Power BI and AI Essentials for Reporting and Visualization

Program Description

While this outline serves as a foundational framework with use cases from multiple industries and functions, the final program is fully customized to your industry and internal workflows.

Participants work on real-world problems, not generic examples. We engage in a pre-workshop alignment to inject your specific organizational datasets, pain points, and proprietary use cases directly into the curriculum.

Learning Objectives

Program Details

Content

Day 1: Foundations, Basics & Automated Visuals

  • A non-technical introduction to the Power BI ecosystem (Desktop, Service, and Mobile) and how it integrates with the existing corporate tech stack.
  • Scenario (General): An executive team needs to understand how raw data from Excel, SQL, and Cloud sources flow into a centralized “Single Source of Truth.”
  • Hands-on: Navigation lab – explore the Power BI interface, connect to a sample dataset, and understand the relationship between “Data,” “Model,” and “Report” views.
  • Expected Impact: Foundation for all subsequent modules; clarity on how data moves from source to insight.
  • Understanding how Power BI’s built-in AI and Microsoft Copilot automate the creation of report structures, visual layouts, and DAX calculations.
  • Scenario (Banking): A leadership team uses Power BI Copilot to instantly draft an executive summary explaining visual variances in a loan portfolio dashboard.
  • Hands-on: Practice “Visual Prompting” – using Power BI’s Q&A feature and Copilot to generate the first draft of a departmental dashboard from a raw CSV file.
  • Expected Impact: Immediate improvement in report turnaround time; foundational ability to use AI as a reporting “thinking partner”.
  • Leveraging AI-powered visuals like “Key Influencers” to support campaign ideation and competitor analysis specifically for the Malaysian retail landscape.
  • Demo: Inputting consumer feedback data into Power BI → The AI identifies what drives supplement purchases and suggests audience personas based on the patterns.
  • Hands-on: Use Power BI’s “Decomposition Tree” to scan competitor positioning data and visualize market share drivers into a 1-page strategic summary.
  • Expected Impact: Strategic proposal preparation in half the time; more data-backed campaign angles with less manual research.
  • Using Power BI’s built-in AI to extract visuals and structure them into high-stakes proposal decks and HQ submission storylines.
  • Scenario (Operations): Inputting a raw operational performance dashboard → Copilot generates slide titles, talking points, and a “what worked/what didn’t” narrative.
  • Hands-on: Create a 6–8 slide outline for a retailer campaign proposal, converting Power BI AI-generated insights into concise executive summaries.
  • Expected Impact: 30–50% reduction in time spent preparing decks; faster turnaround for HQ submissions and partner proposals.

Day 2: Advanced Strategy, Storytelling & Governance

  • Visualizing customer feedback at scale using Power BI’s built-in sentiment analysis capabilities to identify pain points.
  • Scenario (CRM/Service): Inputting 500+ customer samples → Power BI visualizes volume trends and sentiment while Copilot drafts response templates (EN/BM/Chinese).
  • Hands-on: Build a “Sentiment Dashboard” that identifies escalating complaints and drafts internal talking points for the commercial lead.
  • Expected Impact: Faster response time; more consistent brand tone; reduced “CS fatigue” in drafting routine reports.
  • Using Power BI’s “Smart Narrative” and Copilot to go beyond “what happened” to explaining “why it happened” across multiple data sources.
  • Scenario (Sales): Inputting disparate sales data → Power BI’s AI identifies the underlying drivers of a growth spike and drafts a “Best Practice” playbook for other regions.
  • Hands-on: Use Copilot to perform a “Pain-Point” audit on a functional report, identifying bottlenecks and recommending automated visual solutions.
  • Expected Impact: Move from raw visualization to actionable strategic intelligence; improved clarity for HQ communication.
  • Defining guardrails around data privacy (PDPA), health/financial claims, and brand voice in AI-augmented Power BI reporting.
  • Scenario (HR/Legal): Establishing human-in-the-loop checkpoints for content and data visuals that involve medical or healthcare claims sensitivity.
  • Hands-on: Co-create a “Brand Reporting Playbook” outlining data privacy do’s/don’ts and approval steps for AI-generated insights.
  • Expected Impact: Brand integrity structurally embedded in the reporting workflow; significantly reduced risk of regulatory breaches.
  • Identifying and prioritizing Power BI AI and Copilot opportunities that align with specific business goals.
  • The Framework: Evaluating ideas based on Feasibility (Data availability) vs. Business Value (ROI, time saved, or competitive edge).
  • Hands-on: Design a 3–6 month Power BI AI rollout plan for your department with measurable KPIs and assigned owners.
  • Expected Impact: A prioritized “AI Visual Backlog” ready for implementation; clearer path to data-backed decisions.
Data Analytics Training for IT Professionals

List of Deliverables

Upon completion of the program, participants will have produced a tangible “AI Portfolio” including:

Prerequisites

Who Should Attend

Training Methodology

100% HRDC-Claimable

This program is fully registered and compliant with HRDC (Human Resource Development Corporation) requirements under the SBL-Khas scheme, allowing Malaysian employers to offset the training costs against their levy.

Certification of Completion

Participants who successfully complete the program will be awarded a “Professional Certificate in Power BI & Strategic AI Visualization“.

Post-Workshop Consulting (Optional)

For organizations looking to bridge the gap between training and execution, we offer optional, paid consulting services. These engagements provide expertise and technical support for specific pilot development or full-scale operational integration of the data- and AI-driven use cases established during the program.

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