Create Dashboards with Power BI — Lesson 2: Data Preparation and Model Support

Learning Objectives

• Prepare dashboard-ready data with Power Query
• Create relationships, a date table, and core measures
• Validate data quality, totals, granularity, and refresh assumptions

Detailed Topics

• Prepare dashboard-ready data with Power Query: Prepare dashboard-ready data with Power Query
• Create relationships: Create relationships, a date table, and core measures
• Validate data quality: Validate data quality, totals, granularity, and refresh assumptions

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

Build a compact semantic model for a dashboard.

• 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 Data Preparation and Model Support in a realistic scenario and validate the result with documented evidence.