Privitar Announces New Integration with Collibra That Combines Data Intelligence and Privacy Preservation to Accelerate Access to Safe Data
Privitar, the leading data privacy platform provider has announced a new integration with Collibra that combines data intelligence with privacy preservation to accelerate access to safe data. Through the API-based integration, users can democratize access to sensitive data across an organization. Data users can effectively browse and request data within their Collibra Data Catalog, and have Privitar’s data privacy protections seamlessly applied.
“Enterprise data often contains sensitive or confidential information, and the provisioning process needs to deliver safe datasets with minimal or no delays or overhead,” said Marc Moesse, Vice President of Product at Privitar. “The integration between Privitar and Collibra was designed to address those challenges by streamlining and building data privacy protections directly into the data provisioning process, empowering users to unlock the value of data and turn it into a strategic asset. As a result, data users will have quick and easy access to high-quality, trustworthy, de-identified data assets that are safe to use and useful in their analyses and algorithms.”
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How the Integration Works
The integration of the Collibra Platform, providing full visibility of data assets and their properties, and the Privitar Data Privacy Platform’sTM comprehensive de-identification and policy management enables data-driven organizations to accelerate access to safe data. Through the integration, organizations can streamline data provisioning for a broader range of users and use cases, safely accelerating access to insights from even the most sensitive datasets. The integration enables users to:
● Curate data assets across an organization and expose more data to a wider range of approved users
● Empower data users to quickly find data, searching and requesting data for their projects without having to code or script
● Track data requests and provisioned data
● Understand what data to protect, applying consistent privacy protections accordingly
● Optimizing utility while reducing risk with sophisticated de-identification techniques and advanced linkage control
● Centrally manage privacy policies to ensure data privacy is effectively governed across the enterprise
● Accelerate data provisioning of safe datasets via automation that eliminates slow, manual processes
● Automatically track the lineage of safe datasets back to the raw data used to create them
● Benefit from performance and reliability at scale
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