Roles Strategy & Transformation
Lead Business Analyst
Leads the analysis for a complex change and reviews other analysts' work without managing them.
4tasks in this job
46skills it uses
13tools named
The skills this job leans on most
- 1Diagnosing operational handoffs and flowNeeded in 3 of 4 tasks
- 2Writing verifiable requirements and acceptance criteriaNeeded in 2 of 4 tasks
- 3Mapping proposed process changes and control impactsNeeded in 2 of 4 tasks
- 4Eliciting needs through interviews, workshops and observationNeeded in 2 of 4 tasks
- 5Maintaining requirements traceability and change controlNeeded in 2 of 4 tasks
ToolsMicrosoft ExcelMicrosoft Power BIMicrosoft PurviewMicrosoft SQL Server (with SSMS)Google BigQuerySalesforceJiraAzure DevOpsMicrosoft 365 CopilotCelonisSAP SignavioMicrosoft VisioServiceNow
All 4 tasks, where people get stuck and all 46 skills
Tasks
- Define and govern a KPI metric contract
- Assess data quality and fitness for a stated use
- 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
- Governing shared data definitions
- Calculating and verifying KPIs against their specification
- Specifying BI measure semantics: additivity, aggregation and time
All skills
- Diagnosing operational handoffs and flow
- Eliciting needs through interviews, workshops and observation
- Mapping proposed process changes and control impacts
- Writing verifiable requirements and acceptance criteria
- Analysing event sequences, states and durations
- Applying data classification and handling rules
- 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 and validating digital analytics event measurement
- Designing efficient and reproducible investigations
- Designing semantic models for governed analytics
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing incorrect query and calculation results
- 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
- Identifying keys and testing referential integrity
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Judging whether a change exceeds normal variation
- Mapping customer journeys and evidence gaps
- 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
- 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
SQL for Analysis
Answer real business questions with SQL, from your first query to finding why a number moved.
- Modules
- 5 open
- Runs in
- Your browser
6tasks in this job
96skills it uses
14tools named
The skills this job leans on most
- 1Defining measurable outcomes and leading indicatorsNeeded in 3 of 6 tasks
- 2Diagnosing operational handoffs and flowNeeded in 3 of 6 tasks
- 3Governing shared data definitionsNeeded in 2 of 6 tasks
- 4Calculating and verifying KPIs against their specificationNeeded in 1 of 6 tasks
- 5Specifying BI measure semantics: additivity, aggregation and timeNeeded in 1 of 6 tasks
ToolsMicrosoft Power BIMicrosoft PurviewMicrosoft ExcelMicrosoft SQL Server (with SSMS)Google BigQuerySalesforce+ 8 more
All 6 tasks, where people get stuck and all 96 skills
Tasks
- Define and govern a KPI metric contract
- Assess data quality and fitness for a stated use
- Verify project deliverables and accept a controlled handoff
- 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
- Maintaining requirements traceability and change control
- Discovering process variants from event logs
All skills
- Calculating and verifying KPIs against their specification
- Defining measurable outcomes and leading indicators
- Diagnosing operational handoffs and flow
- Governing shared data definitions
- Specifying BI measure semantics: additivity, aggregation and time
- Analysing event sequences, states and durations
- Applying data classification and handling rules
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assigning and operating data ownership and stewardship
- Calculating and interpreting percentages, rates and ratios
- 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
- Comparing periods consistently
- Coordinating accountable handoffs between teams
- Decomposing one's own tasks for AI assistance
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and interpreting hypothesis tests
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing efficient and reproducible investigations
- Designing experiments
- Designing implementable change plans
- Designing semantic models for governed analytics
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Discovering process variants from event logs
- Eliciting needs through interviews, workshops and observation
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating learning transfer and performance outcomes
- Evaluating whether people understood, adopted and sustained a change
- 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
- 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
- Interpreting trends and seasonality
- 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
- Maintaining a risk register with owners, triggers and responses
- Maintaining requirements traceability and change control
- Managing master data identities and exceptions
- 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
- Monitoring data quality and freshness in production
- 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
- Selecting charts for evidence and decisions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying project progress against accepted outcomes
- Verifying reports and dashboards before and after release
- Weighing conflicting evidence by design strength
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
All tools
- Microsoft Power BI
- Microsoft Purview
- Microsoft Excel
- Microsoft SQL Server (with SSMS)
- Google BigQuery
- Salesforce
- Jira
- Azure DevOps
- ServiceNow
- Microsoft 365 Copilot
- SAP S/4HANA
- Microsoft Visio
- SAP Signavio
- Celonis
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
SQL for Analysis
Answer real business questions with SQL, from your first query to finding why a number moved.
- Modules
- 5 open
- Runs in
- Your browser
6tasks in this job
97skills it uses
13tools named
The skills this job leans on most
- 1Defining measurable outcomes and leading indicatorsNeeded in 3 of 6 tasks
- 2Diagnosing operational handoffs and flowNeeded in 3 of 6 tasks
- 3Governing shared data definitionsNeeded in 2 of 6 tasks
- 4Calculating and verifying KPIs against their specificationNeeded in 1 of 6 tasks
- 5Specifying BI measure semantics: additivity, aggregation and timeNeeded in 1 of 6 tasks
ToolsMicrosoft Power BIMicrosoft PurviewMicrosoft ExcelMicrosoft SQL Server (with SSMS)Google BigQuerySalesforce+ 7 more
All 6 tasks, where people get stuck and all 97 skills
Tasks
- Define and govern a KPI metric contract
- Assess data quality and fitness for a stated use
- Verify project deliverables and accept a controlled handoff
- 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
- Maintaining requirements traceability and change control
- Discovering process variants from event logs
All skills
- Calculating and verifying KPIs against their specification
- Defining measurable outcomes and leading indicators
- Diagnosing operational handoffs and flow
- Governing shared data definitions
- Specifying BI measure semantics: additivity, aggregation and time
- Analysing event sequences, states and durations
- Applying data classification and handling rules
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assigning and operating data ownership and stewardship
- Calculating and interpreting percentages, rates and ratios
- 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
- Comparing periods consistently
- Coordinating accountable handoffs between teams
- Decomposing one's own tasks for AI assistance
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and interpreting hypothesis tests
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing efficient and reproducible investigations
- Designing experiments
- Designing implementable change plans
- Designing semantic models for governed analytics
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Discovering process variants from event logs
- Eliciting needs through interviews, workshops and observation
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating learning transfer and performance outcomes
- Evaluating whether people understood, adopted and sustained a change
- 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
- 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
- Interpreting trends and seasonality
- 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
- Maintaining a risk register with owners, triggers and responses
- Maintaining requirements traceability and change control
- Managing master data identities and exceptions
- 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
- Monitoring data quality and freshness in production
- 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 decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying project progress against accepted outcomes
- Verifying reports and dashboards before and after release
- Weighing conflicting evidence by design strength
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
All tools
- Microsoft Power BI
- Microsoft Purview
- Microsoft Excel
- Microsoft SQL Server (with SSMS)
- Google BigQuery
- Salesforce
- ServiceNow
- Jira
- Azure DevOps
- SAP S/4HANA
- Celonis
- Microsoft Visio
- SAP Signavio
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
SQL for Analysis
Answer real business questions with SQL, from your first query to finding why a number moved.
- Modules
- 5 open
- Runs in
- Your browser
About the role
Leads the analysis for a complex change and reviews other analysts' work without managing them. Owns the analysis approach across a whole initiative and is accountable for the quality of requirements that others produce. Data judgement sits in setting success metrics that can be verified from data.