Data Governance Training Course

Participants can assess which data requires formal governance controls and why.

• Material data identified through financial impact, customer harm and regulatory obligations
• Risk scenarios linked to inaccurate, incomplete, untimely and unauthorised data
• Data criticality ratings calibrated using impact and likelihood scoring
• Risk appetite statements converted into data-control priorities and thresholds
• Business processes traced to the data that supports key decisions
• Control intensity matched to materiality, sensitivity, usage frequency and change rate

Participants can design governance bodies, decision rights and escalation paths.

• Centralised, federated and hybrid governance models compared against organisational structures
• Decision rights assigned for definitions, quality rules, access and retention
• Governance council terms of reference defined with voting and escalation rules
• Working groups structured around domains, products, controls and delivery dependencies
• DCAM capability areas used to assess governance operating-model maturity
• Escalation paths established for unresolved ownership, quality and policy decisions

Participants can assign accountable owners and operational stewards for data domains.

• Data owner accountabilities defined for policy, risk acceptance and prioritisation
• Business, technical and operational steward roles differentiated through practical responsibilities
• RACI matrices built for data definition, quality, access and incident decisions
• Delegated authority documented for approvals, exceptions and remediation funding
• Stewardship capacity estimated from domain complexity, incidents and control workload
• Role conflicts resolved between process owners, system owners and data owners

Participants can define data domains and select critical data elements for control.

• Data domains modelled around business entities, processes and analytical use cases
• Critical data elements selected using materiality, regulatory relevance and decision dependency
• CDE registers documented with definitions, owners, systems and control requirements
• Data-element granularity aligned with reporting fields, attributes and reference values
• Source-to-consumption paths mapped for priority customer, product and financial elements
• Control gaps exposed where critical elements lack ownership, rules or lineage

Participants can establish governed definitions and metadata requirements for shared data.

• Business glossary entries structured with definitions, calculations, owners and approved usage
• Metric definitions reconciled when finance, operations and analytics use conflicting terms
• Metadata minimums specified for datasets, fields, reports and data products
• Technical, business and operational metadata linked through common identifiers
• Naming conventions applied to domains, datasets, attributes and reference values
• Approval workflows controlled for glossary changes, deprecations and disputed definitions

Participants can define a data-quality management approach for priority data elements.

• Data-quality dimensions assessed: accuracy, completeness, consistency, timeliness, validity and uniqueness
• Quality policy translated into measurable rules, thresholds, owners and remediation actions
• Preventive, detective and corrective controls distinguished for data-quality management
• Acceptable quality thresholds set according to risk, use case and customer impact
• Quality scorecards designed for executives, owners, stewards and delivery teams
• ISO 8000 concepts applied to data quality, master data and data exchange

Participants can specify profiling tests and business rules for critical data elements.

• Data profiling examines nulls, patterns, ranges, duplicates and referential integrity
• Business rules expressed through permitted values, calculations, dependencies and thresholds
• Completeness tests designed for mandatory attributes and conditional data requirements
• Validity checks compare field values against formats, ranges and approved reference lists
• Reconciliation controls compare totals across source systems, reports and downstream datasets
• Rule ownership assigned for approval, monitoring, exception handling and periodic review

Participants can operate a controlled workflow for data incidents and remediation.

• Data incidents classified by severity, business impact, urgency and affected populations
• Issue records capture symptoms, root causes, owners, deadlines and closure evidence
• Five Whys analysis separates immediate correction from underlying process failure
• Ishikawa diagrams identify people, process, system and supplier causes
• Remediation plans distinguish data correction, control redesign and source-system change
• Service-level targets defined for triage, containment, resolution and recurrence prevention

Participants can specify governance controls for shared master and reference data.

• Master data distinguished from transactional, analytical and reference data assets
• Golden-record rules define survivorship, matching, merging and source precedence
• Reference-data ownership assigned for code lists, hierarchies and controlled vocabularies
• Change requests governed for product classifications, customer segments and geographic codes
• Duplicate detection rules combine deterministic matching with reviewed exception queues
• Distribution controls prevent outdated reference values entering downstream reports

Participants can define lifecycle controls from data creation through secure disposal.

• Data lifecycle stages mapped from creation, capture and use through disposal
• Retention schedules linked to legal, regulatory, contractual and operational requirements
• Records management requirements separated from analytical storage and backup practices
• Disposition approvals documented for deletion, archival, legal holds and exceptions
• Data minimisation applied to collection fields, copies and secondary uses
• Lifecycle controls evidenced through retention registers, audit logs and disposal records

Participants can define governance requirements for sensitive and personal data.

• Data classification schemes distinguish public, internal, confidential and restricted information
• Personal-data inventories record purposes, lawful bases, recipients and retention periods
• Role-based access control aligns permissions with job duties and data sensitivity
• Access recertification identifies dormant accounts, excessive privileges and segregation conflicts
• Privacy impact assessments identify processing risks before new data uses
• Consent, purpose limitation and data-subject rights translated into governance requirements

Participants can apply governance controls to cloud, distributed and high-volume data environments.

• Cloud data responsibilities divided among provider, platform team and business owner
• Data lake zones governed through ingestion standards, classification and promotion criteria
• Schema-on-read risks controlled through metadata, profiling and approved consumption rules
• Distributed data pipelines monitored for lineage, quality checks and failed transformations
• Data residency requirements assessed for storage locations, replication and cross-border transfers
• Platform guardrails defined for encryption, logging, access and retention enforcement

Participants can document lineage and establish governance expectations for reusable data products.

• End-to-end lineage maps sources, transformations, controls, reports and consumption points
• Critical report fields traced backward to systems, datasets and transformation logic
• Data products defined through purpose, owner, contract, quality and service expectations
• Data contracts specify schemas, refresh frequency, permissible use and change notification
• Impact analysis identifies downstream reports affected by source or definition changes
• Lineage evidence maintained for audit, troubleshooting and stakeholder confidence

Participants can establish governance controls for analytical models and AI-supported decisions.

• Analytical datasets assessed for fitness, bias risks, provenance and permitted use
• Feature definitions governed through approved sources, transformations and ownership
• Training-data controls document collection, labelling, quality and representativeness
• Model outputs monitored for drift, fairness concerns and decision-impact thresholds
• Human review requirements defined for high-impact automated recommendations and decisions
• Analytics governance aligns data controls with model-risk management responsibilities

Participants can design evidence-based controls and test their operating effectiveness.

• Control objectives linked to specific risks, policies, systems and accountable owners
• Key controls distinguished from supporting activities and informal working practices
• Control evidence specified through logs, approvals, reconciliations, reports and attestations
• Design effectiveness tested against stated objectives, risks and control frequency
• Operating effectiveness sampled through evidence review, reperformance and exception analysis
• Control deficiencies recorded with remediation owners, dates and validation criteria

Participants can create metrics that demonstrate governance performance and business value.

• Governance KPIs selected for ownership coverage, quality performance and issue resolution
• Key risk indicators track control failures, overdue remediation and unclassified data
• Baseline measures established before remediation to demonstrate improvement over time
• Scorecards segment results by domain, system, owner, process and business unit
• Benefits cases quantify rework reduction, reporting reliability and avoided risk exposure
• Management reporting combines trends, exceptions, decisions required and accountable actions

Participants can plan stakeholder adoption for new governance roles, controls and standards.

• Stakeholder mapping identifies sponsors, owners, stewards, users and potential blockers
• Change impacts assessed for processes, roles, systems, incentives and reporting
• Communications plans explain governance decisions, responsibilities and expected behaviours
• Steward communities established through meeting rhythms, knowledge sharing and escalation support
• Training needs defined for owners, stewards, data users and technical teams
• Adoption measures track policy compliance, participation, issue reporting and control completion

Participants can produce a phased implementation roadmap aligned to risk, value and delivery capacity.

• Current-state assessment scores capabilities across people, process, data and technology
• Target-state design defines priority controls, roles, artefacts and platform dependencies
• Roadmap initiatives sequenced by material risk, business value and implementation effort
• Quick wins selected without undermining longer-term operating-model requirements
• Dependencies mapped across transformation programmes, system changes and regulatory commitments
• Implementation governance defines milestones, decision gates, funding and benefit tracking

Participants can build, review and present a complete data governance plan for a defined business data domain.

• Domain scenario framed with business objectives, risks, stakeholders and material data
• Governance charter drafted with scope, principles, decision rights and success measures
• Ownership model assigns accountable owners, stewards, forums and escalation routes
• Critical data element register specifies definitions, lineage, quality rules and controls
• Roadmap prioritises remediation, metadata, access, retention and measurement initiatives
• Peer review tests feasibility, control evidence, stakeholder adoption and presentation clarity