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Ask a pharma researcher how well they’re able to leverage their organization’s medical imaging data, and you might hear a discouraging response. While most pharma companies have massive amounts of clinical and medical imaging data, often, most of the imaging data isn’t ready for modern research processes and infrastructure. This imaging data is an untapped asset — it’s disorganized, difficult or impossible to query, not normalized and in no way ready for machine learning and AI. The result is innovation is slowed.
Imaging data is a rich source of information that can hold the key to many discoveries, but it is complex to work with. Pharma companies need a sophisticated data management infrastructure to help manage this complexity and seek to scale up their research.
Here are five common data management problems to consider as your organization evaluates its path forward.
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