Make the best use of data to improve customers’ lives and also establish and maintain market leadership.
Why AI Now?
Just as the life sciences are transforming lives, the industry itself is being transformed. Competition from new start-ups and shifting regulations are creating pressure to conduct R&D and clinical trials faster, but also with more transparency and precision. The growing levels of investment needed for successful product development and production is reducing room for error and driving a need for more certainty in every step on the path to revenue. At the same time, the growing volume of data available for research and analysis represents incredible opportunity to find new cures and exciting innovations faster than ever. Data science can better equip life sciences companies to make the best possible use of data from research, clinical trials, the production line and customers themselves. With RapidMiner, life sciences companies can not only improve their customers’ lives but establish and maintain market leadership.
Life Science Industry Use Cases
Highlighted RapidMiner Impact
A European pharmaceutical manufacturer conducted detailed clustering of pharmacy segments and boosted sales with targeted promotion of drug classes.
An R&D team used text analytics on research papers from 8,000+ scientists, created granular subject matter tags, and extracted insights that increased its speed to revenue with new drugs
A clinical research organization developed a proprietary algorithm for patient recruitment, site selection and monitoring, making clinical trials more effective.
A drug manufacturer augmented its master data management approach to mapping active ingredients to 35,000 SKUs, improving data quality which made manufacturing, foreign markets packaging and regional inventory stocking more precise and accurate.
A research organization used text analytics to mine patient responses for cluster and topic analysis to ensure key observations from the protocol were properly factored into the clinical study, vastly improving contextual insights from the trial.
A manufacturer predicted outliers in chemical composition that arose during batch manufacturing processes, which allowed it to find issues sooner, intervene before product shipped and avoid penalties for inadequate quality control.
What Our Customers Say
“I am in love with its easy to use interface and the new functionalities being added.”
– Assistant Professor