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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

Consider the Advantages of Guardrails in the Cloud

Cloud integration has quite deservedly become the go-to digital transformation strategy across industries, particularly for businesses in the pharmaceutical and biotech sectors. By integrating Cloud technology into your IT approach, your organization can access unprecedented flexibility while taking advantage of real-time collaboration tools. What’s more, Cloud solutions deliver sustained value compared to on-premises solutions, which require resources (both time and money) to upgrade and maintain the associated hardware, since companies can easily scale Cloud platforms in tandem with accelerating growth.

At the same time, leaders must carefully balance the flexibility and adaptability of Cloud technology with the need for robust security and access controls. With effective guardrails administered appropriately, emerging biopharma companies can optimize research and development within boundaries that shield valuable data and ensure regulatory compliance. Explore these advantages of adding the right guardrails to your biotech or pharmaceutical organization’s digital landscape to inform your planning process.

Prevent unintended security risks

One of the most appealing aspects of the Cloud is the ability to leverage its incredible ecosystem of knowledge, tools, and solutions within your own platform. Having effective guardrails in place allows your team to quickly install and benefit from these tools, including brand-new improvements and implementations, without inadvertently creating a security risk. 

Researchers can work freely in the digital setting while the guardrail monitors activity and alerts users in the event of a security risk. As a result, the organization can avoid these common issues that lead to data breaches:

• Maintaining open access to completed projects that should have privileges in place
• Disabling firewalls or Secure Shell systems to access remote systems
• Using sensitive data for testing and development purposes
• Collaborating on sensitive data without proper access controls

Honor the shared responsibility model

Biopharma companies tend to appreciate the autonomous, self-service approach of Cloud platforms, as the dynamic infrastructure offers nearly endless experimentation. At the same time, most security issues in the Cloud result from user errors such as misconfiguration. The implementation of guardrails creates a stopgap so that even with the shortest production schedules, researchers won’t accidentally expose the organization to potential threats. Guardrails also help your company comply with your Cloud service provider’s shared responsibility policy, which outlines and defines the security responsibilities of both organizations.

Establish and maintain best practices for data integrity

Adolescent biopharma companies often experience such accelerated growth that they can’t keep up with the need to create and follow organizational best practices for data management. By putting guardrails in place, you also create standardized controls that ensure predictable, consistent operation. Available tools abound, including access and identity management permissions, security groupings, network policies, and automatic enforcement of these standards as they apply to critical Cloud data. 

A solid information security and management strategy becomes even more critical as your company expands. Entrepreneurs who want to prepare for future acquisitions should be ready to show evidence of a culture that prizes data integrity.

According to IBM, the cost of a single Cloud-based security breach in the United States averaged nearly $4 million in 2020. Guardrails provide a solution that truly serves as a golden means, preserving critical Cloud components such as accessibility and collaboration without sacrificing your organization’s valuable intellectual property, creating compliance issues and compromising research objectives.

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.

How to Tell if Your Computing Partner is Actually Adding Value to Your Research Process: Service Model

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

If 2020 and 2021 proved anything to us, it’s that change is inevitable and often comes when we least expect it. The pandemic shifted the way virtually every company operates. While change can feel unnerving, it is important to make changes that better your work and your company. 

The Life Sciences industry is no different. Whether your company shifted drastically in response to the pandemic or not at all, it’s still important to take a look at your business or team operations to see in what areas you can continue to improve. For teams conducting drug discovery,  development or even pre-clinical workce such area that can often be improved is your external scientific computing support. 

We’ve highlighted several items for teams to take into consideration when evaluating their current partners. So far in our five  part blog series we’ve taken a look at following three considerations:

• #1 – Life Science Specialization and Mastery
• #2 –  Bridging the Gap Between Science and IT
• #3 – A High Level of Adaptability

In this installment, we take a deeper look at Consideration #4: A Service Model that Fits Research Goals. 

Consideration #4: A Service Model that Fits Research Goals

It’s no surprise that every company is likely to have different research goals. A one size fits all approach is not an acceptable strategy. Do you know what sets your current partner apart from their competitors? Do they offer a commodity service, or is there a real and tangible value in what they deliver, and how they deliver it? Your partner’s service model can make an enormous difference in the value you get from their expertise. 

There are two models that life science organizations typically use; computing partners operating under a staff augmentation model or a Managed Service providers model. It is no surprise that these two models work in very different ways and in turn offer very different results for the companies that use them. 

IT staff augmentation may allow your organization to scale its IT team up or down based on current needs. This can help scientific IT teams retain project control and get short-term IT support on an as-needed basis, but it often requires the researchers to obtain, deploy and manage human resources on their own. This can be time consuming and tedious for the organization.  Often, outcomes related to staff augmentation services are guided by rigid, standardized service level agreements that prioritize process over results. Unlike in many other industries, these standards can be limiting in the dynamic world of scientific research and discovery, preventing teams from appropriately adapting their scope as project needs and goals change.  

Managed IT services, on the other hand, offer a more balanced approach between operations and project management. This allows research teams to save time they would otherwise spend managing IT processes, and it enables the delivery of specialized services tailored to your team’s specific needs. And, unlike the staff augmentation model that provides an individual resource to “fill a seat,” a managed services model is based on a team approach.  Often a diverse team of experts with a range of specialization work collaboratively to find a solution to a single issue.  This shifts the focus to prioritize outcomes and enables for a fluid and nimple approach, in a cost and time efficient manner. The end result is better Outcomes for all. 

How Your Computing Partner’s Service Model Influences Research Success

Meeting your research goals requires efficiency and expertise and when comparing the staff augmentation model versus the managed IT model, you can see the clear differences. When choosing the managed IT model you’re going to be offered a level of continuity and efficiency that the staff augmentation model can not compete with. When your organization is pressed for time and resources, having a managed IT model allows you to focus and expedite your work, ultimately accelerating the journey toward  your discovery and development goals.

When you work through evaluating your current partners, be sure to consider whether they operate with a service model that fits your research and development needs and goals. 

And stay tuned for the final installment of this series on how to evaluate your external scientific computing resources, in which we’ll discuss our last but certainly not least important consideration: Dedication and Accountability.

If You’re Not Doing These Five Things to Improve Research Outcomes, Start Now

Effective research and development programs are still one of the most significant investments of any biopharma. In fact, Seed Scientific estimates that the current global scientific research market is worth $76 billion, including $33.44 billion in the United States alone. Despite the incredible advancements in technology now aiding scientific discovery, it’s still difficult for many organizations to effectively bridge the gap between the business and IT, and fully leverage innovation to drive the most value out of their R&D product. 

If you’re in charge of your organization’s R&D IT efforts, following tried and true best practices may help. Start with these five strategies to help the business you’re supporting drive better research outcomes.

Tip #1: Practice Active Listening

Instead of jumping to a response when presented with a business challenge, start by listening to research teams and other stakeholders and learning more about their experiences and needs. The process of active listening, which involves asking questions to create a more comprehensive understanding of the issue at hand, can lead to new avenues of inspiration and help internal IT organizations better understand the challenges and opportunities before them. 

Tip #2: Plan Backwards 

Proper planning is a must for most scientific computing initiatives. But one particularly interesting method for accomplishing an important goal, such as moving workloads to the Cloud or optimizing your applications and workflows for global collaboration, is to start with a premortem. During this brainstorming session, team members and other stakeholders can predict possible challenges and other roadblocks and ideate viable solutions before any work begins. Research by Harvard Business Review shows this process can improve the identification of the underlying causes of issues by 30% and ultimately help drive better project and research outcomes.

Tip #3: Consider the Process, Not Just the Solution

Research scientists know all too well that discovering a viable solution is merely the beginning of a long journey to market. It serves R&D IT teams well to consider the same when developing and implementing platform solutions for business needs. Whether a system within a compute environment needs to be maintained, upgraded, or retired, R&D IT teams must prepare to adjust their approach based on the business’ goals, which may shift as a project progresses. Therefore, maintaining a flexible and agile approach throughout the project process is critical.  

Tip #4: Build a Specialized R&D IT Team 

Any member of an IT team working in support of the unique scientific computing needs of the business (as opposed to more common enterprise IT efforts) should possess both knowledge and experience in the specific tools, applications, and opportunities within scientific research and discovery. Moreover, they should have the skills to quickly identify and shift to the most important priorities as they evolve and adapt to new methods and initiatives that support improved research outcomes. If you don’t have these resources on staff, consider working with a specialized scientific computing partner to bridge this gap. 

Tip #5: Prepare for the Unexpected 

In research compute, it’s not enough to have a fall-back plan—you need a back-out plan as well. Being able to pivot quickly and respond appropriately to an unforeseen challenge or opportunity is mission-critical. Better yet, following best practices that mitigate risk and enable contingency planning from the start (like implementing an infrastructure-as-code protocol), can help the business you’re supporting avoid crippling delays in their research process. 

While this isn’t an exhaustive list, these five strategies provide an immediate blueprint to improve collaboration between R&D IT teams and the business, and support better research outcomes through smarter scientific computing. But if you’re looking for more support, RCH Solutions’ specialized Sci-T Managed Services could be the answer.  Learn more about his specialized service, here. 

How to Tell if Your Computing Partner is Actually Adding Value to Your Research Process: Adaptability

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

 If you’re still not sure you’ve sourced the ideal scientific computing partner to help your team realize it’s research compute goals, here’s another quality to evaluate: adaptability. 

By this point, we hope you’ve already read the first two installments in this five part series on how to tell if your partner is adding value (if not, start with #1 – Life Sciences Specialization and Mastery and #2 – The Ability to Bridge the Gap Between Science and IT). Here, we will take a look at the importance of adaptability, and why that quality matters to R&D teams (and their IT counterparts). 

Consideration #3: High Level of Adaptability

In today’s world adaptability is a highly sought after skill. Whether it’s in your personal or professional life, the ability to shift and adjust to change is vital. 

In the context of scientific computing, adaptability is less about survival and more about the ability to see things from different perspectives. In a research environment, though the end goal often remains, the process or needs associated with achieving that goal, can often be fluid.  Being able to or evolve to reach new or shifting goals with precision and performance is a skill not everyone one—or every team—possesses. 

In a research environment, scientists are not always able to predict the results their work will yield, thus needing to work hand in hand with a partner that is able to adjust when necessary. Whether you and your team need a few new resources or a new strategy entirely, a good computing partner will be able to adapt to your needs!

There are even more benefits to having a highly adaptable partner including increased level of performance, smoother transition from one project to the next, and more efficiency in your company’s research. A great scientific computing partner should be able to meet these needs using scalable IT architecture and a flexible service model. If your partner’s service model is too rigid, it may indicate they lack the expertise to readily provide dynamic solutions.

A Better Model for Your Dynamic Needs

Rigid service models may be the norm in many industries, but it does not predict success in the world of life science research. And too often, those partners that fall into the “good enough” category (as we mentioned above) follow these strict SLAs that don’t account for nuance or research environments.  

A partner that is not adaptable will inevitably be incapable of keeping up with the demands of shifting research. Choose a scientific computing partner whose services align with your scientific initiatives and deliver robust, consistent results. Prepare for the next year’s challenges by reaching out to a partner that offers highly specialized scientific computing services to life science research organizations like yours.

As you take all of these points into account, be sure to come back for consideration #4: A Service Model that Fits Research goals. 

Attention R&D IT decision makers: 

If you’re expecting different results in 2022 despite relying on the same IT vendors and rigid support model that didn’t quite get you to your goal last year, it may be time to hit pause on your plan.

At RCH, we’ve spent the past 30+ years paying close attention to what works — and what doesn’t—while providing specialty scientific computing and research IT support exclusively in the Life Sciences. We’ve put together this list of must-do best practices that you, and especially your external IT partner, should move to the center of your strategy to help you to take charge of your R&D IT roadmap. 

And if your partners are not giving you this advice to get your project back track?  Well, it may be time to find a new one.

1. Ground Your Plan in Reality
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 very common. However, this approach is not only ineffective, it’s also expensive. Only when your strategy and plan is created to account for where you are today — not where you’d like to be today — can you be confident that it will take you where you want to go. Otherwise, you’ll be taking two steps forward, and one (or more) step back the entire time.

2. Start with Good Data
Research IT professionals are often asked to support a wide range of data-related projects. But the reality is, scientists can’t use data to drive good insights, if they can’t find or make sense of the data in the first place. Implementing FAIR data practices should be the centerprise of any scientific computing strategy. Once you see the full scope of your data needs, only then can you deliver on high-value projects, such as analytics or visualization.

3. Make “Fit-For-Purpose” Your Mantra
Research is never a one size fits all process. Though variables may be consistent based on the parameters of your organization and what has worked well in the past, viewing each challenge as unique affords you the opportunity to leverage best-of-breed design patterns and technologies to answer your needs. Therefore, resist the urge to force a solution, even one that has worked well in other instances, into a framework if it’s not the optimal solution, and opt for a more strategic and tailored approach. 

4. Be Clear On the Real Source of Risk
Risk exists in virtually all industries, but 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 influence life or death, accuracy is not only everything, it’s the only thing. And so the tendency is to go with what you know. But ask yourself this: does your effort to minimize risk stifle innovation? In a business built on boundary-breaking innovation, mistaking static for “safe” can be costly.  Identifying which projects, processes and/or workloads would be better managed by other, more specialized service providers may actually reduce risk by improving project outcomes.   

Reaching Your R&D IT Goals in 2022:  A Final Thought

Never substitute experience. 

Often, the strategy that leads to many effective scientific and technical computing initiatives within an R&D IT framework differs from a traditional enterprise IT model. And that’s ok because, just as often, the goals do as well. That’s why it is so important to leverage the expertise of R&D IT professionals highly specialized and experienced in this niche space.

Experience takes time to develop. 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, well—‘been there done that. 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. 

It’s this level of specialization — focused expertise combined with real, hands-on experience — that can make all the difference in your ability to realize your outcome. 

And if you’re still on the fence about that, just take a look at some of these case studies to see how it’s working for others.  

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

As you continue to evaluate your strategy for 2022 and beyond, it’s important to ensure all facets of your compute environment are optimized— including the partners you hire to support it. 

Sometimes companies settle for working with partners that are just “good enough,” but in today’s competitive environment, that type of thinking can break you.  What you really need to move the needle is a scientific computing partner who understands both Science and IT. 

In part two of this five-part blog series on what you should be measuring your current providers against, we’ll examine how to tell if your external IT partner has the chops to meet the high demands of science, while balancing the needs of IT. If you haven’t read our first post, Evaluation Consideration #1: Life Science Specialization and Mastery, you can jump over there, first.  

Evaluation Consideration #2: Bridging the Gap Between Science and IT 

While there are a vast number of IT partners available, it’s important to find someone that has a deep understanding of the scientific industry and community. It can be invaluable to work with a specialized IT group, considering being an expert in one or the other is not enough.  The computing consultant that works with clients in varying industries may not have the best combination of knowledge and experience to drive the results you’re looking for.  

Your computing partner should have a vast understanding of how your research drives value for your stakeholders. Their ability to leverage opportunities and implement IT infrastructure that meet scientific goals, is vital. Therefore, as stated in consideration #1: Life Science Specialization and Mastery, it’s vital that your IT partner have significant IT experience.  

This is an evaluation metric best captured during strategy meetings with your scientific computing lead. Take a moment to consider the IT infrastructure options that are presented to you. Do they use your existing scientific infrastructure as a foundation? Do they require IT skills that your research team has? 

These are important considerations because you may end up spending far more than necessary on IT infrastructure that goes underutilized. This will make it difficult for your life science research firm to work competitively towards new discoveries. 

The Opportunity Cost of Working with the Wrong Partner is High

Overspending on underutilized IT infrastructure draws valuable IT resources away from critical research initiatives. Missing opportunities to deploy scientific computing solutions in response to scientific needs negatively impacts research outcomes. 

Determining if your scientific computing partner is up to the task requires taking a closer look at the quality of expertise you receive. Utilize your strategy meetings to gain insight into the experience and capabilities of your current partners, and pay close attention to Evaluation Consideration #2: Bridging the Gap Between Science and IT.  Come back next week to read more about our next critical consideration in your computing partnership, having a High Level of Adaptability. 

A Five-Part Series for Life Sciences Researchers and IT Professionals

The New Year is upon us and for most, that’s a time to reaffirm organizational goals and priorities, then develop a roadmap to achieve them. For many enterprise and R&D IT teams, that includes working with external consultants and providers of specialized IT and scientific computing services. 

But much has changed in the last year, and more change is coming in the next 12 months. Choosing the right partner is essential to the success of your research and, in the business where speed and performance are critical to your objectives, you don’t want to be the last to know when your partner isn’t working out quite as well as you had planned (and hoped). 

But what should you look for in a scientific computing partner?  

This blog series will outline five qualities that are essential to consider … and what you should be measuring your current providers against throughout the year to determine if they’re actually adding value to your research and processes.  

Evaluation Consideration #1: Life Science Specialization and Mastery

There are many different types of scientific computing consultants and many different types of organizations that rely on them. Life science researchers regularly perform incredibly demanding research tasks and need computing infrastructure that can support those needs in a flexible, scalable way.

A scientific computing consultant that works with a large number of clients in varied industries may not have the unique combination of knowledge and experience necessary to drive best-in-class results in the life sciences. 

Managing IT infrastructure for a commercial enterprise is very different from managing IT infrastructure for a life science research organization. Your computing partner should be able to provide valuable, highly specialized guidance that caters to research needs – not generic recommendations for technologies or workflows that are “good enough” for anyone to use.

In order to do this, your computing partner must be able to develop a coherent IT strategy for supporting research goals. Critically, partners should also understand what it takes to execute that strategy, and connect you with the resources you need to see it through.

Today’s Researchers Can’t Settle for “Good Enough”

In the past, the process of scientific discovery left a great deal of room for trial and error. In most cases, there was no alternative but to follow the intuition of scientific leaders, who could spend their entire career focused on solving a single scientific problem.

Today’s research organizations operate in a different environment. The wealth of scientific computing resources and the wide availability of emerging technologies like artificial intelligence (AI) and machine learning (ML) enable brand-new possibilities for scientific discovery.

Scientific research is increasingly becoming a multi-disciplinary process that requires researchers and data scientists to work together in new ways. Choosing the right scientific partner can unlock value for research firms and reduce time-to-discovery significantly.

Best-in-class scientific computing partnerships enable researchers to:

• Predict the most promising paths to scientific discovery and focus research on the avenues most likely to lead to positive outcomes.
• Perform scientific computing on scalable, cloud-enabled infrastructure without overpaying for services they don’t use.
• Automate time-consuming research tasks and dedicate more time and resources to high-impact, strategic initiatives.
• Maintain compliance with local and national regulations without having to compromise on research goals to do so.

If your scientific computing partner is one step ahead of the competition, these capabilities will enable your researchers to make new discoveries faster and more efficiently than ever before.

But finding out whether your scientific computing partner is up to the task requires taking a closer look at the quality of expertise you receive. Pay close attention to Evaluation Consideration #1: Life Science Specialization and Mastery and come back next week to read more about our next critical consideration in your computing partnership, the Ability to Bridge the Gap Between Science and IT.

R enables researchers to leverage reproducible data science environments.

Life science research increasingly depends on robust data science and statistical analysis to generate insight. Today’s discoveries do not owe their existence to single “eureka” moments but the steady analysis of experimental data in reproducible environments over time.

The shift towards data-driven research models depends on new ways to gather and analyze data, particularly in very large datasets. Often, researchers don’t know beforehand whether those datasets will be structured or unstructured or what kind of statistical analysis they need to perform in order to reach research goals.

R has become one of the most popular programming languages in the world of data science because it answers these needs. It provides a clear framework for handling and interpreting large amounts of data. As a result, life science research teams are increasingly investing in R expertise in order to meet ambitious research goals.

How R Supports Life Science Research and Development

R is a programming language and environment designed for statistical computing. It’s often compared to Python because the two share several high-profile characteristics. They are both open-source programming languages that excel at data analysis. 

The key difference is that Python is a general-purpose language. R was designed specifically and exclusively for data science applications. It offers researchers a complete ecosystem for data analysis and comes with an impressive variety of packages and libraries built for this purpose. Python’s popularity relies on it being relatively straightforward and easy to learn. Mastering R is much more challenging but offers far better solutions for data visualization and statistical analysis. R has earned its place as one of the best languages for scientific computing because it is interpreted, vector-based, and statistical:

• As an interpreted language, R runs without the need for a secondary compiler. Researchers can run code directly, which makes it faster and easier to interpret data using R.
• As a vector language, R allows anyone to add functions to a single vector without inserting a loop. This makes R faster and more powerful than non-vector languages.
• As a statistical language, R offers a wide range of data science and visualization libraries ideal for biology, genetics, and other scientific applications.

While the concept behind R is simple enough for some users to get results by learning on the fly, many of its most valuable functions are also its most complex. Life science research teams that employ R experts are well-positioned to address major pain points associated with using R while maximizing the research benefits it provides.

Challenges Life Science Research Teams Face When Implementing R

A large number of life science research teams already use R to some degree. However, fully optimized R implementation is rare in the life science industry. Many teams face steep challenges when obtaining data-driven research results using R:

1. Maintaining Multiple Versions of R Packages Can Be Complex

Reproducibility is the defining component of scientific research and software development. Source code control systems make it easy for developers to track and manage different versions of their software, fix bugs, and add new features. However, distributed versioning is much more challenging when third-party libraries and components are involved. 

Any R application or script will draw from R’s rich package ecosystem. These packages do not always follow any formal management system. Some come with extensive documentation, and others simply don’t. As developers update their R packages, they may inadvertently break dependencies that certain users rely on. Researchers who try to reproduce results using updated packages may get inaccurate outcomes.

Several high-profile R developers have engineered solutions to this problem. Rstudio’s Packrat is a dependency management system for R that lets users reproduce and isolate environments, allowing for easy version control and collaboration between users.

Installing a dependency management system like Packrat can help life science researchers improve their ability to manage R package versions and ensure reproducibility across multiple environments. Life science research teams that employ R experts can make use of this and many other management tools that guarantee smooth, uninterrupted data science workflows.

2. Managing and Administrating R Environments Can Be Difficult 

There is often a tradeoff between the amount of time spent setting up an R environment and its overall reproducibility. It’s relatively easy to create a programming environment optimized for a particular research task in R with minimal upfront setup time. However, that environment may not end up being easily manageable or reproducible as a result.

It is possible for developers to go back and improve the reproducibility of an ad-hoc project after the fact. This is a common part of the R workflow in many life science research organizations and a critical part of production analysis. However, it’s a suboptimal use of research time and resources that could be better spent on generating new discoveries and insights.

Optimizing the time users spend creating R environments requires considering the eventual reproducibility needs of each environment on a case-by-case basis: 

• An ad-hoc exploration may not need any upfront setup since reproduction is unlikely. 
• If an exploration begins to stabilize, users can establish a minimally reproducible environment using the session_info utility. It will still take some effort for a future user to rebuild the dependency tree from here.
• For environments that are likely candidates for reproduction, bringing in a dependency management solution like Packrat from the very beginning ensures a high degree of reproducibility.
• For maximum reproducibility, configuring and deploying containers using a solution like Docker guarantees all dependencies are tracked and saved from the start. This requires a significant amount of upfront setup time but ensures a perfectly reproducible, collaboration-friendly environment in R.

Identifying the degree of reproducibility each R environment should have requires a great degree of experience working within R’s framework. Expert scientific computing consultants can play a vital role in helping researchers identify the optimal solution for establishing R environments.

3. Some Packages Are Complicated and Time-Consuming to Install

R packages are getting larger and more complex, which significantly impacts installation time. Many research teams put considerable effort into minimizing the amount of time and effort spent on installing new R packages. 

This can become a major pain point for organizations that rely on continuous integration (CI) strategies like Travis or GitLab-CI. The longer it takes for you to get feedback on your CI strategy, the slower your overall development process runs as a result. Optimized CI pipelines can help researchers spend less time waiting for packages to install and more time doing research.

Combined with version management problems, package installation issues can significantly drag down productivity. Researchers may need to install and test multiple different versions of the same package before arriving at the expected result. Even if a single installation takes ten minutes, that time quickly adds up.

There are several ways to optimize R package installation processes. Research organizations that frequently install packages directly from source code may be able to use a cache utility to reduce compiling time. Advanced versioning and package management solutions can reduce package installation times even further.

4. Troubleshooting R Takes Up Valuable Research Time

While R is simple enough for scientific personnel to learn and use quickly, advanced scientific use cases can become incredibly complex. When this happens, the likelihood of generating errors is high. Troubleshooting errors in R can be a difficult and time-consuming task and is one of the most easily preventable pain points that come with using R.

Scientific research teams that choose to contract scientific computing specialists with experience in R can bypass many of these errors. Having an R expert on board and ready to answer your questions can mean the difference between spending hours resolving a frustrating error code and simply establishing a successful workflow from the start.

R has a dynamic and highly active community, but complex life science research errors may be well outside the scope of R troubleshooting. In environments with strict compliance and cybersecurity rules in place, you may not be able to simply post your session_info data on a public forum and ask for help.

Life science research organizations need to employ R experts to help solve difficult problems, optimize data science workflows, and improve research outcomes. Reducing the amount of time researchers spend attempting to resolve error codes is key to maximizing their scientific output.

RCH Solutions Provides Central Management for Scientific Workflows in R

Life science research firms that rely on scientific computing partners like RCH Solutions can free up valuable research resources while gaining access to R expertise they would not otherwise have. A specialized team of scientific computing experts with experience using R can help life science teams alleviate the pain points described above.

Life science researchers bring a wide range of incredibly valuable scientific expertise to their organizations. This expertise may be grounded in biology, genetics, chemistry, or many other disciplines, but it does not necessarily predict a great deal of experience in performing data science in R. Scientists can do research without a great deal of R knowledge – if they have a reliable scientific computing partner.

RCH Solutions allows life science researchers to centrally manage R packages and libraries. This enables research workflows to make efficient use of data science techniques without costing valuable researcher time or resources. 

Without central management, researchers are likely to spend a great deal of time trying to install redundant packages. Having multiple users spend time installing large, complex R packages on the same devices is an inefficient use of valuable research resources. Central management prevents users from having to reinvent the wheel every time they want to create a new environment in R.

Optimize Your Life Science Research Workflow with RCH Solutions

Contracting a scientific computing partner like RCH Solutions means your life science research workflow will always adhere to the latest and most efficient data practices for working in R. Centralized management of R packages and libraries ensures the right infrastructure and tools are in place when researchers need to create R environments and run data analyses.

Find out how RCH Solutions can help you build and implement the appropriate management solution for your life science research applications and optimize deployments in R. We can aid you in ensuring reproducibility in data science applications. Talk to our specialists about your data visualization and analytics needs today.

Bio-IT Teams Must Focus on Five Major Areas in Order to Improve Efficiency and Outcomes

Life Science organizations need to collect, maintain, and analyze a large amount of data in order to achieve research outcomes. The need to develop efficient, compliant data management solutions is growing throughout the Life Science industry, but Bio-IT leaders face diverse challenges to optimization.

These challenges are increasingly becoming obstacles to Life Science teams, where data accessibility is crucial for gaining analytic insight. We’ve identified five main areas where data management challenges are holding these teams back from developing life-saving drugs and treatments.

Five Data Management Challenges for Life Science Firms

Many of the popular applications that Life Science organizations use to manage regulated data are not designed specifically for the Life Science industry. This is one of the main reasons why Life Science teams are facing data management and compliance challenges. Many of these challenges stem from the implementation of technologies not well-suited to meet the demands of science.

Here, we’ve identified five areas where improvements in data management can help drive efficiency and reliability.

1. Manual Compliance Processes

Some Life Sciences teams and their Bio-IT partners are dedicated to leveraging software to automate tedious compliance-related tasks. These include creating audit trails, monitoring for personally identifiable information, and classifying large volumes of documents and data in ways that keep pace with the internal speed of science.

However, many Life Sciences firms remain outside of this trend towards compliance automation. Instead, they perform compliance operations manually, which creates friction when collaborating with partners and drags down the team’s ability to meet regulatory scrutiny.

Automation can become a key value-generating asset in the Life Science development process. When properly implemented and subjected to a coherent, purpose-built data governance structure, it improves data accessibility without sacrificing quality, security, or retention.

2. Data Security and Integrity

The Life Science industry needs to be able to protect electronic information from unauthorized access. At the same time, certain data must be available to authorized third parties when needed. Balancing these two crucial demands is an ongoing challenge for Life Science and Bio-IT teams.

When data is scattered across multiple repositories and management has little visibility into the data lifecycle, striking that key balance becomes difficult. Determining who should have access to data and how permission to that data should be assigned takes on new levels of complexity as the organization grows.

Life Science organizations need to implement robust security frameworks that minimize the exposure of sensitive data to unauthorized users. This requires core security services that include continuous user analysis, threat intelligence, and vulnerability assessments, on top of a Master Data Management (MDM) based data infrastructure that enables secure encryption and permissioning of sensitive data, including intellectual properties.

3. Scalable, FAIR Data Principles

Life Science organizations increasingly operate like big data enterprises. They generate large amounts of data from multiple sources and use emerging technologies like artificial intelligence to analyze that data. Where an enterprise may source its data from customers, applications, and third-party systems, Life Science teams get theirs from clinical studies, lab equipment, and drug development experiments.

The challenge that most Life Science organizations face is the management of this data in organizational silos. This impacts the team’s ability to access, analyze, and categorize the data appropriately. It also makes reproducing experimental results much more difficult and time-consuming than it needs to be.

The solution to this challenge involves implementing FAIR data principles in a secure, scalable way. The FAIR data management system relies on four main characteristics:

Findability. In order to be useful, data must be findable. This means it must be indexed according to terms that IT teams, scientists, auditors, and other stakeholders are likely to search for. It may also mean implementing a Master Data Management (MDM) or metadata-based solution for managing high-volume data.

Accessibility. It’s not enough to simply find data. Authorized users must also be able to access it, and easily. When thinking about accessibility—while clearly related to security and compliance, including proper provisioning, permissions, and authentication—ease of access and speed can be a difference-maker, which leads to our next point.

Interoperability. When data is formatted in multiple different ways, it falls on users to navigate complex workarounds to derive value from it. If certain users don’t have the technical skills to immediately use data, they will have to wait for the appropriate expertise from a Bio-IT team member, which will drag down overall productivity.

Reusability. Reproducibility is a serious and growing concern among Life Science professionals. Data reusability plays an important role in ensuring experimental insights can be reproduced by independent teams around the world. This can be achieved through containerization technologies that establish a fixed environment for experimental data.

4. Data Management Solutions

The way your Life Sciences team stores and shares data is an integral component of your organization’s overall productivity and flexibility. Organizational silos create bottlenecks that become obstacles to scientific advancement, while robust, accessible data storage platforms enable on-demand analysis that improves time-to-value for various applications.

The three major categories of storage solutions are Cloud, on-premises, and hybrid systems. Each of these presents a unique set of advantages and disadvantages, which serve specific organizational goals based on existing infrastructure and support. Organizations should approach this decision with their unique structure and goals in mind.

Life Science firms that implement MDM strategy are able to take important steps towards storing their data while improving security and compliance. MDM provides a single reference point for Life Science data, as well as a framework for enacting meaningful cybersecurity policies that prevent unauthorized access while encouraging secure collaboration.

MDM solutions exist as Cloud-based software-as-a-service licenses, on-premises hardware, and hybrid deployments. Biopharma executives and scientists will need to choose an implementation approach that fits within their projected scope and budget for driving transformational data management in the organization.

Without an MDM strategy in place, Bio-IT teams must expend a great deal of time and effort to organize data effectively. This can be done through a data fabric-based approach, but only if the organization is willing to leverage more resources towards developing a robust universal IT framework.

5. Monetization

Many Life Science teams don’t adequately monetize data due to compliance and quality control concerns. This is especially true of Life Science teams that still use paper-based quality management systems, as they cannot easily identify the data that they have – much less the value of the insights and analytics it makes possible.

This becomes an even greater challenge when data is scattered throughout multiple repositories, and Bio-IT teams have little visibility into the data lifecycle. There is no easy method to collect these data for monetization or engage potential partners towards commercializing data in a compliant way.

Life Science organizations can monetize data through a wide range of potential partnerships. Organizations to which you may be able to offer high-quality data include:

• Healthcare providers and their partners
• Academic and research institutes
• Health insurers and payer intermediaries
• Patient engagement and solution providers
• Other pharmaceutical research organizations
• Medical device manufacturers and suppliers

In order to do this, you will have to assess the value of your data and provide an accurate estimate of the volume of data you can provide. As with any commercial good, you will need to demonstrate the value of the data you plan on selling and ensure the transaction falls within the regulatory framework of the jurisdiction you do business in.

Overcome These Challenges Through Digital Transformation

Life Science teams who choose the right vendor for digitizing compliance processes are able to overcome these barriers to implementation. Vendors who specialize in Life Sciences can develop compliance-ready solutions designed to meet the incredibly unique needs of science, making fast, efficient transformation a possibility.

RCH Solutions can help teams like yours capitalize on the data your Life Science team generates and give you the competitive advantage you need to make valuable discoveries. Rely on our help to streamline workflows, secure sensitive data, and improve Life Sciences outcomes.

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 interested in learning how RCH can support your goals, get in touch with us here.