In today’s data-driven landscape, the quality of your data directly impacts the accuracy of AI-driven insights and decision-making. Here’s why data quality matters:

- Trustworthy Insights: Reliable data ensures that AI models generate accurate predictions and recommendations. Without trustworthy data, there’s a risk of eroding trust in AI systems.
- Business Processes and Decision-Making: Poor data quality or incompatible data structures can hinder business processes and decision-making capabilities. Clean, well-structured data is essential for informed choices.

A powerful data platform plays a crucial role in maintaining high data quality. With a robust data platform, you can ensure that your data is consistent, accurate, and readily available for various applications and processes. Leveraging such a platform is foundational for implementing effective data quality measures.

During the session, we will guide you through Microsoft Purview Data Quality:
This comprehensive solution empowers business domain and data owners to assess and oversee data quality. It offers no-code/low-code rules, including out-of-the-box (OOB) and AI-generated rules.

Purview Data Quality incorporates AI-powered data profiling. It recommends columns for profiling, allowing human intervention to refine these recommendations. This iterative process enhances accuracy and improves underlying AI models.

By integrating Microsoft Purview with your data platform, you can apply and monitor data quality processes more effectively. This integration ensures seamless data management and governance across your data estate.

During the session, we will walk you through the Data Quality Life Cycle:

- Assign data quality steward permissions in your data catalog.
- Register and scan data sources in Microsoft Purview Data Map.
- Set up data source connections for quality assessment.
- Configure and run data profiling.
- Define and apply data quality rules.