Driving Success Leveraging Pharmaceutical Big Data
The term Big Data is everywhere these days. As analytics professionals, it’s almost unavoidable; every company, from every industry, seems to be talking about it. But, what does it mean and how does it apply to the pharmaceutical industry? And, what is driving its adoption?
First, let’s define what Big Data means for pharmaceuticals. With the rapid and massive digitization of knowledge – from medical records to social media to lab tests – combined with the more traditional pharma data sets (such as prescription sales data), the Big Data tent has come to include many, varied sources of information, both old and new, under its big top. The data are not heterogeneous and can come in many forms, be it structured or unstructured and may not necessarily come from one of the standard, third party syndicated data providers, such as IMS.
Unlike other industries, Pharma is just beginning to experiment with this new frontier, as the industry acquires and learns from these data sets. Part of the challenge with these types of analyses is that the home office analytics team has been sized to support today’s needs of getting reports out to the Field and performing other types of traditional ad hoc analyses. Additionally, many companies supplement their existing analytics support to outsourced third party vendors. This lack of in-house expertise and bandwidth leaves little time for experimenting with tomorrow’s, new type of analyses.
What is driving the trend today to dive into Big Data analytics? According to Forbes, the top three big data business drivers include: speeding time for operational or analytical workloads (39%); increasing competitive advantage with flexibility of data used in business solutions (34%); and business requirements for higher levels of advanced analytics (31%). While this survey was applied cross-industry, the learnings can certainly be applied to Pharmaceuticals and the pay-offs from these sophisticated analytics can be meaningful – particularly in these days of increased pricing pressure from payers, patent cliffs and focus on outcomes.
In fact, when it comes to Pharma, Big Data can be used in in a variety of ways. On the R&D side of the house, scientists are using Big Data to develop “personalized medicine” where they are reviewing anonymized electronic medical records to review treatments and outcomes.
On the commercial side, pharma is starting to use these additional data sets to better target HCPs, again by leveraging big data to hone in on doctors most likely to prescribe their drug or that see their therapeutic patient base. These analyses can be helpful in formulating closed loop marketing campaigns to physicians, tracking messaging effectiveness or social media patient outreach campaigns, for example. Other types of analyses include determining key influencers from online community usage or using lab tests to unearth overlooked HCPs that have a need for your drug.
All of this can lead to more effective use of sales and marketing spend. By calling on the right doctors, who see the right patients (and reaching out to those patients), all at the right time, resources can be more efficiently utilized.
So for pharmaceuticals, it’s a whole, new world, rife with innovation. As experienced in other industries, the big data business drivers build the business case. The question remains, however, how to best develop a big data strategy, program and resources, as commercial teams move from traditional Rx-based analytics into a future that is still evolving.
Interested in learning more? Download this free whitepaper: A New Analytics Approach for Pharmaceutical Companies
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