Data Observability Best Practices for Reliable Data Pipelines Q&A powered by Community
Summary: collibra_data_citizens introduces a webinar focused on the importance of data quality for AI and analytics, emphasizing the need for evolving data quality rules throughout the data lifecycle— from ingestion and storage to processing and consumption. The session aims to showcase how AI can automate the creation and management of these rules to ensure data reliability. It covers best practices for managing data quality during ingestion, storage, and consumption stages. The webinar includes a demonstration and offers a Q&A section for attendees who wish to explore these topics more deeply.
Artificial Intelligence (AI) and analytics requires high quality data to ensure reliable outputs and accurate decision making. The data quality rules needed for ensuring reliable data evolve as data moves from ingestion and storage, to compute and consumption. This webinar will cover best practices to ensure the right data quality rules at the right points in your data pipelines. The demonstration will show how AI can automate and simplify the creation of rules for monitoring and management of data health to ensure reliable AI and accurate analytics.
In the demonstration, you’ll see how AI can automate and simplify the creation of rules for monitoring and managing data health—ensuring reliable AI and accurate analytics.
During the webinar, you’ll learn how to build and manage data quality rules for:
Data ingestion from source systems and files
Data stored in data warehouses and lakes
Data consumed in AI and analytics processes
Watch the webinar [here], and explore the questions below from our Powered by Community Q&A, which are addressed throughout the session.
Prefer to skip the webinar and dive straight into the Q&A? Click [this link ].
