A tailored course, built for your situation
Deeper command of data governance frameworks for industrial data systems
Build fluency in the standards and structures that define trusted data pipelines in process manufacturing environments
$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.
The situation this course is for
Who this is for
Early-career data professional in a process manufacturing or industrial environment, working at the intersection of data science, operations, and compliance, aiming to transition from technical contributor to trusted governance practitioner
Who this is not for
Executives seeking board-level summaries, consultants selling frameworks, or professionals outside industrial data contexts where process integrity and data traceability are not core compliance drivers
What you walk away with
- Map data governance standards directly to operational data flows in process environments
- Design policy structures that reflect the constraints and requirements of industrial data systems
- Confidently lead data domain definitions with clear stewardship and lineage tracking
- Navigate DCMM maturity levels with precision and practical implementation steps
- Produce governance artefacts that withstand technical and compliance review cycles
The 12 modules (with all 144 chapters)
Module 1. Industrial data systems and the case for governance
Understand why data governance in process manufacturing differs from generic IT or enterprise contexts, with examples from chemical, petrochemical, and bulk material production.
12 chapters in this module
- Data in continuous process environments
- Why batch logic doesn’t apply
- Compliance as a system property
- Trusted data in safety instrumentation
- Case: ethylene pipeline monitoring
- Data drift in sensor networks
- Operational consequences of latency
- Linking data quality to product specs
- Traceability as a legal requirement
- Regulatory scrutiny on process data
- The cost of reprocessing records
- Governance as operational hygiene
Module 2. Core principles of data governance frameworks
Break down the foundational concepts shared across DCMM, DAMA-DMBOK, and ISO 8000, with emphasis on applicability to industrial control and monitoring systems.
12 chapters in this module
- What is a data governance framework
- Domain vs function vs process
- The seven core data domains
- Data stewardship models
- Ownership vs accountability
- Framework maturity levels
- How DCMM defines level 3
- DAMA’s wheel in practice
- ISO 8000 and data quality
- Framework alignment checklist
- Mapping to NIST CSF
- Cross-framework comparison
Module 3. Data lineage in process manufacturing
Trace data from sensor to report, learning how to document and validate lineage in high-throughput, multi-source environments.
12 chapters in this module
- Lineage in continuous systems
- Identifying data touchpoints
- Sensor to historian flow
- Tag-level traceability
- Handling batch exceptions
- Lineage for audit readiness
- Documenting transformation logic
- Using metadata effectively
- Lineage in SAP MES
- Integrating PLC data sources
- Versioning sensor firmware
- Lineage automation options
Module 4. Data stewardship in asset-intensive operations
Define clear stewardship roles that reflect plant-level responsibilities and cross-functional dependencies in large-scale operations.
12 chapters in this module
- Why stewards matter in plants
- Engineering vs IT ownership
- Stewardship in shutdown cycles
- Change control integration
- Defining data custodians
- Escalation paths for disputes
- Stewardship in procurement
- Vendor data accountability
- Documenting role boundaries
- Training for frontline stewards
- Stewardship KPIs
- Review cadence planning
Module 5. Data quality in high-velocity industrial contexts
Apply data quality dimensions to real-time monitoring and control systems where timeliness and accuracy are non-negotiable.
12 chapters in this module
- Timeliness vs latency tradeoffs
- Accuracy in sensor calibration
- Completeness in batch reporting
- Consistency across shifts
- Validity in chemical assays
- Uniqueness in tag identifiers
- Measuring data quality gaps
- Setting industrial thresholds
- Automated flagging rules
- Corrective action workflows
- Data quality dashboards
- Reporting to reliability teams
Module 6. Policy design for industrial compliance
Write governance policies that are enforceable, auditable, and operationally feasible in regulated manufacturing environments.
12 chapters in this module
- Policy vs procedure vs standard
- Writing testable policies
- Incorporating ISA-95 models
- Policy scope definition
- Referencing API and ASTM
- Handling legacy system exceptions
- Enforcement mechanisms
- Audit trail requirements
- Version control practices
- Change management integration
- Stakeholder review cycles
- Policy communication plans
Module 7. Data domain modeling for process systems
Structure data domains around physical assets and processes, not business functions, to align governance with operational reality.
12 chapters in this module
- Physical asset as data anchor
- Unit operations as domains
- Feedstock tracking domains
- Emissions data grouping
- Utility consumption domains
- Maintenance data boundaries
- Lab results integration
- Safety system data scope
- Domain ownership mapping
- Cross-domain dependencies
- Naming conventions for tags
- Domain documentation templates
Module 8. Implementing DCMM in industrial settings
Apply the Data Management Capability Maturity Model to real-world plants and systems, focusing on achievable, verifiable improvements.
12 chapters in this module
- DCMM’s 27 capability areas
- Level 1: initial practices
- Level 2: managed processes
- Level 3: defined standards
- Achieving level 3 in quality
- Documenting process ownership
- Assessment preparation
- Internal audit alignment
- Gap analysis techniques
- Roadmap for level 3
- DCMM and ISO integration
- Reporting maturity gains
Module 9. Integrating data governance with operational technology
Bridge the gap between IT governance frameworks and OT systems, ensuring policies work where data is born.
12 chapters in this module
- OT data lifecycle stages
- Data at the edge
- Firewall zone considerations
- Historian system governance
- Access control for SCADA
- Data retention in OT
- Backup validation for PLCs
- Secure transfer methods
- Change management in OT
- Patch governance alignment
- Incident response coordination
- Joint IT-OT governance teams
Module 10. Governance for AI and analytics in industry
Ensure data used in predictive models and analytics meets governance standards for traceability, quality, and compliance.
12 chapters in this module
- Training data provenance
- Model input validation
- Bias detection in sensor data
- Versioning model features
- Audit trails for predictions
- Explainability in control systems
- Monitoring model drift
- Governance for digital twins
- Approving analytics pipelines
- Lab-to-plant deployment
- Scaling pilot models
- Documentation for validation
Module 11. Creating audit-ready governance artefacts
Produce documentation packages that satisfy both technical and compliance reviewers without rework or delays.
12 chapters in this module
- What auditors look for
- Data governance checklist
- Lineage diagram standards
- Stewardship role evidence
- Policy implementation proof
- Quality measurement logs
- Change history records
- Compliance sign-off templates
- Self-assessment packages
- Preparing for regulator review
- Handling evidence requests
- Audit follow-up workflows
Module 12. Building your governance implementation playbook
Assemble a personalized, actionable playbook that applies everything learned to your specific environment and priorities.
12 chapters in this module
- Assessing your current state
- Identifying quick wins
- Stakeholder alignment plan
- Pilot domain selection
- Roadmap to level 3
- Resource estimation
- Communication strategy
- Tracking progress
- Building reusable templates
- Documenting decisions
- Scaling beyond pilot
- Sustaining governance gains
How this maps to your situation
- When you’re drafting your first data governance policy
- Before an internal audit cycle begins
- After a data quality incident in operations
- When scaling analytics from pilot to production
Before vs. after
Before
Approaching data governance through general frameworks without clear adaptation to industrial systems
After
Confidently applying governance standards to process manufacturing contexts with precise, auditable, and operationally sound practices
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: 45-60 minutes per module, designed to be completed over 6-8 weeks with real-world application between modules
How this compares to the alternatives
Unlike generic data governance courses, this program focuses specifically on industrial data systems, providing templates and examples drawn from petrochemical, manufacturing, and bulk processing environments rather than IT or financial services use cases.
Frequently asked
Is this course relevant if I work in process manufacturing?
Yes, this course is specifically designed for data professionals in asset-intensive industries like chemicals, refining, and industrial materials.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will I get practical tools I can use immediately?
Yes, every module includes downloadable templates and real-world examples applicable to industrial data governance.
$199 one-time. 45-60 minutes per module, designed to be completed over 6-8 weeks with real-world application between modules.
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