Roles Strategy & Transformation
Head of Business Analysis
Accountable for the business analysis discipline and for the quality of requirements and success metrics across the change portfolio.
3tasks in this job
49skills it uses
4tools named
The skills this job leans on most
- 1Writing verifiable requirements and acceptance criteriaNeeded in 2 of 3 tasks
- 2Eliciting needs through interviews, workshops and observationNeeded in 2 of 3 tasks
- 3Maintaining requirements traceability and change controlNeeded in 2 of 3 tasks
ToolsMicrosoft PurviewJiraMicrosoft PowerPointMicrosoft Power BIMicrosoft ExcelQlik SenseMicrosoft Visio
All 3 tasks, where people get stuck and all 49 skills
Tasks
- Define and govern a KPI metric contract
- Elicit, analyse and manage requirements for a data, analytics or system change
- Analyse a current business process from event and operational data and define the future-state process
Where people get stuck
- Eliciting needs through interviews, workshops and observation
- Maintaining requirements traceability and change control
All skills
- Eliciting needs through interviews, workshops and observation
- Writing verifiable requirements and acceptance criteria
- Analysing event sequences, states and durations
- Assessing data source credibility and fitness for a question
- Assigning and operating data ownership and stewardship
- Calculating and interpreting percentages, rates and ratios
- Choosing and justifying missing-data treatments
- Comparing groups
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Describing distributions with summaries and plots
- Designing and interpreting hypothesis tests
- Designing efficient and reproducible investigations
- Designing semantic models for governed analytics
- Diagnosing data quality exceptions
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Exploring data to find patterns that survive validation
- Giving specific developmental feedback on data and AI work
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Mapping customer journeys and evidence gaps
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Profiling data
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Selecting charts for evidence and decisions
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- 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
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
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
Discovering Requirements and Defining Metrics
Turn stakeholder needs into verifiable requirements and governed metric definitions with owners, grain and acceptance tests.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Diagnostic Investigation and Root Cause Analysis
Resolve contradictory evidence and find, verify and prevent the causes of business and data anomalies.
- Modules
- 5 open
- Practice data
- Raket Parts
3tasks in this job
59skills it uses
5tools named
The skills this job leans on most
- 1Writing verifiable requirements and acceptance criteriaNeeded in 2 of 3 tasks
- 2Eliciting needs through interviews, workshops and observationNeeded in 2 of 3 tasks
- 3Maintaining requirements traceability and change controlNeeded in 2 of 3 tasks
ToolsMicrosoft PurviewMicrosoft Power BIJiraMicrosoft PowerPointMicrosoft Visio
All 3 tasks, where people get stuck and all 59 skills
Tasks
- Define and govern a KPI metric contract
- Elicit, analyse and manage requirements for a data, analytics or system change
- Analyse a current business process from event and operational data and define the future-state process
Where people get stuck
- Eliciting needs through interviews, workshops and observation
- Maintaining requirements traceability and change control
All skills
- Eliciting needs through interviews, workshops and observation
- Writing verifiable requirements and acceptance criteria
- Analysing event sequences, states and durations
- Assessing data source credibility and fitness for a question
- Assigning and operating data ownership and stewardship
- Calculating and interpreting percentages, rates and ratios
- Calculating and verifying KPIs against their specification
- Choosing and justifying missing-data treatments
- Comparing groups
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Describing distributions with summaries and plots
- Designing and interpreting hypothesis tests
- Designing and versioning data contracts for consumers
- Designing efficient and reproducible investigations
- Designing semantic models for governed analytics
- Diagnosing data quality exceptions
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Exploring data to find patterns that survive validation
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Making accountable decisions under uncertainty
- Mapping customer journeys and evidence gaps
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Recognising and resolving ethical conflicts in data and AI work
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- 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
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
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
Discovering Requirements and Defining Metrics
Turn stakeholder needs into verifiable requirements and governed metric definitions with owners, grain and acceptance tests.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Diagnostic Investigation and Root Cause Analysis
Resolve contradictory evidence and find, verify and prevent the causes of business and data anomalies.
- Modules
- 5 open
- Practice data
- Raket Parts
4tasks in this job
71skills it uses
7tools named
The skills this job leans on most
- 1Writing verifiable requirements and acceptance criteriaNeeded in 2 of 4 tasks
- 2Eliciting needs through interviews, workshops and observationNeeded in 2 of 4 tasks
- 3Maintaining requirements traceability and change controlNeeded in 2 of 4 tasks
ToolsMicrosoft PurviewJiraMicrosoft PowerPointMicrosoft Power BIMicrosoft ExcelQlik Sense+ 1 more
All 4 tasks, where people get stuck and all 71 skills
Tasks
- Define and govern a KPI metric contract
- Elicit, analyse and manage requirements for a data, analytics or system change
- Track and verify benefits realisation against the business case
- Analyse a current business process from event and operational data and define the future-state process
Where people get stuck
- Eliciting needs through interviews, workshops and observation
- Maintaining requirements traceability and change control
All skills
- Eliciting needs through interviews, workshops and observation
- Writing verifiable requirements and acceptance criteria
- Analysing event sequences, states and durations
- Assessing data source credibility and fitness for a question
- Assigning and operating data ownership and stewardship
- Calculating and interpreting percentages, rates and ratios
- Calculating and verifying KPIs against their specification
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Comparing groups
- Comparing periods consistently
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Describing distributions with summaries and plots
- Designing and interpreting hypothesis tests
- Designing and versioning data contracts for consumers
- Designing efficient and reproducible investigations
- Designing implementable change plans
- Designing semantic models for governed analytics
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing data quality exceptions
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating learning transfer and performance outcomes
- Exploring data to find patterns that survive validation
- Facilitating group decisions using disputed evidence
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Interpreting trends and seasonality
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Making accountable decisions under uncertainty
- Mapping customer journeys and evidence gaps
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reasoning about systems, feedback and second-order effects
- Recognising and resolving ethical conflicts in data and AI work
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying reports and dashboards before and after release
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
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
Discovering Requirements and Defining Metrics
Turn stakeholder needs into verifiable requirements and governed metric definitions with owners, grain and acceptance tests.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Diagnostic Investigation and Root Cause Analysis
Resolve contradictory evidence and find, verify and prevent the causes of business and data anomalies.
- Modules
- 5 open
- Practice data
- Raket Parts
About the role
Accountable for the business analysis discipline and for the quality of requirements and success metrics across the change portfolio. Heads the business analysis managers and decides analysis standards, capacity and how analysts are deployed against portfolio priorities. Differs from the Head of Transformation, who owns the portfolio, by owning the analysis that defines and evidences what it delivers.
Also called Head of Business Analysis and Change