices documented
- [ ] Feedback mechanisms active
- [ ] Recognition program implemented

## Reporting & Analytics

### Level 1: Data Quality Reports

- [ ] Quality scorecards created
- [ ] Trend reports available
- [ ] Exception reports configured
- [ ] Executive dashboards live
- [ ] Drill-down capability enabled

### Level 2: Quality Metrics

- [ ] Completeness percentage tracked
- [ ] Accuracy rates measured
- [ ] Timeliness metrics captured
- [ ] Consistency scores calculated
- [ ] Uniqueness ratios monitored

### Level 3: Business Impact

- [ ] Quality impact on operations assessed
- [ ] Cost of poor quality calculated
- [ ] Revenue impact analyzed
- [ ] Customer satisfaction correlation
- [ ] Compliance risk evaluated

## Tools & Automation

### Level 1: Quality Tools

- [ ] Data quality tools evaluated
- [ ] Cleansing tools implemented
- [ ] Monitoring tools configured
- [ ] Profiling tools utilized
- [ ] Matching tools optimized

### Level 2: Automation

- [ ] Automated quality checks running
- [ ] Scheduled validations active
- [ ] Real-time monitoring enabled
- [ ] Alert automation configured
- [ ] Self-healing processes implemented

## Sign-off & Certification

### Data Quality Certification

- [ ] Technical validation complete
- [ ] Business validation passed
- [ ] Compliance requirements met
- [ ] Performance standards achieved
- [ ] Quality certification granted

### Continuous Improvement

- [ ] Lessons learned documented
- [ ] Improvement opportunities identified
- [ ] Action plans created
- [ ] Resources allocated
- [ ] Success metrics defined