Remember when research and scientific computing in Life Sciences reflected a simpler time?  

Teams in Early Discovery through Development had almost complete liberty to manage their computing environment. Moreover, research I.T. was often separate from general I.T. And business groups were supported by dedicated technical professionals specialized not only in a particular area of science, but also in the compute practices required to properly support it. Combined, these teams had the experience, expertise, and flexibility to implement best practices and tools needed to advance research.

But times have changed.  

Today, the environment we work in is defined in large part by the effects of more than 15 years of consolidation. In 2018  alone, global M&A activity within the pharmaceutical industry reached $265 billion, an increase of more than 25% over 2017. While there are a lot of great business reasons driving consolidation—the growing cost and complexity of effective R&D work, a shifting regulatory landscape, increasing competition within certain domains and from more sources, and the need to balance innovation with the economics of drug development.  The outcome has unintended consequences on the critical and formerly coveted qualities of speed, efficiency, transparency and, ironically, collaboration, despite the fact that many scientific computing specialists have now been brought back into general I.T. 

New and disruptive technologies have also played a role in redefining the look of scientific computing as we know it.  

Look at the advent of the Cloud. Arguably the single most significant I.T. innovation for the better part of the last decade, the Cloud and its capabilities are forcing change and shifting expectations around everything from infrastructure (hardware), to platforms (Window, Linux), and software (application development and deployment). Most significantly, perhaps, is its effect on the “business” of I.T. itself, with the Cloud affecting costs in many ways, including through economies of scale, use of OpEx in place of CapEx, more streamlined deployment of applications, and a better operating model for each business.

Nonetheless, as technology and scientific innovation collide in new ways, we’re seeing more firms facing barriers to scientific innovation. 

A Model Made for the Masses

Under an enterprise I.T. model that promotes standardization over specialization, many scientific computing professionals are choosing other paths, limited by policies and other practices that seldom fit the unique needs of science. 

And with resources thinning while demand for expertise in new and emerging technologies (like the Cloud, AI and ML) grows, many companies turn to outsourced support from ‘cost-effective’ vendors who fill seats and route support tickets, but bring little if any specialized research computing experience. 

More good people leave. 

Service—and science—continues to suffer. 

And business groups are left to choose between poor support or no support at all. 

A Better Way

I’ve often used this metaphor as I can think of no industry where it is more relatable than ours.

If you need a routine physical, chances are you’d be confident that a generalist like your family physician is more than qualified to perform the exam and provide an accurate assessment of your general health.  

But what if you also had a heart condition?  Would you not seek a physician who specializes in cardiac care?  With experience in a broad range of health topics, your family doctor undoubtedly plays an important role in helping you maintain your overall well-being. However, seeking the care of (or not) an expert with specialized experience in a more specific area of medicine when warranted, could mean the difference between life and death. 

While perhaps an oversimplification of an issue for effect, the point is this: This same principle applies to modern scientific computing environments in the Life Sciences. 

Like it or not, many companies have evolved away from a model that embeds dedicated research computing professionals within the business unit at a time when that unique skill-set and focused expertise is needed most.

Businesses that attempt to meet that need through the support of I.T. generalists, rather than turning to dedicated specialists in the Life Sciences, are at a clear disadvantage.  So while we’re thinking about times past through the lens of where they have brought us today, those who fail to leverage the expertise that is available may very-well find themselves asking the same question, remember when, but for a very different reason. 

RCH Solutions is a global provider of computational science expertise, helping Life Sciences and Healthcare firms of all sizes clear the path to discovery for nearly 30 years. If you’re interesting in learning how RCH can support your goals, get in touch with us here.