Data Analytics
Snowflake Summit 26: What Does It Take To Achieve Data Quality?
Data quality and strong data governance are key to having a strong foundation for artificial intelligence initiatives. Without clean, quality and accurate data, AI and analytics platforms could respond to queries with incorrect information, misleading users who might not know there is a quality issue.
The need for consistent data governance and quality monitoring is only growing, but many organizations don’t know where to start.
At Snowflake Summit 26 in San Francisco, BizTech spoke with Snowflake Head of Horizon Catalog and Collaboration Prasanna Krishnan about the difference between data quality and data governance, common obstacles, how to navigate data quality when data is stored in multiple locations, and how enterprises can improve their approach using Snowflake solutions.
DISCOVER: Turn data into insights and accelerate artificial intelligence initiatives.
Check out this page for our complete coverage of Snowflake Summit 26.
Participants
Prasanna Krishnan, Head of Horizon Catalog and Collaboration, Snowflake
Video Highlights
- If an enterprise has a data quality issue, then artificial intelligence agents will give confident answers that are grounded in inaccurate data.
- Data governance includes three pillars: knowing, protecting and auditing.
- Data quality requires strong data governance and a consistent effort. Snowflake enables enterprises to establish data quality monitoring with a variety of customizable data metric functions.
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