Data Analytics for Managers Training Course

Participants can translate a management issue into a bounded analytical question and decision brief.

• SMART decision objectives linked to accountable business owners
• Problem statements separated from assumed solutions and preferred answers
• Hypothesis trees used to structure possible drivers of performance
• Decision criteria defined before selecting measures or analysing data
• Scope boundaries set by population, period, geography and process
• Analytical question canvas records assumptions, constraints and intended actions

Participants can define decision-useful measures and connect them to operational drivers.

• KPI design using purpose, formula, owner, target and cadence
• Metric trees connect outcome measures with controllable driver measures
• Numerators, denominators and units specified to prevent false comparisons
• Targets distinguished from thresholds, benchmarks and control limits
• Balanced scorecard perspectives adapted for operational performance measures
• Data dictionary entries document definitions, sources, transformations and exceptions

Participants can identify suitable data sources and specify governance controls for managerial analysis.

• Operational systems, surveys, finance records and external datasets compared
• Primary keys and identifiers assessed before joining separate data sources
• Data lineage traces reported measures back to source records
• GDPR principles applied to personal data collection and analysis
• Role-based access controls matched to data sensitivity and responsibilities
• Retention schedules and purpose limitation considered during data requests

Participants can profile data quality and decide whether a dataset is fit for its intended use.

• Completeness, validity, accuracy, consistency, timeliness and uniqueness quality dimensions
• Data profiling identifies missing values, duplicates and invalid categories
• Reconciliation checks compare record totals with trusted control totals
• Outliers investigated using business rules before statistical treatment
• Sampling bias assessed against the target population and decision
• Data quality issue log assigns impact, owner and remediation

Participants can structure and analyse management data in Excel using repeatable techniques.

• Excel Tables create structured ranges for filtering and formula consistency
• XLOOKUP and INDEX-MATCH reconcile records across reference datasets
• PivotTables summarise performance by period, segment and operational unit
• Conditional formatting highlights exceptions against defined management thresholds
• SUMIFS and COUNTIFS calculate segmented measures from detailed records
• Data validation controls standardise manual inputs and category labels

Participants can prepare analysis-ready data while retaining an auditable record of changes.

• Tidy data principles organise variables, observations and values consistently
• Text-to-columns and trimming functions repair inconsistent field formats
• Date parsing creates usable reporting periods, cohorts and ageing measures
• Derived fields calculate margins, durations, rates and variance measures
• Missing values treated through documented rules rather than silent deletion
• Transformation log records source fields, logic, owner and approval

Participants can use core SQL queries to retrieve, filter and summarise business data.

• SELECT statements retrieve named fields from relational database tables
• WHERE clauses filter records using dates, categories and thresholds
• GROUP BY aggregates counts, sums and averages by business dimension
• INNER JOIN combines related records through matching key fields
• CASE expressions classify records into management reporting categories
• Query results validated against expected totals and sample records

Participants can summarise data distributions and select statistics appropriate to the management question.

• Mean, median and mode selected for different distribution shapes
• Range, variance and standard deviation describe performance variability
• Percentiles reveal service levels hidden by average measures
• Frequency distributions expose concentration, gaps and unusual patterns
• Weighted averages prevent distorted results across unequal group sizes
• Base sizes reported alongside percentages to support reliable interpretation

Participants can select and critique charts that communicate a specific management message accurately.

• Line charts show trends, seasonality and intervention effects over time
• Bar charts compare categories using consistent scales and ordering
• Scatter plots test relationships between two continuous measures
• Control charts distinguish common-cause variation from special-cause signals
• Small multiples compare segments without overloading a single visual
• Chart titles state the finding rather than merely naming variables

Participants can investigate material performance changes using structured analytical methods.

• Variance analysis separates volume, price, mix and timing effects
• Pareto analysis prioritises categories causing the largest cumulative impact
• Five Whys tests plausible operational causes behind observed results
• Fishbone diagrams organise people, process, system and policy factors
• Drill-down paths move from enterprise metrics to transaction detail
• Waterfall charts explain movement between opening and closing positions

Participants can assess relationships in data without making unsupported causal claims.

• Correlation coefficients measure direction and strength between numeric variables
• Confounding variables explain misleading associations in observational data
• Causal claims require plausible mechanisms, comparison groups and timing
• A/B test design defines treatment, control, outcome and sample
• Statistical significance distinguished from material business significance
• Regression outputs interpreted through coefficients, confidence intervals and residuals

Participants can segment populations to target services, interventions and communications more effectively.

• RFM analysis groups customers by recency, frequency and monetary value
• Behavioural segmentation identifies usage patterns, channels and service needs
• Cohort analysis compares retention and activity from common start periods
• Segmentation rules tested for stability, actionability and fairness
• Persona summaries link quantified segments to service design decisions
• Protected characteristics reviewed to identify potential discriminatory outcomes

Participants can produce and evaluate practical forecasts for management planning decisions.

• Time-series components separate trend, seasonality, cycles and irregular variation
• Moving averages smooth short-term fluctuations in demand series
• Exponential smoothing weights recent observations for near-term forecasting
• Forecast assumptions document known events, capacity constraints and policy changes
• Mean absolute percentage error compares forecast accuracy across periods
• Prediction intervals communicate plausible ranges rather than single-point certainty

Participants can specify a decision-focused dashboard and assess whether it supports management action.

• Dashboard audience, decisions and refresh cadence defined before visual design
• Power BI data model connects fact tables with shared dimensions
• DAX measures calculate reusable ratios, variances and period comparisons
• Slicers support controlled comparison by segment, geography and period
• Exception-based design highlights measures requiring management intervention
• Dashboard acceptance criteria test accuracy, usability, access and actionability

Participants can communicate analytical findings as a concise, evidence-led management recommendation.

• Pyramid Principle structures recommendations, evidence and supporting detail
• Situation-complication-question-answer framing clarifies the management narrative
• Executive summaries state decision, recommendation, impact and required action
• Annotations explain material changes, risks and analytical limitations
• Confidence statements separate observed facts, estimates and assumptions
• Meeting packs pair dashboard views with decisions and accountable owners

Participants can commission analytics work with clear scope, controls and review criteria.

• CRISP-DM phases align business understanding with analytical delivery activities
• Analytics brief specifies decision, users, measures, data and timeline
• User stories define management needs from the stakeholder perspective
• Acceptance criteria establish quality checks before analysis is released
• RACI matrix clarifies analyst, manager, data owner and approver roles
• Review meetings challenge assumptions, methods, exceptions and decision implications

Participants can identify ethical, legal and operational risks in management analytics.

• Algorithmic bias assessed through representation, outcomes and error differences
• Data minimisation limits collection to justified analytical requirements
• Model risk registers document assumptions, limitations and monitoring triggers
• Explainability requirements matched to decision impact and stakeholder rights
• Independent review tests calculations, logic and reproducibility before release
• Escalation routes address material errors, privacy incidents and harmful outcomes

Participants can convert analytical findings into prioritised actions with measurable follow-through.

• Decision matrices compare options against impact, cost, risk and feasibility
• Impact-effort mapping prioritises analytical recommendations for implementation
• SMART action plans assign owners, milestones, measures and review dates
• Benefits realisation plans link interventions to expected measurable outcomes
• Test-and-learn cycles monitor results and adjust operational responses
• Post-implementation reviews compare realised results with original assumptions

Participants can build, review and present a complete management analytics case pack addressing a defined business decision.

• Decision brief defines the management question, scope and success criteria
• Data quality assessment documents readiness, limitations and remediation priorities
• Analysis workbook produces segmented measures, trends and validated findings
• Dashboard specification presents KPI logic, visual requirements and user actions
• Recommendation narrative states evidence, options, risks and accountable actions
• Peer review tests calculations, assumptions, clarity and decision usefulness