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Data Governance

A comprehensive framework for managing data principles, governance standards, and symbols table architecture at AIC Holdings.

Core Data Principles

Our data governance framework is built on foundational principles that ensure data quality, accessibility, security, and business value across all data science initiatives.

🎯 Data Quality First

Principle: Data quality is non-negotiable - accurate, complete, and timely data drives reliable insights

  • Accuracy: Data must be correct and validated at source
  • Completeness: Missing data patterns must be identified and addressed
  • Timeliness: Data freshness requirements must be met for each use case
  • Consistency: Data definitions and formats must be standardized across systems

🔒 Security by Design

Principle: Data security and privacy protections are embedded throughout the data lifecycle

  • Access Controls: Role-based permissions with least privilege principle
  • Data Classification: Sensitive data must be identified and protected appropriately
  • Audit Trails: All data access and modifications must be logged and traceable
  • Privacy Compliance: GDPR, CCPA, and industry regulations must be followed

📊 Business Value Alignment

Principle: Data initiatives must directly support business objectives and decision-making

  • Use Case Driven: Data collection and processing must have clear business purposes
  • ROI Measurement: Data investments must demonstrate measurable business value
  • Stakeholder Engagement: Business users must be involved in data requirement definition
  • Impact Assessment: Data quality issues must be evaluated based on business impact

🔄 Lifecycle Management

Principle: Data must be managed systematically from creation to archival

  • Data Lineage: Full traceability from source to consumption must be maintained
  • Retention Policies: Data lifecycle rules must be defined and automated
  • Version Control: Data schema and processing changes must be tracked
  • Change Management: Data modifications must follow controlled processes

🌐 Interoperability Standards

Principle: Data must be accessible and usable across systems and teams

  • Standard Formats: Common data formats and APIs must be used
  • Metadata Management: Comprehensive documentation must accompany all data assets
  • Integration Patterns: Consistent approaches for data ingestion and distribution
  • Schema Evolution: Backward-compatible data schema changes when possible

Governance Framework

Data Stewardship

  • Data Owners: Business stakeholders responsible for data accuracy and usage policies
  • Data Custodians: Technical teams responsible for data storage, processing, and security
  • Data Users: Consumers of data who must follow usage guidelines and report issues

Quality Assurance

  • Validation Rules: Automated data quality checks at ingestion and processing
  • Monitoring: Continuous monitoring of data quality metrics and alerts
  • Issue Tracking: Systematic process for reporting and resolving data quality problems
  • Quality Metrics: KPIs for data accuracy, completeness, and timeliness

Compliance & Controls

  • Data Catalog: Centralized inventory of all data assets with metadata
  • Access Management: Identity and access controls for all data systems
  • Audit Capabilities: Comprehensive logging and reporting for compliance requirements
  • Risk Assessment: Regular evaluation of data-related risks and mitigation strategies

Symbols Table Architecture

Coming Soon: Detailed framework for implementing standardized data symbols and reference architectures

Planned Components

  • Symbol Registry: Centralized catalog of data symbols, definitions, and relationships
  • Reference Data Management: Master data governance for critical business entities
  • Data Dictionary: Standardized definitions and business rules for all data elements
  • Integration Standards: APIs and protocols for symbol resolution and validation

Implementation Roadmap

Phase 1: Foundation (Current)

  • Establish core data principles and governance committee
  • Implement basic data quality monitoring and validation rules
  • Create initial data catalog and documentation standards

Phase 2: Symbols Framework (Planned)

  • Design and implement symbols table architecture
  • Deploy reference data management capabilities
  • Establish symbol resolution and validation services

Phase 3: Advanced Governance (Future)

  • Implement advanced data lineage and impact analysis
  • Deploy automated policy enforcement and compliance reporting
  • Establish data marketplace and self-service analytics capabilities

Getting Started

For Data Consumers

  1. Review Data Catalog: Understand available data assets and their quality characteristics
  2. Follow Usage Guidelines: Adhere to access controls and data handling requirements
  3. Report Issues: Use established channels for data quality problems and enhancement requests

For Data Producers

  1. Implement Quality Checks: Build validation rules into data processing pipelines
  2. Maintain Documentation: Keep data catalogs and metadata current and accurate
  3. Monitor Performance: Track data quality metrics and respond to alerts promptly

For Data Stewards

  1. Define Policies: Establish clear governance rules and business requirements
  2. Monitor Compliance: Regular audits and compliance reporting
  3. Drive Improvements: Identify opportunities for data quality and governance enhancements

Governance Committee

Data governance decisions are made collaboratively by stakeholders from business, technology, and compliance teams.

Start Small

Begin with high-impact, high-visibility data assets and expand governance coverage incrementally based on business priorities.