Super-Size Me: Optimizing the Information Explosion - Pharmaceutical Executive

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Super-Size Me: Optimizing the Information Explosion


Pharmaceutical Executive


The Human Element

In the continuum that stretches from acquiring Big Data on the one end to reporting findings from it on the other, what should pharma keep "close to the vest?" What functions/responsibilities ought to remain in-house?



It likely will be impractical for pharma to be in the business of acquiring Big Data, given the vast number of sources and privacy rules associated with this. At the same time, it my be highly inefficient for pharma to process and deliver the data, as cloud-based computing and storage has made owning storage and processing impractical for most entities at the scale the cloud affords.

The IT infrastructure for dealing with Big Data will be in private and public clouds and maintained by someone else. Routine analytics will be outsourced. Reporting will be outsourced. What companies must have, though, will be people responsible for:

Setting the company's technology strategy and designing the architecture for internal systems
Implementing and overseeing data governance
Managing the company's many business partners
Designing or approving new analytical algorithms and approaches
Understanding the insights that analytics provide and using them for applications that provide strategic competitive advantage
Setting key performance indicators

How Should Companies Proceed?

As a first step, firms must recognize that partnerships with third-party providers will be required, given the considerable costs involved. Data aggregators, cloud solution providers, and analytical software companies all will play some role in the necessary capabilities. The fast-changing environment requires being open to new and evolving data and technology solutions.

Second, companies would do well to create a Data Governance Council—a cross-functional team with representatives from IT, business strategy, HEOR, clinical development, market research, commercial analytics, legal, and compliance. This team should be charged with developing an enterprise-wide information strategy that covers:

The types of data that are needed
Where the data will come from
What level of quality is needed (this will differ by application)
How quality will be maintained
How the data will need to be structured

Allowing separate research, brand, marketing, and managed markets groups to acquire data separately will sub-optimize the company's path forward. Data acquisitions should be considered for their potential appeal to multiple users and ensure that companies gain the maximum value from their investment; the cost of Big Data will preclude functions from buying data without regard for how it might be used elsewhere. Indeed, the lines between clinical and commercial applications of data are already becoming blurred.

Each company will obviously have to find its own "path to enlightenment" with Big Data, but it will almost certainly involve a new way of working. This will be apparent in what remains proprietary IP versus what is outsourced, as well as through a new level of collaboration across the enterprise on data purchases, data management practices, and data uses.

Don Ragas is Chief Architect, Global Operations at IMS Health. He can be reached at


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