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How Digital and Analytics Revamp Life Sciences

By Life Sciences Review | Wednesday, October 21, 2020
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Life-sciences organizations are on the cusp of a digital revolution, and there are wondrous opportunities to leverage digital and analytics to address increasing needs.


FREMONT, CA: Life sciences organizations are trying to keep pace with customer demands. They are shifting from treatment to preventive scenarios. But how can they use the vital patient information available to them to the best of their advantage to manage health outcomes? Just like the other sectors, it is time that the life sciences firms adopt digital and data analytics and leverage the insights strategically to their advantage. Pioneering life-sciences firms are now discovering the solution, however, by approaching digital transformations differently.  Here is how.


There are several industry battlegrounds in life sciences, where it is possible to deliver value at scale through data sets, data and analytics platforms, analytical models, and digital experiences for customers. Companies are designing solutions that use artificial intelligence (AI) and real-world data to create hypotheses at scale and enhance testing efficiencies for several use cases across the value chain.


This can dramatically advance a firm’s understanding of disease progression and drug effectiveness, boost the search for new indications for existing drugs, and shape value-based pricing decisions.


In the real-time customer and patient-engagement area, technology is deployed to build an agile, customer-centric, commercial model that gives the right message through the right channel to the right customer at the right time. Personalized content is offered at the market's speed, with testing conducted to rapidly refine content and the results measured in real-time. Campaign cycles are measured in days. The incorporation of new methods like reinforcement learning creates a continuous learning loop, while modern technology ensures everything is smoothly integrated across channels.


Technological advancement is constant, which means today’s battlegrounds will be less matured tomorrow. Cutting-edge analytics and other digital techniques like deep learning, transfer learning, and reinforcement learning will revolutionize areas. Technology can drive further productivity advances in life sciences business processes. Many companies have made steps toward transforming their businesses with digital and analytics. Their future competitiveness depends on how fast they are scaling up.


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