agentic-data-stack-community/data-core/tasks/create-data-contract.md

AI Agentic Data Stack Framework - Community Edition. Open source data engineering framework with 4 core agents, essential templates, and 3-dimensional quality validation.

# Task: Create Data Contract

## Overview
Creates a comprehensive, interactive data contract that serves as the foundational agreement between data producers and consumers. This task implements enterprise best practices for data governance, quality standards, and stakeholder collaboration.

## Prerequisites
- Business requirements or problem statement
- Stakeholder identification and contact information
- Initial data source information (if available)
- Compliance and regulatory context

## Dependencies
- Templates: `interactive-data-contract-tmpl.yaml`
- Checklists: `data-contract-validation.md`
- Tasks: `advanced-data-elicitation.md`

## Steps

### 1. **Stakeholder Analysis and Engagement**
   - Identify all data stakeholders (owners, stewards, consumers, governance)
   - Map stakeholder roles, responsibilities, and approval levels
   - Set up multi-stakeholder collaboration channels
   - **Validation**: Stakeholder matrix complete with contact information and roles

### 2. **Business Context Elicitation**
   - Define business purpose and value proposition
   - Identify success metrics and measurement criteria
   - Map data requirements to business outcomes
   - Document regulatory and compliance requirements
   - **Quality Check**: Business case clearly articulates measurable value

### 3. **Data Requirements Analysis**
   - Catalog source systems and data availability
   - Define data freshness and update frequency requirements
   - Specify data volume and scalability needs
   - Document data lineage and dependencies
   - **Validation**: Technical feasibility confirmed with Data Architect

### 4. **Schema and Quality Framework Design**
   - Define complete data schema with field specifications
   - Establish quality rules across all quality dimensions:
     - Completeness (required fields, null rate thresholds)
     - Accuracy (format validation, range checks)
     - Consistency (cross-system validation, referential integrity)
     - Validity (data type validation, business rules)
     - Uniqueness (duplicate handling, constraint definition)
     - Timeliness (freshness requirements, SLA targets)
   - **Quality Check**: Quality framework covers all six dimensions with measurable thresholds

### 5. **Governance and Compliance Integration**
   - Define access controls and security requirements
   - Document privacy and data protection measures
   - Establish regulatory compliance framework
   - Create audit trail and change management procedures
   - **Validation**: Governance requirements reviewed and approved by Data Governance Owner

### 6. **Service Level Agreement Definition**
   - Specify availability, performance, and quality SLAs
   - Define monitoring and alerting requirements
   - Establish incident response procedures
   - Document escalation and resolution processes
   - **Quality Check**: SLAs are realistic and measurable

### 7. **Interactive Validation and Approval**
   - Conduct multi-stakeholder review sessions
   - Implement progressive disclosure for complex sections
   - Collect evidence for all requirements and decisions
   - Obtain formal approvals from all required stakeholders
   - **Final Validation**: All stakeholders have provided digital approval

## Interactive Features

### Progressive Disclosure
- **Basic Mode**: Essential sections for simple data contracts
- **Advanced Mode**: Comprehensive sections for complex enterprise data
- **Expert Mode**: Full governance and compliance framework

### Multi-Agent Collaboration
- **Data Architect**: Technical feasibility validation
- **Data Quality Engineer**: Quality framework review
- **Data Governance Owner**: Compliance and governance validation
- **Business Stakeholders**: Business value and requirements approval

### Real-Time Validation
- **Schema Validation**: Real-time field validation against data standards
- **Quality Rule Testing**: Automated feasibility checking of quality thresholds
- **Compliance Checking**: Automated regulatory compliance validation
- **Business Impact Scoring**: Dynamic calculation of business value metrics

## Outputs

### Primary Deliverable
- **Interactive Data Contract** (`interactive-data-contract.md`)
  - Complete specification following template structure
  - All sections validated and approved
  - Evidence collection documentation included
  - Digital stakeholder approvals recorded

### Supporting Documents
- **Stakeholder Matrix** - Roles, responsibilities, and contact information
- **Quality Framework Specification** - Detailed quality rules and thresholds
- **Governance Policy References** - Applicable policies and compliance requirements
- **Evidence Collection Log** - Supporting documentation and validation evidence

## Success Criteria

### Quality Gates
- **Business Alignment Score**: ≥ 90/100
- **Technical Feasibility Score**: ≥ 85/100
- **Quality Framework Completeness**: ≥ 95/100
- **Stakeholder Approval Score**: ≥ 95/100
- **Compliance Validation Score**: ≥ 95/100

### Stakeholder Validation
- [ ] Business Owner approval with value confirmation
- [ ] Data Architect technical feasibility sign-off
- [ ] Data Quality Engineer quality framework approval
- [ ] Data Governance Owner compliance validation
- [ ] All consumer representatives acceptance

### Evidence Requirements
- Documented business case with quantified value
- Technical architecture validation
- Quality threshold feasibility analysis
- Compliance gap analysis and remediation plan
- Stakeholder consensus documentation

## Validation Framework

### Interactive Quality Scoring
- **Real-time calculation** of contract completeness percentage
- **Dynamic quality metrics** based on framework complexity
- **Stakeholder engagement scoring** measuring collaboration effectiveness
- **Evidence quality assessment** ensuring validation rigor

### Multi-Stage Validation
1. **Draft Validation**: Internal consistency and completeness check
2. **Technical Validation**: Architecture and feasibility review
3. **Business Validation**: Value proposition and requirements alignment
4. **Governance Validation**: Compliance and policy adherence
5. **Final Approval**: Multi-stakeholder consensus and sign-off

### Continuous Improvement
- Track contract effectiveness post-implementation
- Collect feedback for template and process improvement
- Monitor contract adherence and adaptation needs
- Update best practices based on lessons learned

## Risk Mitigation

### Common Pitfalls
- **Scope Creep**: Use progressive disclosure to manage complexity
- **Stakeholder Misalignment**: Implement structured consensus-building process
- **Technical Infeasibility**: Early architect involvement and validation
- **Quality Threshold Issues**: Automated feasibility checking and evidence collection

### Quality Assurance
- Peer review of all contract sections
- Technical validation before stakeholder approval
- Business impact verification with quantified metrics
- Compliance review for all regulatory requirements

## Notes
This task is foundational to the entire framework - invest time in getting it right. A well-crafted data contract prevents issues downstream and enables effective multi-agent collaboration throughout the project lifecycle.