Speeding Time-to-Market in Life Sciences

Speeding Time-to-Market in Life Sciences

The Goal: Medical device and drug development is a lengthy, complex, and costly process, entrenched with a high degree of uncertainty that expensive development will actually succeed. Life Science executives are laser focused on using data analytics to avoid delays and rejections that risk investments or cost revenue.

 

The Requirements: Throughout each phase of design, testing, approval and in-market product use, life sciences companies produce huge volumes of valuable patient-related data. This data is federated across multiple heterogeneous systems, some of which are owned by external parties which provide work under contract. Business managers in operations, quality control, regulatory affairs and client service each need to make use of combined data sets to solve their problems without exposing that data in any unauthorized manner.

 

The Challenge: The data underlying drug and medical device development is highly sensitive, proprietary and regulated – and generated by a variety of organizations.  Blending raw data from all the sources required is highly complex. Sensitive data cannot be exposed directly to analysts but must be available for the analytics. Each data owner has to ensure sensitive data in not exposed in violation of corporate policies and regulations.

 

The Problems:  Conventional systems can be configured to allow users to expose or change data owned by another party.  If data Owner A shares raw data with Owner B, using a conventional system, then Owner B can use the combined data sets in violation of Owner A’s rules.  Legal agreements cannot be relied upon to prevent data misuse. Most data sharing projects get stuck as a result. Reliance on 3rd party services do not provide the capability for self-service analytics, and do not provide the data quality and transparency required to ensure the analytics are generating accurate results.

 

The Myngl Concordance Solution: Secure Analytic Containers work differently than conventional systems which allow users to view or change data from another owner.  Concordance decentralizes security, governance and compliance controls to each data owner separately and simultaneously. Users, admins, even Myngl staff, are blocked from direct source data access to change or override owners’ rules. This removes vulnerabilities and prevents data risks, misuse, and breaches.

 

Analysts can make use of sensitive data without exposing it.  Secure Analytic Containers provide analysts with a secure analytic sandbox utilizing open-source Zeppelin Notebooks, SQL, Python, TensorFlow and other powerful tools. Analytic jobs are separately authorized by data owners to make use of restricted data. Automated scheduling is orchestrated via microservices and results are segregated from source data. This allows analytic apps to use data elements which are hidden from direct Subscriber view.

 


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