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BI tools are all set to uncover new business opportunities, evaluate new strategies in life sciences, and put them into action right away.
FREMONT, CA: Business leaders in life sciences have more specific business intelligence requirements than their peers in other industries. Besides the common business drivers like marketing, profit and loss, and customer churn, life sciences companies need data regarding patients and clinical trial outcomes. Long histories of clinical trials can be complex to sift through to find insights into planning future ones, and as Business Intelligence (BI) in life sciences, utilizing advanced tools is the key to success in this business, which is based on the data generation. Here are three major applications of Bi in life sciences.
• Planning Clinical Trials
BI offers can help life sciences companies optimize their clinical trials using predictive analytics. BI system uses clinical data in different ways, depending on the data analysis needs of the client. It can create predictive models related to a patient's response to the drug being studied and simulate to show the optimal number of patients might be needed for testing that drug. It can also detect chemical signals received by sensors as data and predict a drug's effect before it is given to a patient.
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• Conducting Clinical Trials
BI platform can help life science and pharmaceutical companies find insights regarding business operations and bring drugs to market faster using predictive analytics. It also compares clients with the success of their peer organizations. BI solution uses operational data from clinical trials for drugs or similar practices to show the differences in success. Apart from the focus areas of cycle time, enrollment, and costs, BI also provides risk evaluations of patients to be monitored more closely if need be.
BI can help pharmaceutical companies gain operational insights and model marketing situations using predictive analytics. This can drastically improve the business decision-making process. Marketing conditions could comprise a seasonal rise in demand for a drug or which retailers see the most sales of that drug. The BI model is trained using thousands of data points from sales, marketing investments, and marketing campaign projects.