Tag PII from Great Expectations

Great Expectations, an open-source data validation framework, plays a crucial role in managing and protecting Personal Identifiable Information (PII) by ensuring that data pipelines adhere to defined expectations, thereby maintaining data quality and integrity. While Great Expectations itself is not a data storage or analysis tool, it helps identify and alert on anomalies or deviations in data that could indicate a breach or improper handling of PII. By defining expectations for what PII should look like and how it should be handled within datasets, organizations can prevent the exposure of sensitive information and ensure compliance with data protection standards. This proactive approach to data validation helps organizations maintain trust and adherence to privacy regulations, ensuring that PII is managed responsibly throughout their data ecosystems.

Secoda's integration with Great Expectations allows you to streamline your data management processes. By building custom workflows, you can easily tag Personally Identifiable Information (PII) within Great Expectations, saving time and reducing manual errors. The automation feature in Secoda consists of triggers and actions. Triggers enable you to schedule workflows, ensuring subsequent actions are executed in a structured manner. Actions encompass various operations, such as filtering and updating metadata, allowing you to create detailed workflows tailored to your team's requirements. Additionally, Secoda empowers you to perform bulk updates to metadata in Great Expectations, further enhancing your data management capabilities.

Secoda, an AI data governance platform, offers a comprehensive integration with Great Expectations. With Secoda's all-in-one data search, catalog, lineage, monitoring, and governance capabilities, data teams can easily find and utilize their data. The integration with Great Expectations further enhances data governance by providing advanced data quality validation and monitoring features. This powerful combination simplifies the data stack and empowers data teams to ensure the accuracy and reliability of their data.