Tag PHI in Great Expectations with Secoda. Learn more about how you can automate workflows to turn hours into seconds. Do more with less and scale without the chaos.
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Great Expectations, an open-source Python library, offers a framework for defining and validating data states. It aids data teams in establishing a common understanding of their data by enabling quality testing, documentation, and profiling. The library promotes data quality and collaboration by allowing users to specify expectations, validate them, and document the data they handle. In the context of ensuring data governance at scale, Secoda can be used to tag PHI (Protected Health Information) residing in Great Expectations.
Tagging PHI (Protected Health Information) in Great Expectations, a tool for data validation, enables teams to define and enforce expectations on health data, ensuring it's accurately identified, validated, and protected. By establishing specific expectations around PHI data, organizations can enhance data quality, maintain compliance with healthcare regulations, and ensure PHI is handled securely throughout data pipelines. This tagging process underscores the commitment to data integrity and patient privacy, essential in healthcare data management and analytics.
The integration 'Great Expectations' allows users to tag PHI through Secoda. An Automation consists of Triggers and Actions, where Triggers activate the workflow based on defined schedules. Actions encompass various operations such as filtering and updating metadata, enabling users to create customized workflows. With Secoda, bulk updates to metadata in Great Expectations can be performed efficiently.
Secoda, the AI data governance platform, offers seamless integration with various data models, databases, warehouses, and BI tools. By connecting to these resources, Secoda enables effortless data discovery and utilization for both data and business stakeholders. With its comprehensive data management platform, Secoda consolidates data catalog, lineage, documentation, and monitoring, providing a centralized solution for efficient data management.
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