Do You Need Support with Your Cloud Strategy?

Cloud services are swiftly becoming standard for those looking to create an IT strategy that is both scalable and elastic. But when it comes time to implement that strategy—particularly for those working in life sciences R&D—there are a number of unique combinations of services to consider. 

Here is a checklist of key areas to examine when deciding if you need expert support with your Cloud strategy. 

• Understand the Scope of Your Project
  Just as critical as knowing what should be in the cloud is knowing what should not be. The act of mapping out the on-premise vs. cloud-based solutions in your strategy will help demonstrate exactly what your needs are and where some help may be beneficial. 
• Map Out Your Integration Points
  Speaking of on-premise vs. in the Cloud, do you have an integration strategy for getting cloud solutions talking to each other as well as to on-premise solutions? 
• Does Your Staff Match Your Needs?
  When needs change on the fly, often your staff needs to adjust. However, those adjustments are not always so easily implemented, which can lead to gaps. So when creating your cloud strategy, ensure you have the right team to help understand the capacity, uptime and security requirements unique to a cloud deployment.

Check our free eBook, Cloud Infrastructure Takes Research Computing to New Heights, to help uncover the best cloud approach for your team. Download Now

• Do Your Solutions Meet Your Security Standards?
  There are more than enough examples to show the importance of data security.  It’s no longer enough however, to understand just your own data security needs. You now must know the risk management and data security policies of providers as well. 
• Don’t Forget About Data
  Life Sciences is awash with data and that is a good thing. But all this data does have consequences, including within your cloud strategy so ensure your approach can handle all your bandwidth needs. 
• Agree on a Timeline
  Finally, it is important to know the timeline of your needs and determine whether or not your team can achieve your goals. After all, the right solution is only effective if you have it at the right time. That means it is imperative you have the capacity and resources to meet your time-based goals. 

Using RCH Solutions to Implement the Right Solution with Confidence

Leveraging the Cloud to meet the complex needs of scientific research workflows requires a uniquely high level of ingenuity and experience that is not always readily available to every business. Thankfully, our Cloud Managed Service solution can help. Steeped in more than 30 years of experience, it is based on a process to uncover, explore, and help define the strategies and tactics that align with your unique needs and goals. 

We support all the Cloud platforms you would expect, such as AWS and others, and enjoy partner-level status with many major Cloud providers. Speak with us today to see how we can help deliver objective advice and support on the solution most suitable for your needs. 

Studied benefits of Cloud computing in the biotech and pharma fields.

Cloud computing has become one of the most common investments in the pharmaceutical and biotech sectors. If your research and development teams don’t have the processing power to keep up with the deluge of available data for drug discovery and other applications, you’ve likely looked into the feasibility of a digital transformation.

Real-world research reveals these examples that highlight the incredible effects of Cloud-based computing environments for start-up and growing biopharma companies.

Competitive Advantage

As more competitors move to the Cloud, adopting this agile approach saves your organization from lagging behind. Consider these statistics:

• According to a February 2022 report in Pharmaceutical Technology, keywords related to Cloud computing increased by 50% between the second and third quarters of 2021. What’s more, such mentions increased by nearly 150% over the five-year period from 2016 to 2021. 
• An October 2021 McKinsey & Company report indicated that 16 of the top 20 pharmaceutical companies have referenced the Cloud in recent press releases.
• As far back as 2020, a PwC survey found that 60% of execs in pharma had either already invested in Cloud tech or had plans for this transition underway. 

Accelerated Drug Discovery

In one example cited by McKinsey, Moderna’s first potential COVID-19 vaccine entered clinical trials just 42 days after virus sequencing. CEO Stéphane Bancel credited Cloud technology, that enables scalable and flexible access to droves of existing data and as Bancel put it, doesn’t require you “to reinvent anything,” for this unprecedented turnaround time. 

Enhanced User Experience

Both employees and customers prefer to work with brands that show a certain level of digital fluency. In the survey by PwC cited above, 42% of health services and pharma leaders reported that better UX was the key priority for Cloud investment. Most participants – 91% – predicted that this level of patient engagement will improve individual ability to manage chronic disease that require medication.

Rapid Scaling Capabilities

Cloud computing platforms can be almost instantly scaled to fit the needs of expanding companies in pharma and biotech. Teams can rapidly increase the  capacity of these systems to support new products and initiatives without the investment required to scale traditional IT frameworks. For example, the McKinsey study estimates that companies can reduce the expense associated with establishing a new geographic location by up to 50% by using a Cloud platform. 

Are you ready to transform organizational efficiency by shifting your biopharmaceutical lab to a Cloud-based environment? Connect with RCH today to learn how we support our customers in the Cloud with tools that facilitate smart, effective design and implementation of an extendible, scalable Cloud platform customized for your organizational objectives. 

References
https://www.mckinsey.com/industries/life-sciences/our-insights/the-case-for-Cloud-in-life-sciences
https://www.pharmaceutical-technology.com/dashboards/filings/Cloud-computing-gains-momentum-in-pharma-filings-with-a-50-increase-in-q3-2021/
https://www.pwc.com/us/en/services/consulting/fit-for-growth/Cloud-transformation/pharmaceutical-life-sciences.html

Prepare for the next generation of R&D innovation.

As biotech and pharmaceutical start-ups experience accelerated growth, they often collide with computing challenges as the existing infrastructure struggles to support the increasingly complex compute needs of a thriving research and development organization.

By anticipating the need to scale the computing environment in the early stages of action for your pharma or biotech enterprise, you can shield your start-up from the impact of these five common concerns associated with rapid expansion.

Insufficient storage space

Life sciences companies conducting R&D particularly have to reckon with an incredible amount of data. Research published by the Journal of the American Medical Association indicates that each organization in this sector could easily generate ten terabytes of data daily, or about a million phone books’ worth of data. Start-ups without a plan in place to handle that volume of information will quickly overwhelm their computing environments. Forbes notes that companies must address both the cost of storing several copies of necessary data and the need for a comprehensive data management strategy to streamline and enhance access to historical information.

Collaboration and access issues

As demonstrated by the COVID-19 pandemic and its aftermath, remote work has become essential across industries, including biotech and pharma. In addition, global collaborations are more common than ever before, emphasizing the need for streamlined access and connectivity from anywhere. Next-generation cloud-based environments allow you to optimize access and automate processes to facilitate collaboration, including but not limited to supply chain, production, and sales workflows.

Ineffective data security

Security threats compromise the invaluable intellectual property of your biotech or pharmaceutical start-up. As the team scales the company’s ability to process and analyze data, it proportionally increases the likelihood of a data breach. The world’s top 20 pharma companies by market sector experienced more than 9,000 breaches from January 2018 to September 2021, according to a Constella study reported by FiercePharma. Nearly two-thirds of these incidents occurred in the final nine months of the research period.

If your organization accesses and uses patient information, you are also creating exposure to costly HIPAA violations. Consider investing in a next-generation tech platform that provides proactive data security, with advanced measures like intelligent system integrations and new methods to validate and verify access requests.

Limited data processing power

As biotech and pharmaceutical companies increasingly invest in artificial intelligence, organizations without the infrastructure to implement next-generation analysis and processing tools will be at a significant disadvantage. AI and other types of machine learning dramatically reduce the time it takes to sift through seemingly endless data to find potential drug matches for disease states, understand mechanisms of action, and even predict possible side effects for drugs still in development. 

Last year, The Guardian reported that 90% of large global pharmaceutical companies invested in AI in 2020, and most of their smaller counterparts have quickly followed suit. The Forbes article cited above projected AI spending of $2.45 billion in the biotech and pharmaceutical industries by 2025, an increase of nearly 430% over 2019 numbers.

Modernization and scale 

Cloud-first environments can scale in tandem with your organization’s accelerated growth more easily than an on-prem server system. Whether you need to support expanding geographic locations or expanding performance needs, the cloud compute space can flex to accommodate an adolescent biotech company’s coming of age. 

When your organization commits to the cloud platform, place best practices at the forefront of implementation. A framework based on data fidelity will prevent future access, collaboration and security issues. The cloud relies on infrastructure as code, a system that maintains stability through every phase of iterative growth. 

Concerns about compliance

McKinsey & Company identified the need for better-quality assurance measures in response to ever-increasing regulatory scrutiny nearly ten years ago in its 2014 report “Rapid growth in biopharma: Challenges and opportunities.” Since that time, the demands of domestic agencies such as the Food and Drug Administration have been compounded by the need to comply with numerous global regulations and quality benchmarks. Efficient, robust data processes can help adolescent biopharma companies keep up with these voluminous and constantly evolving requirements. 

With a keen understanding of these looming challenges, research teams can leverage smart IT partnerships and emerging technologies in response. The 2014 McKinsey report correctly predicted that to successfully address the tech challenges of growth, organizations must expand capacity to adopt new technologies and take risks in terms of capital expenditures to scale the computing environment. Taking advantage of existing cloud platforms with innovative tools designed specifically for R&D can save your team the time and money of building a brand-new infrastructure for your tech needs.

Part Five in a Five-Part Series for Life Sciences Researchers and IT Professionals  

Because scientific research is increasingly becoming a multi-disciplinary process that requires researcher scientists, data scientists and technical engineers to work together in new ways, engaging an IT partner that has the specialized skills, service model and experience to meet your compute environment needs can be a difference-maker in the success of your research initiatives.  

If you’re unsure what specifically to look for as you evaluate your current partners, you’ve come to the right place!  In this five-part blog series, we’ve provided a range of considerations important to securing a partner that will not only adequately support your research compute environment needs, but also help you leverage innovation to drive greater value out of your research efforts. Those considerations and qualities include: 

1. Unique Life Sciences Specialization and Mastery
2. The Ability to Bridge the Gap Between Science and IT
3. A High Level of Adaptability
4. A Service Model That Fits Research Goals

In this last installment of our 5 part series, we’ll cover one of the most vital considerations when choosing your IT partner: Dedication and Accountability. 

You’re More than A Service Ticket

Working with any vendor requires dedication and accountability from both parties, but especially in the Life Sciences R&D space where project goals and needs can shift quickly and with considerable impact.

Deploying the proper resources necessary to meet your goals requires a partner who is proactive, rather than reactive, and who brings a deep understanding and vested interest in your project outcomes (if you recall, this is a critical reason why a service model based on SLA’s rather than results can be problematic).  

Importantly, when scientific computing providers align themselves with their customers’ research goals, it changes the nature of their relationship. Not only will your team have a reliable resource to help troubleshoot obstacles and push through roadblocks, it will also have a trusted advisor to provide strategic guidance and advice on how to accomplish your goals in the best way for your needs (and ideally, help you avoid issues before they surface). And if there is a challenge that must be overcome? A vested partner will demonstrate a sense of responsibility and urgency to resolve it expeditiously and optimally, rather than simply getting it done or worse—pointing the finger elsewhere.   

It’s this combination of knowledge, experience and commitment that will make a tangible difference in the value of your relationship.

Don’t Compromise

Now you have all 5 considerations, it’s time to put them into practice. Choose a scientific computing partner whose services reflect the specialized IT needs of your scientific initiatives and can deliver robust, consistent results. 

RCH Solutions Is Here to Help You Succeed

RCH has long been a provider of specialized computing services exclusively to the Life Sciences. For more than 30 years, our team has been called upon to help biotechs and pharmas across the globe architect, implement, optimize and support compute environments tasked with driving performance for scientific research teams.  Find out how RCH can help support your research team and enable faster, more efficient scientific discoveries, by getting in touch with our team here.

Building an effective computing environment early on helps ensure positive research outcomes later. 

Now more than ever, Life Science research and development is driven by technological innovation.

That doesn’t mean human ingenuity has become any less important. It simply depends on accurate, well-structured data more than ever before. The way Life Science researchers capture, store, and communicate that data is crucial to their success.

This is one of the reasons why Life Science professionals are leaving major pharmaceutical firms and starting their own new ventures. Startups have the unique opportunity to optimize their infrastructure from the very start and avoid being hamstrung by technology and governance limitations the way many large enterprises often are.

Optimized tech and data management models offer a significant competitive advantage in the Life Science and biopharma industries. For example, implementing AI-based or other predictive software and lean workflows makes it easier for scientists to synthesize data and track toward positive results or, equally as important, quickly see the need to pivot their strategy to pursue a more viable solution.  The net effect is a reduction in the time and cost of discovery, which not only gives R&D teams a competitive upper hand but improves outcomes for patients.

Time is Money in R&D

Life Science research and development is a time-consuming, resource-intensive process that does not always yield the results scientists or stakeholders would like. But startup executives who optimize discovery processes using state-of-the-art technology early on can mitigate two significant risks:

• Fixing Broken Environments. Building and deploying optimized computing infrastructure is far easier and less expensive than repairing a less-than-ideal computing environment once you hit an obstacle. 

• Losing Research Data Value. Suboptimal infrastructure makes it difficult to fully leverage data to achieve research goals. This means spending more time manually handling data and less time performing critical analysis. In a worst-case scenario, a bad infrastructure can lead to even good data being lost or mismanaged, rendering it useless. 

Startups that get the experience and expertise they need early on can address these risks and deploy a solid compute model that will generate long-lasting value.  

5 Areas of Your Computing Model to Optimize for Maximum Success

There are five foundational areas startup researchers should focus on when considering and developing their compute model:

1. Technology

Research teams need to consider how different technologies interact with one another and what kinds of integrations they support. They should identify the skillset each technology demands of its users and, if necessary,  seek objective guidance from a third-party consultant when choosing between technology vendors. 

2. Operating Systems

Embedded systems require dependable operating systems, especially in Life Sciences. Not only must operating systems support every tool in the researchers’ tech stack; but individual researchers must also be well-acquainted with the way those systems work. Researchers need resource management solutions that share information between stakeholders easily and securely.

3. Applications and Software

Most Life Science organizations use a variety of on-prem, Cloud-enabled, open-source, and even home-grown applications procured on short-term contracts. This offers flexibility, but organizations cannot easily coordinate between software and applications with different implementation and support requirements. Since these tools come from different sources and have varying levels of post-sale documentation and support, scientists often have to take up the heavy burden of harmonizing their tech stack on their own.

4. Workflows

Researchers have access to more scientific instruments than ever before. Manufacturers routinely provide assistance and support in implementing these systems, but that is not always enough. Startups need expert guidance in establishing workflows that utilize technological and human resources optimally. 

But building and optimizing scientific workflows is not a one-size-fits-all endeavor; teams with multiple research goals may need separate workflows optimized differently to accommodate each specific research goal.

5. Best Practices

Optimizing a set of research processes to deliver predictable results is not possible without a stable compute environment in place. For Life Science research organizations to develop a robust set of best practices, they must first implement the scientific computing model that makes those practices possible. This takes expert guidance and implementation from professionals who specialize in IT considerations unique to a research and development environment that, many times, lean startups simply don’t have on the team.

Maximize the Impact of Research Initiatives 

Emerging Life Science and biotech research companies have to empower their scientific teams to make the most of the tools now available. But architecting, hinging, and implementing a robust and effective compute model requires experience and expertise in the very specific area of IT unique to research and discovery. If the team lacks such a resource, scientists will often jump into the role of solving IT problems, pulling them away from the core value of their expertise.

The right bio-IT partner can be instrumental in helping organizations design, develop, and implement their computing environment, enabling scientists to remain focused on science and helping to position the organization for long-term success.   

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. 

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. 

By now we’ve all heard of the now legendary self-help playbook, Outliers: The Story of Success, by Malcolm Gladwell, in which he shares his take on how to achieve success in any particular endeavor: practice correctly for roughly 10,000 hours and you’re on your way to a high performance, no matter the field. 

While the merit of the rule can and will continue to be debated by evangelical naysayers (after all, there are exceptions to every rule), Gladwell’s research attempts to debunk the myth that achievement is based on luck or chance. And though the roles of family, culture and friendship are considered by the author, the value of time, focus, and effort seem to almost always emerge as the most essential elements in the formula for success. Let’s look at a few of my favorite examples. 

Overnight Sensation– Or Not?

The music industry’s often most assumed overnight sensation and the biggest rock band in history, The Beatles, arrived on the rock ‘n’ roll scene (by way of the Ed Sullivan show) as part of the British Invasion in the mid-1960’s. Captivating American youth what seems like instantly, in reality it took the band several years playing together and multiple name changes (and even more haircuts) before they would form the mold for the pop cultural icon they would become. 

Bill Gates is another example. Starting his first venture in computer science in 1970 at just 15, his climb to the top was not accelerated, but rather long and consistent. And today, as only the second richest person in the world, he may not be finished yet. 

The subjects of both examples—arguably two of the greatest influencers of our modern culture, albeit in different ways—clearly put in a lot of hard work well-before they became well-known and successful, which, as Gladwell would claim, would amass to at least 10,000 hours of honing their craft. 

As Big As the Beatles?

At RCH Solutions, we believe there is no substitute for experience and have spent more than 27 years honing our craft—scientific computing specifically within the Life Sciences. During that time, we’ve changed our business model to reflect the unique and evolving demands of our customers, while maintaining a culture crafted for learning and achieving. 

Our customer-base has been built through years of focused work in a very specific area. And while we find that many of our relationships have grown organically driven by good results, we sometimes joke about how often we hear comments like, ‘we didn’t’ know RCH did that.” (Cue marketing). The reality, though, is that the model we follow has created a culture that is very much like a supportive family and a good group of friends. We encourage exploration and joy in our work for both employees and customers, and will prioritize quality over quantity any day of the week. 

So, while an appearance on the Ed Sullivan Show and ensuing fame may not be an option for us, we will happily continue to be relentless in our practice and pursuit for innovation, challenging ourselves to deliver a ground-breaking computing experiences for our clients every day, so that they can deliver life-saving science to humanity. 

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. 

If you’re in charge of your organization’s R&D IT efforts and expecting different results from the same initiatives, it’s time to reassess your Bio-IT roadmap.

Start here with this list of best practices curated from almost 30 years of experience exclusively focused on scientific computing within the Life Sciences:

• Don’t make good decisions with bad data. As Bio-IT professionals, we’re often asked to support a wide range of data-related projects. After all, data is currency in the life sciences—the more you have, the more you can do. But even petabytes of data don’t do you any good if you can’t retrieve or make sense of it. While delivering big on a high-value project (analytics is the Holy Grail) may be on your team’s radar, good intentions alone won’t get you results; your plan and your processes are critical. Which leads me to my next point.
• Start from where you are, rather than where you want to be.  Would you ever consider building a house on a foundation you weren’t sure to be solid?  Probably not. However, in the high-stakes and often-demanding environment of R&D IT, the tendency to move toward solutioning before fully exposing and diagnosing the full extent of the issue or opportunity is all too common.  However, this approach is not only backward, it’s also costly. Only when you know where it is that you’re starting from, can you accurately identify the right strategy or tools to get you where you want to go.
• Listen (a lot).  This one may seem like a no brainer, but human nature primes us to ready a response while we should actually still be listening. In the field of scientific computing, listening should take place as part of a constructive engagement between I.T. and the business with the goal to understand each side’s particular challenges and needs.
• Clarify risk. In a highly regulated environment, the concept of mitigating risk is ubiquitous, and for good reason. When the integrity of data or processes drives outcomes that can actually mean life or death for living things, accuracy is not only everything it’s the only thing. But different projects, business units, and even companies have varying appetites for risk. Before assuming what will and won’t work because of the perceived risk involved, be sure to clearly understand where and what may be influencing a particular way of thinking.
• Let the shape of the solution fit the shape of the problem. Square peg, round hole—you know the outcome. Though variables may be consistent based on the parameters of your organization and what’s worked well in the past, viewing each challenge as unique affords you the opportunity (or luxury) to leverage best-of-breed design patterns and technologies to answer your needs. Resist the urge to jam a prefab solution into framework not equipped to support it and opt for a more strategic (and tailored) approach.
• Leverage small projects to build momentum for big initiatives.  Ever try to get your kids to clean their room? Psychology 101 has taught us that humans are programmed to respond more favorably when our big goals are broken into a series of smaller, more attainable steps. The same applies when affecting change within a company.  If you want buy-in to tackle a big initiative—like converting to the cloud, for example—start first on smaller elements of the effort. Agree on success then build upon that platform.
• Plan for the life of the solution. Once a solution is delivered—no matter what type it may be—it’s only the beginning. Solutions have to be maintained, oftentimes upgraded, and eventually retired.  Future-proof your systems when architecting them by thinking about your operations today AND years from today.
• Build agility into your team.  Often, the efficiency and flexibility of the team implementing your compute solutions are just as critical as the solutions themselves. Knowing how to structure your team and workflows—like when and why to introduce a DevOp or a DevSecOps model, for example—can help you bridge the gaps that often impede innovation.
• Always have a Plan B.  No IT or Bio-IT operation should move forward without a fall-back—or rather, a “back-out”— plan. Being able to pivot quickly and respond appropriately in the event of an unforeseen challenge (or opportunity if you’re an optimist) can mean the difference between the ability to try again, or not.
• Trust the process.  This is a phrase infinitely more famous for what it means to Philadelphia 76er fans, but unequivocally applicable in scientific computing.  Often, the strategy that leads to many effective scientific and technical computing initiatives within an R&D IT framework is different from a traditional enterprise IT model. And that’s ok because just as often the goals are different as well.  Leverage the expertise of R&D IT professionals highly specialized and experienced in this niche space, and trust the process.

Finally, I’ll leave you with this:  Never substitute experience.  The value in solutioning is a mix of skill, dedication, resources, time, and experience.  

Skill can be developed (often quickly) when needed

Dedication is generally a factor of personality but can be fostered

Resources are often fixed as a factor of the company

And with only 24 hours in each day, time is beyond our control; to be successful, you must use is wisely. 

Experience on the other hand, takes time to develop. It’s a product of resources and dedication applied to build skills.  It’s not simply knowing what solutions work or don’t, but rather understanding the types of solutions or solution paths that are optimal for a particular goal, because you’ve tried it before. It’s having the ability to project potential outcomes, in order to influence priorities and workflows. And ultimately, it’s knowing how to find the best design patterns.  

After nearly three decades of support for Bio-IT initiatives in the life sciences and healthcare,  I can say with certainty, experience is the single constant in a world otherwise fueled by innovation and change. 

Didn’t make it to the event?  Read on for key insights from this year’s expo.

Last week, hundreds of the industry’s top minds converged on the Seaport World Trade Center in Boston for the annual Bio-IT World conference and expo. As usual, it was an information-packed event and an invaluable opportunity to connect with colleagues, friends, partners, clients, and thought-leaders across the industry.  

While there was no shortage of ideas and information exchanged, there were a few themes that emerged as the most pressing.

Unsurprisingly, the use of data and analytics, as well as the applicability of the Cloud, continue to prompt as many questions as they do answers for those exploring how to evolve a compute infrastructure to support—and accelerate—scientific innovation. In fact, the industry as a whole may not be able to realize the full value of these powerful capabilities until it collectively habituates to the buzz that surrounds them. Until then (and likely after as well), the need for an experienced partner to help navigate these trends will be more important than ever.

Beyond that, here are my other top takeaways from the week:

5. Scientific Computing is still evolving.
Platforms such as the Cloud are moving beyond adoption toward transformation. Emerging areas such as AI and ML are beginning to bear fruit for certain applications. It is exciting to see the promise of realized utility and applicability of these platforms.

4. Data is exploding and the blast is not yet well contained.
John Conway of AstraZeneca made a number of key observations including the need to treat data differently. He commented during his presentation that data needs to be treated and managed like currency. Only then will it be seen as truly valuable and the appropriate strategies for storing, sharing, and managing be applied.

3. Analytics are only one piece of the equation (and equations need to be balanced).
While the tools are plenty, the process is paramount and still taking shape. Recently, I was asked by the global CIO for R&D of a top 10 pharmaceutical company about our experience with analytics. My response was a question to the effect of, “where is the data is located and how will it be curated then managed?” In other words, analytical tools can be powerful factors in your complete data strategy, but they are only one of several factors. Mapping out an effective process and workflow for mining and aggregating the data is equally as important.  

2. Don’t believe the Cloud hype … unless you’ve done these 3 things first.
Lance Smith of Celgene noted several steps any Life Sciences organization must take before they’re in a position to enjoy the long term benefits of the Cloud.

One: Assess which applications are best-suited for on-prem vs. in the Cloud
(because not all things are created equal)

Two: Establish a full-time, dedicated, Cloud team
(because effective Cloud implementation and evolution can’t be done part-time)

Three: Get multiple levels of management on board
(because turning a big ship can require a big effort)

I’ll go a step further and add a fourth: Choose your Cloud partner wisely. If the Cloud is a critical element of your innovation roadmap, take the time to make sure your partner brings the right levels of experience in the right areas — the industry, the business, and with the applications critical to advancing your goals.    

1. Companies are seeking new ways to solve new (and nuisance) challenges.
As the demand on the business to do more with less grows, the value of an IT partner who offers speed, specialization, and scientific computing experience is becoming more clear. Anecdotally, our booth had more traffic and interest than ever before. While some may snicker that our salmon-colored sweaters played a role in that (thanks, marketing), I take it as a sign that the shift away from large, one-size-fits-all vendors is underway. After all, as Klaus Schwab once said, “In the new world, it’s not the big fish that eats the small fish, it’s the fast fish that eats the slow fish.”

Looking ahead, one thing is clear: It’s going to be a fun ride as nimble organizations like RCH take the reins to help Life Sciences and Healthcare companies steer through some of these uncharted lands.

Until next year, Bio-IT World …