Advanced Power BI + Copilot (AI Feature)

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Duration 5 Days – 40 hrs.

 

Overview

This advanced training program is designed for professionals responsible for building, maintaining, and enhancing centralized dashboards across multiple business functions. It focuses on strengthening technical Power BI expertise, improving data processing efficiency, and leveraging Microsoft Copilot AI features to accelerate analytics, automate reporting workflows, and enhance decision-making.

Participants will move beyond traditional reporting by learning advanced data modeling, DAX optimization, performance tuning, and AI-assisted insights generation. The course also addresses common challenges such as managing manual data files, improving data governance, and scaling analytics solutions across the organization.

By the end of the training, participants will be equipped to deliver high-performance dashboards, automate reporting pipelines, and utilize AI-powered analytics using Copilot for more strategic business insights.

 

Objectives

  • Enhance data modeling and transformation techniques using Power Query 
  • Develop advanced DAX calculations for complex business scenarios 
  • Design high-performance, scalable dashboards 
  • Optimize Power BI reports for speed and usability 
  • Automate data refresh and reporting workflows 
  • Implement data governance, security, and access control

    Leverage Microsoft Copilot in Power BI for: 

  • Automated insights generation 
  • Natural language queries 
  • AI-assisted report creation 
  • Reduce dependency on manual data processing 
  • Enable data storytelling and business-driven analytics

 

Target Audience

  • Power BI Developers / Analysts 
  • Data Analysts / Reporting Specialists 
  • Business Intelligence Professionals 
  • MIS / Reporting Teams 
  • IT and Data Teams supporting analytics platforms 
  • Professionals handling centralized dashboards and reporting operations

 

Prerequisites 

  • Basic to Intermediate knowledge of Power BI

    Familiarity with: 

  • Data sources (Excel, SQL, APIs, etc.) 
  • Basic DAX and Power Query 
  • Experience in creating reports/dashboards is recommended 
  • Understanding of business reporting processes is an advantage

 

Course Outline 

 

Day 1: Advanced Data Modeling & Transformation

Module 1: Data Architecture & Modeling Best Practices

  • Star schema vs. Snowflake schema 
  • Fact and dimension tables design 
  • Handling large datasets efficiently 
  • Data normalization vs denormalization 

Module 2: Advanced Power Query (M Language)

  • Query optimization techniques 
  • Parameterization and reusable queries 
  • Handling messy and manual data sources 
  • Data cleansing automation strategies 

Module 3: Data Integration

  • Combining multiple data sources 
  • Incremental data loading strategies 
  • API and cloud-based data integration 

 

Day 2: Advanced DAX & Analytical Modeling

Module 4: Advanced DAX Concepts

  • Context (Row vs Filter context deep dive) 
  • CALCULATE and advanced filter manipulation 
  • Time intelligence (YTD, MTD, rolling periods) 

Module 5: Complex Business Calculations

  • Dynamic measures 
  • Scenario analysis 
  • Ranking and segmentation 
  • Financial and operational KPI modeling 

Module 6: Optimization Techniques

  • Reducing DAX complexity 
  • Improving calculation performance 
  • Debugging and troubleshooting DAX 

 

Day 3: Dashboard Design, UX & Performance Tuning

Module 7: Advanced Dashboard Design

  • UX/UI principles for dashboards 
  • Visual hierarchy and storytelling 
  • Designing for executive vs operational users 

Module 8: Advanced Visualizations

  • Custom visuals and advanced charts 
  • Drill-through, bookmarks, and tooltips 
  • Interactive dashboard techniques 

Module 9: Performance Optimization

  • Data model optimization 
  • Reducing report load time 
  • Performance Analyzer usage 

 

Day 4: Automation, Governance & Enterprise Deployment

Module 10: Automation & Workflow Efficiency

  • Scheduled refresh and data pipelines 
  • Automating reporting processes 
  • Integration with Power Automate 

Module 11: Data Governance & Security

  • Row-Level Security (RLS) 
  • Data access management 
  • Workspace governance and lifecycle 

Module 12: Deployment & Collaboration

  • Publishing and sharing reports 
  • Version control and workspace management 
  • Best practices for enterprise BI environments 

 

Day 5: Power BI Copilot & AI-Driven Analytics

Module 13: Introduction to Microsoft Copilot in Power BI

  • Overview of Copilot capabilities 
  • AI-assisted report generation 
  • Natural language to dashboard 

Module 14: AI-Powered Insights & Automation

  • Generating insights using Copilot 
  • Automated summaries and narratives 
  • Smart data exploration 

Module 15: Practical Use Cases

  • Reducing manual reporting effort 
  • Accelerating dashboard creation 
  • Enhancing decision-making with AI 

Module 16: Capstone Project (Hands-On)

  • Build an end-to-end dashboard using: 
    • Advanced modeling 
    • DAX 
    • Performance optimization 
    • Copilot features 
  • Presentation and feedback session 

Key Value for Trainee (Aligned to Current Role)

  • Replacing manual data handling with automated workflows 
  • Improving dashboard scalability and performance 
  • Enabling faster report development using Copilot AI 
  • Enhancing ability to support multiple business units efficiently 
  • Transitioning from report builder → analytics strategist 

 

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  • Explain warehouse processes and their impact on inventory accuracy and service performance.
  • Evaluate warehouse layouts, storage systems, and product locations.
  • Identify appropriate controls for receiving, put-away, storage, picking, and dispatch.
  • Apply First-In, First-Out (FIFO) and First-Expired, First-Out (FEFO) principles.
  • Compare picking strategies based on order characteristics and operational requirements.
  • Identify causes of inventory discrepancies and recommend corrective actions.
  • Develop a cycle counting approach based on inventory value, movement, and risk.
  • Recognize sources of shrinkage and recommend preventive and detective controls.
  • Interpret warehouse productivity and inventory performance KPIs.
  • Review warehouse processes using supporting records, observations, and control evidence.
        Target Audience  
  • Warehouse supervisors, team leaders, and managers.
  • Inventory controllers and stock control personnel.
  • Logistics and distribution supervisors.
  • Internal auditors and operational compliance personnel.
  • Supply chain and operations professionals.
  • Personnel responsible for reviewing warehouse controls and performance.
  Prerequisites  
  • Basic familiarity with warehouse, logistics, or inventory activities.
  • General understanding of business processes and operational documentation.
  • Basic numerical skills, including percentages and simple calculations.
  • Basic spreadsheet skills are helpful.
  • No prior experience with a specific Warehouse Management System (WMS) is required.
   

Course Outline

  Day 1: Warehouse Processes, Layout, and Storage Controls Module 1: Warehouse Operations and Process Controls  
  • Warehouse roles within the supply chain.
  • End-to-end flow: receiving, put-away, storage, replenishment, picking, packing, and dispatch.
  • Physical stock movement and corresponding system transactions.
  • Responsibilities, authorization, and segregation of duties.
  • Common operational weaknesses and audit evidence.
  Module 2: Warehouse Layout and Storage Systems  
  • Functional zones, movement paths, and space utilization.
  • Receiving, storage, picking, staging, and dispatch areas.
  • Storage options: floor stacking, shelving, pallet racking, and bin storage.
  • Fixed and dynamic location systems.
  • Product slotting based on movement, size, handling needs, and compatibility.
  • Layout bottlenecks and their effects on productivity and inventory control.
  Module 3: Receiving and Put-Away  
  • Purchase orders, delivery documents, and expected receipts.
  • Quantity, condition, and item identification checks.
  • Recording shortages, overages, damage, and discrepancies.
  • Controls for acceptance, quarantine, and release.
  • Put-away rules and location confirmation.
  • Traceability through labels, barcodes, lot numbers, and serial numbers.
  • Timely recording of receipts and stock movements.
  Module 4: Storage, Stock Rotation, and Replenishment  
  • Storage discipline and location integrity.
  • FIFO and FEFO principles, applications, and limitations.
  • Batch, lot, and expiry-date management.
  • Controls for slow-moving, obsolete, damaged, and expired stock.
  • Replenishment of picking locations.
  • Supervision and review of stock rotation compliance.
    Day 2: Order Fulfillment and Inventory Accuracy Module 5: Picking Strategies and Order Fulfillment  
  • Discrete, batch, zone, and wave picking.
  • Selecting picking strategies for different order profiles.
  • Pick paths, location sequencing, and travel reduction.
  • Replenishment coordination and stock availability.
  • Picking verification and error prevention.
  • Packing, staging, and dispatch confirmation.
  • Handling short picks, substitutions, cancellations, and returns.
  Module 6: Inventory Records and Accuracy Controls  
  • Item master data, units of measure, and location records.
  • On-hand, available, allocated, and quarantined stock.
  • Transaction controls for receipts, transfers, issues, returns, and adjustments.
  • Common causes of physical and system stock mismatches.
  • Barcode scanning and WMS control points.
  • Inventory reconciliation and supporting documentation.
  • Measuring inventory accuracy and interpreting discrepancies.
  Module 7: Cycle Counting and Physical Inventory  
  • Cycle counting versus full physical inventory.
  • ABC classification and risk-based count priorities.
  • Count frequency based on value, movement, and discrepancy history.
  • Count preparation, blind counting, and movement controls.
  • Recounts, variance investigation, and adjustment approval.
  • Root cause analysis and corrective action follow-up.
  • Reviewing the effectiveness of the counting program.
  Module 8: Shrinkage Reduction and Inventory Protection  
  • Sources of shrinkage: loss, damage, theft, misplacement, and recording errors.
  • Access controls and accountability for stock movements.
  • Controls for high-value and sensitive inventory.
  • Returns, write-offs, scrap, and disposal controls.
  • Exception reporting and unusual transaction patterns.
  • Investigating recurring losses and unexplained adjustments.
  • Preventive and detective controls for shrinkage reduction.
    Day 3: Performance Management, Process Review, and Improvement Module 9: Warehouse Productivity and Inventory KPIs  
  • Receiving productivity and dock-to-stock time.
  • Put-away productivity and replenishment performance.
  • Picking productivity and order accuracy.
  • Inventory accuracy and cycle count variance.
  • Space utilization and storage capacity.
  • Shrinkage, damage, and expiry indicators.
  • Defining KPI formulas, data sources, and reporting boundaries.
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  • Reviewing procedures against actual warehouse practices.
  • Tracing transactions from documents to systems and physical stock.
  • Reviewing approvals, access rights, and segregation of duties.
  • Identifying control gaps and recurring process exceptions.
  • Documenting findings with clear supporting evidence.
  • Prioritizing corrective actions and tracking closure.
  Module 11: Warehouse Improvement Planning  
  • Identifying bottlenecks, unnecessary movement, and rework.
  • Improving layout, slotting, and picking arrangements.
  • Addressing inventory discrepancy root causes.
  • Standardizing transaction recording and exception handling.
  • Assigning improvement owners and measurable targets.
  • Monitoring results and sustaining operational improvements.
  Module 12: Capstone — Warehouse Operations and Inventory Improvement Plan  
  • Review a warehouse scenario containing layout, transaction, inventory, and performance information.
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  • Develop a cycle counting and inventory reconciliation plan.
  • Propose controls to reduce shrinkage and improve traceability.
  • Select relevant KPIs and define their calculation and reporting requirements.
  • Produce a prioritized improvement plan with responsibilities and expected outcomes.
 

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