Roles Strategy & Transformation
Change Manager
Manages a change team and is accountable for the quality of its change plans and adoption evaluation.
3tasks in this job
32skills it uses
7tools named
The skills this job leans on most
- 1Designing implementable change plansNeeded in 3 of 3 tasks
- 2Evaluating whether people understood, adopted and sustained a changeNeeded in 3 of 3 tasks
- 3Measuring campaign outcomes with valid denominatorsNeeded in 3 of 3 tasks
- 4Reasoning about systems, feedback and second-order effectsNeeded in 2 of 3 tasks
- 5Mapping stakeholders' decisions, constraints and needsCore to this role
ToolsMicrosoft ExcelMicrosoft SharePointMicrosoft Power BIQualtricsServiceNowWorkdaySympa HR
All 3 tasks, where people get stuck and all 32 skills
Tasks
- Plan a data, AI or system-enabled change with measurable adoption outcomes
- Evaluate a communications or change campaign against its objectives
- Assess the combined change load on affected groups and recommend sequencing
Where people get stuck
- Reasoning about systems, feedback and second-order effects
- Critically evaluating marketing attribution claims
All skills
- Designing implementable change plans
- Evaluating whether people understood, adopted and sustained a change
- Measuring campaign outcomes with valid denominators
- Applying research ethics and participant consent
- Choosing an analytical method to fit the question and data
- Controlling disclosure risk in shared data and outputs
- Defining measurable outcomes and leading indicators
- Designing and appraising samples
- Designing and quality-checking survey instruments
- Designing decision-oriented research with customers, users or employees
- Designing experiments
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Escalating unresolved risks with evidence and clear ownership
- Estimating and challenging causal effects
- Evaluating employee experience evidence responsibly
- Evaluating experimental results and integrity
- Evaluating learning transfer and performance outcomes
- Giving specific developmental feedback on data and AI work
- Independently checking analytical conclusions and claims
- Interpreting customer feedback and qualitative evidence
- Interpreting trends and seasonality
- Judging the materiality of defects and deficiencies for the intended use
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying reports and dashboards before and after release
- Weighing conflicting evidence by design strength
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
Experimentation and Causal Impact Evaluation
Answer "did it work" with randomised experiments where possible and quasi-experimental methods where not.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Leading Analytics and AI Change
Lead adoption, diagnose learning needs, coach and assure team capability.
- Modules
- 5 open
- Practice data
- Raket Parts
4tasks in this job
71skills it uses
9tools named
The skills this job leans on most
- 1Evaluating whether people understood, adopted and sustained a changeNeeded in 4 of 4 tasks
- 2Designing implementable change plansNeeded in 3 of 4 tasks
- 3Measuring campaign outcomes with valid denominatorsNeeded in 3 of 4 tasks
- 4Reasoning about systems, feedback and second-order effectsNeeded in 2 of 4 tasks
- 5Mapping stakeholders' decisions, constraints and needsCore to this role
ToolsMicrosoft SharePointMicrosoft ExcelMicrosoft Power BIServiceNowMicrosoft PowerPointQualtrics+ 3 more
All 4 tasks, where people get stuck and all 71 skills
Tasks
- Plan a data, AI or system-enabled change with measurable adoption outcomes
- Evaluate a communications or change campaign against its objectives
- Design, administer and quality-check a professional survey
- Assess the combined change load on affected groups and recommend sequencing
Where people get stuck
- Reasoning about systems, feedback and second-order effects
- Critically evaluating marketing attribution claims
All skills
- Designing implementable change plans
- Evaluating whether people understood, adopted and sustained a change
- Measuring campaign outcomes with valid denominators
- Analysing event sequences, states and durations
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Building decision-focused reports and dashboards
- 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
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- 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 appraising samples
- Designing and interpreting hypothesis tests
- Designing and quality-checking survey instruments
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- 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 and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating employee experience evidence responsibly
- Evaluating experimental results and integrity
- Evaluating learning transfer and performance outcomes
- 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
- Giving specific developmental feedback on data and AI work
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting trends and seasonality
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether uncertainty changes the decision
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Reading charts and dashboards correctly, including their state
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
- Training reproducible machine learning models
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying reports and dashboards before and after release
- Weighing conflicting evidence by design strength
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- Microsoft SharePoint
- Microsoft Excel
- Microsoft Power BI
- ServiceNow
- Microsoft PowerPoint
- Qualtrics
- Microsoft 365 Copilot
- Workday
- Sympa HR
Paths to start
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
Skill Path
Experimentation and Causal Impact Evaluation
Answer "did it work" with randomised experiments where possible and quasi-experimental methods where not.
- Modules
- 5 open
- Practice data
- Raket Parts
5tasks in this job
81skills it uses
6tools named
The skills this job leans on most
- 1Evaluating whether people understood, adopted and sustained a changeNeeded in 5 of 5 tasks
- 2Designing implementable change plansNeeded in 4 of 5 tasks
- 3Measuring campaign outcomes with valid denominatorsNeeded in 4 of 5 tasks
- 4Reasoning about systems, feedback and second-order effectsNeeded in 3 of 5 tasks
- 5Mapping stakeholders' decisions, constraints and needsCore to this role
ToolsMicrosoft Power BIMicrosoft PowerPointMicrosoft SharePointMicrosoft ExcelQualtricsWorkday
All 5 tasks, where people get stuck and all 81 skills
Tasks
- Assess data and AI capability gaps in a function and plan their development
- Plan a data, AI or system-enabled change with measurable adoption outcomes
- Evaluate a communications or change campaign against its objectives
- Design, administer and quality-check a professional survey
- Assess the combined change load on affected groups and recommend sequencing
Where people get stuck
- Reasoning about systems, feedback and second-order effects
- Critically evaluating marketing attribution claims
All skills
- Designing implementable change plans
- Evaluating whether people understood, adopted and sustained a change
- Measuring campaign outcomes with valid denominators
- Reasoning about systems, feedback and second-order effects
- Analysing event sequences, states and durations
- Analysing organisation structure, spans and critical-role cover
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Building decision-focused reports and dashboards
- 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
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing options against explicit decision criteria
- 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 appraising samples
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and quality-checking survey instruments
- Designing auditable decision models
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- 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 and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating employee experience evidence responsibly
- Evaluating experimental results and integrity
- Evaluating learning transfer and performance outcomes
- 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
- Giving specific developmental feedback on data and AI work
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- 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 uncertainty changes the decision
- Modelling workforce demand and supply
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Reading charts and dashboards correctly, including their state
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Running sensitivity and break-even analysis on model assumptions
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
- Training reproducible machine learning models
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying reports and dashboards before and after release
- Weighing conflicting evidence by design strength
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
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
Skill Path
Experimentation and Causal Impact Evaluation
Answer "did it work" with randomised experiments where possible and quasi-experimental methods where not.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Leading Analytics and AI Change
Lead adoption, diagnose learning needs, coach and assure team capability.
- Modules
- 5 open
- Practice data
- Raket Parts
About the role
Manages a change team and is accountable for the quality of its change plans and adoption evaluation. Allocates change leads across several changes and judges when the combined change load is too much for people to absorb. Data judgement sits in setting adoption measures that show real behaviour change.