Roles Strategy & Transformation
Change Analyst
Accountable for gathering and analysing evidence on how a change affects people and whether it has been adopted.
3tasks in this job
0skills it uses
8tools named
The skills this job leans on most
- 1Evaluating whether people understood, adopted and sustained a changeNeeded in 3 of 3 tasks
- 2Designing implementable change plansNeeded in 2 of 3 tasks
- 3Mapping stakeholders' decisions, constraints and needsCore to this role
ToolsMicrosoft ExcelWorkdayMicrosoft SharePointSAP SuccessFactorsSympa HRQualtricsCornerstone OnDemandMicrosoft PowerPoint
All 3 tasks, where people get stuck and all 0 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
Where people get stuck
- Designing and quality-checking survey instruments
- Designing and appraising samples
- Designing decision-oriented research with customers, users or employees
All skills
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
Leading Analytics and AI Change
Lead adoption, diagnose learning needs, coach and assure team capability.
- 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
3tasks in this job
64skills it uses
12tools named
The skills this job leans on most
- 1Designing and quality-checking survey instrumentsNeeded in 2 of 3 tasks
- 2Designing and appraising samplesNeeded in 2 of 3 tasks
- 3Designing decision-oriented research with customers, users or employeesNeeded in 2 of 3 tasks
- 4Designing implementable change plansNeeded in 2 of 3 tasks
- 5Mapping stakeholders' decisions, constraints and needsCore to this role
ToolsMicrosoft ExcelMicrosoft SharePointJiraWorkdayMicrosoft Power BIServiceNow+ 6 more
All 3 tasks, where people get stuck and all 64 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
Where people get stuck
- Designing decision-oriented research with customers, users or employees
- Critically evaluating marketing attribution claims
All skills
- Designing and appraising samples
- Designing and quality-checking survey instruments
- Designing decision-oriented research with customers, users or employees
- Designing implementable change plans
- 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 interpreting hypothesis tests
- 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
- 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
- Evaluating whether people understood, adopted and sustained a change
- 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
- 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 whether a change exceeds normal variation
- Judging whether uncertainty changes the decision
- Measuring campaign outcomes with valid denominators
- 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
- 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 benefits realisation and attribution
- Weighing conflicting evidence by design strength
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- Microsoft Excel
- Microsoft SharePoint
- Jira
- Workday
- Microsoft Power BI
- ServiceNow
- Pendo
- Cornerstone OnDemand
- Microsoft PowerPoint
- Qualtrics
- Microsoft Forms
- Netigate
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
Coming soonProduct and Digital Behaviour Analytics
4tasks in this job
71skills it uses
12tools 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
- 4Mapping stakeholders' decisions, constraints and needsCore to this role
- 5Critically evaluating marketing attribution claimsCore to this role
ToolsMicrosoft ExcelMicrosoft SharePointWorkdayPendoMicrosoft Power BIServiceNow+ 6 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
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 Excel
- Microsoft SharePoint
- Workday
- Pendo
- Microsoft Power BI
- ServiceNow
- Microsoft PowerPoint
- Qualtrics
- Netigate
- Microsoft Forms
- SAP SuccessFactors
- 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
About the role
Accountable for gathering and analysing evidence on how a change affects people and whether it has been adopted. Produces stakeholder maps, impact assessments, training logistics and adoption reports without managing people. Data judgement sits in measuring real behaviour change rather than activity such as attendance or emails sent.
Also called Change Coordinator