What is the Role of Predictive Analytics in Life Sciences?

Life science companies are looking to utilize the massive amounts of structured and unstructured data they own to achieve these goals through big data (predictive analytics) analysis.

Fremont, CA: With being faced by many challenges, the Life Sciences industry is navigating health care reform, delivering innovation and value, complying with regulatory changes, optimizing the supply chain, and operating in a globalized economy organization are looking to implement business intelligence solutions that can allow for a more agile approach to their operations.

To properly use and implement these BI solutions, the life science enterprise must have data that is consistent, usable, accessible, accurate, reliable, and secure across the enterprise. An organization's success in managing and using that data starts with building a framework to establish a comprehensive data management process. 

Recent life sciences big data analytics efforts have focused primarily on applying advanced analytics to improve their research and development efficiency. Using insights from big data sources, like genetic and claims data, could reduce trials' cost by enrolling patients most likely to respond to the treatment, improving the trial design, and reducing the length of trials.

Life sciences companies are in the midst of a rapidly changing reimbursement environment where payers are changing the incentives for participants in the health care system based on the demonstrable value they deliver. 

Proper rewards for their innovations will require companies to generate real world outcomes data that show their drug is a significant improvement over current standards of care. This means companies will have to learn to capture and analyze data from patient-related social media channels, payer claims, and EMR (Electronic Medical Records). This effective application of data analytics on these unstructured data sources provides new insights and opportunities to take advantage of that data by engaging with payers much earlier in the R&D cycle, which could improve market access.

A partnership amidst the business and technology groups is essential to achieve this new leverage with big data and analytics. Data Governance is not only an IT function; it must be a cooperative effort between management, IT, and the end-users of that data. It includes the people, roles, assets, procedures, policies, and standards needed to successfully administer and manage a company's information resources, spread across disparate systems, and be owned by different departments. By utilizing less traditional data and data sources, such as customer sentiment data derived from social media and other digital channels, organizations can more effectively analyze activity across marketing channels, but a more human face on the brand and break down information silos between various internal entities like R&D and commercial operations by, for instance, applying insights on patient preferences to future development efforts.

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