APR Nov/Dec 2022 - 73

« FORMULATION AND DEVELOPMENT
Thinking of accessibility in another way, data access can also be
improved between the enterprise and an external collaborator
or CRO. With the right configuration, new data from an external
collaborator or CRO can appear in the organization's catalog as it
is captured, instead of researchers having to wait for the data to
be delivered in a package at designated time points.
*
Interoperability is simplified with APIs and tools for working
with the data, including export, analysis and integration with
other data types.
* Reusability is enabled by letting teams repurpose the
same data for analysis and large-scale processing across the
enterprise, which is easily possible if the data and associated
metadata are well-described and stored.
Aside from the efficiency benefits that can be achieved from desiloing
data, organizations should also consider how modern data
management can simplify compliance and regulatory approvals. First,
de-identification tools can be configured to remove personal health
information (PHI) and personal identifiable information (PII) " on the
edge, " before it comes into the platform and is made accessible to
researchers, ensuring consistency across departments. Additionally,
audit trails can be easily captured that show access logs, versioning,
and processing actions.
With a centralized, standardized and scalable infrastructure that
performs these functions, pharma research teams can enable FAIRdriven
processes, meet their greater R&D goals, and plan for more
ambitious data-driven objectives in the future. The ability to efficiently
and securely access massive amounts of curated data on the cloud
repeatedly can enable research that wasn't possible in the past.
These tools allow R&D groups to meet project deadlines focused
on processing large, complex objects with varying data formats in a
standardized and reproducible (and repeatable) manner while not
being burdened with manual data management.
By combining a sophisticated approach to data management with wellthought-out
goals for complex analysis and/or AI/ML, organizations
can avoid the pitfalls that have hindered digital transformations thus
far. Scientists and researchers can focus on analyzing and processing
datasets, rather than managing them-resulting in accelerated R&D
and innovation to bring therapeutics to market faster.
Author Biography
Jim Olson is CEO of Flywheel, a biomedical research
informatics platform. The company uses cloud-scale
computing infrastructure to address the increasing
complexity of modern computational science and
machine learning. Jim is a " builder " at his core. His passion is
developing teams and growing companies. Jim has over 35 years of
leadership experience in technology, digital product development,
business strategy, high-growth companies, and healthcare. He has
worked for large and startup companies, including West Publishing,
now Thomson Reuters, Iconoculture, Livio Health Group and Stella/
Blue Cross Blue Shield of Minnesota.
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APR Nov/Dec 2022

Table of Contents for the Digital Edition of APR Nov/Dec 2022
