In this video, Matt Peterson, a trainer at Pragmatic Works, demonstrates how to leverage built-in artificial intelligence features in Power BI to quickly uncover insights from your data. While many users rely on manual filtering and exploration, Power BI includes powerful AI-driven tools that can automatically explain trends, increases, and differences in datasets.
This session focuses on helping users work smarter by using AI features that are often overlooked or hidden within Power BI.
Matt Peterson begins by highlighting that Power BI contains several AI capabilities designed to simplify data analysis. These tools allow users to gain insights without manually applying multiple filters or building complex visuals.
These features are especially useful when working with large datasets that include multiple dimensions.
One of the key features demonstrated is the ability to explain increases or decreases in metrics over time. In the example, Matt analyzes total sales and profit margin between 2006 and 2007.
Instead of manually filtering data, users can:
Power BI then uses AI to evaluate all related fields and determine which factors contributed most to the change.
For example, the AI identifies that certain product attributes, such as color, significantly impacted sales growth. Black-colored products showed a substantial increase, with sales rising from approximately 1.7 million to 3 million, representing a 122% increase.
This insight allows users to ask better business questions, such as whether product offerings or marketing strategies influenced the change.
Another powerful aspect of this feature is the automatic generation of supporting visuals. Power BI creates multiple charts to help explain the data:
These visuals provide a deeper understanding of trends without requiring manual creation. Additionally, users can add these AI-generated visuals directly to their reports for sharing with stakeholders.
Matt Peterson also demonstrates another AI feature designed for categorical analysis: Find where this distribution is different. This feature is useful when comparing categories such as countries or regions.
For instance, when analyzing total sales by country, a noticeable drop between the United States and Australia can be investigated using this feature.
Steps include:
The AI then evaluates how different filters affect the distribution and highlights key differences.
The resulting visual compares the original dataset with filtered subsets. For example, temperature ranges such as cold, cool, and warm may significantly impact sales distribution.
Key observations include:
This allows users to quickly identify areas for further investigation without manually testing multiple filters.
One of the most practical benefits of these AI features is the ability to incorporate generated visuals directly into Power BI reports. With a single click, users can add these insights to dashboards and presentations.
This improves:
By leveraging these tools, users can deliver more impactful and data-driven reports.
Matt Peterson emphasizes the importance of using AI features in Power BI to streamline analysis and uncover insights more efficiently. Rather than relying solely on manual exploration, these tools enable users to quickly identify trends and key drivers in their data.
Whether analyzing time-based trends or categorical differences, Power BI’s AI capabilities help users work smarter and gain deeper insights with minimal effort. For those interested in expanding their knowledge, Pragmatic Works offers comprehensive training covering all AI features within Power BI.
Don't forget to check out the Pragmatic Works' on-demand learning platform for more insightful content and training sessions on Power BI and other Microsoft applications. Be sure to subscribe to the Pragmatic Works YouTube channel to stay up-to-date on the latest tips and tricks.