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Perspectives on Big Data in Pharmaceuticals

Posted by Matt Breese on Aug 1, 2016

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In May we posted a “Perspectives on Big Data in Healthcare” blog. We acknowledged that leveraging data is a critical component for any industry that is future oriented, and that it certainly has a place in healthcare. To recap, Big Data is the term for very large, comprehensive data sets that can be used for complex analytics to reveal patterns, trends and relationships. In healthcare it is a credible tool to optimize innovation, improve efficiencies in research and clinical trials, and individualize provider, payer and pharma partnerships.

For pharma, properly leveraging Big Data can be resource intensive, but it can produce a big payoff if properly applied to strategic planning and marketing.

Why invest in Big Data?

With constrained resources – both financial and staffing – why would pharma want to invest in collecting and analyzing Big Data?

The answer is quite simple. Any tool that helps an organization improve access to its products, follow population and/or healthcare trends and better read its competitive market has value.

Collecting Big Data for the sake of having access to an enormous amount of information does not have value unless pharmaceutical manufacturers also have the ability to:

  • Collect the right type of data
  • Ensure diverse, yet relevant, data sources
  • Create appropriate relationships among the various data sets
  • Analyze the data in a logical and actionable way
  • Use the analysis to improve business and the future value of products for patients (operations, product development, marketing, etc.)

With the correct skill set, analysts can mine Big Data to help pharma:

  • Understand trends in population health and healthcare services
  • Evaluate and identify value-added clinical trial sites
  • Consider multiple factors that affect R&D
  • Target marketing strategies, sales and managed market efforts
  • Evaluate competitors, market potential and the payer landscape

Zephyr Health, a leading resource for data analysis, produced a report in conjunction with pharmaphorum™ that highlights the challenges associated with accessing, analyzing and using Big Data. The report underscores the importance of global healthcare data in today’s complex business environment. Simply stated, pharma’s goal in using Big Data is to:

  • Make smart R&D decisions
  • Be competitive
  • Market products wisely and effectively
  • Enhance the bottom line

A straightforward cost/benefit analysis can help organizations determine whether the resource investment will pay dividends in the future. “In the future” is the key. Big Data is not a quick-fix tool, so manufacturers who expect or need immediate answers may be better served by other decision-making tools.

Resourcing Big Data

Appropriate resourcing turns Big Data into smart data. Many organizations wrestle with the decision to staff full-time, in-house analysts or to contract for services that will produce comprehensive, actionable results.

“The world is hungry for people to turn [Big Data] into business action because most of the world’s data lies untapped, with its full potential yet to be revealed,” says James Weatherall, head of AstraZeneca’s Advanced Analytics Center. “I can see companies specializing in data analytics for pharma, or certain parts of pharma R&D, coming to the fore.”

Today’s diverse data sets include the traditional data that has been used for years (sales, prescriptions, claims, etc.), but also electronic health data, digitized publications, and large registries and clinical and government databases. To create smart data, analysts must be able to collect, integrate, analyze and report information that pharma’s administrators and senior executives can translate into strategies and tactics.

Whether Big Data analysis is an in-house resource or a contracted service, the mapping and predicting capabilities can be a catalyst for pharma to:

  • Bring a team together under shared goals
  • Crystalize best practices
  • Drive R&D and marketing innovation
  • Optimize product development and financial growth

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Topics: Industry Trends, Data & Analytics