Technology stack
Customer’s challenges
The project addressed several key challenges, including the need for enhanced data governance to support strategic initiatives and the consolidation of existing solutions for improved efficiency. The customer sought a framework that promoted transparency and collaboration while balancing data democratization with central governance to maintain data integrity. One of the major challenges was the difficulty in identifying and tracking data access patterns, including which users were accessing specific data in Snowflake from Power BI, identifying frequently used schemas and objects in Snowflake, detecting redundant semantic models in Power BI, and pinpointing heavy Snowflake users among Power BI users.
Solution
To tackle these issues, we defined comprehensive processes and tools for effective data governance and quality management. This structured approach clarified user roles within the data ecosystem and included developing metrics to evaluate data qualityconsistently.
The use case for Generative AI features in this project involved utilizing Snowflake Cortex LLM functions to analyze Power Query code. This analysis helped in identifying how Power BI semantic models connected to Snowflake datasets, extracting details about accessed objects, and presenting this information in a structured format. The AI capabilities enabled automated discovery and mapping of data connections, which would otherwise be a manual and time-consuming process. The information extracted using Generative AI was presented using Power BI reports to business stakeholders and data owners. This approach enhanced data governance by providing insights into data usage patterns and facilitating the optimization of Power BI models.
We also aimed to drive usage of the Snowflake data warehouse and build a guild of data champions among analysts and business users.
The approach was highly effective, resulting in documented governance processes and the introduction of metrics for ongoing data quality assessment and data access patterns. A tailored framework for Snowflake and Power BI ensured reliable information access, while standardized processes for data discovery improved user experience.
Benefits
Within six months, usage of the Snowflake warehouse increased by 25%
(in the number of active users), enabling more efficient decision-making.
The customer became well-prepared to launch AI initiatives,
leveraging high-quality data for advanced analytics.
The project laid a strong foundation for future growth
through enhanced data governance and quality standards.
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