Challenges and Solutions for Data Management in the Life Science Industry

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. 

There are good reasons to balance Cloud infrastructure between multiple vendors.

In Part One of this series, we discussed some of the applications and workflows best-suited for public Cloud deployment. But public Cloud deployments are not the only option for life science researchers and biopharmaceutical IT teams. Hybrid Cloud and multi-Cloud environments can offer the same benefits in a way that’s better aligned to stakeholder interests.

What is a Multi-Cloud Strategy?

Multi-Cloud refers to an architectural approach that uses multiple Cloud computing services in parallel. Organizations that adopt a multi-Cloud strategy are able to distribute computing resources across their deployments and minimize over-reliance on a single vendor.

Multi-Cloud deployments allow Life Science researchers and Bio-IT teams to choose between multiple public Cloud vendors when distributing computing resources. Some Cloud platforms are better suited for certain tasks than others, and being able to choose between multiple competing vendors puts the organization at an overall advantage.

Why Bio-IT Teams Might Want to Adopt a Multi-Cloud Strategy

Working with a single Cloud computing provider for too long can make it difficult to move workloads and datasets from one provider to another. Especially as needs and requirements change which, as we know, is quite often within Life Sciences organizations.  Highly centralized IT infrastructure tends to accumulate data gravity – the tendency for data analytics and other applications to converge on large data repositories, making it difficult to scale data capabilities outwards.

This may go unnoticed until business or research goals demand migrating data from one platform to another. At that point, the combination of data gravity and vendor lock-in can suddenly impose unexpected technical, financial, and legal costs.

Cloud vendors do not explicitly prevent users from migrating data and workflow applications. However, they have a powerful economic incentive to make the act of migration as difficult as possible. Letting users flock to their competitors is not strictly in their interest.

Not all Cloud vendors do this, but any Cloud vendor can decide to. Since Cloud computing agreements can change over time, users who deploy public Cloud technology with a clear strategy for avoiding complex interdependencies will generally fare better than users who simply go “all in” with a single vendor.

Multi-Cloud deployments offer Life Science research organizations a structural way to eliminate over-reliance on a single Cloud vendor. Working with multiple vendors from the start demands researchers and IT teams plan for data and application portability from the beginning.

Multi-Cloud deployments also allow IT teams to better optimize diverse workflows with scalable computing resources. When researchers demand new workloads, their IT partners can choose an optimal platform for each one of them on a case-by-case basis.

This allows researchers and IT teams to coordinate resources more efficiently. One research application’s use of sensitive data may make it better suited for a particular Cloud provider, while another workflow demands high-performance computing resources only available from a different provider. Integrating multiple Cloud providers under a single framework can enable considerable efficiencies through each stage of the research process.

What About Hybrid Cloud?

Hybrid Clouds are IT architectures that rely on a combination of private Cloud resources alongside public Cloud systems. Private Cloud resources are simply Cloud-based architectures used exclusively by one organization.

For example, imagine your life science firm hosts some research applications on its own internal network but also uses Microsoft Azure and Amazon AWS. This is a textbook example of a multi-Cloud architecture that is also hybrid.

Hybrid Cloud environments may offer benefits to Life Science researchers that need security and compliance protection beyond what public Cloud vendors can easily offer. Private Cloud frameworks are ideal for processing and storing sensitive data.

Hybrid Cloud deployments may also present opportunities to reduce overall operating expenses over time. If researchers are sure they will consistently use certain Cloud computing resources frequently for years, hosting those applications on a private Cloud deployment may end up being more cost-efficient over that period. 

It’s common for Bio-IT teams to build private on-premises Cloud systems for small, frequently used applications and then use easily scalable public Cloud resources to handle less frequent high-performance computing jobs. This hybrid approach allows life science research organizations to get the best of both worlds.

Optimize your Cloud Strategy for Achieving Research Goals 

Life Science research organizations generate value by driving innovation in a dynamic and demanding field. Researchers who can perform computing tasks on the right platform and infrastructure for their needs are ideally equipped to make valuable discoveries. Public Cloud, multi-Cloud, and hybrid Cloud deployments are all viable options for optimizing Life Science research with proven technology.

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. 

Life Science researchers are beginning to actively embrace public Cloud technology. Research labs that manage IT operations more efficiently have more resources to spend on innovation.

As more Life Science organizations migrate IT infrastructure and application workloads to the public Cloud, it’s easier for IT leaders to see what works and what doesn’t. The nature of Life Science research makes some workflows more Cloud-friendly than others.

Why Implement Public Cloud Technology in the Life Science Sector?

Most enterprise sectors invest in public Cloud technology in order to gain cost benefits or accelerate time to market. These are not the primary driving forces for Life Science research organizations, however.

Life Science researchers in drug discovery and early research see public Cloud deployment as a way to consolidate resources and better utilize in-house expertise on their core deliverable—data. Additionally, the Cloud’s ability to deliver on-demand scalability plays well to Life Science research workflows with unpredictable computing demands.

These factors combine to make public Cloud deployment a viable solution for modernizing Life Science research and fostering transformation. It can facilitate internal collaboration, improve process standardization, and extend researchers’ IT ecosystem to more easily include third-party partners and service providers.

Which Applications and Workflows are Best-Suited to Public Cloud Deployment?

For Life Science researchers, the primary value of any technology deployment is its ability to facilitate innovation. Public Cloud technology is no different. Life Science researchers and IT leaders are going to find the greatest and most immediate value utilizing public Cloud technology in collaborative workflows and resource-intensive tasks.

1. Analytics

Complex analytical tasks are well-suited for public Cloud deployment because they typically require intensive computing resources for brief periods of time. A Life Science organization that invests in on-premises analytics computing solutions may find that its server farm is underutilized most of the time.

Public Cloud deployments are valuable for modeling and simulation, clinical trial analytics, and other predictive analytics processes that enable scientists to save time and resources by focusing their efforts on the compounds that are likely to be the most successful. They can also help researchers glean insight from translational medicine applications and biomarker pathways and ultimately, bring safer, more targeted, and more effective treatments to patients. Importantly, they do this without the risk of overpaying and underutilizing services.

2. Development and Testing

The ability to rapidly and securely build multiple development environments in parallel is a collaborative benefit that facilitates Life Science innovation. Again, this is an area where life science firms typically have the occasional need for high-performance computing resources – making on-demand scalability an important cost-benefit.

Public Cloud deployments allow IT teams to perform large system stress tests in a streamlined way. System integration testing and user acceptance testing are also well-suited to the scalable public Cloud environment.

3. Infrastructure Storage

In a hardware-oriented life science environment, keeping track of the various development ecosystems used to glean insight is a challenge. It is becoming increasingly difficult for hardware-oriented Life Science research firms to ensure the reproducibility of experimental results, simply because of infrastructural complexity.

Public Cloud deployments enable cross-collaboration and ensure experimental reproducibility by enabling researchers to save infrastructure as data. Containerized research applications can be opened, tested, and communicated between researchers without the need for extensive pre-configuration.

4. Desktop and Devices

Research firms that invest in public Cloud technology can spend less time and resources provisioning validated environments. They can provision virtual desktops to vendors and contractors in real-time, without having to go through a lengthy and complicated hardware process.

Life Science research organizations that share their IT platform with partners and contractors are able to utilize computing resources more efficiently and reduce its data storage needs. Instead of storing data in multiple places and communicating an index of that data to multiple partners, all of the data can be stored securely in the cloud and made accessible to the individuals who need it.

5. Infrastructure Computing

Biopharmaceutical manufacturing is a non-stop process that requires a high degree of reliability and security. Reproducible high-performance cloud (HPC) computing environments allow researchers to create and share computational biology data and biostatistics in a streamlined way.

Cloud-enabled infrastructure computing also helps Life Science researchers monitor supply chains more efficiently. Interacting with supply chain vendors through a Cloud-based application enables researchers to better predict the availability of research materials, and plan their work accordingly.

Hybrid Cloud and Multi-Cloud Models May Offer Greater Efficiencies 

Public Cloud technology is not the only infrastructural change happening in the Life Science industry. Certain research organizations can maximize the benefits of cloud computing through hybrid and multi-Cloud models, as well. The second part of this series will cover what those benefits are, and which Life Science research firms are best-positioned to capitalize on them.

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. 

Transformative change means rethinking the scientific computing workflow. 

The need to embrace and enhance data science within the Life Sciences has never been greater. Yet, many Life Sciences organizations performing  drug discovery face significant obstacles when transforming their legacy workflows.

Multiple factors contribute to the friction between the way Life Science research has traditionally been run and the way it needs to run moving forward. Companies that overcome these obstacles will be better equipped to capitalize on tomorrow’s research advances.

5 Obstacles to the Cloud-First Data Strategy and How to Address Them 

Life Science research organizations are right to dedicate resources towards maximizing research efficiency and improving outcomes. Enabling the full-scale Cloud transformation of a biopharma research lab requires identifying and addressing the following five obstacles.

1. Cultivating a Talent Pool of Data Scientists

Life Science researchers use a highly developed skill set to discover new drugs, analyze clinical trial data, and perform biostatistics on the results. These skills do not always overlap with the demands of next-generation data science infrastructure. Life Science research firms that want to capitalize on emerging data science opportunities will need to cultivate data science talent they can rely on.

Aligning data scientists with therapy areas and enabling them to build a nuanced understanding of drug development is key to long-term success. Biopharmaceutical firms need to embed data scientists in the planning and organization of clinical studies as early as possible and partner them with biostatisticians to build productive long-term relationships.

2. Rethinking Clinical Trials and Collaborations

Life Science firms that begin taking a data science-informed approach to clinical studies in early drug development will have to ask difficult questions about past methodologies:

• Do current trial designs meet the needs of a diverse population?
• Are we including all relevant stakeholders in the process?
• Could decentralized or hybrid trials drive research goals in a more efficient way?
• Could we enhance patient outcomes and experiences using the tools we have available?
• Will manufacturers accept and build the required capabilities quickly enough?
• How can we support a global ecosystem for real-world data that generates higher-quality insights than what was possible in the past?
• How can we use technology to make non-data personnel more capable in a cloud-first environment?
• How can we make them data-enabled?

All of these questions focus on the ability for data science-backed cloud technology to enable new clinical workflows. Optimizing drug discovery requires addressing inefficiencies in clinical trial methodology.

3. Speeding Up the Process of Achieving Data Interoperability

Data silos are among the main challenges that Life Science researchers face with legacy systems. Many Life Science organizations lack a company-wide understanding of the total amount of data and insights they have available. So much data is locked in organizational silos that merely taking stock of existing data assets is not possible.

The process of cleaning and preparing data to fuel AI-powered data science models is difficult and time-consuming. Transforming terabyte-sized databases with millions of people records into curated, AI-ready databases manually is slow, expensive, and prone to human error.

Automated interoperability pipelines can reduce the time spent on this process to a matter of hours. The end result is a clean, accurate database fully ready for AI-powered data science. Researchers can now create longitudinal person records (LPRs) with ease.

4. Building Infrastructure for Training Data Models

Transforming legacy operations into fast, accurate AI-powered ones requires transparent access to many different data sources. Setting up the infrastructure necessary takes time and resources. Additionally, it can introduce complexity when identifying how to manage multiple different data architectures. Data quality itself may be inconsistent between sources.

Building a scalable pipeline for training AI data models requires scalable cloud technology that can work with large training datasets quickly. Without reputable third-party infrastructure in place, the process of training data models can take months.

5. Protecting Trade Secrets and Patient Data

Life Science research often relies on sensitive technologies and proprietary compounds that constitute trade secrets for the company in question. Protecting intellectual property has always been a critical challenge in the biopharmaceutical industry, and today’s cybersecurity landscape only makes it more important.

Clinical trial data, test results, and confidential patient information must be protected in compliance with privacy regulations. Life Science research organizations need to develop centralized policies that control the distribution of sensitive data to internal users and implement automated approval process workflows for granting access to sensitive data.

Endpoint security solutions help ensure sensitive data is only downloadable to approved devices and shared according to protocol. This enables Life Science researchers to share information with partners and supply chain vendors without compromising confidentiality.

A Robust Cloud-First Strategy is Your Key to Life Science Modernization

Deploying emergent technologies in the Life Science industry can lead to optimal research outcomes and better use of company resources. Developing a cloud computing strategy that either supplements or replaces aspects of your legacy system requires input and buy-in from every company stakeholder it impacts. Consult with the expert Life Science research consultants at RCH Solutions to find out how your research team can capitalize on the digital transformation taking place in Life Science.

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. 

Key Takeaways from NVIDIA’s GTC Conference Keynote

The Value of an AI Ecosystem in Drug Discovery

The Cliff’s Notes version of how NVIDIA plans to operationalize its vision (and my take on it), is this: 

• Application Sharing: NVIDIA touted Omniverse as a collaborative platform — “universal” sharing of applications and 3D. 
• Data Centralization: The software-defined data center (BlueField-2 & 3 / DPU) was also quite compelling, though in the world of R&D we live in at RCH, it’s really more about Science and Analytics than Infrastructure. Nonetheless, I think we have to acknowledge the potential here.
• Virtualization: GPU virtualization was also impressive (though like BlueField, this is not new but evolved). In my mind, I wrestle with virtualization for density when it comes to Scientific Compute, but we (collectively) need to put more thought into this.
• Processing: NVIDIA is pushing its own CPU as the final component in the mix, which is an ARM-based processor. ARM is clearly going to be a force moving forward, and Intel x86_64 is aging … but we also have to acknowledge that this will be an evolution and not a flash-cut.

What’s interesting is how this approach could play to enhance in-silico Science. 

Our world is Cloud-first. Candidly, I’m a proponent of that for what I see as legitimate reasons (you can read more about that here). But like any business, Public Cloud vendors need to cater to a wide audience to better the chances of commercial success. While this philosophy leads to many beneficial services, it can also be a blocker for specialized/niche needs, like those in drug R&D. 

To this end, Edge Computing (for those still catching up, a high-bandwidth and very low latency specialty compute strategy in which co-location centers are topologically close to the Cloud), is a solution. 

Edge Computing is a powerful paradigm in Cloud Computing, enabling niche features and cost controls while maintaining a Cloud-first tact. Thus, teams are able to take advantage of the benefits of a Public Cloud for data storage, while augmenting what Public Cloud providers can offer by maintaining compute on the Edge. It’s a model that enables data to move faster than the more traditional scenario; and in NVIDIA’s equation, DGX and possibly BlueField work as the Edge of the Cloud.

More interestingly, though, is how this strategy could help Life Sciences companies dip their toes into the still unexplored waters of Quantum Computing through cuQuantum … Quantum (qubit) simulation on GPU … for early research and discovery. 

I can’t yet say how well this works in application, but the idea that we could use a simulator to test Quantum Compute code, as well as train people in this discipline, has the potential to be downright disruptive. Talking to those in the Quantum Compute industry, there are anywhere from 10 – 35 people in the world who can code in this manner (today). I see this simulator as a more cost-effective way to explore technology, and even potentially grow into a development platform for more user-friendly OS-type services for Quantum.

A Solution for Reducing the Pain of Data Movement

In summary, what NVIDIA is proposing may simplify the path to a more synergistic computing paradigm by enabling teams to remain—or become—Cloud-first without sacrificing speed or performance. 

Further, while the Public Cloud is fantastic, nothing is perfect. The Edge, enabled by innovations like what NVIDIA is introducing, could become a model that aims to offer the upside of On-prem for the niche while reducing the sometimes-maligned task of data movement. 

While only time will tell for sure how well NVIDIA’s tools will solve Scientific Computing challenges such as these, I have a feeling that Jensen and his team—like our most ancient of ancestors who first carved stone into a circle—just may be on to something here. 

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. 

Containers resolve deployment and reproducibility issues in Life Science computing.

Bioinformatics software and scientific computing applications are crucial parts of the Life Science workflow. Researchers increasingly depend on third-party software to generate insights and advance their research goals.

These third-party software applications typically undergo frequent changes and updates. While these updates may improve functionalities, they can also impede scientific progress in other ways.

Research pipelines that rely on computationally intensive methodologies are often not easily reproducible. This is a significant challenge for scientific advancement in the Life Sciences, where replicating experimental results – and the insights gleaned from analyzing those results – is key to scientific progress.

The Reproducibility Problem Explained 

For Life Science researchers, reproducibility falls into four major categories:

Direct Replication is the effort to reproduce a previously observed result using the same experimental conditions and design as an earlier study.

Analytic Replication aims to reproduce scientific findings by subjecting an earlier data set to new analysis.

Systemic Replication attempts to reproduce a published scientific finding under different experimental conditions.

Conceptual Replication evaluates the validity of an experimental phenomenon using a different set of experimental conditions.

Researchers are facing challenges in some of these categories more than others. Improving training and policy can help make direct and analytic replication more accessible. Systemic and conceptual replication is significantly harder to address effectively.

These challenges are not new. They have been impacting research efficiency for years. In 2016, Nature published a study showing that out of 1,500 life science researchers, more than 70% failed to reproduce another scientist’s experiments.

There are multiple factors responsible for the ongoing “reproducibility crisis” facing the life sciences. One of the most important challenges scientists need to overcome is the inability to easily assemble software tools and their associated libraries into research pipelines.

This problem doesn’t fall neatly into one of the categories above, but it impacts each one of them differently. Computational reproducibility forms the foundation that direct, analytic, systemic, and conceptual replication techniques all rely on.

Challenges to Computational Reproducibility 

Advances in computational technology have enabled scientists to generate large, complex data sets during research. Analyzing and interpreting this data often depends heavily on specific software tools, libraries, and computational workflows.

It is not enough to reproduce a biotech experiment on its own. Researchers must also reproduce the original analysis, using the computational techniques that previous researchers used, and do so in the same computing environment. Every step of the research pipeline has to conform with the original study in order to truly test whether a result is reproducible or not.

This is where advances in bioinformatic technology present a bottleneck to scientific reproducibility. Researchers cannot always assume they will have access to (or expertise in) the technologies used by the scientists whose work they wish to reproduce. As a result, achieving computational reproducibility turns into a difficult, expensive, and time-consuming experience – if it’s feasible at all.

How Containerization Enables Reproducibility 

Put simply, a container consists of an entire runtime environment: an application, plus all its dependencies, libraries, and other binaries, and configuration files needed to run it, bundled into one package. By containerizing the application platform and its dependencies, differences in OS distributions and underlying infrastructure are abstracted away.

If a researcher publishes experimental results and provides a containerized copy of the application used to analyze those results, other scientists can immediately reproduce those results with the same data. Likewise, future generations of scientists will be able to do the same regardless of upcoming changes to computing infrastructure.

Containerized experimental analyses enable life scientists to benefit from the work of their peers and contribute their own in a meaningful way. Packaging complex computational methodologies into a unique, reproducible container ensure that any scientist can achieve the same results with the same data.

Bringing Containerization to the Life Science Research Workflow

Life Science researchers will only enjoy the true benefits of containerization once the process itself is automatic and straightforward. Biotech and pharmaceutical research organizations cannot expect their researchers to manage software dependencies, isolate analyses away from local computational environments, and virtualize entire scientific processes for portability while also doing cutting-edge scientific research.

Scientists need to be able to focus on the research they do best while resting assured that their discoveries and insights will be recorded in a reproducible way. Choosing the right technology stack for reproducibility is a job for an experienced biotech IT consultant with expertise in developing R&D workflows for the biotech and pharmaceutical industries.

RCH Solutions helps Life Science researchers develop and implement container strategies that enable scalable reproducibility. If you’re interested in exploring how a container strategy can support your lab’s ability to grow, contact our team to learn more.

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. 

Certified AWS engineers bring critical expertise to research workflows and data architecture. 

Organizations of every kind increasingly measure their success by their ability to handle data. 

Whether conducting scientific research or market research, the efficiency of your data infrastructure is key. It will either give you a leading competitive edge or become an expensive production bottleneck.

For many executives and IT professionals, Amazon’s AWS service is the go-to Cloud computing solution. Amazon isn’t the only vendor on the market, but it is the most popular one, even if Microsoft, Azure, and Google Cloud aren’t far behind.

Both Research teams and IT professionals looking to increase their data capacities are always looking for good tech talent. In a world of uncertainties, official certification can make all of the difference when it comes to deploying new technologies.

AWS Certifications: What They Mean for Organizations

Amazon offers 11 globally recognized certifications for its industry-leading Cloud technologies. Studies show that professionals who pursue AWS certification are faster, more productive troubleshooters than non-certified employees.

One of the highest levels of certification that an AWS professional can obtain is the AWS Solutions Architect – Professional certification. This represents a technical professional who can design and deploy entire Cloud system frameworks from the ground up, creating efficient data flows and solving difficult problems along the way.

Professional Architect certification holders have earned this distinction by demonstrating the following:

The ability to create dynamically scalable fault-tolerant AWS applications.

The expertise to select appropriate AWS services based on project requirements.

The ability to implement successful cost-control strategies.

Experience migrating complex, multi-tier applications on the AWS platform.

While everything in the AWS certification system relies on Amazon technology, the fundamental processes involved are essentially vendor agnostic. Every growing organization needs to migrate complex applications between platforms while controlling costs and improving data efficiency – AWS is just one tool of many that can get the job done.

This is especially important for research organizations that work in complex Cloud environments. Being able to envision an efficient, scalable Cloud architecture solution and then deploy that solution in a cost-effective way is clearly valuable to high-pressure research environments.

Meet The AWS-Certified Solutions Architects on the RCH Team

At RCH Solutions, we pride ourselves on leveraging the best talent and providing best-in-class Cloud support for our customers. When we have AWS problems to solve, they go to our resident experts, Mohammad Taaha and Yogesh Phulke, both who have obtained AWS Solutions Architect certification.

Mohammad has been with us since 2018. Coming from the University of Massachusetts, he served as a Cloud Engineer responsible for some of our most exciting projects:

• Creating extensive solutions for AWS EC2 with multiple frameworks (EBS, ELB, SSL, Security Groups and IAM), as well as RDS, CloudFormation, Route 53, CloudWatch, CloudFront, CloudTrail, S3, Glue, and, Direct Connect.
• Deploying a successful high-performance computing (HPC) cluster on AWS for a Life-Sciences customer, using Parallel Cluster running SGE scheduler for the purpose.
• Automating operational tasks including software configuration, server scaling and deployments, and database setups in multiple AWS Cloud environments with the use of modern application and configuration management tools (e.g. CloudFormation and Ansible).
• Working closely with clients to design networks, systems, and storage environments that effectively reflect their business needs, security, and service level requirements.
• Architecting and migrating data from on-premises solutions (Isilon) to AWS (S3 & Glacier) using industry-standard tools (Storage Gateway, Snowball, CLI tools, Datasync, among others).
• Designing and deploying plans to remediated accounts affected by IP overlap after a recent merger.

All of these tasks have served to boost the efficiency of data-oriented processes for clients and make them better-able to capitalize on new technologies and workflows.

AWS Isn’t the Only Vendor Out There

Though It’s natural to focus on Amazon AWS thanks to its position as the industry leader, RCH Solutions is vendor agnostic, which means we support a range of Cloud service providers and our team has competencies in all of the major Cloud technologies on the market. If your organization is better served by Microsoft Azure, Google Cloud, or any other vendor, you can rest assured RCH Solutions can support your Cloud computing efforts. 

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. 

Essential Questions to Ask When Evaluating Your Options

Cloud computing is having a fundamental impact on the biotech industry. Tasks that were extremely time consuming or simply not possible even a decade ago can now be performed quickly and efficiently in the Cloud. 

Take big data storage and analysis. Amazon Web Service, Microsoft Azure, and Google Cloud – to name just the three biggest – offer storage and Cloud computing services that allow companies to store massive data sets and provide the computing power required to analyze it.

Affordable access to these powerful tools shortens timelines and allows even small companies to perform tasks that, until recently, were limited to deep-pocket companies that could afford to buy the hardware needed.

Cloud computing solutions also transfer the power of selection and implementation into the hands of the functional areas. IT is no longer the rate-limiting step in implementation; cloud pay-to-play solutions can be turned on just as quickly as credit card information can be transmitted.  

And there are capital considerations as well. The difference is upfront capital expense (CapEx) in on-prem storage, vs. operational expense (OpEx) for the cloud.  Not having to come up with large sums of money immediately is an advantage of the Cloud.

But there is a flip side. In the biotech/biopharma world, compliance with regulatory requirements, such as 21 CFR Part 11, rank high on the list of issues, and Cloud-based systems might not afford the necessary protection. Security is another important consideration. After all, Cloud computing means sharing your company’s sensitive information to a third-party service provider. 

Not to mention, the many benefits to on-premise options, including the ability to tailor your environment to meet very specific company needs. 

For these reasons, conversations centered on the implementation or better execution of Cloud solutions permeate research and IT teams, especially as the working world shifts toward higher adoption of virtual work and collaboration practices. 

If you’re exploring which Cloud vs. on-prem solutions are right for your work and team, consider the following critical considerations before making any moves:

• Business objective. What is the main objective of migrating your business to the cloud, and how will the cloud support your broader R&D or data goals?
• Impact.  How will a migration impact your organization’s ability to maintain productivity, and can you afford outages if needed? 
• Readiness.  Are you prepared to support a cloud infrastructure?  What steps must you take now to ensure compatibility between current on-premise deployments and cloud?
• Workflow. What applications make sense to keep on-premise and which would be ripe for the cloud?  One size (or in this case, storage strategy) does not fit all. 
• Capital.  Have you assessed costs, including expenses related to the dedicated human resources necessary to support the migration?
• Time. Have you thought about realistic timelines and possible roadblocks that could increase migration times?
• Risk Mitigation. What are some known risks or cons that may make you, or your organization, hesitant, and how will your CSP support efforts to reduce risk through all phases of your relationship?
• Security. Will your data be secure? What security protocols can you trust your cloud service provider to follow to ensure you realize the many benefits of the cloud without sacrificing security?  
• Compliance.  Will your cloud service provider meet client compliance?
• Business Continuity and Disaster Recovery.  How will your cloud service provider accommodate and plan for the unknown … a requirement we know all too well following COVID-19. 

And this list could go on. 

The bottom line?  As compelling as a complete move into the Cloud may sound, teams need to carefully consider all the many factors before operationalizing a plan.  And when in doubt, an experienced Cloud computing expert can be the navigator organizations need to ensure the decisions they make are right for their needs and goals. 

You can find more information about how RCH Solutions can help develop your Cloud strategy here.

Looking for support for your AI Initiatives?

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. 

An experienced Bio-IT partner can help you determine if AI is right for your project. At RCH, we’ve helped our customers successfully navigate and leverage an evolving technology landscape to best meet their R&D IT needs for nearly three decades. Talk to us about how we can help you, too.

Last week we attended AWS re:Invent 2019, the premier event for any organization stepping—or running—into the Cloud as part of their IT strategy. It was our 4th time attending and, as expected, it didn’t disappoint.

re:Invent was jam-packed with engaging speakers covering important topics, opportunities to connect with existing customers and partners, and of course, the chance to snag some ever-coveted Amazon swag. But, what struck me most, was the growing and noticeable presence of Life Sciences attendees. From large pharma to start-ups, the Life Sciences were better represented than ever before. It’s a clear sign that we’ve reached an inflection point within our industry, and the Cloud’s role within an effective a Bio-IT strategy has been cemented.

Equally as important, AWS showed their interest in continuing to enhance their solution-set to meet the specific needs of medical research and discovery. 

Here are seven quick takeaways from the event that demonstrate how AWS is moving the needle to better support Cloud computing in the Life Sciences:

1. Linux Deployment It’s estimated that Linux deployments in AWS are over 4X that of Windows in the Cloud. The reasons are many but those that pertain to Life Sciences include availability, reliability, scalability, and scalability.

2. Quantum Computing Amazon Braket is the new Quantum Computing Service, which can help the acceleration of significant breakthroughs in science.

3. Elastic Kubernetes Service (EKS) Running Kubernetes pods on AWS Fargate, the serverless compute engine built for containers on AWS, makes it easier than ever to build and run your Kubernetes apps in the Cloud.

4. SageMaker AWS has added over 50 new enhancements, including those for the Deep Graph Library (DGL). With DGL, you can improve the prediction accuracy of recommendations, fraud detection, and Drug Discovery systems using Graph Neural Networks (GNNs).

5. New EC2 Gravitron 2 Powered ARM Instances and Inf1 ML Inference Optimized Instances. These are ideal for scientific computing and high-performance machine learning workloads. These are extremely promising, as they provide high performance and the lowest cost machine learning in the Cloud.

6. Amazon S3 Access Points Easily manage access for shared datasets on S3, with the ability to create hundreds of access points per bucket, each with a name and permissions customized for the application.

7. Amazon Redshift Update Next-Generation Compute Instances and Managed Analytics-Optimized Storage should streamline the process to manage data workflows and findings. This enables you to save data transformation and enrichment you have done in Amazon Redshift into your Amazon S3 data lake in an open format.You can then analyze your data with Redshift Spectrum and other AWS services such as Amazon Athena, Amazon EMR, and Amazon SageMaker. See something I missed?  Share your top takeaways from AWS re:Invent below. 

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. 

Today, the Cloud is a critical component of most Bio-IT strategies. The benefits of incorporating the Cloud into your workflows are many and include the ability to open and accelerate access to critical research data and foster greater processing capabilities at scale.  There’s also significant opportunity for cost savings.  Instead of investing in costly on-prem servers and data centers to potentially use only a fraction of its capacity, using the “pay-as-you-need” model based on your consumption of compute resources ultimately helps you achieve a lower cost. 

However, what many IT teams don’t accurately account for is the potential for the Cloud to become a significant cost-center in-and-of-itself. Excess users, unused databases, and duplicate workflows—these are all very common yet costly contributors of unnecessary expense within your Cloud environment. Without the right strategies, processes, and controls in place to guard against missteps such as these, departments may find their costs growing out of control and see the need, as one of our customers said best, to “put their Cloud on a diet.” 

If your Cloud environment is starting to feel a little tight around the middle, keep these tips in mind: 

1. Define clear and measurable Cloud goals to set a realistic budget.

Ideally, goal-setting is done in advance of implementation, but it’s never too late to solidify a strategy for better outcomes. 

By defining (or redefining) your goals clearly, your team can carefully budget for your Cloud needs. This begins with an accurate estimation of total Cloud users (think groups and individuals across the full span of the workflow) since services are offered on a per-user or a usage basis. Budgets should accommodate a fluctuation in users, and ongoing monitoring to remove old or unwanted accounts should be performed regularly. Ironically, underestimating an organization’s demand for a service can lead to exceeding the budget. Luckily, most Cloud providers offer helpful tools for accurate budgeting purposes.  

2. Understand how the Cloud influences workflow.

The Cloud is only as effective—and efficient—as you set it up to be. Be sure to address processes and workflows not only within your Cloud but also happening around the Cloud to streamline processes or services and avoid duplication. 

Here are a few ways to do that when working with some of the most notable Cloud providers: 

Manage usage with features like auto-shutdown on instances that are not currently in use.

Use Amazon CloudWatch alarms to detect and shut down unused EC2 instances automatically to avoid accumulating unnecessary usage charges

MS Azure has auto-shutdown for VMs using Azure Resource Manager

Google’s Cloud Scheduler provides a straightforward solution to automatically stop and start VMs

 

Manage storage costs by removing duplicate files and utilizing a tiered-storage model.

Amazon S3 Intelligent-Tiering is designed for customers who want to optimize storage costs automatically when data access patterns change, without performance impact or operational overhead

Microsoft Azure storage offers different access tiers, which allow you to store blob object data in the most cost-effective manner

 

Use tools that provide historical metrics on Cloud usage to identify unused services.

Enable detailed monitoring for resources, such as your instances, or publish your own application metrics

Amazon CloudWatch can load all the metrics in your account for search, graphing, and alarms

Microsoft Azure Monitor collects and aggregates data from a variety of sources into a common data platform where it can be used for analysis, visualization, and alerting

Google Cloud Metrics Explorer lets you build ad-hoc charts for any metric collected by your project

Finally, it’s always a good idea to integrate services across management platforms to maintain consistency and deploy your application in multiple regions with lower latency to local customers. The latter provides disaster recovery from region-wide outages and enables low-latency global access to applications and data.

3. Work with a vendor who is familiar with the unique needs in Life Sciences

There are several benefits of working with a vendor who brings specific experience in implementing or optimizing Cloud computing workflows within the Life Sciences. 

The most significant is helping organizations still determining how the Cloud will work for their needs, set goals, and adapt workflows based on best practices and the unique demand of their business. 

For these teams, they typically see a dramatic increase in agility for the organization, since the cost and time it takes to experiment and develop is significantly lower. 

4. Focus On Outcomes

The bottom line: Cloud computing allows you to focus on outcomes, rather than racking, cabling, and powering servers, and offers the potential to significantly increase the pace of innovation and discovery within R&D teams. However, if not implemented or appropriately optimized, it can introduce an entirely new set of challenges—and cost—many organizations are not prepared to face. Partnering with a vendor who brings first-hand experience supporting organizations as they navigate this new and rapidly evolving territory—and being involved in defining and executing solution sets—is invaluable.

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.