Data Governance Manager
Accountable for managing a team of data stewards across several domains and for the governance programme that coordinates their work.
5tasks in this job
65skills it uses
6tools named
The skills this job leans on most
- 1Assessing lawful basis, purpose compatibility and minimisation for data reuseNeeded in 4 of 5 tasks
- 2Controlling disclosure risk in shared data and outputsNeeded in 2 of 5 tasks
- 3Interpreting policy scope and delegated authority limitsNeeded in 2 of 5 tasks
ToolsCollibraServiceNowOneTrustMicrosoft Power BIMicrosoft PowerPointTableauMicrosoft PurviewMicrosoft ExcelAlationMicrosoft SharePoint
All 5 tasks, where people get stuck and all 65 skills
Tasks
- Review a proposed use, reuse, disclosure of or access to data and record the permitted-use decision
- Prepare a periodic performance or risk report for a management or governance committee
- Assign data ownership and stewardship and decide a domain's data standards
- Monitor and correct data quality in a domain or system of record and drive remediation at source
- Maintain the AI use register and AI management system evidence across all AI systems
Where people get stuck
- Writing evidence-led findings and auditable recommendations with their limits
- Presenting and defending evidence-based recommendations to decision-makers
- Assigning and operating data ownership and stewardship
All skills
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Controlling disclosure risk in shared data and outputs
- Interpreting policy scope and delegated authority limits
- Applying data classification and handling rules
- Assessing data source credibility and fitness for a question
- Assessing privacy risk through data protection impact assessments
- Assigning and operating data ownership and stewardship
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Communicating sensitive or unwelcome analytical findings
- Constructing clear and honest charts
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing decision rights and dispute routes for data and AI work
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- Designing testable data controls
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Discovering undeclared data processing and AI use from operational evidence
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Explaining technical work to accountable non-specialists
- Facilitating evidence-led retrospectives and corrective actions
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and prioritising critical data elements
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting correlation without inferring cause
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Locating and acting on personal data for individual rights requests
- Managing master data identities and exceptions
- Mapping personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Profiling data
- Reading charts and dashboards correctly, including their state
- Recognising and containing suspected personal data breaches
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Record-keeping requirements for processing activities and AI systems
- Repairing data defects reversibly under owner authority
- Resolving consent and suppression status across systems
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
Paths to start
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- Modules
- 5 open
- Practice data
- Raket Parts
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
8tasks in this job
89skills it uses
10tools named
The skills this job leans on most
- 1Assessing lawful basis, purpose compatibility and minimisation for data reuseNeeded in 5 of 8 tasks
- 2Designing testable data controlsNeeded in 5 of 8 tasks
- 3Assigning and operating data ownership and stewardshipNeeded in 4 of 8 tasks
- 4Writing evidence-led findings and auditable recommendations with their limitsNeeded in 1 of 8 tasks
- 5Presenting and defending evidence-based recommendations to decision-makersNeeded in 1 of 8 tasks
ToolsCollibraServiceNowOneTrustMicrosoft Power BIMicrosoft PowerPointTableau+ 4 more
All 8 tasks, where people get stuck and all 89 skills
Tasks
- Review a proposed use, reuse, disclosure of or access to data and record the permitted-use decision
- Prepare a periodic performance or risk report for a management or governance committee
- Design and review governed data-access entitlement policy
- Maintain a data and AI risk register and record risk treatment and acceptance
- Assign data ownership and stewardship and decide a domain's data standards
- Test the design and operating effectiveness of a data-dependent control
- Monitor and correct data quality in a domain or system of record and drive remediation at source
- Maintain the AI use register and AI management system evidence across all AI systems
Where people get stuck
- Enforcing and auditing data access
- Maintaining a risk register with owners, triggers and responses
- Rating risks against appetite and choosing proportionate treatment
All skills
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assigning and operating data ownership and stewardship
- Designing testable data controls
- Presenting and defending evidence-based recommendations to decision-makers
- Writing evidence-led findings and auditable recommendations with their limits
- Analysing error patterns and failure modes in models and AI systems
- Applying data classification and handling rules
- Assessing data source credibility and fitness for a question
- Assessing data-service continuity and recoverability
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Designing and appraising samples
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing automated pipelines and processes that fail safely and recover
- Designing decision rights and dispute routes for data and AI work
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- Designing role-based access entitlement models
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Discovering undeclared data processing and AI use from operational evidence
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining technical work to accountable non-specialists
- Facilitating evidence-led retrospectives and corrective actions
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and prioritising critical data elements
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Judging whether uncertainty changes the decision
- Locating and acting on personal data for individual rights requests
- Managing master data identities and exceptions
- Mapping personal data processing and flows
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Monitoring and testing data control effectiveness
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Preserving incident evidence and timeline
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading charts and dashboards correctly, including their state
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Record-keeping requirements for processing activities and AI systems
- Repairing data defects reversibly under owner authority
- Resolving consent and suppression status across systems
- Running incident command: severity, timeline and status updates
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Testing the completeness and accuracy of reports and data that controls rely on
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
Paths to start
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- Modules
- 5 open
- Practice data
- Raket Parts
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
7tasks in this job
90skills it uses
11tools named
The skills this job leans on most
- 1Enforcing and auditing data accessNeeded in 5 of 7 tasks
- 2Assessing lawful basis, purpose compatibility and minimisation for data reuseNeeded in 4 of 7 tasks
- 3Controlling disclosure risk in shared data and outputsNeeded in 2 of 7 tasks
- 4Designing enforceable data governance policiesNeeded in 1 of 7 tasks
- 5Maintaining a risk register with owners, triggers and responsesCore to this role
ToolsCollibraServiceNowMicrosoft Power BIMicrosoft PowerPointTableauMicrosoft Purview+ 5 more
All 7 tasks, where people get stuck and all 90 skills
Tasks
- Review a proposed use, reuse, disclosure of or access to data and record the permitted-use decision
- Prepare a periodic performance or risk report for a management or governance committee
- Design and review governed data-access entitlement policy
- Maintain a data and AI risk register and record risk treatment and acceptance
- Test the design and operating effectiveness of a data-dependent control
- Classify an AI system's risk and assure its documentation, evaluation and oversight evidence
- Maintain the AI use register and AI management system evidence across all AI systems
Where people get stuck
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Rating risks against appetite and choosing proportionate treatment
- Interpreting policy scope and delegated authority limits
All skills
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Controlling disclosure risk in shared data and outputs
- Designing enforceable data governance policies
- Enforcing and auditing data access
- Analysing error patterns and failure modes in models and AI systems
- Applying data classification and handling rules
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Assigning and operating data ownership and stewardship
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Constructing clear and honest charts
- Designing and appraising samples
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing automated pipelines and processes that fail safely and recover
- Designing decision rights and dispute routes for data and AI work
- Designing efficient and reproducible investigations
- Designing role-based access entitlement models
- Designing testable data controls
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Discovering undeclared data processing and AI use from operational evidence
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining technical work to accountable non-specialists
- Facilitating evidence-led retrospectives and corrective actions
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and prioritising critical data elements
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Judging whether uncertainty changes the decision
- Locating and acting on personal data for individual rights requests
- Managing master data identities and exceptions
- Mapping personal data processing and flows
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Monitoring and testing data control effectiveness
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading charts and dashboards correctly, including their state
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Record-keeping requirements for processing activities and AI systems
- Repairing data defects reversibly under owner authority
- Resolving consent and suppression status across systems
- Running incident command: severity, timeline and status updates
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Testing the completeness and accuracy of reports and data that controls rely on
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- Collibra
- ServiceNow
- Microsoft Power BI
- Microsoft PowerPoint
- Tableau
- Microsoft Purview
- Alation
- Microsoft SharePoint
- Credo AI
- OneTrust
- Microsoft Excel
Paths to start
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- Modules
- 5 open
- Practice data
- Raket Parts
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
About the role
Accountable for managing a team of data stewards across several domains and for the governance programme that coordinates their work. Sets governance priorities and standards across domains and decides policy exceptions within delegated authority. Manages people and cross-domain priorities, where a Data Governance Lead owns one domain's decisions.