Power BI & AI for Data Analytics — Lesson 2: AI-Assisted Data Analysis

Learning Objectives

• Use AI-supported features to identify patterns, drivers, anomalies, and segments
• Apply natural-language questions, explanations, and assisted report creation
• Review generated insights for evidence, assumptions, and business relevance

Detailed Topics

• Use AI-supported features to identify patterns: Use AI-supported features to identify patterns, drivers, anomalies, and segments
• Apply natural-language questions: Apply natural-language questions, explanations, and assisted report creation
• Review generated insights for evidence: Review generated insights for evidence, assumptions, and business relevance

Guided Demonstration

1. Review the requirement, users, data, dependencies, permissions, risks, and expected result.
2. Demonstrate the relevant configuration, application, automation, analysis, or administration workflow step by step.
3. Capture settings, outputs, logs, screenshots, calculations, test results, and other useful evidence.
4. Compare the observed result with acceptance criteria and explain any corrective action.

Hands-On Activity

Investigate a dataset using AI-assisted analytical features.

• Document assumptions, prerequisites, and the planned sequence before starting.
• Complete the activity and preserve evidence of normal, boundary, and failure behavior.
• Record issues, corrective actions, reusable notes, and the next improvement priority.

Scenario Analysis

• Identify the business or technical decision that must be made.
• Compare valid implementation approaches and their trade-offs.
• Consider security, privacy, accessibility, performance, support, and operational impact.

Troubleshooting Focus

• Separate symptoms from causes and collect evidence before applying a fix.
• Check configuration, data, permissions, dependencies, capacity, connectivity, and recent changes.
• Validate rollback, recovery, ownership, user impact, and business continuity.

Validation Checklist

• The requirement, scope, inputs, and expected outcome are clear.
• The selected design or configuration is documented and follows approved standards.
• Normal, boundary, failure, security, accessibility, and operational conditions are reviewed.
• The result is supported by evidence and the next improvement is prioritized.

Lesson Outcome

Learners can explain and apply AI-Assisted Data Analysis in a realistic scenario and validate the result with documented evidence.