Time for biopharma to jump on the "big data" train?

Reuters

Article Highlights

  • The question of core competencies is more than semantics – it’s the most important strategic question facing biopharma.

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  • The real question for big pharmas is when to jump on board the big data train. @DShaywitz

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  • Once big data analytics firmly establishes itself, serious biopharma players will have no choice but to consider it vital.

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In a piece just posted at TheAtlantic.com, I discuss what I see as the next great quest in applied science: the assembly of a unified health database, a “big data” project that would collect in one searchable repository all the parameters that measure or could conceivably reflect human well-being. 

I don’t expect the insights gained from these data will obsolete physicians, but rather empower them (as well as patients and other stakeholders) and make them better, informing their clinical judgment without supplanting their empathy.

I also discuss how many companies and academic researchers are focusing their efforts on defined subsets of the information challenge, generally at the intersection of data domains.  I observe that one notable exception seems to be big pharma, as many large drug companies seem to have decided that hefty big data analytics is a service to be outsourced, rather than a core competency to be built.  I then ask whether this is savvy judgment or a profound miscalculation, and suggest that if you were going to create the health solutions provider of the future, arguably your first move would be to recruit a cutting-edge analytics team.

The question of core competencies is more than just semantics – it is perhaps the most important strategic question facing biopharma companies as they peer into a frightening and uncertain future. 

In my experience, a company’s view of its core competencies translates directly into how it prosecutes its mission, as well as in the quality of talent it’s able to recruit and retain.   An enterprise that sees itself as defined by world-class sales and marketing would be expected to look very different, and emphasize different things, than a company that specializes in making novel biologics, for example, or a company that’s focused on clinical development.  Companies are usually built around what they do well, and can underestimate the complexity of other areas, especially those selected for outsourcing.  You don’t know what you don’t know.

In the case of big data analytics, this means that unless a pharma company is deliberately built around this capability – or this function is developed and nurtured in a fenced-off, skunkworks fashion – it’s unlikely to get adequate traction, and will be vulnerable to the usual corporate antibodies, especially when budget time comes around.

Conversely, I suspect that a biopharma company built entirely around analytics would suffer from a problem similar to that experienced by many academic researchers who seek to drive their laboratory discoveries into real-world application:  it’s far more difficult to successfully develop, manufacture, receive approval for, and market a new medical product than most outside the business appreciate.

(Perhaps a stretch, but you might also argue health portals such as Google Health and Microsoft HealthVault failed in part because sponsors were so focused on the data and analytic opportunities that they lost sight of essential real-world considerations, and didn’t have a sufficiently granular sense of what’s required to be a successful business in the always-difficult health space.)

Presumably, once big data analytics firmly establishes itself as an essential capability, serious biopharma players will have no choice but to consider this function vital and integral – and I suspect most eventually will.  Thus, the real question for big pharmas is when to jump on board the big data train; they’ve been burned in the past by premature investments in overhyped technologies, so you can certainly appreciate their reluctance.

At the same time, given both the overwhelming amount of available data and the fact that traditional pharma approaches to innovation seem to have largely run out of steam, you’d think that a bet on big data analytics might make a lot of sense now.  Given the headwinds facing the industry, it’s a bold play you’ve got to believe someone will be wise enough, brave enough, or desperate enough to make.

 

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David
Shaywitz

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