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.

1. 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

2. 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.
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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.

3. 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.