Business Analytics for Managers Training Course

Participants can identify suitable data sources and define basic governance requirements for analysis.

• Transactional, master, reference and external data distinguished by business purpose
• Relational tables interpreted through entities, attributes, keys and relationships
• Data lineage mapped from source system through transformation to dashboard
• Ownership, stewardship and access responsibilities assigned using a RACI matrix
• Personal data handling aligned with purpose limitation and access controls
• Data dictionaries specify fields, definitions, formats, units and permitted values

Participants can profile, clean and validate a dataset before analytical use.

• Data profiling checks completeness, uniqueness, validity, consistency and timeliness
• Missing values classified as random, systematic or structurally absent
• Duplicate records detected using identifiers, matching rules and exception reports
• Outliers investigated through distribution plots, business rules and source checks
• Cleaning logs record transformations, assumptions, exceptions and responsible reviewers
• Reconciliation compares record counts, totals and balances with source reports

Participants can use Excel to prepare, calculate and analyse management data efficiently.

• Structured tables support controlled formulas, filters, references and refreshes
• XLOOKUP and INDEX-MATCH combine reference data with transaction records
• PivotTables summarise performance by period, region, product and customer
• Conditional formatting highlights threshold breaches, trends and unusual values
• Power Query imports, transforms and documents repeatable data preparation steps
• Error checks identify broken formulas, inconsistent ranges and circular references

Participants can write SQL queries that retrieve and aggregate business data accurately.

• SELECT statements retrieve named fields from defined source tables
• WHERE clauses filter records using dates, categories, values and conditions
• INNER and LEFT JOIN operations combine related business tables
• GROUP BY aggregates calculate counts, sums, averages and grouped measures
• CASE expressions create business categories and conditional performance flags
• Query validation compares output totals against independent control calculations

Participants can describe business performance using appropriate measures, comparisons and distributions.

• Measures of central tendency compare mean, median and mode appropriately
• Range, variance and standard deviation quantify operational performance variation
• Percentiles reveal service distributions hidden by average performance measures
• Period comparisons separate absolute change, percentage change and index movements
• Cohort analysis tracks behaviour from common customer or employee starting points
• Pareto analysis prioritises categories responsible for most observed impact

Participants can investigate performance gaps using structured diagnostic methods and evidence.

• Variance analysis separates volume, price, mix and efficiency effects
• Drill-down paths trace headline KPI movements to contributing segments
• Five Whys tests causal explanations beyond initial symptom descriptions
• Fishbone diagrams organise possible causes across process, people and technology
• Control charts distinguish common-cause variation from special-cause variation
• Driver trees quantify relationships between operational activities and outcomes

Participants can design dashboard views that support rapid, accurate management interpretation.

• Visual hierarchy directs attention from headline status to supporting evidence
• Chart selection matches comparisons, trends, distributions, relationships and composition
• Pre-attentive attributes use position, length, colour and size deliberately
• Dashboard wireframes organise KPIs, filters, commentary and drill-through paths
• Reference lines show targets, prior periods, benchmarks and tolerance ranges
• Accessibility checks address colour contrast, labels, legends and readable scales

Participants can build a usable Power BI model with reliable relationships and measures.

• Star schemas separate fact tables from dimensions for reporting performance
• Relationship cardinality and filter direction prevent ambiguous analytical results
• Date tables support consistent time intelligence and period comparisons
• DAX measures calculate ratios, totals and context-sensitive business metrics
• Calculated columns distinguished from measures through storage and evaluation context
• Model documentation records table purpose, relationships, measures and refresh dependencies

Participants can produce interactive Power BI dashboards for defined management users.

• Report pages align user questions with appropriate visual and filter choices
• Slicers provide controlled segmentation by period, geography, product and channel
• Drill-through pages connect summary exceptions to detailed supporting records
• Tooltips add contextual measures without overcrowding primary report views
• Bookmarks organise alternative management views within a single report
• Dashboard testing verifies calculations, interactions, labels and business interpretation

Participants can interpret sampling results, confidence intervals and hypothesis tests responsibly.

• Sampling frames define populations, units, inclusion rules and coverage limitations
• Confidence intervals express plausible ranges around estimated business measures
• Hypothesis tests compare observed differences against expected random variation
• P-values interpreted alongside effect size, sample size and decision context
• Type I and Type II errors clarify risks of incorrect decisions
• Correlation coefficients assess association without claiming causal relationships

Participants can plan and assess controlled tests for business changes and interventions.

• A/B test hypotheses specify treatments, outcomes, populations and decision rules
• Random assignment reduces selection bias between treatment and control groups
• Sample size planning considers baseline rates, minimum effect and statistical power
• Guardrail metrics detect unintended effects on cost, risk or service
• Test duration accounts for cycles, seasonality and delayed customer responses
• Experiment readouts report effect sizes, uncertainty and recommended next actions

Participants can develop and evaluate practical forecasts for planning and resource decisions.

• Time-series components separate level, trend, seasonality and irregular variation
• Moving averages smooth short-term fluctuations in operational demand data
• Exponential smoothing weights recent observations for responsive baseline forecasts
• Seasonal indices adjust forecasts for recurring monthly or quarterly patterns
• Forecast accuracy uses MAE, MAPE and bias measures appropriately
• Rolling forecasts refresh assumptions as actual results and conditions change

Participants can segment customers or service users and identify commercially relevant differences.

• RFM analysis ranks customers by recency, frequency and monetary value
• Segmentation criteria combine behavioural, value, needs and risk characteristics
• Customer retention measures distinguish active bases, cohorts and churn definitions
• Funnel analysis identifies conversion loss between customer journey stages
• Cross-sell analysis examines product combinations and relevant customer opportunities
• Segment profiles translate analytical patterns into differentiated management actions

Participants can use operational data to identify process constraints and improvement priorities.

• Process maps identify activities, handoffs, queues, controls and failure points
• Cycle time measures separate touch time, waiting time and rework
• Capacity analysis compares available resources with workload and demand patterns
• Throughput metrics reveal constraints limiting end-to-end service performance
• Service-level measures track timeliness, quality, backlog and first-time resolution
• Root-cause evidence prioritises improvement actions by impact and feasibility

Participants can compare decision options using scenarios, sensitivities and quantified assumptions.

• Scenario planning defines base, upside, downside and stress-case assumptions
• Sensitivity analysis tests outcome changes from key input movements
• Decision trees compare choices, probabilities, payoffs and contingent actions
• Expected value calculations combine potential outcomes with estimated likelihoods
• Break-even analysis identifies volumes required to cover fixed and variable costs
• Assumption registers document sources, owners, confidence and review dates

Participants can apply governance and ethical controls to analytical products and recommendations.

• CRISP-DM stages connect business understanding, data work, modelling and deployment
• Metric governance assigns definitions, owners, approval rules and review cycles
• Bias assessments examine representation, measurement, historical and selection bias
• Explainability requirements match model complexity with decision consequence and accountability
• Access controls restrict sensitive data according to role and business need
• Model monitoring tracks performance drift, data changes and unintended outcomes

Participants can present analytical findings as clear recommendations for management action.

• Executive narratives state decision, evidence, implication, recommendation and requested action
• Message pyramids organise supporting analysis beneath a clear central conclusion
• Uncertainty communication distinguishes estimates, ranges, assumptions and known limitations
• Recommendation options compare benefits, costs, risks, dependencies and timing
• Management slides use annotations to explain material movements and exceptions
• Challenge questions test data credibility, causality, feasibility and ownership

Participants can plan the implementation and ongoing use of a business analytics product.

• User stories define report users, decisions, tasks and information needs
• Product backlogs prioritise analytics requirements by value, effort and risk
• Acceptance criteria specify data, calculations, visuals and performance expectations
• Refresh schedules align data availability with management decision cycles
• Adoption measures track usage, user feedback, action completion and benefit realisation
• Operating procedures define support, change control and periodic metric review

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

• Business problem statement defines decision owner, scope, measures and constraints
• Prepared dataset includes documented checks, transformations and reconciliation results
• Analytical model combines descriptive findings, drivers, forecast and scenario evidence
• Power BI dashboard presents KPIs, exceptions, segments and drill-through detail
• Recommendation pack specifies actions, expected impact, risks and accountable owners
• Peer review tests analytical validity, dashboard clarity and decision usefulness