Fueling breakthroughs in pharma AI: 3 critical factors 

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Big data and AI offer massive opportunities to the pharmaceutical industry — in theory. In reality, many companies are struggling to realize the potential of these tools. Some organizations have been hesitant or resistant to leveraging the technologies. Others may have attempted to embrace them early on but are now beginning their second or third incarnations of “digital transformation,” likely with some layoffs along the way.

Why the difficulty? Digital transformation is, of course, a massive undertaking — requiring enterprise-wide coordination and a clear, focused vision. In the real world, organizations have struggled with defining a focus for their AI efforts and sustaining the investments necessary to reach them. It’s easy to get excited about the prospect of using AI to solve everything under the sun, but more often, successes are coming when teams stay focus…

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5 common data management problems affecting drug discovery

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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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