Analytics Manager
Accountable for the delivery, priorities, quality assurance and people development of an analytics or data science team.
4tasks in this job
74skills it uses
6tools named
The skills this job leans on most
- 1Verifying reports and dashboards before and after releaseNeeded in 3 of 4 tasks
- 2Establishing and declaring data grainNeeded in 2 of 4 tasks
- 3Combining tables safely: cardinality, fan-out and conservation of totalsNeeded in 1 of 4 tasks
- 4Building decision-focused reports and dashboardsNeeded in 1 of 4 tasks
- 5Specifying BI measure semantics: additivity, aggregation and timeNeeded in 1 of 4 tasks
ToolsWorkdaySympa HRSQL (query language)Microsoft Power BIJiraMicrosoft ExcelSmartsheetTableauMicrosoft PowerPoint
All 4 tasks, where people get stuck and all 74 skills
Tasks
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Answer a commissioned business question with reproducible query-based analysis
- Deliver and sustain a trusted BI report or dashboard
- Set and maintain working standards for a team's analytical, reporting and modelling work products
Where people get stuck
- Prioritising work by evidenced value, cost, risk and confidence
- Planning delivery dependencies and the critical path
- Verifying benefits realisation and attribution
All skills
- Building decision-focused reports and dashboards
- Combining tables safely: cardinality, fan-out and conservation of totals
- Establishing and declaring data grain
- Specifying BI measure semantics: additivity, aggregation and time
- Verifying reports and dashboards before and after release
- 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
- Building data protection by design into data products and pipelines
- 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
- Classifying AI uses against regulatory obligations
- Comparing groups
- Comparing periods consistently
- Controlling disclosure risk in shared data and outputs
- 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 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 implementable change plans
- Designing semantic models for governed analytics
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- Formulating answerable data questions
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and prioritising critical data elements
- 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
- Judging whether and how to use AI for a task
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Preparing analysis-ready datasets reproducibly
- Profiling data
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reconciling conflicting reports
- 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 changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Verifying AI-assisted outputs against sources
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
Paths to start
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
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
8tasks in this job
132skills it uses
9tools named
The skills this job leans on most
- 1Verifying reports and dashboards before and after releaseNeeded in 7 of 8 tasks
- 2Establishing and declaring data grainNeeded in 4 of 8 tasks
- 3Combining tables safely: cardinality, fan-out and conservation of totalsNeeded in 3 of 8 tasks
- 4Building decision-focused reports and dashboardsNeeded in 2 of 8 tasks
- 5Specifying BI measure semantics: additivity, aggregation and timeNeeded in 2 of 8 tasks
ToolsWorkdaySympa HRSQL (query language)Microsoft Power BIJiraMicrosoft Excel+ 3 more
All 8 tasks, where people get stuck and all 132 skills
Tasks
- Assess data and AI capability gaps in a function and plan their development
- Investigate inconsistent BI reporting
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Answer a commissioned business question with reproducible query-based analysis
- Deliver and sustain a trusted BI report or dashboard
- Prepare a periodic performance or risk report for a management or governance committee
- Develop and document a fit-for-purpose analytical or ML model
- Set and maintain working standards for a team's analytical, reporting and modelling work products
Where people get stuck
All skills
- Building decision-focused reports and dashboards
- Combining tables safely: cardinality, fan-out and conservation of totals
- Establishing and declaring data grain
- Specifying BI measure semantics: additivity, aggregation and time
- Verifying reports and dashboards before and after release
- Analysing event sequences, states and durations
- Analysing organisation structure, spans and critical-role cover
- Applying data classification and handling rules
- Applying research ethics and participant consent
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assigning and operating data ownership and stewardship
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- 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
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- 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 comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing auditable decision models
- Designing automated pipelines and processes that fail safely and recover
- Designing decision rights and dispute routes for data and AI work
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- 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 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
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating learning transfer and performance outcomes
- Evaluating product outcomes and metric integrity
- Evaluating whether people understood, adopted and sustained a change
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- Formulating answerable data questions
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and prioritising critical data elements
- Identifying and quantifying decision downside and exposure
- 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 correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Investigating workforce movement and attrition trends
- 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 external evidence applies to the decision context
- Judging whether uncertainty changes the decision
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Modelling workforce demand and supply
- Monitoring data quality and freshness in production
- Planning delivery dependencies and the critical path
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- Reasoning about systems, feedback and second-order effects
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Repairing data defects reversibly under owner authority
- Revising prior decisions when evidence changes
- Running sensitivity and break-even analysis on model assumptions
- 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 changes to data services and business systems for business meaning and downstream impact
- 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
- 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 testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
Paths to start
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
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
9tasks in this job
132skills it uses
9tools named
The skills this job leans on most
- 1Verifying reports and dashboards before and after releaseNeeded in 8 of 9 tasks
- 2Establishing and declaring data grainNeeded in 4 of 9 tasks
- 3Combining tables safely: cardinality, fan-out and conservation of totalsNeeded in 3 of 9 tasks
- 4Building decision-focused reports and dashboardsNeeded in 2 of 9 tasks
- 5Specifying BI measure semantics: additivity, aggregation and timeNeeded in 2 of 9 tasks
ToolsWorkdaySympa HRSQL (query language)JiraMicrosoft ExcelSmartsheet+ 3 more
All 9 tasks, where people get stuck and all 132 skills
Tasks
- Assess data and AI capability gaps in a function and plan their development
- Investigate inconsistent BI reporting
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Answer a commissioned business question with reproducible query-based analysis
- Deliver and sustain a trusted BI report or dashboard
- Prepare a periodic performance or risk report for a management or governance committee
- Develop and document a fit-for-purpose analytical or ML model
- Design and govern the operating model for data, analytics and AI work in a function
- Set and maintain working standards for a team's analytical, reporting and modelling work products
Where people get stuck
All skills
- Building decision-focused reports and dashboards
- Combining tables safely: cardinality, fan-out and conservation of totals
- Establishing and declaring data grain
- Specifying BI measure semantics: additivity, aggregation and time
- Verifying reports and dashboards before and after release
- Analysing event sequences, states and durations
- Analysing organisation structure, spans and critical-role cover
- Applying data classification and handling rules
- Applying research ethics and participant consent
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assigning and operating data ownership and stewardship
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- 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
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- 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 comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing auditable decision models
- Designing automated pipelines and processes that fail safely and recover
- Designing decision rights and dispute routes for data and AI work
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- 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 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
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating learning transfer and performance outcomes
- Evaluating product outcomes and metric integrity
- Evaluating whether people understood, adopted and sustained a change
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- Formulating answerable data questions
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and prioritising critical data elements
- Identifying and quantifying decision downside and exposure
- 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 correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Investigating workforce movement and attrition trends
- 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 external evidence applies to the decision context
- Judging whether uncertainty changes the decision
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Modelling workforce demand and supply
- Monitoring data quality and freshness in production
- Planning delivery dependencies and the critical path
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- Reasoning about systems, feedback and second-order effects
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Repairing data defects reversibly under owner authority
- Revising prior decisions when evidence changes
- Running sensitivity and break-even analysis on model assumptions
- 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 changes to data services and business systems for business meaning and downstream impact
- 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
- 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 testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
All tools
- Workday
- Sympa HR
- SQL (query language)
- Jira
- Microsoft Excel
- Smartsheet
- Microsoft Power BI
- Tableau
- Microsoft PowerPoint
Paths to start
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
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
About the role
Accountable for the delivery, priorities, quality assurance and people development of an analytics or data science team. Decides what the team works on, which reports and models are trusted and how capability is built. The Lead Data Scientist owns technical modelling standards. Differs from the Head of Data & Analytics, who owns the operating model and investment for the whole Function.
Also called Data Manager (when the role manages a team), BI Manager, Data Science Manager