A tailored course, built for your situation
Pragmatic AI Data Lineage Practices for Cross-Functional Programs
Implementation-grade practices for business and technology leaders driving AI governance at scale
$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 slow AI adoption and weaken governance confidence
The situation this course is for
As AI initiatives grow, teams struggle to maintain clear visibility across data sources, transformations, and model dependencies. Without a unified approach, compliance becomes reactive, audits take longer, and engineering rework increases, all while business leaders wait for trustworthy outputs.
Who this is for
Business and technology professionals leading or contributing to AI governance, data strategy, or cross-functional program delivery
Who this is not for
Individuals seeking introductory data concepts or vendor-specific tool training
What you walk away with
- Establish clear, auditable data lineage across AI workflows
- Align technical teams with compliance and business stakeholders
- Reduce rework and accelerate time-to-approval for AI deployments
- Implement standardized reporting that satisfies governance requirements
- Build stakeholder trust through transparent data practices
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Data Lineage
Define core principles and organizational value of AI data lineage
12 chapters in this module
- Defining data lineage in AI contexts
- Distinguishing lineage from data provenance
- Core components of a lineage system
- Role of metadata in traceability
- Linking data to model behavior
- Governance drivers for lineage
- Common misconceptions
- Benefits across functions
- Integration with MLOps
- Regulatory expectations
- Industry benchmarks
- Setting baseline maturity
Module 2. Cross-Functional Stakeholder Alignment
Map lineage needs across engineering, compliance, and business units
12 chapters in this module
- Identifying key stakeholders
- Understanding legal requirements
- Engineering team expectations
- Business user needs
- Compliance reporting demands
- Creating shared definitions
- Facilitating joint workshops
- Managing conflicting priorities
- Establishing feedback loops
- Documenting agreement points
- Maintaining alignment over time
- Scaling communication frameworks
Module 3. Data Source Identification and Classification
Systematically catalog inputs for AI systems
12 chapters in this module
- Inventorying data sources
- Classifying by sensitivity level
- Assessing reliability and freshness
- Documenting ownership
- Tagging for regulatory relevance
- Mapping to use cases
- Versioning source definitions
- Handling third-party data
- Managing consent status
- Automating source detection
- Validating source integrity
- Updating source records
Module 4. End-to-End Traceability Frameworks
Build systems that connect data to decisions
12 chapters in this module
- Designing traceable pipelines
- Embedding lineage capture
- Tracking transformations
- Linking features to models
- Storing lineage metadata
- Querying lineage paths
- Visualizing data flows
- Ensuring temporal accuracy
- Handling schema changes
- Validating trace links
- Testing traceability
- Auditing trace records
Module 5. Metadata Management Strategies
Implement scalable metadata practices
12 chapters in this module
- Types of metadata
- Choosing metadata stores
- Designing metadata schemas
- Capturing technical metadata
- Recording business context
- Managing metadata quality
- Synchronizing metadata
- Enriching metadata
- Governance of metadata
- Access controls
- Metadata versioning
- Metadata audit trails
Module 6. Automated Lineage Capture Techniques
Leverage tooling to reduce manual effort
12 chapters in this module
- Instrumenting data pipelines
- Parsing code for lineage
- Using observability tools
- Integrating with ETL
- Capturing model inputs
- Logging data access
- Tracking feature engineering
- Monitoring pipeline changes
- Validating automation accuracy
- Handling edge cases
- Scaling automation
- Maintaining automation systems
Module 7. Policy Development for Data Lineage
Define standards that guide implementation
12 chapters in this module
- Setting data accountability
- Defining data quality rules
- Establishing documentation norms
- Setting retention policies
- Defining access rights
- Creating change controls
- Enforcement mechanisms
- Compliance validation
- Policy review cycles
- Training on policies
- Auditing policy adherence
- Updating policies
Module 8. Implementation Playbook Development
Create actionable guides for team adoption
12 chapters in this module
- Assessing team readiness
- Identifying quick wins
- Building rollout plans
- Creating step-by-step guides
- Developing templates
- Preparing training materials
- Setting success metrics
- Running pilot projects
- Gathering feedback
- Iterating on playbooks
- Scaling implementation
- Maintaining playbooks
Module 9. Audit Readiness and Compliance Reporting
Prepare for internal and external reviews
12 chapters in this module
- Understanding audit requirements
- Preparing documentation
- Generating lineage reports
- Demonstrating compliance
- Responding to auditor questions
- Preparing for regulatory exams
- Creating evidence trails
- Maintaining audit logs
- Conducting self-assessments
- Addressing findings
- Improving over time
- Reporting to leadership
Module 10. Scaling Across Programs and Teams
Extend lineage practices beyond pilot teams
12 chapters in this module
- Assessing scalability needs
- Standardizing approaches
- Creating center of excellence
- Sharing best practices
- Managing cross-team dependencies
- Integrating with SDLC
- Incorporating into onboarding
- Measuring adoption
- Optimizing for reuse
- Reducing duplication
- Managing technical debt
- Sustaining momentum
Module 11. Tooling and Technology Integration
Select and deploy enabling technologies
12 chapters in this module
- Evaluating lineage tools
- Integrating with data catalog
- Connecting to MLOps platforms
- Working with ETL tools
- API integration patterns
- Data warehouse compatibility
- Cloud platform considerations
- Open source options
- Vendor evaluation
- Pilot testing tools
- Deployment strategies
- Maintaining tool integrations
Module 12. Sustaining and Evolving Lineage Practices
Ensure long-term effectiveness and relevance
12 chapters in this module
- Monitoring adoption
- Tracking effectiveness
- Updating for new regulations
- Adapting to new technologies
- Responding to incidents
- Refreshing training
- Updating documentation
- Incorporating lessons learned
- Benchmarking performance
- Planning improvements
- Engaging stakeholders
- Future-proofing practices
How this maps to your situation
- Organizations launching first AI governance initiative
- Teams scaling AI across multiple business units
- Companies preparing for regulatory scrutiny
- Leaders building cross-functional data trust
Before vs. after
Before
Unclear ownership, inconsistent documentation, reactive compliance, and fragmented tooling slow AI progress and increase risk exposure.
After
Clear data provenance, aligned teams, proactive audit readiness, and scalable practices enable faster, more trusted AI deployment.
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, 60 hours of self-paced learning, designed for integration into real-time project workflows.
If nothing changes
Without structured data lineage, organizations face increased rework, delayed deployments, compliance gaps, and eroding stakeholder trust in AI systems.
How this compares to the alternatives
Unlike generic data governance courses or vendor-specific certifications, this program delivers implementation-grade practices tailored to cross-functional AI programs, with a focus on practical execution over theory.
Frequently asked
Who is this course designed for?
Business and technology professionals involved in AI governance, data strategy, compliance, or cross-functional program leadership.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course technical or business-oriented?
It bridges both perspectives, with implementation methods relevant to engineers and strategic framing for business leaders.
$199 one-time. Approximately 45, 60 hours of self-paced learning, designed for integration into real-time project workflows..
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