Roles Strategy & Transformation
Business Analysis Manager
Manages a team of business analysts and allocates them across the organisation's changes.
3tasks in this job
67skills 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 PowerPointMicrosoft Power BIJiraAzure DevOpsMicrosoft VisioMicrosoft Excel
All 3 tasks, where people get stuck and all 67 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
- Maintaining requirements traceability and change control
- 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
- Combining tables safely: cardinality, fan-out and conservation of totals
- Comparing groups
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Defining the analysis population: filters, nulls, dates and time zones
- 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
- Discovering process variants from event logs
- 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
- Facilitating evidence-led retrospectives and corrective actions
- Fitting, checking and interpreting inferential regression models
- Formulating answerable data questions
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- 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
- Preparing analysis-ready datasets reproducibly
- 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
- 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
- Training reproducible machine learning models
- 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
Paths to start
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
Skill Path
Exploratory Data Analysis and Pattern Discovery
Describe a distribution honestly and decide which unusual records need checking before anyone acts.
- 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
4tasks in this job
70skills it uses
6tools named
The skills this job leans on most
- 1Writing verifiable requirements and acceptance criteriaNeeded in 3 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 PurviewMicrosoft Power BIJiraAzure DevOpsMicrosoft VisioMicrosoft Excel
All 4 tasks, where people get stuck and all 70 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
- Forecast delivery capacity against portfolio demand and recommend allocation of people and supplier capacity
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
- Maintaining requirements traceability and change control
- 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
- Combining tables safely: cardinality, fan-out and conservation of totals
- Comparing groups
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Defining the analysis population: filters, nulls, dates and time zones
- 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
- Discovering process variants from event logs
- 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
- Facilitating evidence-led retrospectives and corrective actions
- Fitting, checking and interpreting inferential regression models
- Formulating answerable data questions
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting policy scope and delegated authority limits
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping customer journeys and evidence gaps
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Preparing analysis-ready datasets reproducibly
- 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
- 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
- Training reproducible machine learning models
- 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
Paths to start
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
Skill Path
Exploratory Data Analysis and Pattern Discovery
Describe a distribution honestly and decide which unusual records need checking before anyone acts.
- 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
5tasks in this job
76skills it uses
7tools named
The skills this job leans on most
- 1Writing verifiable requirements and acceptance criteriaNeeded in 4 of 5 tasks
- 2Eliciting needs through interviews, workshops and observationNeeded in 2 of 5 tasks
- 3Maintaining requirements traceability and change controlNeeded in 2 of 5 tasks
ToolsMicrosoft PurviewMicrosoft PowerPointMicrosoft Power BIJiraAzure DevOpsMicrosoft Visio+ 1 more
All 5 tasks, where people get stuck and all 76 skills
Tasks
- Define and govern a KPI metric contract
- Verify project deliverables and accept a controlled handoff
- 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
- Forecast delivery capacity against portfolio demand and recommend allocation of people and supplier capacity
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
- Maintaining requirements traceability and change control
- 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
- Combining tables safely: cardinality, fan-out and conservation of totals
- Comparing groups
- Coordinating accountable handoffs between teams
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Defining the analysis population: filters, nulls, dates and time zones
- 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
- Discovering process variants from event logs
- 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
- Facilitating evidence-led retrospectives and corrective actions
- Fitting, checking and interpreting inferential regression models
- Forecasting project cost and schedule from progress evidence
- Formulating answerable data questions
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting policy scope and delegated authority limits
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping customer journeys and evidence gaps
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Planning delivery dependencies and the critical path
- Preparing analysis-ready datasets reproducibly
- Prioritising work by evidenced value, cost, risk and confidence
- 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
- Sampling and reviewing a team's work against standards
- 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
- Training reproducible machine learning models
- Validating records by matching, recomputation and end-to-end tracing
- Verifying AI-assisted outputs against sources
- Verifying project progress against accepted outcomes
- 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
Paths to start
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
Skill Path
Exploratory Data Analysis and Pattern Discovery
Describe a distribution honestly and decide which unusual records need checking before anyone acts.
- 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
Manages a team of business analysts and allocates them across the organisation's changes. Owns analysis standards and the quality of requirements across those changes, including how success metrics are verified from data. Differs from the Lead Business Analyst in managing people and standards across many initiatives rather than leading analysis on one.