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Preparing Data for AI in Power BI
Learn how to prepare AI-ready data in Power BI with Justin Vogel. Clean and standardize columns, handle missing values, build a solid model, and avoid common data issues
In Preparing Data for AI in Power BI, Justin Vogel shows you how to get your data into the right shape for AI-driven analysis. You’ll learn how to clean, standardize, and structure datasets so AI tools can produce more accurate summaries, insights, and results. The course focuses on practical preparation steps inside the Power BI ecosystem, including common data issues that cause AI outputs to be misleading, inconsistent, or unusable.
You’ll also learn how to think about “AI-ready” data, clear column meaning, consistent formats, trustworthy categories, and well-designed relationships. Justin covers best practices for reducing noise, handling missing values, and organizing your model so downstream AI features work smoothly. By the end, you’ll have a repeatable preparation workflow you can apply before using Copilot, Q&A, or other AI experiences in Power BI.
Course Outline ( Free Preview)
Module 01 - Introduction
Justin sets the stage by explaining what “AI-ready” data means in Power BI and why preparation matters before you rely on AI-driven insights. You’ll preview the workflow you’ll follow in this course, reduce complexity, add guidance for AI, and validate outputs, so you understand how each step improves accuracy, consistency, and trust.
Module 02 - Simplifying the Data Schema
This module focuses on making your model easier for both humans and AI to understand. Justin shows how to reduce unnecessary complexity, clarify relationships, and standardize naming so the schema tells a clean story. You’ll learn practical ways to make tables and fields more intuitive, which directly improves the quality of AI summaries, Q&A results, and downstream recommendations.
Module 03 - Add AI Instructions15 min.
Learn how to guide AI features in Power BI by providing clear instructions and context. Justin covers what good instructions look like, how to define terms and expectations, and how to reduce ambiguity so AI outputs stay aligned to your business meaning. The goal is to make AI assistance more predictable, consistent, and useful for real users.
Module 04 - Create Verified Answers11 min.
AI is only valuable when it’s trusted. In this module, Justin shows how to create and validate “verified” responses so users get consistent answers to common questions. You’ll learn how to test outputs, correct misunderstandings, and lock in reliable results that support self-service analytics without confusion.
Module 05 - Class Wrap Up3 min.
Congratulations on completing the course, you now have a repeatable checklist for preparing AI-ready data in Power BI. Justin recaps the key steps, simplifying your schema, adding instructions, and verifying answers, so you can apply the same approach to future datasets. You’ll leave with clear next steps for maintaining accuracy as your model grows and changes.
Justin Vogel taught high school Advanced Placement® psychology and academic research for 15 years. He holds an MS in Psychology and spent several summers teaching psychology to educators new to the subject. He has also scored AP exams, reviewed textbooks for the Florida Department of Education, and written exam questions for test banks. He has presented at professional learning conferences at the local and national level and now puts his teaching and presentation skills to use on the Power BI team at Pragmatic Works.
