Data Governance

Logic AI provides robust data governance features to help organizations manage, protect, and derive value from their data. This guide outlines the key aspects of data governance within the platform.

Data Lifecycle Management

Collection Phase

• Data Minimization: Configure workflows to collect only necessary data
• Consent Management: Tools to track and enforce user consent preferences
• Source Tracking: Maintain records of data origins and collection methods
• Ingestion Controls: Apply validation rules to incoming data
• Documentation: Record data schemas and collection parameters

Processing Phase

• Transformation Rules: Define how data is processed and normalized
• Quality Assurance: Set up automated checks for data quality
• Processing Logs: Maintain detailed records of all data transformations
• Version Control: Track changes to data processing workflows
• Enrichment Tracking: Document how and when data is enriched

Storage Phase

• Classification System: Categorize data based on sensitivity and type
• Retention Policies: Enforce time-based data retention requirements
• Archiving Rules: Specify conditions for data archiving
• Storage Optimization: Balance performance and cost for different data types
• Backup Protocols: Define data backup frequency and scope

Usage Phase

• Access Controls: Specify who can access different data categories
• Usage Tracking: Monitor how data is used across workflows
• Purpose Limitation: Enforce restrictions on data usage
• Attribution: Maintain linkage between outputs and source data
• Audit Trails: Record all data access and usage events

Deletion Phase

• Deletion Triggers: Define conditions that initiate data deletion
• Verification Process: Confirm data has been properly deleted
• Deletion Certificates: Generate records of completed deletions
• Retention Exceptions: Manage legal holds and other retention requirements
• Anonymization Options: Convert data to anonymized form as alternative to deletion

Data Classification Framework

Implement a consistent data classification system:

graph TD
    A[Data Intake] --> B{Classification}
    B -->|Public| C[No Restrictions]
    B -->|Internal| D[Limited Access]
    B -->|Confidential| E[Strict Controls]
    B -->|Restricted| F[Highest Protection]
    C --> G[Apply Governance Rules]
    D --> G
    E --> G
    F --> G

Classification Levels

• Public: Information safe for public disclosure
• Internal: Information for use within the organization only
• Confidential: Sensitive information requiring controlled access
• Restricted: Highly sensitive information with stringent protections

Classification Metadata

• Sensitivity Level: The assigned classification level
• Data Owner: Person or department responsible for the data
• Review Date: When classification should be reviewed
• Legal Basis: Legal justification for data processing
• Compliance Tags: Relevant compliance requirements (GDPR, HIPAA, etc.)

Regulatory Compliance

Compliance Frameworks

• GDPR Compliance: Features for European data protection requirements
• CCPA/CPRA Compliance: California privacy law requirements
• HIPAA Compliance: Healthcare data protection features
• SOX Compliance: Financial reporting controls
• Industry-Specific: Tools for sector-specific regulations

Compliance Tools

• Data Subject Requests: Process for responding to individual rights requests
• Compliance Reporting: Generate reports for regulatory submissions
• Cross-Border Controls: Manage international data transfer restrictions
• Compliance Monitoring: Track compliance status across workflows
• Documentation Generation: Create compliance documentation automatically

Data Quality Management

Quality Dimensions

• Accuracy: Correctness of data values
• Completeness: All required data is present
• Consistency: Data is consistent across the system
• Timeliness: Data is up-to-date
• Validity: Data adheres to defined formats and rules

Quality Monitoring

• Data Profiling: Automated analysis of data properties
• Quality Scoring: Assign scores based on quality dimensions
• Exception Handling: Process for managing quality exceptions
• Remediation Workflows: Automated correction of quality issues
• Quality Dashboards: Visualize data quality metrics

Data Access Controls

Access Model

• Role-Based Access: Permissions based on job functions
• Attribute-Based Access: Permissions based on data attributes
• Purpose-Based Access: Access tied to specific business purposes
• Time-Limited Access: Temporary access permissions
• Contextual Access: Access based on context (location, device, etc.)

Implementation Tools

• Access Policies: Centralized definition of access rules
• Authorization Workflows: Approval processes for access requests
• Masking and Tokenization: Hide sensitive data elements
• Row/Column Filtering: Limit access to specific data subsets
• Access Certification: Periodic review of access rights

Data Lineage and Traceability

Lineage Tracking

• Visual Lineage Maps: Graphical representation of data flows
• Transformation Tracking: Record all changes to data
• Dependency Analysis: Identify relationships between data assets
• Impact Analysis: Assess effects of changes to data sources
• Root Cause Analysis: Trace issues back to their source

Implementation Approaches

graph LR
    A[Data Source] --> B[Ingestion]
    B --> C[Processing]
    C --> D[Storage]
    D --> E[Analysis]
    E --> F[Output]

    G[Lineage Metadata] -.-> A
    G -.-> B
    G -.-> C
    G -.-> D
    G -.-> E
    G -.-> F

Metadata Management

Metadata Types

• Technical Metadata: Schemas, data types, relationships
• Business Metadata: Business definitions, owners, purposes
• Operational Metadata: Processing statistics, quality metrics
• Administrative Metadata: Access controls, retention policies
• Reference Metadata: Lookup values, taxonomies, hierarchies

Metadata Repository

• Centralized Catalog: Single source of truth for metadata
• Search Capabilities: Find data assets based on metadata
• API Access: Programmatic access to metadata
• Version Control: Track changes to metadata over time
• Integration: Connect with external metadata systems

Data Governance Roles

Core Roles

• Chief Data Officer: Executive responsible for data strategy
• Data Governance Committee: Cross-functional oversight group
• Data Owners: Responsible for specific data domains
• Data Stewards: Day-to-day management of data quality
• Data Custodians: Technical implementation of governance

Responsibilities Matrix

Governance Implementation

Implementation Phases

1. Assessment: Evaluate current state and gaps
2. Design: Develop governance framework and policies
3. Implementation: Deploy tools and processes
4. Monitoring: Track governance effectiveness
5. Optimization: Continuously improve governance program

Success Metrics

• Policy Compliance: Adherence to governance policies
• Data Quality Scores: Improvement in quality metrics
• Risk Reduction: Decreased data-related incidents
• Efficiency Gains: Reduced time for data discovery and access
• Value Creation: Business benefits from improved data governance

Integration with Logic AI

Platform Capabilities

• Policy Automation: Enforce governance policies in workflows
• Metadata Integration: Connect with enterprise metadata systems
• Compliance Templates: Pre-built workflows for common regulations
• Governance Dashboards: Monitor governance status
• Audit Capabilities: Comprehensive traceability and audit features

Implementation Guide

graph TD
    A[Define Governance Requirements] --> B[Configure Logic AI Policies]
    B --> C[Integrate with Existing Systems]
    C --> D[Implement Monitoring]
    D --> E[Regular Governance Reviews]
    E --> F[Continuous Improvement]

Next Steps

• Develop your governance strategy using our Governance Worksheet
• Learn about Security Guidelines for protecting sensitive data
• Explore Enterprise Features for advanced governance capabilities