A tailored course, built for your situation
Production-Grade Data Quality Programs for Senior Leaders
Build enterprise-grade data integrity systems with confidence and strategic clarity
$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.
Data quality initiatives often fail due to misalignment between technical execution and leadership expectations
The situation this course is for
Senior leaders are increasingly accountable for data integrity but lack structured frameworks to guide program design, stakeholder alignment, and sustainable operations. Without a production-grade approach, efforts remain fragmented, under-resourced, and disconnected from enterprise outcomes.
Who this is for
Senior business and technology leaders in regulated or data-intensive environments who influence or lead data governance, compliance, analytics, or digital transformation initiatives
Who this is not for
Individual contributors focused only on data cleaning tools, entry-level analysts, or engineers seeking coding-heavy instruction
What you walk away with
- Lead enterprise data quality programs with structured, repeatable methodologies
- Align data integrity initiatives with compliance, risk, and operational goals
- Design governance models that scale across complex organizations
- Integrate data quality into existing data architecture and delivery pipelines
- Communicate value and progress effectively to executive and board-level stakeholders
The 12 modules (with all 144 chapters)
Module 1. Foundations of Production-Grade Data Quality
Establish core principles, terminology, and strategic context for enterprise data quality leadership
12 chapters in this module
- Defining production-grade data quality
- Evolution of data quality in regulated environments
- Leadership roles in data integrity programs
- Data quality as a business enabler
- Core components of a scalable program
- Aligning with enterprise risk and compliance
- Stakeholder mapping and influence
- Assessing organizational readiness
- Common failure patterns and how to avoid them
- Building the business case
- Measuring strategic impact
- Setting program vision and scope
Module 2. Strategic Alignment and Executive Engagement
Learn how to gain and maintain executive sponsorship and cross-functional buy-in
12 chapters in this module
- Communicating data quality to non-technical leaders
- Translating technical needs into business value
- Engaging C-suite and board stakeholders
- Creating executive dashboards
- Positioning data quality as strategic infrastructure
- Managing competing priorities
- Building coalition across departments
- Facilitating leadership workshops
- Developing governance charters
- Securing budget and resources
- Managing change at scale
- Sustaining momentum over time
Module 3. Data Quality Assessment and Benchmarking
Conduct comprehensive assessments to identify gaps and set baselines
12 chapters in this module
- Designing assessment frameworks
- Selecting key data domains
- Measuring accuracy, completeness, timeliness
- Using metadata to inform quality evaluation
- Benchmarking against industry standards
- Prioritizing data assets by impact
- Conducting stakeholder interviews
- Documenting current state workflows
- Identifying root causes of poor quality
- Quantifying business impact of data issues
- Reporting findings to leadership
- Setting realistic improvement targets
Module 4. Designing Enterprise Data Quality Rules
Develop enforceable, maintainable, and scalable data quality rules
12 chapters in this module
- Categorizing data quality dimensions
- Defining rule types: validity, consistency, uniqueness
- Creating business-friendly rule definitions
- Mapping rules to data systems
- Versioning and change management for rules
- Automating rule validation
- Balancing precision and flexibility
- Handling exceptions and overrides
- Integrating with data dictionaries
- Testing rule effectiveness
- Documenting rule ownership
- Scaling rule sets across domains
Module 5. Data Quality Monitoring and Alerting
Implement continuous monitoring systems with actionable insights
12 chapters in this module
- Designing monitoring architectures
- Selecting real-time vs batch approaches
- Configuring thresholds and tolerances
- Building alert workflows
- Integrating with incident management
- Reducing alert fatigue
- Visualizing data quality trends
- Creating service-level agreements for data
- Monitoring third-party data feeds
- Auditing monitoring effectiveness
- Escalation protocols
- Reporting on system health
Module 6. Root Cause Analysis and Remediation
Drive systemic fixes rather than temporary patches
12 chapters in this module
- Conducting structured root cause investigations
- Using fishbone and 5-why techniques
- Distinguishing symptoms from causes
- Engaging technical and business teams
- Prioritizing remediation efforts
- Designing corrective action plans
- Validating fix effectiveness
- Preventing recurrence
- Documenting lessons learned
- Integrating fixes into SDLC
- Tracking remediation ROI
- Scaling remediation across systems
Module 7. Data Quality in Data Integration Pipelines
Embed quality controls into ETL, ELT, and streaming workflows
12 chapters in this module
- Assessing pipeline risk points
- Validating data at ingestion
- Handling schema drift and inconsistency
- Implementing data cleansing steps
- Logging and auditing transformations
- Ensuring referential integrity
- Managing data lineage for quality
- Testing pipeline resilience
- Monitoring pipeline performance
- Handling backpressure and failures
- Versioning pipeline logic
- Documenting data flow assumptions
Module 8. Governance, Ownership, and Stewardship
Establish clear roles, responsibilities, and decision rights
12 chapters in this module
- Defining data ownership models
- Assigning data stewardship roles
- Creating RACI matrices for data quality
- Onboarding and training stewards
- Establishing governance committees
- Resolving data disputes
- Managing policy exceptions
- Conducting stewardship reviews
- Integrating with enterprise governance
- Measuring stewardship effectiveness
- Scaling governance across regions
- Maintaining policy currency
Module 9. Data Quality in Cloud and Hybrid Environments
Adapt practices for modern, distributed data architectures
12 chapters in this module
- Assessing cloud-specific risks
- Ensuring consistency across cloud and on-prem
- Managing multi-cloud data quality
- Leveraging cloud-native monitoring tools
- Securing data quality in shared environments
- Handling serverless and event-driven architectures
- Optimizing cost-performance tradeoffs
- Integrating with cloud data warehouses
- Managing data residency and sovereignty
- Auditing cloud data operations
- Scaling quality controls automatically
- Designing for elasticity
Module 10. Compliance, Risk, and Audit Readiness
Ensure data quality programs meet regulatory and audit requirements
12 chapters in this module
- Mapping to GDPR, CCPA, HIPAA, FERPA
- Supporting SOX and financial reporting
- Preparing for internal and external audits
- Documenting controls and evidence
- Demonstrating data lineage and provenance
- Managing data retention and deletion
- Handling subject access requests
- Reporting on compliance posture
- Integrating with enterprise risk management
- Conducting control self-assessments
- Responding to regulatory inquiries
- Maintaining audit trails
Module 11. Scaling and Sustaining Data Quality Programs
Transition from pilot to enterprise-wide adoption
12 chapters in this module
- Developing a multi-year roadmap
- Phasing rollout by business unit
- Building centers of excellence
- Creating training and enablement programs
- Measuring program maturity
- Adapting to organizational change
- Incorporating feedback loops
- Optimizing program operations
- Managing vendor and partner relationships
- Sustaining funding and support
- Celebrating milestones and wins
- Evolving the program over time
Module 12. Leading the Future of Data Quality
Anticipate emerging trends and position your organization ahead
12 chapters in this module
- Emerging technologies and their impact
- AI and machine learning in data quality
- Predictive quality monitoring
- Automated rule generation
- Natural language for rule definition
- Integrating with data catalogs
- Adopting data contracts
- Building data product mindsets
- Fostering a culture of data ownership
- Measuring long-term data health
- Influencing industry standards
- Positioning as a thought leader
How this maps to your situation
- Leading a new data governance initiative
- Responding to audit findings or compliance gaps
- Scaling data analytics or AI/ML programs
- Modernizing legacy data infrastructure
Before vs. after
Before
Data quality efforts are reactive, siloed, and lack executive visibility
After
Data quality is proactive, integrated, and recognized as a strategic asset
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 60, 70 hours of focused learning, designed for senior leaders to complete at their own pace over 8, 12 weeks.
If nothing changes
Without a structured approach, data quality programs remain inconsistent, underfunded, and unable to scale, limiting the organization’s ability to trust its data for critical decisions.
How this compares to the alternatives
Unlike generic data management courses or tool-specific certifications, this program focuses exclusively on the leadership, design, and operational challenges of running enterprise-grade data quality programs in complex, regulated environments.
Frequently asked
Who is this course designed for?
Senior business and technology leaders responsible for data governance, compliance, analytics, or digital transformation in complex organizations.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate upon completion?
Yes, a digital certificate of completion is issued through the learning environment after finishing all modules.
$199 one-time. Approximately 60, 70 hours of focused learning, designed for senior leaders to complete at their own pace over 8, 12 weeks..
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