Business Data Analysis Training Course

Participants can identify suitable data sources and document the meaning of essential business fields.

• Primary, secondary and administrative data matched to decision requirements
• Relational tables distinguished by entities, keys, records and attributes
• Fact tables and dimension tables identified within reporting data models
• Data dictionaries documenting field definitions, formats, owners and refresh cycles
• Metric definitions reconciled across finance, operations and customer reporting
• Grain assessed before combining transaction, customer and monthly summary datasets

Participants can profile dataset quality and apply documented cleaning rules before analysis.

• Completeness, validity, accuracy, consistency, timeliness and uniqueness quality dimensions
• Frequency tables expose missing values, duplicates, invalid codes and unexpected categories
• Outlier checks distinguish data-entry errors from valid exceptional observations
• Cleaning rules documented before altering source values or removing records
• Standardised dates, currencies, identifiers and category labels improve comparability
• Reconciliation totals confirm cleaned data against trusted control figures

Participants can build transparent Excel models for summarising and analysing business data.

• Excel Tables create structured references, controlled ranges and reusable formulas
• XLOOKUP, INDEX-MATCH and SUMIFS combine reference and transaction data
• PivotTables summarise revenue, volumes, costs and service performance measures
• Calculated columns apply business rules consistently across detailed records
• Conditional formatting highlights threshold breaches, outliers and missing information
• Formula auditing traces precedents, dependents and calculation errors

Participants can create repeatable data preparation steps using Power Query.

• Power Query imports files, folders and workbook tables into repeatable workflows
• Applied Steps record transformations for review, refresh and error tracing
• Split, merge and replace operations standardise inconsistent source fields
• Append queries combine monthly extracts with matching column structures
• Merge queries join lookup tables using validated business keys
• Data types assigned early prevent incorrect calculations and loading errors

Participants can write SQL queries to retrieve, filter, summarise and join business data.

• SELECT statements retrieve required fields without unnecessary source columns
• WHERE clauses filter records using dates, categories, values and null conditions
• GROUP BY aggregates calculate counts, totals, averages and minimum values
• INNER JOIN and LEFT JOIN combine related business tables correctly
• CASE expressions create transparent categories and conditional business measures
• Query validation checks row counts, duplicates and aggregate reconciliation

Participants can calculate and interpret measures that track business performance and exceptions.

• KPIs linked to objectives, owners, targets, tolerances and action triggers
• Variance analysis separates actual, budget, prior-period and forecast comparisons
• Rate calculations use appropriate denominators for meaningful performance comparisons
• Waterfall analysis explains changes in revenue, cost and operating margin
• Service-level measures track timeliness, backlog, resolution and failure rates
• Exception reports prioritise material deviations requiring management attention

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

• Mean, median and mode selected according to distribution shape and purpose
• Range, variance and standard deviation quantify spread around central values
• Percentiles identify service thresholds, customer behaviour and performance dispersion
• Histograms reveal skewness, clusters, gaps and potential data quality issues
• Box plots compare distributions across branches, products and customer segments
• Cross-tabulations reveal patterns between categorical variables and performance outcomes

Participants can segment business data and compare groups using consistent measures and bases.

• Customer segmentation uses behavioural, value, demographic and lifecycle variables
• Cohort analysis tracks groups sharing acquisition dates or programme entry periods
• Pareto analysis identifies categories responsible for most volume, cost or defects
• ABC classification prioritises inventory, suppliers and customers by business value
• Benchmark comparisons use consistent definitions, periods and relevant peer groups
• Segment sizes checked before drawing conclusions from small populations

Participants can investigate relationships in data while separating association from causal claims.

• Scatter plots examine relationships between two quantitative business variables
• Correlation coefficients measure direction and strength of linear association
• Confounding variables considered before attributing outcomes to observed drivers
• Five Whys structures investigation of recurring operational performance failures
• Fishbone diagrams organise possible causes across people, process and technology
• Root-cause evidence distinguishes observed patterns from untested explanations

Participants can assess whether observed differences are likely to support a business decision.

• Population, sample and sampling frame defined for a valid analysis
• Random, stratified and convenience samples compared for selection bias
• Confidence intervals express uncertainty around estimated rates and averages
• Null and alternative hypotheses stated before inspecting test results
• t-tests compare average performance between two relevant business groups
• Chi-square tests assess associations between categorical variables and outcomes

Participants can analyse time-based patterns and prepare simple forecasts with stated assumptions.

• Time series decomposed into trend, seasonality, cycles and irregular variation
• Moving averages smooth short-term fluctuations in demand and service volumes
• Seasonal indices adjust forecasts for recurring monthly or quarterly patterns
• Forecast accuracy measured using MAD, MAPE and bias calculations
• Scenario assumptions document expected changes in prices, demand and capacity
• Forecast ranges communicate uncertainty rather than presenting single-point certainty

Participants can select and design visualisations that make business evidence easy to interpret.

• Chart selection matches comparisons, distributions, relationships and time-series questions
• Bar charts, line charts and scatter plots used for distinct analytical purposes
• Visual hierarchy directs attention toward material findings and decision thresholds
• Colour palettes distinguish categories without obscuring values or accessibility
• Axis scales, labels and annotations prevent misleading visual interpretation
• Small multiples compare segments without overcrowding a single chart

Participants can build a Power BI model with appropriate relationships and business measures.

• Power BI relationships connect fact tables with shared dimension tables
• Star schemas support reliable filtering, aggregation and report performance
• DAX measures calculate totals, ratios and time-based performance indicators
• CALCULATE modifies filter context for controlled business measure logic
• Date tables support month-to-date, year-to-date and prior-period comparisons
• Model validation reconciles Power BI outputs with approved source totals

Participants can design management dashboards that support monitoring, prioritisation and action.

• Dashboard purpose defined around users, decisions, review frequency and action owners
• Executive pages highlight outcomes, exceptions, drivers and required decisions
• Drill-through views connect headline metrics to supporting operational detail
• Slicers allow controlled comparison by period, region, product and segment
• Targets and reference lines show performance against agreed expectations
• Narrative annotations explain significant changes, risks and recommended actions

Participants can apply practical controls for responsible, secure and auditable business analysis.

• Purpose limitation restricts personal data use to defined business needs
• Data minimisation removes fields unnecessary for the intended analysis
• Role-based access controls limit sensitive datasets to authorised users
• Anonymisation and pseudonymisation reduce exposure in analysis extracts
• Bias checks examine unequal representation, outcomes and measurement quality
• Analysis logs record sources, transformations, assumptions and approval decisions

Participants can present analytical findings with evidence, limitations and recommended business actions.

• Pyramid Principle structures messages from recommendation to supporting evidence
• Executive summaries state the decision, finding, implication and required action
• Assumptions, limitations and confidence levels disclosed without obscuring conclusions
• Recommendations specify owners, timing, expected benefit and monitoring measures
• Storyboards sequence context, evidence, options and decision requests clearly
• Challenge questions prepare analysts to defend methods, numbers and interpretation

Participants can build, review and present a complete business analysis from decision question to recommended action.

• Decision question, stakeholder needs and success measures finalised for analysis
• Source data profiled, cleaned and documented using agreed quality rules
• SQL, Excel or Power Query outputs assembled into auditable analysis tables
• Measures, segments, tests and forecasts selected for the stated decision
• Power BI dashboard presents findings, exceptions, drivers and decision options
• Peer review tests data quality, logic, visual clarity and evidence claims
• Presentation communicates recommendation, implementation actions, risks and monitoring measures