AI-powered insights: Leveraging Data Agents in Microsoft Fabric

Leveraging artificial intelligence (AI) technologies has become an imperative for companies looking to stay competitive in their respective markets. Data Agent (formally AI Skill), a powerful new addition to the Microsoft Fabric platform, does just that by seamlessly integrating AI into the data analytics process. Data Agent in Microsoft Fabric will transform the way users are able to interact with their data, upgrading their data analytics capabilities and ultimately driving organizational success.

Utilizing Data Agent in Microsoft Fabric

Microsoft Fabric’s Data Agent feature offers a significant edge over traditional methods as it enables the creation of custom chatbots, similar to Chat GTP, that can interact with data models and provide insights directly from your organizational data. These chatbots can query data lakes and access all three data levels of the medallion architecture:

1. Gold layer: The most refined data layer where data is aggregated, summarized and optimized for reporting and analytics. This layer contains high-quality, ready-to-use data for business intelligence and decision-making.
2. Silver layer: Contains cleaned and enriched data as data in this layer has undergone some levels of transformation and is more refined than the bronze layer.
3. Bronze layer: The raw data layer where data is ingested in its original format from various sources without any transformations.

While most queries only access the gold layer, chatbots powered by Data Agent can be configured to query the three different data layers based on users’ needs, allowing for more granular data analysis and real-time operational insights. For example, a chatbot can be set up to access the gold layer for high-level summaries and insights, the silver layer for more detailed and cleaned data and the bronze layer for raw data analysis.

Companies can also leverage Data Agent to automate data analysis by generating queries, eliminating the need for manual data preselection as the chatbot will access the appropriate data layer based on the user’s question. This capability allows users to uncover trends that may not have been previously identified and quickly obtain actionable insights. For example, a sales team could use Data Agent to analyze real-time transaction data and identify emerging market trends, enabling them to adjust their strategies accordingly.