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Shadow™ is a leader in automating data and document de-identification and data anonymization tool.
Our team created this data anonymization tool to produce solution that allow you to focus on science and results.
Shadow™ alleviates the pain points of homegrown software utilities that often prove to be inefficient, difficult to maintain, and slow to match quickly evolving requirements.
Our team created this data anonymization tool to produce solution that allow you to focus on science and results.
Shadow™ alleviates the pain points of homegrown software utilities that often prove to be inefficient, difficult to maintain, and slow to match quickly evolving requirements.
Key Features
Shadow™ is a fully featured, Health Canada, France’s MR001-MR004, EMA Policy 0070 (Phase 1 and 2) compliant solution
Looking to enhance your organization’s impact? GENINVO’s technology-empowered experts are available to assist you in the following areas:
Shadow™ is a comprehensive solution to address the evolving landscape for data and/or document de-identification and anonymization
Reports to drive, describe and monitor your anonymization project and the effectiveness of your teams.
Determination of the risk for re-identification of your data before and after de-identification to ensure you meet required thresholds.
Built from the ground up by the innovative time at GENINVO, a team comprised of both Life Sciences and software development professionals
Options to use the software’s “canned” (predefined) de-identification strategies for CDISC SDTM and ADaM along with those you develop to support your own data and document standards
Guidance for you and your project team members through de-identification, review/approval, and anonymization
Visibility of the effects of your de-identification strategies as you apply them to determine and tailor their effectiveness
Evaluation of the impact of your applied de-identification strategies to ensure consumers’ ability to reproduce analyses
Assessment of the precision/efficiency of strategy application and effectiveness of the de-ID teams/personnel
