Data Governance Frameworks Training Course

Participants can select and tailor a governance framework and maturity assessment approach.

• EDM Council DCAM domains applied to governance capability assessment
• ISO 8000 principles for data quality, exchange and master data
• COBIT governance objectives connected to information and technology controls
• Framework selection criteria: regulatory exposure, data scale, operating complexity
• Maturity scoring scales calibrated with observable evidence and control outcomes
• Target-state principles translate framework requirements into local design choices

Participants can design governance structures, decision rights and escalation mechanisms.

• Federated, centralised and hybrid models compared against organisational structures
• Decision rights defined for standards, exceptions, investments and risk acceptance
• Governance council charter specifies membership, authority, cadence and quorum
• Domain working groups coordinate standards, issues and cross-functional dependencies
• Escalation paths separate operational remediation from policy and investment decisions
• Terms of reference document mandates, inputs, decisions and meeting records

Participants can assign practical governance roles and accountabilities for critical data.

• Data owner accountabilities for purpose, quality, access and lifecycle decisions
• Data steward responsibilities for definitions, controls, issues and stakeholder communication
• Data custodian duties covering storage, backup, technical access and recovery
• RACI matrices expose duplicated authority, accountability gaps and approval delays
• Role descriptions distinguish business stewardship from technical metadata administration
• Stewardship capacity planning estimates domain coverage, workload and required expertise

Participants can identify critical data elements and document unambiguous business definitions.

• Critical data elements selected through risk, value, usage and regulatory criteria
• Definition templates capture meaning, calculation, source, owner and permitted values
• Business terms separated from physical fields, reports and application labels
• Calculation rules document inclusions, exclusions, aggregation and reporting periods
• Reference data values governed through approval, versioning and effective dates
• Definition conflicts resolved using evidence, decision rights and documented rationale

Participants can establish glossary, metadata and lineage requirements that support data use.

• Business glossary structures connect terms, policies, owners and related assets
• Metadata minimum fields cover source, sensitivity, quality and lifecycle status
• Technical, business and operational metadata differentiated by user requirements
• Data catalog workflows manage asset registration, review, approval and retirement
• Lineage documentation traces source, transformation, consumption and control points
• Metadata standards define naming conventions, mandatory attributes and validation rules

Participants can design data quality controls, measures and issue remediation processes.

• Data quality dimensions: accuracy, completeness, consistency, timeliness, validity and uniqueness
• Profiling techniques quantify nulls, duplicates, outliers and format violations
• Quality rules specify thresholds, tolerances, exceptions, owners and monitoring frequency
• Control types distinguish preventive, detective, corrective and compensating measures
• Root cause analysis separates source defects from transformation and process failures
• Issue logs record impact, priority, remediation owner, due date and closure evidence

Participants can produce quality scorecards that support accountable management decisions.

• Scorecard measures combine rule results, business impact and trend analysis
• Pass rates calculated against approved populations, thresholds and reporting periods
• Data quality KPIs distinguish control performance from underlying data condition
• Heat maps prioritise domains by risk exposure, defect volume and business impact
• Exception reporting documents accepted risk, expiry dates and accountable approvers
• Management packs link quality trends to remediation progress and investment decisions

Participants can apply classification and lifecycle controls to sensitive information assets.

• Classification schemes distinguish public, internal, confidential and restricted data
• Personal data inventories identify processing purpose, lawful basis and recipients
• Retention schedules align business need, legal duties and disposal requirements
• Privacy by design embeds minimisation, purpose limitation and consent controls
• Data protection impact assessments identify high-risk processing and mitigation actions
• Disposal controls cover deletion, anonymisation, archival and destruction evidence

Participants can define access approval, sharing and monitoring controls for governed data.

• Role-based access control maps job functions to approved data permissions
• Attribute-based access control applies contextual rules for sensitive data access
• Access requests capture purpose, justification, owner approval and expiry dates
• Segregation of duties prevents conflicting data creation, approval and release actions
• Data sharing agreements specify purpose, fields, safeguards, retention and liabilities
• Periodic access recertification confirms continuing need and removes dormant permissions

Participants can govern master and reference data through controlled lifecycle processes.

• Master data domains identify authoritative entities, identifiers and golden records
• Golden record rules resolve duplicates, survivorship and source-system conflicts
• Reference data governance controls code creation, approval, mapping and retirement
• Match-and-merge methods use deterministic rules, probabilistic scores and review queues
• Change requests document business rationale, downstream impact and effective dates
• Crosswalk tables manage mappings between local codes and enterprise standards

Participants can connect governance requirements to architectures, data flows and control locations.

• Conceptual, logical and physical models serve distinct governance decisions
• Data flow diagrams identify collection, transformation, storage, sharing and consumption
• Control points positioned at ingestion, transformation, publication and archival stages
• System-of-record decisions establish authoritative sources for critical data elements
• Lineage evidence supports auditability, impact assessment and defect investigation
• Data product contracts define interfaces, quality commitments and consumer responsibilities

Participants can establish governance controls for analytics datasets, models and AI use cases.

• Analytics data products require defined owners, consumers, quality and service levels
• Model inventory records purpose, owner, training data and approval status
• Bias assessments test representation, outcome disparity and proxy variable risks
• Model documentation records assumptions, performance limits and intended use
• Human oversight controls define review points, override authority and accountability
• AI use policies address prohibited uses, transparency, monitoring and incident response

Participants can create governance documents that are actionable, testable and maintainable.

• Policy statements define mandatory principles, scope, accountabilities and enforcement
• Standards translate policies into measurable requirements and minimum control criteria
• Procedures specify repeatable steps, records, approvals and exception handling
• Control libraries link requirements, risks, tests, owners and evidence
• Exception processes document justification, compensating controls, expiry and review
• Document governance establishes version control, approval authority and review cycles

Participants can operate issue management, control testing and assurance reporting processes.

• Issue triage assesses severity, data impact, customer harm and regulatory exposure
• Problem statements distinguish symptoms, root causes and required control changes
• Corrective action plans assign milestones, owners, dependencies and validation criteria
• Control testing samples evidence against defined design and operating effectiveness
• Assurance reporting tracks overdue actions, repeat issues and accepted risks
• Lessons learned update policies, standards, training and preventive controls

Participants can plan adoption activities that embed governance in operational work.

• Stakeholder mapping identifies sponsors, opponents, decision makers and affected teams
• Change impact assessments examine processes, roles, systems, measures and workload
• Communication plans match governance messages to audience concerns and decisions
• Training plans define role-specific knowledge, practice requirements and evidence
• Communities of practice support stewards through clinics, standards and peer review
• Adoption measures track participation, policy compliance, issue resolution and feedback

Participants can prioritise governance initiatives and create a credible implementation roadmap.

• Roadmap initiatives grouped into foundations, priority domains and scaling capabilities
• Prioritisation matrices compare risk reduction, value, effort and dependency scores
• Business cases quantify remediation cost, control benefits and avoided loss
• Implementation waves sequence policies, roles, tooling, data domains and change actions
• Milestones define decision gates, deliverables, ownership and acceptance criteria
• Benefits realisation plans track outcomes, measures, baselines and review dates

Participants can build, review and present a complete data governance framework for a priority data domain.

• Governance mandate links business objectives, material risks and selected data domain
• Operating model defines councils, decision rights, roles and escalation paths
• Critical data element register documents definitions, owners, rules and controls
• Quality scorecard sets measures, thresholds, reporting cadence and remediation actions
• Policy and standards pack addresses classification, access, retention and metadata
• Phased roadmap prioritises initiatives, dependencies, costs, milestones and success measures
• Peer review tests framework coherence, feasibility, evidence requirements and stakeholder fit