sf-agent-framework/sf-core/tasks/data-assessment.md

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

# Data Assessment

## Purpose

Conduct comprehensive assessment of data quality, volume, and complexity to
inform migration strategies and data architecture decisions.

## Instructions

1. **Data Inventory and Profiling**
   - Catalog all data sources and systems
   - Document data entities and relationships
   - Analyze data volumes and growth rates
   - Profile data quality metrics
   - Identify master data sources

2. **Data Quality Analysis**
   - Assess completeness of required fields
   - Validate data accuracy and consistency
   - Check for duplicate records
   - Review data standardization
   - Evaluate referential integrity

3. **Data Complexity Assessment**
   - Map data relationships and dependencies
   - Identify complex hierarchies
   - Document business rules and validations
   - Assess transformation requirements
   - Review historical data needs

4. **Volume and Performance Analysis**
   - Calculate current data volumes
   - Project future growth rates
   - Identify large data volume (LDV) scenarios
   - Assess archival requirements
   - Evaluate query performance impacts

5. **Integration Dependencies**
   - Map system integrations
   - Document data flow patterns
   - Identify real-time requirements
   - Assess API dependencies
   - Review batch processing needs

6. **Compliance and Security**
   - Classify data sensitivity levels
   - Identify PII and regulated data
   - Document retention requirements
   - Assess encryption needs
   - Review access control requirements

## Input Requirements

- Source system documentation
- Data dictionaries
- Current data volumes
- Integration specifications
- Business process documentation
- Compliance requirements

## Output Format

- Data Assessment Report including:
  - Executive summary
  - Data quality scorecard
  - Volume analysis and projections
  - Complexity assessment matrix
  - Risk identification
  - Recommendations
  - Migration approach options

## Assessment Metrics

- **Quality Dimensions**
  - Completeness: % of populated required fields
  - Accuracy: % of correct values
  - Uniqueness: % of duplicate records
  - Consistency: % matching business rules
  - Timeliness: Data currency metrics

## Best Practices

- Use automated profiling tools
- Sample statistically significant data
- Involve business stakeholders
- Document assumptions clearly
- Plan for iterative assessment
- Consider performance early
- Address quality at source