A tailored course, built for your situation
Production-Grade AI Data Lineage Practices for Cross-Functional Programs
Master implementation-grade data lineage for AI governance, compliance, and engineering alignment
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Siloed data practices lead to inconsistent lineage, audit delays, and compliance friction across teams
The situation this course is for
As AI initiatives scale, teams struggle to establish shared data truth. Compliance requires traceability, engineering demands precision, and business units expect clarity. Without a unified lineage practice, programs stall under coordination overhead and governance gaps.
Who this is for
Business and technology professionals in data governance, risk, compliance, engineering, IT, or program leadership roles guiding AI initiatives
Who this is not for
This is not for data scientists focused solely on modeling, or for individuals seeking introductory AI literacy content
What you walk away with
- Design and implement end-to-end data lineage architectures for AI systems
- Align lineage practices across engineering, compliance, and business functions
- Integrate lineage into CI/CD, model validation, and audit workflows
- Apply metadata standards and traceability frameworks in production environments
- Lead cross-functional lineage initiatives with governance and operational clarity
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Data Lineage
Establish core concepts, scope, and organizational value of lineage in AI systems
12 chapters in this module
- Defining data lineage in AI contexts
- Distinguishing lineage from provenance and traceability
- Business drivers for lineage adoption
- Regulatory expectations and reporting needs
- Common misconceptions and pitfalls
- Lineage as a cross-functional enabler
- Assessing organizational readiness
- Stakeholder mapping and influence paths
- Current market maturity benchmarks
- Integration with AI governance frameworks
- Case study: Early-stage lineage adoption
- Self-assessment: Lineage maturity evaluation
Module 2. Metadata Architecture for Traceability
Design metadata models that support end-to-end lineage tracking
12 chapters in this module
- Core metadata types for AI systems
- Structured vs. unstructured data tracking
- Metadata capture at ingestion points
- Model input and output labeling standards
- Versioning data and transformations
- Linking metadata to pipeline execution
- Schema change impact tracking
- Automated metadata extraction methods
- Metadata storage options and trade-offs
- Querying metadata for audit support
- Ensuring metadata consistency
- Worked example: Metadata schema design
Module 3. Data Provenance and Lineage Capture
Implement technical controls for accurate lineage capture across pipelines
12 chapters in this module
- Identifying lineage capture points
- Instrumenting ETL/ELT processes
- Tracking data transformations
- Handling joins and aggregations
- Capturing feature engineering steps
- Model training data lineage
- Inference-time data tracking
- Real-time vs batch lineage capture
- Cross-system lineage challenges
- Third-party data integration
- Validating lineage completeness
- Worked example: End-to-end capture workflow
Module 4. Cross-Functional Stakeholder Alignment
Enable collaboration between data, engineering, compliance, and business teams
12 chapters in this module
- Mapping stakeholder lineage needs
- Translating technical lineage into business terms
- Compliance reporting requirements
- Risk team expectations for auditability
- Engineering needs for debugging and reproducibility
- Product team use cases for transparency
- Establishing shared definitions
- Lineage in change management processes
- Conflict resolution in data ownership
- Cross-functional governance models
- Workshop: Aligning stakeholder requirements
- Worked example: Stakeholder needs matrix
Module 5. Integration with AI Governance Frameworks
Embed lineage into model lifecycle governance
12 chapters in this module
- Lineage in model risk management
- Connecting lineage to model validation
- Documentation for model review boards
- Version control integration
- Model rollback and lineage traceability
- Explainability and bias investigation
- Regulatory examination readiness
- Internal audit support workflows
- Third-party model oversight
- Model decommissioning and data retention
- Policy enforcement via lineage
- Worked example: Governance workflow integration
Module 6. Automation and Tooling Strategies
Select and deploy tools that scale lineage practices
12 chapters in this module
- Open-source vs commercial tool comparison
- APIs for lineage integration
- Automated lineage generation
- Custom parser development
- Tooling for unstructured data
- Cloud-native lineage solutions
- Vendor assessment criteria
- Tool interoperability challenges
- Scalability considerations
- Security and access controls
- Cost-benefit analysis
- Worked example: Tool selection matrix
Module 7. Lineage in DevOps and MLOps
Integrate lineage into CI/CD and model deployment pipelines
12 chapters in this module
- Versioning data with code
- Lineage in testing environments
- Automated lineage tagging in pipelines
- Model deployment traceability
- Canary release data tracking
- Rollback impact assessment
- Monitoring data drift with lineage
- Alerting on lineage anomalies
- Infrastructure as code integration
- Pipeline metadata logging
- End-to-end automation patterns
- Worked example: CI/CD lineage integration
Module 8. Policy and Compliance Integration
Align lineage practices with regulatory and internal policy requirements
12 chapters in this module
- GDPR and data subject rights
- CCPA and data transparency
- Financial services regulations
- Healthcare data traceability
- Internal audit frameworks
- Data retention policies
- Cross-border data flow tracking
- Consent tracking via lineage
- Policy exception handling
- Compliance reporting templates
- Regulatory examination preparation
- Worked example: Compliance gap analysis
Module 9. Operationalizing Lineage at Scale
Deploy and maintain lineage systems across large portfolios
12 chapters in this module
- Phased rollout strategies
- Pilot program design
- Change management for adoption
- Training and enablement plans
- Support model design
- Performance monitoring
- Handling lineage debt
- Managing technical debt in metadata
- Scaling metadata storage
- Cross-domain integration
- Sustaining executive sponsorship
- Worked example: 12-month rollout plan
Module 10. Advanced Lineage Patterns
Address complex scenarios in federated and hybrid environments
12 chapters in this module
- Federated data architectures
- Cross-system lineage mapping
- Hybrid cloud tracking
- Third-party vendor lineage
- Open banking and data sharing
- Blockchain-based provenance
- Event-driven architecture patterns
- Streaming data lineage
- Graph-based lineage models
- AI-generated data tracking
- Synthetic data lineage
- Worked example: Cross-cloud tracking
Module 11. Validation and Quality Assurance
Ensure lineage accuracy, completeness, and reliability
12 chapters in this module
- Defining lineage quality metrics
- Automated validation checks
- Sampling for audit verification
- Reconciliation with source systems
- Handling missing lineage data
- False positive reduction
- Accuracy testing methodologies
- Completeness scoring
- Staleness detection
- User feedback loops
- Root cause analysis for gaps
- Worked example: Validation dashboard design
Module 12. Strategic Leadership in Data Lineage
Lead enterprise-wide lineage initiatives and drive cultural change
12 chapters in this module
- Building a lineage competency center
- Talent development strategies
- Budgeting for lineage programs
- Measuring ROI and business impact
- Communicating value to leadership
- Creating lineage standards
- Vendor and partner alignment
- Industry benchmarking
- Future trends in AI traceability
- Succession planning
- Scaling best practices
- Worked example: Strategic roadmap development
How this maps to your situation
- Implementing data lineage in regulated AI deployments
- Leading cross-functional alignment on traceability standards
- Responding to audit or compliance examination needs
- Scaling AI governance with production-ready lineage infrastructure
Before vs. after
Before
Unclear ownership, inconsistent tracking, reactive responses to audits
After
Proactive lineage design, cross-functional alignment, audit-ready systems
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 45 hours of self-paced learning, designed for professionals balancing active roles.
If nothing changes
Organizations without robust data lineage face increased audit friction, longer time-to-resolution for compliance issues, and higher coordination costs as AI programs scale across teams.
How this compares to the alternatives
Unlike general AI ethics or data governance overviews, this course delivers implementation-grade practices focused specifically on data lineage, with cross-functional integration patterns not covered in tool-specific or platform-limited training.
Frequently asked
Who is this course designed for?
Professionals in data governance, compliance, risk, engineering, or program leadership roles who need to implement robust data lineage in AI systems.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is prior experience with data lineage required?
No, but the course assumes familiarity with data systems and AI workflows. It is designed to take practitioners to implementation-grade proficiency.
$199 one-time. Approximately 45 hours of self-paced learning, designed for professionals balancing active roles..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours