Advanced Power BI & Enterprise Analytics Training Course

• Enterprise BI operating models and analytical maturity
• Power BI architecture and the modern Microsoft data ecosystem
• Data sources, ingestion patterns and analytical layers
• Semantic models and enterprise reporting architecture
• Designing for scale, maintainability and reuse
• Executive analytics requirements and decision use cases
• Architecture assessment workshop

Practical session: Enterprise analytics architecture exercise. Map a real or simulated organisation's data sources, reporting consumers, analytical layers and decision requirements, then produce a target Power BI architecture and prioritised implementation roadmap.

• Dimensional modelling and star-schema architecture
• Fact and dimension design
• Relationship design and filter propagation
• Role-playing dimensions and many-to-many relationships
• Date tables and advanced calendar design
• Model granularity, cardinality and storage considerations
• Enterprise modelling case workshop

Practical session: Advanced semantic model build. Design a star schema from a multi-table case dataset, create dimensions and fact tables, establish relationships, test filter propagation and document modelling decisions.

• Power Query architecture and M concepts
• Advanced transformations and reusable queries
• Parameters, functions and dynamic data preparation
• Combining, merging and appending complex datasets
• Data quality, validation and exception handling
• Query folding and transformation performance
• Data preparation laboratory

Practical session: Power Query engineering lab. Ingest multiple deliberately inconsistent sources, profile data quality, build reusable transformation logic, create parameters and functions, and produce a refresh-ready analytical dataset.

• DAX evaluation context and calculation logic
• Measures versus calculated columns
• CALCULATE and filter context manipulation
• Variables, iterators and table functions
• Advanced aggregation patterns
• Context transition and virtual tables
• Complex DAX problem-solving laboratory

Practical session: DAX engineering lab. Build a measure library for revenue, margin, growth, contribution, ranking and variance analysis, then troubleshoot context and calculation errors using controlled test cases.

• Advanced time intelligence
• Period-over-period and rolling analysis
• Dynamic segmentation and ranking
• Cohort and contribution analysis
• Dynamic KPIs and benchmark measures
• Scenario and what-if calculations
• Advanced DAX challenge

Practical session: Advanced analytical measures lab. Develop time-intelligence, rolling-period, cohort, segmentation and scenario measures and validate them against independently calculated control totals.

• Executive dashboard architecture
• Analytical visual selection and information hierarchy
• Drill-through, bookmarks and report navigation
• Dynamic titles, tooltips and interaction design
• Narrative techniques for complex data
• Designing board and executive performance views
• Dashboard critique and redesign workshop

Practical session: Executive dashboard design studio. Convert a complex management-information requirement into an executive dashboard, applying visual hierarchy, navigation, drill-through, tooltips and decision-focused storytelling.

• Power BI performance principles
• Model optimisation and cardinality management
• DAX performance analysis
• Query and visual optimisation
• Performance Analyzer and diagnostic techniques
• Capacity considerations and scalable design
• Performance tuning laboratory

Practical session: Performance optimisation clinic. Diagnose a deliberately slow Power BI model using Performance Analyzer and modelling techniques, then optimise relationships, calculations, queries and report visuals and document the performance improvement.

• Workspace and access architecture
• Row-level and object-level security
• Data governance and stewardship
• Deployment pipelines and lifecycle management
• Version control and release discipline
• Monitoring adoption and report quality
• Enterprise governance workshop

Practical session: Enterprise governance design. Configure a simulated workspace and security structure, define roles and access rules, map deployment stages and produce a governance checklist covering ownership, certification, release and monitoring.

• Integrating finance, operations, customers and performance data
• Building executive KPI frameworks
• Advanced variance and exception analysis
• Risk and opportunity identification
• Scenario modelling for management decisions
• Data storytelling for senior leadership
• Integrated solution development

Practical session: Integrated enterprise analytics case. Combine finance, operations and customer data into a unified model, develop management KPIs, identify exceptions and produce an executive insight pack with recommended actions.

• Define a complex organisational decision problem
• Build and validate the analytical model
• Develop advanced measures and executive dashboards
• Apply performance and governance principles
• Interpret findings and formulate management insights
• Present the solution to an executive review panel
• Capstone assessment and feedback