Quality vs. Quantity: A Simple Scale for Success

Quality vs. Quantity: A Simple Scale for Success

In Life Sciences, and medical fields in particular, there is a premium on expertise and the role of a specialist. When it comes to scientists, researchers, and doctors, even a single high-performer who brings advanced knowledge in their field often contributes more value than a few average generalists who may only have peripheral knowledge. Despite this premium placed on specialization or top-talent as an industry norm, many life science organizations don’t always follow the same measure when sourcing vendors or partners, particularly those in the IT space. 

And that’s a mis-step. Here’s why.

Why “A” Talent Matters

I’ve seen far too many organizations that had, or still have, the above strategy, and also many that focus on acquiring and retaining top talent. The difference? The former experienced slow adoption which stalled outcomes which often had major impacts to their short and long term objectives. The latter propelled their outcomes out of the gates, circumventing cripping mistakes along the way. For this reason and more, I’m a big believer in attracting and retaining only “A” talent. The best talent and the top performers (Quality) will always outshine and out deliver a bunch of average ones. Most often, those individuals are inherently motivated and engaged, and when put in an environment where their skills are both nurtured and challenged, they thrive.  

Why Expertise Prevails

While low-cost IT service providers with deep rosters may similarly be able to throw a greater number of people at problems, than their smaller, boutique counterparts, often the outcome is simply more people and more problems.  Instead, life science teams should aim to follow their R&D talent acquisition processes and focus on value and what it will take to achieve the best outcomes in this space. Most often, it’s not about quantity of support/advice/execution resources—but about quality.

Why Our Customers Choose RCH

Our customers are like minded and also employ top talent, which is why they value RCH—we consistently service them with the best. While some organizations feel that throwing bodies (Quantity) at a problem is one answer, often one for optics, RCH does not. We never have. Sometimes you can get by with a generalist, however, in our industry, we have found that our customers require and deserve specialists. The outcomes are more successful. The results are what they seek— Seamless transformation.  

In most cases, we are engaged with a customer who has employed the services of a very large professional services or system integration firm. Increasingly, those customers are turning to RCH to deliver on projects typically reserved for those large, expensive, process-laden companies.  The reason is simple. There is much to be said for a focused, agile and proven company.  

Why Many Firms Don’t Restrategize

So why do organizations continue to complain but rely on companies such as these? The answer has become clear—risk aversion. But the outcomes of that reliance are typically just increased costs, missed deadlines or major strategic adjustments later on – or all of the above. But why not choose an alternative strategy from inception? I’m not suggesting turning over all business to a smaller organization. But, how about a few? How about those that require proven focus, expertise and the track record of delivery? I wrote a piece last year on the risk of mistaking “static for safe,” and stifling innovation in the process. The message still holds true. 

We all know that scientific research is well on its way to becoming, if not already, a multi-disciplinary, highly technical process that requires diverse and cross functional teams to work together in new ways. Engaging a quality Scientific Computing partner that matches that expertise with only “A” talent, with the specialized skills, service model and experience to meet research needs can be a difference-maker in the success of a firm’s research initiatives. 

My take? Quality trumps quantity—always in all ways. Choose a scientific computing partner whose services reflect the specialized IT needs of your scientific initiatives and can deliver robust, consistent results. Get in touch with me below to learn more. 

 

How Big Data Is Powering Precision Medicine

Data science has earned a prominent place on the front lines of precision medicine – the ability to target treatments to the specific physiological makeup of an individual’s disease. As cloud computing services and open-source big data have accelerated the digital transformation, small, agile research labs all over the world can engage in development of new drug therapies and other innovations.

Previously, the necessary open-source databases and high-throughput sequencing technologies were accessible only by large research centers with the necessary processing power. In the evolving big data landscape, startup and emerging biopharma organizations have a unique opportunity to make valuable discoveries in this space. 

The drive for real-world data

Through big data, researchers can connect with previously untold volumes of biological data. They can harness the processing power to manage and analyze this information to detect disease markers and otherwise understand how we can develop treatments targeted to the individual patient. Genomic data alone will likely exceed 40 exabytes by 2025 according to 2015 projections published by the Public Library of Science journal Biology. As data volume increases, its accessibility to emerging researchers improves as the cost of big data technologies decreases. 

A recent report from Accenture highlights the importance of big data in downstream medicine, specifically oncology. Among surveyed oncologists, 65% said they want to work with pharmaceutical reps who can fluently discuss real-world data, while 51% said they expect they will need to do so in the future. 

The application of artificial intelligence in precision medicine relies on massive databases the software can process and analyze to predict future occurrences. With AI, your teams can quickly assess the validity of data and connect with decision support software that can guide the next research phase. You can find links and trends in voluminous data sets that wouldn’t necessarily be evident in smaller studies. 

Applications of precision medicine

Among the oncologists Accenture surveyed, the most common applications for precision medicine included matching drug therapies to patients’ gene alterations, gene sequencing, liquid biopsy, and clinical decision support. In one example of the power of big data for personalized care, the Cleveland Clinic Brain Study is reviewing two decades of brain data from 200,000 healthy individuals to look for biomarkers that could potentially aid in prevention and treatment. 

AI is also used to create new designs for clinical trials. These programs can identify possible study participants who have a specific gene mutation or meet other granular criteria much faster than a team of researchers could determine this information and gather a group of the necessary size. 

A study published in the journal Cancer Treatment and Research Communications illustrates the impact of big data on cancer treatment modalities. The research team used AI to mine National Cancer Institute medical records and find commonalities that may influence treatment outcomes. They determined that taking certain antidepressant medications correlated with longer survival rates among the patients included in the dataset, opening the door for targeted research on those drugs as potential lung cancer therapies. 

Other common precision medicine applications of big data include:

  • New population-level interventions based on socioeconomic, geographic, and demographic factors that influence health status and disease risk
  • Delivery of enhanced care value by providing targeted diagnoses and treatments to the appropriate patients
  • Flagging adverse reactions to treatments
  • Detection of the underlying cause of illness through data mining
  • Human genomics decoding with technologies such as genome-wide association studies and next-generation sequencing software programs

These examples only scratch the surface of the endless research and development possibilities big data unlocks for start-ups in the biopharma sector. Consult with the team at RCH Solutions to explore custom AI applications and other innovations for your lab, including scalable cloud services for growing biotech and pharma research organizations.

Checklist: Do You Need Support with Your Cloud Strategy

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. 

 

 

Does the Cloud Live up to Its Transformative Reputation?

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

Solve These 5 Future Compute Environment Challenges By Preparing Your Biopharma for Scale Today

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.

How to Tell If Your Computing Partner is Actually Adding Value to Your Research Process: Dedication and Accountability

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.

Optimize Your Life Science Startup for Success From the Start: Develop a Solid Compute Model

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 technology innovation.

That does not 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 make 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 are able to 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; 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 implementing these systems, but that is not always enough. Startups need expert guidance 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 to solve 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?

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. 

10,000 Hours and The Reality of Hard Work

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. 

Best Practices for R&D IT

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 examplecan 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. 

Top 5 Takeaways from Bio-IT 2019

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 …