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Data analytics and business teams rely on trusted data to train their AI models, find accurate answers to abstract problems, and provide their internal customers with key insights. Leveraging untrusted data can have especially catastrophic impacts when used to inform strategic decision-making. Data quality tools address these problems by proactively detecting, resolving, and preventing data health issues. However, various data quality tools specialize in different capabilities and solutions, meaning no one tool can apply to every business.

With the Open Data Quality Framework, Alation customers can strengthen data governance for Snowflake by making data quality information visible. This allows governance teams to document and enforce data quality policies and rules, assign data quality stewards, determine if data is fit for use, and manage the entire data quality lifecycle from one platform. And with Alation’s best-of-breed approach to data quality, customers choose the best solution for their business rather than following a one-size-fits-all approach.

Alation Open Data Quality Framework for Snowflake enhances governance with:

Data Health Tab: Gain visibility into the complete health profile for Snowflake tables and columns – including quality, reliability, and performance.
Trust Flags: Configure data quality results to automatically flag Snowflake data as endorsed, warned, or deprecated to signal whether data should or shouldn’t be used.
Data Profiling: Extend Alation’s column profiling of min, max, mean, and null Snowflake values with insightful data quality and observability information to action data cleansing and remediation.
Data Quality Policies: Capture Snowflake-specific data quality policies in Alation Policy Center to understand how and why data quality rules are applied by the implemented tool.