PHEMI Data Privacy Manager

PHEMI Systems Corporation

Military-grade privacy, security, governance, and data management system for Microsoft Azure.

PHEMI Data Privacy Manager

With the explosion of data in the health and life sciences industry, a whole new world of operational efficiency and scientific progress is possible. With almost daily data breaches and the onset of new privacy legislation, it is becoming increasingly vital to generate value from data without compromising privacy and security.

PHEMI provides the most advanced cloud-based privacy, security, and governance data management system with military-grade privacy for the Microsoft Azure ecosystem.

Data Privacy

PHEMI’s Data Privacy Management System enables enterprise-level health and life sciences organizations to extract the most value from their sensitive data. It facilitates data sharing, protects against data breaches and provides a governance framework to assist with privacy regulation compliance. PHEMI uses best of breed Privacy By Design end-to-end architecture to ensure that privacy measures are integrated into every step of the data pipeline and access to sensitive data is restricted to those allowed under the access policy.

Management Simplicity

The single pane of glass interface for non-expert users and integration with popular analytics tools enables self-serve analytics and the optimization of internal IT and analyst expertise to achieve dramatic improvements in operational efficiencies, clinical and research discoveries. Turnkey managed services and system integration accelerates time to analytics, machine learning, and AI.

PHEMI augments your data lake by helping you:


Any type of structured or unstructured data can be ingested from anywhere; the cloud or on-premises.


Original data is stored in any form, unchanged, and new subsets have full auditability. Catalog, inventory, and index all your data for quick query performance. Versioning provides a data time-machine. Data is analytics-ready. Data pipelines are automated from ingesting, to metadata curation and consumption access controls.


Sensitive data is de-identified by masking, rounding, tokenizing or encryption while still preserving the value inherent in the data. De-identification is done once to create multiple forms of anonymized data for different levels of access.


Ensure privacy is uniformly and consistently enforced at scale using Attribute-Based Access Controls (ABAC). Access controls are kept data and metadata centric making it highly scalable.


Self-service data analytics is enabled via a variety of standard protocols and popular tools: ODBC compliant tools like Tableau or PowerBI, Spark-based data science tools such as Apache Zeppelin or Cloudera Data Science Workbench, Spark MLlib, H2O Sparkling Water, or the PHEMI REST API to a custom application. Or data can be exported to downstream targets.

D'una ullada