What’s the best way to ensure data quality and governance in large-scale Data and AI solutions?

Techguys180

As organizations scale their Data and AI solutions, maintaining data quality and governance becomes a major challenge. With data coming from multiple sources — cloud storage, on-premises systems, IoT devices, and third-party APIs — it’s difficult to ensure consistency, accuracy, and compliance across the entire data lifecycle.

I’m looking for best practices, tools, and frameworks that can help establish a solid data governance strategy while ensuring high data quality for analytics and machine learning models. Specifically, I’m interested in:

(1)Methods for automating data validation, cleansing, and lineage tracking

(2)Ways to enforce data privacy, access control, and compliance (GDPR, HIPAA, etc.)

(3)How to integrate governance tools with Azure Purview, Databricks, or Snowflake

Any real-world insights, architectural diagrams, or tool recommendations would be really helpful.