Continuous Improvement Manager
Accountable for the improvement method and the portfolio of improvement work across operations and for the benefits it delivers.
4tasks in this job
65skills it uses
8tools named
The skills this job leans on most
- 1Defining measurable outcomes and leading indicatorsNeeded in 4 of 4 tasks
- 2Estimating and challenging causal effectsNeeded in 3 of 4 tasks
- 3Diagnosing operational handoffs and flowNeeded in 2 of 4 tasks
- 4Mapping proposed process changes and control impactsNeeded in 1 of 4 tasks
- 5Evaluating experimental results and integrityCore to this role
ToolsSmartsheetMicrosoft Power BIQlik SenseTableauMicrosoft ExcelCelonisSAP SignavioMicrosoft Visio
All 4 tasks, where people get stuck and all 65 skills
Tasks
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Evaluate statistical and causal evidence for an intervention's impact
- 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
All skills
- Defining measurable outcomes and leading indicators
- Estimating and challenging causal effects
- 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
- Comparing periods consistently
- Constructing clear and honest charts
- 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 efficient and reproducible investigations
- Designing experiments
- Designing implementable change plans
- 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 quantities and differences with intervals
- Evaluating and improving chart accessibility
- 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
- 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 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
- Measuring campaign outcomes with valid denominators
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Reading business metrics
- 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
- Testing competing explanations with discriminating evidence
- Testing, implementing and sustaining measured improvements
- Training reproducible machine learning models
- Variance analysis
- 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
Coming soonService, Process and Operations Improvement
5tasks in this job
104skills it uses
9tools named
The skills this job leans on most
- 1Defining measurable outcomes and leading indicatorsNeeded in 5 of 5 tasks
- 2Estimating and challenging causal effectsNeeded in 3 of 5 tasks
- 3Diagnosing operational handoffs and flowNeeded in 3 of 5 tasks
- 4Mapping proposed process changes and control impactsNeeded in 2 of 5 tasks
- 5Evaluating experimental results and integrityCore to this role
ToolsMicrosoft Power BICelonisSmartsheetQlik SenseTableauMicrosoft Excel+ 3 more
All 5 tasks, where people get stuck and all 104 skills
Tasks
- Diagnose operational and fulfilment exceptions and agree corrective ownership
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Evaluate statistical and causal evidence for an intervention's impact
- 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
All skills
- Defining measurable outcomes and leading indicators
- Diagnosing operational handoffs and flow
- Estimating and challenging causal effects
- Mapping proposed process changes and control impacts
- Analysing event sequences, states and durations
- Analysing queue behaviour, capacity and service constraints
- 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
- Assigning and operating data ownership and stewardship
- Balancing territories and quotas from account potential
- Building cohort and retention analyses with correct denominators
- 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
- Comparing periods consistently
- Constructing clear and honest charts
- Converting demand into capacity requirements
- 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 comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Designing implementable change plans
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing data quality exceptions
- 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 quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating learning transfer and performance outcomes
- Evaluating service performance and quality trade-offs
- Evaluating whether people understood, adopted and sustained a change
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Facilitating evidence-led retrospectives and corrective actions
- Fitting, checking and interpreting inferential regression models
- Forecasting operational demand at interval and site grain
- Formulating answerable data questions
- Giving specific developmental feedback on data and AI work
- Identifying and quantifying decision downside and exposure
- 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
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether external evidence applies to the decision context
- Judging whether uncertainty changes the decision
- Maintaining a risk register with owners, triggers and responses
- Maintaining requirements traceability and change control
- Making accountable decisions under uncertainty
- Mapping customer journeys and evidence gaps
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- 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
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- 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 reports and dashboards before and after release
- Weighing conflicting evidence by design strength
- Working through stakeholder conflict over evidence and responsibility
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
All tools
- Microsoft Power BI
- Celonis
- Smartsheet
- Qlik Sense
- Tableau
- Microsoft Excel
- SAP Signavio
- SAP S/4HANA
- Microsoft Visio
Paths to start
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
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
6tasks in this job
113skills it uses
9tools named
The skills this job leans on most
- 1Defining measurable outcomes and leading indicatorsNeeded in 5 of 6 tasks
- 2Investigating anomaliesNeeded in 4 of 6 tasks
- 3Diagnosing operational handoffs and flowNeeded in 4 of 6 tasks
- 4Estimating and challenging causal effectsNeeded in 3 of 6 tasks
- 5Evaluating experimental results and integrityCore to this role
ToolsMicrosoft Power BICelonisSmartsheetQlik SenseTableauMicrosoft Excel+ 3 more
All 6 tasks, where people get stuck and all 113 skills
Tasks
- Diagnose operational and fulfilment exceptions and agree corrective ownership
- Define and govern a KPI metric contract
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Evaluate statistical and causal evidence for an intervention's impact
- 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
- Evaluating experimental results and integrity
- Testing competing explanations with discriminating evidence
- Weighing conflicting evidence by design strength
All skills
- Defining measurable outcomes and leading indicators
- Diagnosing operational handoffs and flow
- Estimating and challenging causal effects
- Investigating anomalies
- Analysing event sequences, states and durations
- Analysing queue behaviour, capacity and service constraints
- 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
- Assigning and operating data ownership and stewardship
- Balancing territories and quotas from account potential
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- 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
- 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
- Converting demand into capacity requirements
- 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 comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and versioning data contracts for consumers
- Designing decision-oriented research with customers, users or employees
- 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 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 quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating learning transfer and performance outcomes
- Evaluating service performance and quality trade-offs
- Evaluating whether people understood, adopted and sustained a change
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Facilitating evidence-led retrospectives and corrective actions
- Fitting, checking and interpreting inferential regression models
- Forecasting operational demand at interval and site grain
- Formulating answerable data questions
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- 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
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether external evidence applies to the decision context
- Judging whether uncertainty changes the decision
- Maintaining a risk register with owners, triggers and responses
- Maintaining requirements traceability and change control
- Making accountable decisions under uncertainty
- 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
- 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
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Revising prior decisions when evidence changes
- 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
- 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 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
- Celonis
- Smartsheet
- Qlik Sense
- Tableau
- Microsoft Excel
- SAP Signavio
- SAP S/4HANA
- Microsoft Visio
Paths to start
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
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
About the role
Accountable for the improvement method and the portfolio of improvement work across operations and for the benefits it delivers. Leads a small improvement team and decides which opportunities are worked on and when a benefit is proven. Data judgement sits in measuring a process before and after change, where an Operations Manager runs a unit and a Business Analysis Manager owns requirements.
Also called Operational Excellence Manager, Lean Manager