Tag PII from Datafold

Datafold, a data reliability platform, plays a crucial role in managing Personal Identifiable Information (PII) by providing tools that help ensure the accuracy and integrity of data throughout the analytics process. By offering features like data diff, column-level lineage, and anomaly detection, Datafold enables organizations to monitor and protect PII effectively. These features assist in identifying unexpected changes or exposures of PII, facilitating rapid response to potential data breaches or compliance issues. Additionally, Datafold's capabilities support data governance initiatives, helping organizations maintain control over PII and ensure that it is handled in accordance with privacy regulations and best practices. Through its comprehensive toolset, Datafold aids organizations in safeguarding sensitive data, thus upholding trust and compliance in their data ecosystems.

Secoda's integration with Datafold allows you to streamline your data management processes by building custom workflows. One of the key features is the ability to tag personally identifiable information (PII) that resides in Datafold, saving time and reducing manual errors. These workflows consist of triggers and actions. Triggers can be scheduled at specific intervals or customized to activate subsequent actions. Actions encompass various operations, such as filtering and updating metadata, which can be stacked to create detailed workflows tailored to your team's requirements. Additionally, Secoda empowers you to perform bulk updates to metadata in Datafold, further enhancing your data management capabilities.

Secoda, an AI data governance platform, offers a comprehensive integration with Datafold, enhancing its capabilities. With Secoda's all-in-one data search, catalog, lineage, monitoring, and governance platform, data teams can easily find and utilize their data. The integration with Datafold further streamlines the data management process, providing users with a seamless experience. By combining the strengths of both platforms, data teams can efficiently govern and optimize their data workflows, ensuring data quality and reliability throughout the entire data lifecycle.