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Practical Data Product Management for Established Enterprises

$199.00
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A tailored course, built for your situation

Practical Data Product Management for Established Enterprises

Turn data governance maturity into measurable product outcomes

$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 initiatives stall despite strong governance foundations

The situation this course is for

Organizations invest heavily in data quality, metadata, and pipelines, yet struggle to deliver repeatable value. Projects remain siloed, ownership is unclear, and business stakeholders disengage. The missing link? A product management discipline applied to data, complete with ownership, roadmaps, user feedback, and lifecycle planning.

Who this is for

Business and technology professionals in established organizations advancing data governance, compliance, or analytics programs who need to deliver measurable outcomes

Who this is not for

Startups building first data stacks, individuals seeking coding bootcamp-style training, or those focused solely on data science modeling

What you walk away with

  • Define and operationalize data product contracts across domains
  • Align data initiatives with business outcome metrics
  • Implement governance that enables speed, not friction
  • Structure cross-functional delivery teams with clear ownership
  • Measure and scale data product maturity across the enterprise

The 12 modules (with all 144 chapters)

Module 1. From Data Stewardship to Product Thinking
Reframe governance roles as product enablers
12 chapters in this module
  1. The evolution of data ownership models
  2. Defining product mindset in data contexts
  3. Mapping compliance requirements to product features
  4. Shifting from project to product funding
  5. Identifying internal data consumers
  6. Building product charters for data domains
  7. Integrating privacy by design
  8. Aligning with enterprise architecture principles
  9. Defining minimum viable data products
  10. Establishing feedback loops with stakeholders
  11. Documenting data product contracts
  12. Measuring initial adoption signals
Module 2. Establishing Data Product Ownership
Define roles, responsibilities, and accountability
12 chapters in this module
  1. Product owner vs. data steward: clarifying overlap
  2. Assigning ownership across domains
  3. Creating RACI frameworks for data products
  4. Onboarding owners with playbooks
  5. Balancing central oversight with domain autonomy
  6. Defining escalation paths for conflicts
  7. Integrating with existing IT governance
  8. Measuring product owner effectiveness
  9. Managing turnover in product roles
  10. Training non-technical owners
  11. Linking performance goals to data health
  12. Documenting ownership transitions
Module 3. Designing Data Product Contracts
Standardize expectations between producers and consumers
12 chapters in this module
  1. Core components of a data product contract
  2. Defining availability SLAs
  3. Specifying freshness and latency expectations
  4. Documenting lineage and provenance
  5. Incorporating data quality rules
  6. Managing change control processes
  7. Versioning contract updates
  8. Automating contract validation
  9. Linking contracts to API specifications
  10. Handling deprecation and sunsetting
  11. Storing contracts in metadata systems
  12. Auditing compliance with contracts
Module 4. Roadmapping for Data Products
Create strategic, prioritized delivery plans
12 chapters in this module
  1. Assessing consumer business impact
  2. Estimating effort and dependencies
  3. Balancing technical debt and new features
  4. Creating multi-quarter roadmaps
  5. Visualizing roadmap commitments
  6. Communicating roadmap changes
  7. Integrating with enterprise planning cycles
  8. Aligning with fiscal budgeting
  9. Incorporating regulatory timelines
  10. Prioritizing based on risk exposure
  11. Managing stakeholder expectations
  12. Tracking roadmap completion rates
Module 5. Scaling with Domain-Driven Data Architecture
Structure data organization by business capability
12 chapters in this module
  1. Identifying bounded data contexts
  2. Mapping domains to organizational units
  3. Designing domain-specific ontologies
  4. Establishing domain-level KPIs
  5. Managing cross-domain dependencies
  6. Defining integration patterns
  7. Implementing domain data hubs
  8. Securing domain-to-domain access
  9. Monitoring domain health metrics
  10. Standardizing tooling per domain
  11. Facilitating domain collaboration
  12. Evolving domains over time
Module 6. Integrating Governance into Product Workflows
Embed compliance into delivery, not as afterthought
12 chapters in this module
  1. Automating policy checks in CI/CD
  2. Tagging data for regulatory scope
  3. Validating data lineage at release
  4. Enforcing encryption standards
  5. Auditing access patterns
  6. Generating compliance evidence automatically
  7. Integrating with GRC platforms
  8. Responding to audit requests
  9. Maintaining documentation as code
  10. Updating policies across versions
  11. Training teams on governance expectations
  12. Reducing manual oversight burden
Module 7. Measuring Data Product Success
Define and track meaningful KPIs
12 chapters in this module
  1. Distinguishing usage from value
  2. Tracking downstream consumption
  3. Measuring time-to-insight reduction
  4. Assessing data reliability incidents
  5. Calculating cost per data product
  6. Benchmarking against peer domains
  7. Linking data quality to business outcomes
  8. Monitoring user satisfaction
  9. Auditing access patterns for fairness
  10. Reporting to executive sponsors
  11. Setting improvement targets
  12. Visualizing maturity over time
Module 8. Building Cross-Functional Delivery Teams
Assemble and align product, tech, and business roles
12 chapters in this module
  1. Defining team composition models
  2. Integrating data engineers into product teams
  3. Onboarding business analysts as co-owners
  4. Managing hybrid reporting lines
  5. Establishing team rituals
  6. Running effective sprint reviews
  7. Documenting decisions in shared logs
  8. Resolving prioritization conflicts
  9. Measuring team velocity
  10. Improving collaboration tools
  11. Conducting retrospectives
  12. Scaling team patterns across domains
Module 9. Managing Technical Debt in Data Products
Identify, prioritize, and reduce legacy burdens
12 chapters in this module
  1. Classifying types of data debt
  2. Assessing risk exposure levels
  3. Tracking debt accumulation trends
  4. Prioritizing reduction efforts
  5. Allocating time in roadmaps
  6. Communicating debt impact to leadership
  7. Measuring reduction progress
  8. Preventing recurrence with automation
  9. Incorporating debt reviews in planning
  10. Balancing new features and cleanup
  11. Creating visibility into debt metrics
  12. Reducing documentation gaps
Module 10. Scaling Data Literacy Across the Enterprise
Enable broader data consumption and contribution
12 chapters in this module
  1. Assessing current literacy levels
  2. Creating role-based training paths
  3. Developing self-service onboarding
  4. Curating learning resources
  5. Certifying data competency
  6. Gamifying knowledge acquisition
  7. Measuring behavior change
  8. Reducing support burden
  9. Promoting data champions
  10. Integrating literacy into onboarding
  11. Tracking adoption of best practices
  12. Scaling peer mentoring
Module 11. Automating Data Product Operations
Apply DevOps principles to data workflows
12 chapters in this module
  1. Implementing CI/CD for data pipelines
  2. Automating testing and validation
  3. Monitoring data product health
  4. Alerting on SLA breaches
  5. Managing deployments across environments
  6. Versioning data artifacts
  7. Rolling back failed releases
  8. Auditing deployment history
  9. Securing pipeline access
  10. Integrating with observability tools
  11. Reducing manual intervention
  12. Improving deployment frequency
Module 12. Sustaining Data Product Maturity
Evolve practices to meet growing demands
12 chapters in this module
  1. Assessing organizational readiness
  2. Identifying scaling bottlenecks
  3. Refining ownership models
  4. Updating contracts at scale
  5. Managing portfolio growth
  6. Optimizing resource allocation
  7. Incorporating lessons learned
  8. Sharing best practices across domains
  9. Evolving governance frameworks
  10. Adapting to new regulations
  11. Investing in tooling improvements
  12. Measuring long-term ROI

How this maps to your situation

  • Leading a data governance program needing stronger product discipline
  • Scaling analytics or AI initiatives with inconsistent outcomes
  • Managing compliance requirements across distributed teams
  • Transitioning from project-based to product-based delivery

Before vs. after

Before
Data initiatives lack clear ownership, stall in governance phases, and fail to deliver consistent business value
After
Teams operate with defined data product contracts, measurable outcomes, and integrated governance enabling faster, compliant delivery

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, 75 hours of content, designed for self-paced learning with implementation milestones

If nothing changes
Continuing with project-based data efforts risks escalating technical debt, inconsistent compliance, and missed opportunities to institutionalize data as a strategic asset

How this compares to the alternatives

Unlike generic data governance courses, this program focuses on actionable product management practices proven in mid-sized to large enterprises with complex compliance needs. It bridges strategy and execution, avoiding theoretical overviews in favor of implementation-grade tools.

Frequently asked

Who is this course designed for?
Business and technology professionals leading data governance, compliance, or analytics programs in established organizations who need to deliver measurable, repeatable outcomes.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate upon completion?
Yes, a digital badge and certificate are awarded upon finishing all modules and assessments.
$199 one-time. Approximately 60, 75 hours of content, designed for self-paced learning with implementation milestones.

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
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