DGSP
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DGSP
ICCP Data Governance and Stewardship Professional (DGSP) equips practitioners with the skills to design, implement, and optimize enterprise data governance frameworks, steward data operations, ensure regulatory compliance, and manage data quality, metadata, and business glossaries.
90
Minutes
Who Should Take This
Data governance analysts, stewards, and managers with two to ten years of experience who lead or support data programs in midsize to large private‑sector enterprises will benefit. They seek to deepen expertise in framework design, operational stewardship, compliance, quality, and metadata governance to drive reliable, compliant data assets across the organization.
What's Covered
1
Domain 1: Data Governance Frameworks and Strategy
2
Domain 2: Data Stewardship Operations
3
Domain 3: Regulatory Compliance and Privacy Governance
4
Domain 4: Data Quality Governance
5
Domain 5: Metadata and Business Glossary Governance
6
Domain 6: Governance Program Measurement and Communication
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
57 learning goals
1
Domain 1: Data Governance Frameworks and Strategy
3 topics
Governance framework design and implementation
- Implement a data governance framework by selecting and adapting industry models including DAMA DMBoK, DGI framework, and DCAM to organizational requirements and maturity levels.
- Implement data governance policies covering data access, data sharing, data retention, data quality standards, and acceptable use aligned with business objectives and regulatory requirements.
- Analyze data governance maturity using structured assessment models to identify capability gaps, prioritize improvement areas, and benchmark against industry standards.
- Design a multi-year data governance roadmap that sequences capability building, policy deployment, and organizational change to progressively increase governance maturity.
Governance operating models and organizational design
- Implement data governance organizational structures including centralized, federated, and hybrid operating models with defined roles, accountability, and decision rights.
- Implement data governance council operations including charter development, meeting cadence, escalation procedures, decision-making protocols, and performance reporting.
- Analyze governance operating model effectiveness by evaluating decision velocity, stakeholder engagement, issue resolution rates, and alignment with business outcomes.
- Design a governance change management strategy that addresses organizational culture, executive sponsorship, stakeholder resistance, and communication plans for governance adoption.
Data governance tools and technology
- Implement data governance platform capabilities including policy management, workflow automation, data catalog integration, and compliance reporting dashboards.
- Analyze data governance tool effectiveness by evaluating user adoption rates, workflow efficiency, policy enforcement accuracy, and integration coverage across data systems.
- Design a data governance technology strategy that selects appropriate platforms, defines integration requirements, and establishes evaluation criteria for governance tooling investments.
2
Domain 2: Data Stewardship Operations
4 topics
Stewardship roles and responsibilities
- Implement data stewardship programs with defined roles for business data stewards, technical data stewards, and coordinating data stewards with clear responsibilities and authority.
- Implement data ownership assignment processes that define data owner responsibilities, establish accountability chains, and link ownership to organizational management structures.
- Analyze stewardship program effectiveness by measuring issue resolution rates, data quality improvements, stakeholder satisfaction, and steward engagement levels.
- Design a stewardship community of practice that fosters knowledge sharing, skill development, best practice dissemination, and recognition for data stewardship contributors.
Data issue management and remediation
- Implement data issue management workflows including issue identification, classification, assignment, root cause analysis, remediation tracking, and closure verification.
- Apply data quality remediation techniques including manual correction, automated cleansing, source system fixes, and process improvements to address systemic data issues.
- Analyze data issue trends to identify systemic causes, predict emerging quality problems, and recommend preventive controls that address root causes rather than symptoms.
Stewardship training and enablement
- Implement data stewardship training programs including onboarding curricula, role-specific skill development, certification tracks, and continuing education requirements.
- Analyze stewardship skill gaps by assessing competency levels across the stewardship network and identifying training needs for business and technical stewards.
- Design a stewardship enablement strategy that provides tools, templates, knowledge bases, and collaboration platforms to maximize steward productivity and effectiveness.
Cross-functional data governance coordination
- Implement cross-functional governance coordination mechanisms including data domain working groups, cross-department issue resolution, and enterprise-wide policy harmonization.
- Analyze cross-functional governance challenges including conflicting departmental priorities, inconsistent data definitions, and siloed stewardship practices.
- Design a federated governance coordination model that balances domain autonomy with enterprise consistency through shared standards, common tools, and aligned metrics.
3
Domain 3: Regulatory Compliance and Privacy Governance
3 topics
Compliance program management
- Implement data compliance monitoring processes including automated policy checks, regulatory reporting, audit evidence collection, and compliance dashboard maintenance.
- Apply privacy governance practices including data protection impact assessments, consent management, data subject rights fulfillment, and breach notification procedures.
- Analyze regulatory compliance gaps by mapping data processing activities to applicable regulations and identifying control deficiencies requiring remediation.
- Design a cross-jurisdictional compliance strategy that harmonizes governance controls across GDPR, CCPA, HIPAA, and industry-specific regulatory requirements.
Data classification and protection governance
- Implement enterprise data classification programs including taxonomy design, classification criteria, automated discovery, labeling workflows, and handling requirements per classification level.
- Apply data protection controls aligned with classification levels including access restrictions, encryption requirements, masking rules, and retention policies for each data sensitivity tier.
- Analyze data protection effectiveness by auditing classification accuracy, control enforcement consistency, and access violation patterns to strengthen governance controls.
- Design a data protection governance strategy that integrates classification, access controls, encryption standards, and monitoring into a unified protection framework across all data tiers.
Data retention and disposal governance
- Implement data retention policies that define retention periods by data category, automate disposition workflows, and maintain audit trails for compliance verification.
- Apply data disposal procedures including secure deletion, anonymization, and archival processes that satisfy regulatory requirements while preserving analytically valuable datasets.
- Analyze data retention compliance by auditing adherence to retention schedules, identifying over-retained data posing unnecessary risk, and validating disposal completeness.
4
Domain 4: Data Quality Governance
2 topics
Data quality standards and monitoring
- Implement data quality standards including dimension definitions, measurement methodologies, threshold criteria, and reporting frameworks aligned with governance policies.
- Implement data quality service level agreements that define quality targets for critical data elements, monitoring frequency, escalation triggers, and remediation timelines.
- Analyze data quality trends across the enterprise to identify deteriorating data domains, correlate quality issues with business impact, and prioritize governance interventions.
- Design a data quality governance strategy that integrates quality management with stewardship operations, linking quality improvement to business value and regulatory compliance.
- Implement data quality certification processes that formally validate critical datasets for fitness-for-use before they are consumed by business processes, regulatory reports, or analytical models.
Data quality issue management
- Implement data quality incident management processes including severity classification, root cause investigation, remediation tracking, and preventive control implementation.
- Apply data profiling techniques to assess data quality dimensions across critical data elements, establishing baselines and monitoring trends over time.
- Analyze the business impact of data quality issues by correlating quality metrics with operational KPIs, customer satisfaction scores, and regulatory compliance outcomes.
5
Domain 5: Metadata and Business Glossary Governance
2 topics
Business glossary and semantic governance
- Implement a business glossary governance process including term proposal, review, approval, publication, and retirement workflows with defined ownership and versioning.
- Apply metadata governance practices to establish standards for metadata capture, lineage documentation, catalog maintenance, and cross-system metadata reconciliation.
- Analyze business glossary adoption and consistency by measuring term usage rates, definition conflicts, and semantic alignment across business domains and data systems.
- Design an enterprise semantic governance strategy that ensures consistent business terminology, data definitions, and metric calculations across all reporting and analytics systems.
Data lineage and impact governance
- Implement data lineage governance including lineage capture requirements, lineage quality standards, and impact analysis procedures for data transformation changes.
- Apply data impact analysis using lineage information to assess the downstream effects of proposed data changes on reports, applications, and regulatory submissions.
- Analyze data lineage coverage gaps by comparing documented lineage with actual data flows, identifying ungoverned data paths, and prioritizing lineage documentation efforts.
6
Domain 6: Governance Program Measurement and Communication
2 topics
Governance metrics and value demonstration
- Implement governance program KPIs including policy compliance rates, data quality scores, stewardship activity metrics, issue resolution timelines, and business impact measures.
- Apply governance value communication techniques to demonstrate ROI through reduced regulatory fines, improved decision quality, operational efficiency gains, and risk reduction.
- Analyze governance program performance by evaluating trend data against targets, comparing results across business domains, and identifying underperforming governance areas.
- Design a governance communication strategy that tailors messaging for executives, business users, and technical teams to maintain engagement and support across organizational levels.
Governance program sustainability
- Implement governance program sustainability practices including continuous funding justification, executive reporting cadences, and stakeholder engagement maintenance.
- Analyze governance program risks including executive sponsor changes, budget pressures, organizational restructuring, and stakeholder fatigue to develop contingency plans.
- Design a long-term governance sustainability strategy that embeds governance into organizational culture, standard operating procedures, and performance management systems.
Scope
Included Topics
- Comprehensive data governance and stewardship practices as tested on the ICCP Data Governance and Stewardship Professional exam, covering governance frameworks, organizational models, policy management, and compliance monitoring.
- Data stewardship operations including business data stewardship, technical data stewardship, data ownership models, stewardship councils, and cross-functional collaboration for data quality improvement.
- Data governance strategy and program management including governance maturity assessment, roadmap development, executive sponsorship, change management, and value measurement for governance initiatives.
- Data quality management within governance contexts including quality dimensions, profiling, monitoring, remediation, and continuous improvement aligned with governance policies and standards.
- Regulatory compliance and privacy governance including GDPR, CCPA, HIPAA, SOX, data sovereignty, cross-border data transfer, and privacy impact assessments.
- Metadata governance and business glossary management including enterprise data catalogs, semantic consistency, data lineage, impact analysis, and metadata standards enforcement.
Not Covered
- Deep technical database administration, storage engineering, or infrastructure management beyond what governance professionals need to understand.
- Advanced data science, machine learning, and statistical modeling techniques covered by the CDS certification.
- Big data distributed systems engineering and platform architecture covered by the CBDP certification.
- Public sector-specific data governance requirements and frameworks covered by the PSDGP certification.
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