Architectural Considerations & Optimization Best Practices

The integration of high-performance computing (HPC) in the Cloud is not just about scaling up computational power; it’s about architecting systems that can efficiently manage and process the vast amounts of data generated in Biotech and Pharma research. For instance, in drug discovery and genomic sequencing, researchers deal with datasets that are not just large but also complex, requiring sophisticated computational approaches.

However, designing an effective HPC Cloud environment comes with challenges. It requires a deep understanding of both the computational requirements of specific workflows and the capabilities of Cloud-based HPC solutions. For example, tasks like ultra-large library docking in drug discovery or complex genomic analyses demand not just high computational power but also specific types of processing cores and optimized memory management.

In addition, the cost-efficiency of Cloud-based HPC is a critical factor. It’s essential to balance the computational needs with the financial implications, ensuring that the resources are utilized optimally without unnecessary expenditure.

Understanding the need for HPC in Bio-IT

In Life Sciences R&D, the computational demands require sophisticated computational capabilities to extract meaningful insights. HPC plays a pivotal role by enabling rapid processing and analysis of extensive datasets. For example, HPC facilitates multi-omics data integration, combining genomics with transcriptomics and metabolomics for a comprehensive understanding of biological processes and disease. It also aids in developing patient-specific simulation models, such as detailed heart or brain models, which are pivotal for personalized medicine.

Furthermore, HPC is instrumental in conducting large-scale epidemiological studies, helping to track disease patterns and health outcomes, which are essential for effective public health interventions. In drug discovery, HPC accelerates not only ultra-large library docking but also chemical informatics and materials science, fostering the development of new compounds and drug delivery systems.

This computational power is essential not only for advancing research but also for responding swiftly in critical situations like pandemics. Additionally, HPC can integrate environmental and social data, enhancing disease outbreak models and public health trends. The advanced machine learning models powered by HPC, such as deep neural networks, are transforming the analytical capabilities of researchers.

HPC’s role in handling complex data also involves accuracy and the ability to manage diverse data types. Biotech and Pharma R&D often deal with heterogeneous data, including structured and unstructured data from various sources. The advanced data visualization and user interface capabilities supported by HPC allow for intricate data patterns to be revealed, providing deeper insights into research data.

HPC is also key in creating collaboration and data-sharing platforms that enhance the collective research efforts of scientists, clinicians, and patients globally. HPC systems are adept at integrating and analyzing these diverse datasets, providing a comprehensive view essential for informed decision-making in research and development.

Architectural Considerations for HPC in the Cloud

In order to construct an HPC environment that is both robust and adaptable, Life Sciences organizations must carefully consider several key architectural components:

• Scalability and flexibility: Central to the design of Cloud-based HPC systems is the ability to scale resources in response to the varying intensity of computational tasks. This flexibility is essential for efficiently managing workloads, whether they involve tasks like complex protein-structure modeling, in-depth patient data analytics, real-time health monitoring systems, or even advanced imaging diagnostics.
• Compute power: The computational heart of HPC is compute power, which must be tailored to the specific needs of Bio-IT tasks. The choice between CPUs, GPUs, or a combination of both should be aligned with the nature of the computational work, such as parallel processing for molecular modeling or intensive data analysis.
• Storage solutions: Given the large and complex nature of datasets in Bio-IT, storage solutions must be robust and agile. They should provide not only ample capacity but also fast access to data, ensuring that storage does not become a bottleneck in high-speed computational processes.
• Network architecture: A strong and efficient network is vital for Cloud-based HPC, facilitating quick and reliable data transfer. This is especially important in collaborative research environments, where data sharing and synchronization across different teams and locations are common.
• Integration with existing infrastructure: Many Bio-IT environments operate within a hybrid model, combining Cloud resources with on-premises systems. The architectural design must ensure a seamless integration of these environments, maintaining consistent efficiency and data integrity across the computational ecosystem.

Optimizing HPC Cloud environments

HPC in the Cloud is as crucial as its initial setup. This optimization involves strategic approaches to common challenges like data transfer bottlenecks and latency issues.

Efficiently managing computational tasks is key. This involves prioritizing workloads based on urgency and complexity and dynamically allocating resources to match these priorities. For instance, urgent drug discovery simulations might take precedence over routine data analyses, requiring a reallocation of computational resources.

But efficiency isn’t just about speed and cost; it’s also about smooth data travel. Optimizing the network to prevent data transfer bottlenecks and reducing latency ensures that data flows freely and swiftly, especially in collaborative projects that span different locations.

In sensitive Bio-IT environments, maintaining high security and compliance standards is another non-negotiable. Regular security audits, adherence to data protection regulations, and implementing robust encryption methods are essential practices. 

Maximizing Bio-IT potential with HPC in the Cloud

A well-architected HPC environment in the Cloud is pivotal for advancing research and development in the Biotech and Pharma industries.

By effectively planning, considering architectural needs, and continuously optimizing the setup, organizations can harness the full potential of HPC. This not only accelerates computational workflows but also ensures these processes are cost-effective and secure.

Ready to optimize your HPC/Cloud environment for maximum efficiency and impact? Discover how RCH can guide you through this transformative journey.

Sources:
https://www.rchsolutions.com/high-performance-computing/
https://www.nature.com/articles/s41586-023-05905-z
https://www.rchsolutions.com/ai-aided-drug-discovery-and-the-future-of-biopharma/
https://www.nature.com/articles/s41596-021-00659-2
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10318494/
https://pubmed.ncbi.nlm.nih.gov/37702944/
https://link.springer.com/article/10.1007/s42514-021-00081-w
https://www.rchsolutions.com/resource/scaling-your-hpc-environment-in-a-cloud-first-world/ https://www.rchsolutions.com/how-high-performance-computing-will-help-scientists-get-ahead-of-the-next-pandemic/ https://www.scientific-computing.com/analysis-opinion/how-can-hpc-help-pharma-rd
https://www.rchsolutions.com/storage-wars-cloud-vs-on-prem/
https://www.rchsolutions.com/hpc-migration-in-the-cloud/
https://www.mdpi.com/2076-3417/13/12/7082
https://www.rchsolutions.com/resource/hpc-migration-to-the-cloud/

Considerations for Enhancing Your In-house Bio-IT Team

As research becomes increasingly data-driven, the need for a robust IT infrastructure, coupled with a team that can navigate the complexities of bioinformatics, is vital to progress. But what happens when your in-house Bio-IT services team encounters challenges beyond their capacity or expertise?

This is where strategic augmentation comes into play. It’s not just a solution but a catalyst for innovation and growth by addressing skill gaps and fostering collaboration for enhanced research outcomes.

Assessing in-house Bio-IT capabilities

The pace of innovation demands an agile team and diverse expertise. A thorough evaluation of your in-house Bio-IT team’s capabilities is the foundational step in this process. It involves a critical analysis of strengths and weaknesses, identifying both skill gaps and bottlenecks, and understanding the nuances of your team’s ability to handle the unique demands of scientific research.

For startup and emerging Biotech organizations, operational pain points can significantly alter the trajectory of research and impede the desired pace of scientific advancement. A comprehensive blueprint that includes team design, resource allocation, technology infrastructure, and workflows is essential to realize an optimal, scalable, and sustainable Bio-IT vision.

Traditional models of sourcing tactical support often clash with these needs, emphasizing the necessity of a Bio-IT Thought Partner that transcends typical staff augmentation and offers specialized experience and a willingness to challenge assumptions.

Identifying skill gaps and emerging needs

Before sourcing the right resources to support our team, it’s essential to identify where the gaps lie. Start by:

1. Prioritizing needs.  While prioritizing “everything” is often the goal, it’s also the fastest way to get nothing done. Evaluate the overarching goals of your company and team, and decide what skills and efforts represent support mission-critical, versus “nice to have” efforts.
2. Auditing current capabilities: Understand the strengths and weaknesses of your current team. Are they adept at handling large-scale genomic data but struggle with real-time data processing? Recognizing these nuances is the first step.
3. Project forecasting: Consider upcoming projects and their specific IT demands. Will there be a need for advanced machine learning techniques or Cloud-based solutions that your team isn’t familiar with?
4. Continuous training: While it’s essential to identify gaps, it’s equally crucial to invest in continuous training for your in-house team. This ensures that they remain updated with the latest in the field, reducing the skill gap over time.

Evaluating external options

Once you’ve identified the gaps, the next step is to find the right partners to fill them. Here’s how:

1. Specialized expertise: Look for partners who offer specialized expertise that complements your in-house team. For instance, if your team excels in data storage but lacks in data analytics, find a partner who can bridge that gap.
2. Flexibility: The world of Life Sciences is dynamic. Opt for partners who offer flexibility in terms of scaling up or down based on project requirements.
3. Cultural fit: Beyond technical expertise, select an external team that aligns with your company’s culture and values. This supports smoother collaboration and integration. 

Fostering collaboration for optimal research outcomes

Merging in-house and external teams can be challenging. However, with the right strategies, collaboration can lead to unparalleled research outcomes.

1. Open communication: Establish clear communication channels. Regular check-ins, updates, and feedback loops help keep everyone on the same page.
2. Define roles: Clearly define the roles and responsibilities of each team member, both in-house and external. This prevents overlaps and ensures that every aspect of the project is adequately addressed.
3. Create a shared vision: Make sure the entire team, irrespective of their role, understands the end goal. A shared vision fosters unity and drives everyone towards a common objective.
4. Leverage strengths: Recognize and leverage the strengths of each team member. If a member of the external team has a particular expertise, position them in a role that maximizes that strength.

Making the right choice

For IT professionals and decision-makers in Pharma, Biotech, and Life Sciences, the decision to augment the in-house Bio-IT team is not just about filling gaps; it’s about propelling research to new heights, ensuring that the IT infrastructure is not just supportive but also transformative.

When making this decision, consider the long-term implications. While immediate project needs are essential, think about how this augmentation will serve your organization in the years to come. Will it foster innovation? Will it position you as a leader in the field? These are the questions that will guide you toward the right choice.

Life Science research outcomes can change the trajectory of human health, so there’s no room for compromise. Augmenting your in-house Bio-IT team is a commitment to excellence. It’s about recognizing that while your team is formidable, the right partners can make them invincible. Strength comes from recognizing when to seek external expertise.

 Pick the right team to supplement yours. Talk to RCH Solutions today.

Sources:
https://www.rchsolutions.com/harnessing-collaboration/
https://www.rchsolutions.com/press-release-rch-introduces-solution-offering-designed-to-help-biotech-startups/
https://www.rchsolutions.com/what-is-a-bio-it-thought-partner-and-why-do-you-need-one/
 https://www.rchsolutions.com/our-people-are-our-key-point-of-difference/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3652225/
https://www.forbes.com/sites/forbesbusinesscouncil/2023/01/10/three-best-practices-when-outsourcing-in-a-life-science-company/?sh=589b57a55575
https://www.cio.com/article/475353/avoiding-the-catch-22-of-it-outsourcing.html 

In the Life Sciences, analyzing vast datasets like genomic sequences or clinical trial results is routine. Ensuring the security and integrity of this data is crucial, especially under tight regulatory oversight.

Amazon Web Services (AWS) provides a platform with certified experts that cater to these needs. AWS offers tools that help streamline data processes, meet compliance standards, and safeguard intellectual property. The key is to use these tools efficiently to maintain data integrity and confidentiality.

AWS’ framework for Life Sciences compliance

AWS solutions are designed to meet the specific demands of Life Sciences, such as upholding GxP compliance and guarding sensitive patient data. By aligning with these requirements, organizations can adhere to regulatory standards while tapping into the benefits of Cloud technology. Moreover, AWS’s voluntary participation in the CSA Security, Trust & Assurance Registry (STAR) Self-Assessment showcases its transparency in compliance with best practices, establishing even more trust for users.

AWS’s commitment to integrating compliance controls means it’s woven through the entire system, from data centers to intricate IT processes. For example, when handling genomic data, AWS ensures encrypted storage for raw sequences, controlled access for processing, and traceable logs for any data transformations, all while adhering to regulatory standards.

Data governance & traceability in AWS

This tailored AWS infrastructure offers Life Sciences organizations unparalleled control. Imagine researchers working on a groundbreaking vaccine. As they collect vast amounts of patient data, they need a system that can not only securely store this information but also track every modification or access to it.

With AWS, an automatic log is generated each time a researcher accesses or modifies a patient’s record. This means that six months later, if there’s a question about who made a specific change to a patient’s data, the researchers can quickly pull up this log, verifying the exact date, time, and individual responsible. Data management on AWS is about ensuring data is traceable, consistent, and always under the organization’s purview.

Ensuring security through AWS best practices

Life Sciences data, whether it’s genomic sequences or computational studies, needs robust security. This data’s sensitivity and proprietary nature mean any breach could harm research outcomes, patient confidentiality, and intellectual property rights.

To tackle these concerns, AWS provides:

• Encryption at rest and in transit: AWS’s encryption mechanisms shield sensitive data in storage and during transfer, so that critical information like genomic data or computational chemistry results remain confidential and tamper-proof.

• IAM (Identity and Access Management): Fine-grained access control is essential in Life Sciences to prevent unauthorized data breaches. With AWS’s IAM, organizations can meticulously determine who accesses specific datasets, down to actions they’re permitted to take—be it viewing, modifying, or sharing.

• VPC (Virtual Private Cloud): Given the sensitive nature of research data, such as precision medicine studies or bioinformatics analyses, an extra layer of protection is often required. AWS’s VPC offers isolated computing resources, enabling organizations to craft custom network architectures that suit their security and compliance needs. This ensures that data remains protected in a dedicated environment.
• Physical security measures: Beyond digital protections, AWS takes extensive precautions to safeguard the physical infrastructure housing this data. Data centers benefit from tight access control, with staff passing through multiple authentication stages. Routine audits and surveillance bolster the integrity of the physical environment where data resides.

Audit preparedness & continuous compliance monitoring with AWS

Navigating the maze of regulatory requirements in the Life Sciences sector can be daunting. AWS offers tools designed specifically to ease this journey.

AWS Artifact stands out as it provides on-demand access to AWS’s compliance reports. With information at their fingertips, companies can confidently maintain regulatory compliance without the traditional runaround of audit requests.

Further strengthening the compliance arsenal, AWS Config offers a dynamic solution. Rather than periodic checks, AWS Config continuously monitors and records the configurations of AWS resources. For instance, if a Life Sciences firm were to deploy a genomic database, AWS Config would ensure its settings align with internal policies and external regulatory standards. Utilizing machine learning, AWS Config can also predict and alert potential non-compliance issues before they become critical.

This continuous oversight eliminates gaps that might arise between audits, allowing for consistent adherence to the best practices and regulatory norms.

Integrating advanced governance with AWS and Turbot

AWS provides a solid foundation for data management and compliance, but sometimes, specific industries demand specialized solutions. This is where third-party tools like Turbot come into play. It’s tailored for sectors such as Life Sciences thanks to its real-time automation, governance, and compliance features.

Consider a pharmaceutical company conducting clinical trials across multiple countries, each with unique compliance criteria. Turbot ensures that every AWS resource aligns with these diverse regulations, minimizing non-compliance risks.

Beyond mere monitoring, if Turbot detects any discrepancies or non-compliant resources, it immediately rectifies the situation without waiting for human intervention. This proactive approach ensures robust security measures are consistently in place.

Security and compliance in AWS for Life Sciences

Life Science industries operate within complex regulatory landscapes, and handling vast datasets requires meticulous attention to security, traceability, and compliance. AWS’s platform is designed to cater to these needs with sophisticated tools for data governance, security, and audit preparedness, however, without a specialized Life Sciences scientific computing provider with deep AWS expertise, like RCH, you may be leaving AWS opportunities and capabilities in the balance. To maximize the potential of these tools and navigate the intricate junction where science and IT overlap in the AWS Cloud, and beyond, a subject matter expert is crucial.

Contact our certified AWS experts to empower your research with specialized Bio-IT expertise and help streamline your journey to groundbreaking discoveries.

Sources: 

https://aws.amazon.com/solutions/health/data-security-and-compliance/

https://aws.amazon.com/health/solutions/gxp/

https://www.rchsolutions.com/cloud-computing/ 

https://docs.aws.amazon.com/whitepapers/latest/gxp-systems-on-aws/aws-certifications-and-attestations.html

https://aws.amazon.com/health/genomics/

https://aws.amazon.com/solutions/case-studies/innovators/moderna/

https://aws.amazon.com/blogs/aws/introducing-amazon-omics-a-purpose-built-service-to-store-query-and-analyze-genomic-and-biological-data-at-scale/

https://aws.amazon.com/compliance/data-center/controls/

https://aws.amazon.com/blogs/aws/new-aws-config-rules-now-support-proactive-compliance/

https://turbot.com/guardrails/blog/2018/11/healthcare-and-life-sciences

https://turbot.com/guardrails/blog/2018/04/gartner-cspm

https://www.rchsolutions.com/resource/elevated-perspectives-security-in-the-cloud/

Overcoming Operational Challenges with AI Drug Discovery

While computer-assisted drug discovery has been around for 50 years, the need for advanced computing tools has never been so crucial.  

Today, machine learning is proving invaluable in managing some of the intricacies of R&D and powering breakthroughs thanks to its ability to process millions of data points in mere seconds.

Of course, AI drug discovery and development tools have their own complex operational demands. Ensuring their integration, operation, and security requires high-performance computing and tools that help manage and make sense of massive data output.

Aligning Biochemistry, AI, and system efficiency

The process of creating and refining pharmaceuticals and biologics is becoming more complex, precise, and personalized, largely due to the robust toolkit of artificial intelligence. As a result, combining complex scientific disciplines, AI-aided tools, and expansive IT infrastructure has come to pose some interesting challenges.

Now, drug discovery and development teams require tools and AI that can:

• Optimize data and applicable storage and efficiently preprocess massive molecular datasets.
• Support high-throughput screening as it sifts through millions of molecular predictions.
• Enable rapid and accurate prediction of molecular attributes.
• Integrate large and diverse datasets from clinical trials, genomic insights, and chemical databases.
• Scale up as computational power as demands surge.

Challenges in Bio-IT for drug discovery and development

Drug discovery and development calls for a sophisticated toolset. The following challenges demonstrate the obstacles such tools must overcome.

• The magnitude and intricacy of the molecular datasets needed to tackle the challenges of drug discovery and development require more than storage solutions. These solutions must be tailored to the unique character of molecular structures.
• High-throughput screening (HTS)—a method that can rapidly test thousands to millions of compounds and identify those that may have a desired therapeutic effect—also requires immense processing power. Systems must be capable of handling immediate data feeds and performing fast, precise analytics.
• Predicting attributes for millions of molecules isn’t just about speed; it’s about accuracy and efficiency. As a result, the IT infrastructure must be equipped to handle these instantaneous computational needs, ensuring there are no delays in data processing, which could bottleneck the research process.
• The scalability issue extends far beyond capacity. Tackling this requires foresight and adaptability. Planning for future complexities in algorithms and computation means pharma teams need a robust and adaptive infrastructure.
• Integrating data into a holistic model poses significant challenges. Teams must find ways to synthesize clinical findings, genomic insights, and chemical information into a unified, coherent data model. This requires finding tech partners with expertise in AI-driven systems and data management strategies; these partners should also recognize and address the peculiarities of each domain, all while providing options for context-driven queries.

As we can see, high-level Bio-IT isn’t just an advantage; it’s a necessity. And it’s one that requires the right infrastructure and expertise from an experienced IT partner.

Mastering the Machine Learning Workflow

Bridging the nuances of drug discovery with the technicalities of artificial intelligence demands specialized knowledge, including: 

• Machine learning algorithms. Each drug discovery dataset has unique characteristics, and the AI model should mirror these idiosyncrasies. Initial testing in a sandbox environment ensures scalability and efficiency before amplification across larger datasets.
• Data preprocessing. High-quality data drives accurate predictions. Effective preprocessing ensures datasets are robust, balanced, capable of interpolating gaps, and free from redundancies. In the pharmaceutical realm, this is the bedrock of insightful machine-learning models.
• Distributed computing. When handling petabytes of data, traditional computational methods may falter. Enter distributed computing. Platforms like Apache Spark enable the distributed processing essential for the seamless analysis of massive datasets and drawing insights in record time.
• Hyperparameter tuning. For pharma machine learning models, tweaking hyperparameters is key to the best performance. The balancing act between trial-and-error, Bayesian optimization, and structured approaches like grid search can dramatically impact model efficiency.
• Feedback mechanisms. Machine learning thrives on feedback. The tighter the loop between model predictions and real-world validations, the sharper and more accurate the predictions become.
• Model validation. Ensuring a model’s robustness is critical. Cross-validation tools and techniques ensure that the model generalizes well without losing its specificity.
• Integration with existing Bio-IT systems. Interoperability is key. Whether through custom APIs, middleware solutions, or custom integrations, models must be seamlessly woven into the existing IT fabric.
• Continuous model training. The drug discovery landscape is ever-evolving. Models require a mechanism that constantly feeds new insights and allows them to evolve, adapt, and learn with every new dataset.

Without the right Bio-IT infrastructure and expertise, AI drug discovery cannot reach its full potential. Integrating algorithms, data processing, and computational methods is essential, but it’s their combined synergy that truly sparks groundbreaking discoveries.

Navigating Bio-IT in drug discovery

As the pharmaceutical industry advances, machine learning is guiding drug discovery and development to unprecedented heights through enabling the creation of sophisticated data models. 

By entrusting scientific computing strategies and execution to experts who understand the interplay between research, technology, and compliance, research teams can remain focused on their primary mission: groundbreaking discoveries.

Get in touch with our team if you’re ready to start a conversation about harnessing the full potential of Bio-IT for your drug discovery endeavors.

Unleash your full potential with effective scientific computing solutions that add value and align with your business needs.

Finding the right Bio-IT partner to navigate the complex landscape that is science-IT is no easy task. With a multitude of factors to consider (such as expertise, outcomes, scalability, data security, and adherence to industry regulations), evaluating potential partners can be an overwhelming process. That’s where a Bio-IT scorecard approach comes in handy. 

By using a structured evaluation approach, organizations can focus on what truly matters—aligning their organizational requirements with the capabilities and expertise of potential Bio-IT partners. Not the other way around. Here’s how using a scorecard can help streamline decision-making and ensure successful collaborations.

1. Bio-IT Requirements Match

Every Biopharma is on a mission, whether it’s to develop and deliver new, life-changing therapeutics, or advance science to drive innovation and change. While they share multiple common needs, such as the ability to process large and complex datasets, the way in which each organization uses IT and technology can vary. 

Biopharma companies must assess how well their current or potential Bio-IT partner’s services align with the organization’s unique computing needs, such as data analysis, HPC, cloud migration, or specialized software support. And that’s where a Bio-IT scorecard can be helpful. For example, a company with multiple locations must enable easy, streamlined data sharing between facilities while ensuring security and authorized-only access. A single location may also benefit from user-based privileges, but their needs and processes will vary since users are under the same roof. 

Organizations must also evaluate the partner’s proficiency in addressing specific Bio-IT challenges relevant to their operations by asking questions such as:

• Can they provide examples of successfully tackling similar challenges in the past, showcasing their domain knowledge?
• Can they demonstrate proficiency in utilizing relevant technologies, such as high-performance computing, cloud infrastructure, and data security?
• How do they approach complex Bio-IT challenges?
• Can they share any real-world examples of solving challenges related to data integration, interpretation, or regulatory compliance?

Questions like these on your own Bio-IT scorecard can help your organization better understand a potential partner’s proficiency in areas specific to your needs and objectives. And this ultimately helps your team understand if the partner is capable of helping prepare your firm scale by reducing bottlenecks and clearing a path to discovery.

2. Technical Proficiency and Industry Experience

Organizations can rate their shortlist partners on relevant technologies, platforms, and scientific computing to learn more about technical proficiency. It’s equally important to apply proficiency to the unique needs of Biopharmas.

According to a industry survey, respondents agree that IT and digital innovation are needed to solve fundamental challenges that span the entire spectrum of operations, including “dedicated funding (59%), a better digital innovation strategy (49%), and the right talent (47%) to scale digital innovation.” It’s essential that IT partners can connect solutions to these and other business needs to ensure organizations are poised for growth.

It’s also critical to verify the partner’s track record of delivering Bio-IT services to organizations within the Life Sciences industry specifically. And the associated outcomes they’ve achieved for similar organizations. To do this, organizations can obtain references and ask specific questions about technical expertise, such as:

• Whether the company proposed solutions that met core business needs
• Whether the IT technology provided a thorough solution
• Whether the solutions were implemented on time and on budget
• How the company continues to support the IT aspect

Successful IT partners are those who can speak from a place of authority in both science and IT. This means being able to understand the technical aspect as well as applying that technology to the nuances of companies conducting pre-clinical and clinical R&D. While IT companies are highly skilled in the former, very few are specialized enough to also embrace the latter. It’s essential to work with a specialized partner that understands this niche segment – the Life Sciences industry. And creating a Bio-IT scorecard based on your unique needs can help you do that.

3. Research Objectives Alignment

IT companies make it their goal to provide optimal solutions to their clients. However, they must also absorb their clients’ goals as their own to ensure they’re creating and delivering the technology needed to drive breakthroughs and accelerate discovery and time-to-value.

Assess how well the partner’s capabilities and services align with your specific research objectives and requirements by asking: 

• Do they have expertise in supporting projects related to your specific research area, such as genomics, drug discovery, or clinical trials?
• Can they demonstrate experience in the specific therapeutic areas or biological processes relevant to our research objectives?
• What IT infrastructure and tools do they have in place to support our data-intensive research?

The more experience in servicing research areas that are similar to yours, the less guesswork involved and the faster they can implement optimal solutions.

4. Scalability and Flexibility

In the rapidly evolving field of Life Sciences, data generation rates are skyrocketing, making scalability and extensibility vital for future growth. Each project may require unique analysis pipelines, tools, and integrations with external software or databases. A Bio-IT partner should be able to customize its solutions based on individual requirements and handle ever-increasing volumes of data efficiently without compromising performance. To help uncover their ability to do that, your team might consider:

• Ask about their approach to adapting to changing requirements, technologies, and business needs. Inquire about their willingness to customize solutions to fit your specific workflows and processes. 
• Request recent and similar examples of projects where the Bio-IT partner has successfully implemented scalable solutions. 

By choosing a Bio-IT partner that prioritizes flexibility and scalability, organizations can future-proof their research infrastructure from inception. They can easily scale up resources as their data grows exponentially while also adapting to changing scientific objectives seamlessly. This agility allows scientists to focus more on cutting-edge research rather than getting bogged down in technical bottlenecks or outdated systems. The potential for groundbreaking discoveries in healthcare and biotechnology becomes even more attainable.

5. Data Security and Regulatory Compliance

In an industry governed by strict regulations such as HIPAA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulation), partnering with a Bio-IT company that is fully compliant with these regulations is essential. Compliance ensures that patient privacy rights are respected, data is handled ethically, and legal implications are avoided.

As part of your due diligence, you should consider the following as it relates to a potential partner’s approach to data security and regulatory compliance: 

• Verify their data security measures, encryption protocols, and adherence to industry regulations (e.g., HIPAA, GDPR, 21 CFR Part 11) applicable to the organization’s Bio-IT data.
• Ensure they have undergone relevant audits or certifications to demonstrate compliance. 
• Ask about how they stay up-to-date on compliance and regulatory changes and how they communicate their ongoing certifications and adherence to their clients.

6. Collaboration and Communication

A strong partnership relies on open lines of communication, where both parties can share and leverage their subject matter expertise in order to work towards a common goal. Look for partners who have experience working with diverse and cross-functional teams, and have successfully integrated technology into various workflows. 

Evaluate the partner’s communication channels, responsiveness, and willingness to collaborate effectively with the organization’s IT team and other important stakeholders. Consider their approach to project management, reporting, and transparent communication, and how it aligns with your internal processes and preferences.

Conclusion

The value of developing and using a Bio-IT scorecard to ensure a strong alignment between the organization’s Bio-IT needs and the right vendor fit cannot be overstated. Using a scorecard model gives you a holistic, systematic, objective way to evaluate potential or current partners to ensure your needs are being met—and expectations hopefully exceeded.

Biotechs and Pharmas can benefit greatly from specialized Bio-IT partners like RCH Solutions. With more than 30 years of exclusive focus servicing the Life Sciences industry, organizations can gain optimal IT solutions that align with business objectives and position outcomes for today’s needs and tomorrow’s challenges. Learn more about what RCH Solutions offers and how we can transform your Bio-IT environment.

Sources:

https://www.genome.gov/genetics-glossary/Bioinformatics

https://www2.deloitte.com/us/en/insights/industry/life-sciences/biopharma-digital-transformation.html

In today’s fast-paced and rapidly evolving world of Life Sciences, successful organizations know innovation is the key to success. But game-changing innovation without effective collaboration is not possible.

Think about it. Bringing together diverse minds, specialized skill sets, and unique perspectives is crucial for making breakthroughs in scientific research, data analysis, and clinical advancements. It’s like the X-factor that can unlock new discoveries, achieve remarkable results, and fast-track time-to-value.

But as always — the $64,000 question remains: But how?  

In leading RCH and working with dozens of different teams across the Life Sciences space,  I’ve seen what works—some things better than others—within organizations looking to foster a greater sense of collaboration to drive innovation.  

Here are my top 5 strategies for your team to consider:    

• Break Down Silos for Collective Success:

One of the critical advantages of collaboration in the Life Sciences is the ability to leverage diverse perspectives and expertise. But traditionally, many organizations have functioned within siloed structures, with each department working independently towards their goals. However, this approach often leads to fragmented progress, limited knowledge sharing, and missed opportunities. By embracing cross-functional collaboration, Life Sciences organizations can break down these barriers and foster an environment that encourages the free flow of ideas, expertise, and resources. As the saying goes, “Two heads are better than one,” which is all the more true in the case of collaboration—the potential for breakthrough solutions expands exponentially.

• Leverage the Power of Advisors:

By collaborating with specialized service providers, organizations can leverage their expertise and extend to a broader ecosystem to help streamline processes, implement robust data management strategies, and ensure compliance with regulatory requirements. Such partnerships bring fresh perspectives, complementary expertise and can help drive efficiencies, by leveraging specialized resources, experience and expertise. This ultimately allows Life Sciences companies to focus on their core competencies—research and science. 

• Drive Innovation Through Interdisciplinary Teams:

Life Sciences is a multidisciplinary field that requires expertise in biology, research and development, information technology, data analysis, and more. Creating cross-functional teams that bring together individuals with diverse backgrounds can foster creativity and innovation through the pooling of data, the sharing of insights, and the generation of new hypotheses—ultimately leading to faster insights and more meaningful outcomes. When scientists, data analysts, bioinformaticians, software developers, and domain experts collaborate, they can collectively develop novel solutions, generate new insights, and optimize processes—more efficiently.

• Enhance Problem-Solving Capabilities:

Collaboration and the power of strategic partnerships allows Life Sciences organizations to tackle complex problems—and opportunities—from multiple angles. By leveraging the collective intelligence of cross-functional teams, and external specialists, organizations can tap into a wealth of knowledge and experience. This enables them to analyze challenges from different perspectives, identify potential blind spots, and develop comprehensive solutions. The synergy created by collaboration often leads to breakthrough discoveries and more efficient problem-solving.

• Agile Adaptation to Rapid Technological Advances:

As we all know, technology is constantly evolving, and keeping pace with the latest advancements can be a daunting task. Collaborating with the right Bio-IT partner helps Life Sciences organizations remain at the forefront of innovation. By fostering partnerships with R&D IT experts, organizations gain access to cutting-edge tools, methodologies, and insights, enabling them to adopt new technologies swiftly and effectively. The ideal Bio-IT partner also has a deep understanding of the complete life cycle of the Cloud journey, for both enterprise and emerging Biopharmas, and from inception to optimization and beyond, enabling them to provide tailored and specialized support at any stage of their distinct Cloud journey. This ultimately assists them in attaining their individual discovery goals and again, allows scientists the bandwidth to focus on their core competencies—research, science and discovery.

Final Thoughts

In the world of Life Sciences, I truly believe that collaboration, both internally through cross-functional teams, and externally through strategic partnerships, is the key to unlocking transformative breakthroughs. And organizations that aren’t focused on creating and sustaining a collaborative culture or cross-functional strategy? They’ll get left behind. 

By harnessing collaboration, organizations can tap into a wealth of knowledge that can drive innovation, enhance problem-solving capabilities, and adapt to rapid technological advances. Embracing collaboration not only accelerates progress but also cultivates a culture of continuous learning and excellence. And the latter is the type of organization that top talent will flock to and thrive within. 

As a leading Bio-IT organization, the team at RCH Solutions believes that it is essential to prioritize collaboration, foster meaningful partnerships, and nurture cross-functional teams to shape the future of the Life Sciences industry. Why? Because we’ve seen the accelerative power it brings—driving breakthroughs, accelerating discovery and smashing outcomes—time and time again. 

Create cutting-edge data architecture for highly specialized Life Sciences

Data pipelines are simple to understand: they’re systems or channels that allow data to flow from one point to another in a structured manner. But structuring them for complex use cases in the field of genomics is anything but simple. 

Genomics relies heavily on data pipelines to process and analyze large volumes of genomic data efficiently and accurately. Given the vast amount of details involving DNA and RNA sequencing, researchers require robust genomics pipelines that can process, analyze, store, and retrieve data on demand. 

It’s essential to build genomics pipelines that serve the various functions of genomics research and optimize them to conduct accurate and efficient research faster than the competition. Here’s how RCH is helping your competitors implement and optimize their genomics data pipelines, along with some best practices to keep in mind throughout the process.

Early-stage steps for implementing a genomics data pipeline

Whether you’re creating a new data pipeline for your start-up or streamlining existing data processes, your entire organization will benefit from laying a few key pieces of groundwork first. These decisions will influence all other decisions you make regarding hosting, storage, hardware, software, and a myriad of other details.

Defining the problem and data requirements

All data-driven organizations, and especially the Life Sciences, need the ability to move data and turn them into actionable insights as quickly as possible. For organizations with legacy infrastructures, defining the problems is a little easier since you have more insight into your needs. For startups, a “problem” might not exist, but a need certainly does. You have goals for business growth and the transformation of society at large, starting with one analysis at a time. So, start by reviewing your projects and goals with the following questions: 

• What do your workflows look like? 
• How does data move from one source to another? 
• How will you input information into your various systems? 
• How will you use the data to reach conclusions or generate more data? 

Leaning into your projects and goals and the outcomes of the above questions in the planning phase will  lead to an architecture that will be laid out to deliver the most efficient results based on how you work. The answers to the above questions (and others) will also reveal more about your data requirements, including storage capacity and processing power, so your team can make informed and sustainable decisions.

Data collection and storage

The Cloud has revolutionized the way Life Sciences companies collect and store data. AWS Cloud computing creates scalable solutions, allowing companies to add or remove space as business dictates. Many companies still use on-premise servers, while others are using a hybrid mix. 

Part of the decision-making process may involve compliance with HIPAA, GDPR, the Genetics Diagnostics Act, and other data privacy laws. Some regulations may prohibit the use of public Cloud computing.  Decision-makers will need to consider every angle, every pro, and every con to each solution to ensure efficiency without sacrificing compliance.

Data cleaning and preprocessing

Raw sequencing data often contains noise, errors, and artifacts that need to be corrected before downstream analysis. Pre-processing involves tasks like trimming, quality filtering, and error correction to enhance data quality. This helps maintain the integrity of the pipeline while improving outputs.

Data movement

Generated data typically writes to local storage and is then moved elsewhere, such as the Cloud or network-attached storage (NAS). This gives companies more capacity, plus it’s cheaper. It also frees up local storage for instruments which is usually limited.   

The timeframe when the data gets moved should also be considered. For example, does the data get moved at the end of a run or as the data is generated? Do only successful runs get moved?  The data format can also change. For example, the file format required for downstream analyses may require transformation prior to ingestion and analysis. Typically,  raw data is read-only and retained. Future analyses (any transformations or changes) would be performed on a copy of that data.

Data disposal

What happens to unsuccessful run data? Where does the data go? Will you get an alert? Not all data needs to be retained, but you’ll need to specify what happens to data that doesn’t successfully complete its run. 

Organizations should also consider upkeep and administration. Someone should be in charge of responding to failed data runs as well as figuring out what may have gone wrong. Some options include adding a system response, isolating the “bad” data to avoid bottlenecks, logging the alerts, and identifying and fixing root causes. 

Data analysis and visualization

Visualizations can help speed up analysis and insights. Users can gain clear-cut answers from data charts and other visual elements and take decisive action faster than reading reports. Define what these visuals should look like and the data they should contain.

Location for the compute

Where the compute is located for cleaning, preprocessing, downstream analysis, and visualization is also important. The closer the data is to the computing source, the shorter distance it has to travel, which translates into faster data processing. 

Optimization techniques for genomics data pipelines

Establishing a scalable architecture is just the start. As technology improves and evolves, opportunities to optimize your genomic data pipeline become available. Some of the optimization techniques we apply include: 

Parallel processing and distributed computing

Parallel processing involves breaking down a large task into smaller sub-tasks which can happen simultaneously on different processors or cores within a single computer system. The workload is divided into independent parts, allowing for faster computation times and increased productivity.

Distributed computing is similar, but involves breaking down a large task into smaller sub-tasks that are executed across multiple computer systems connected to one another via a network. This allows for more efficient use of resources by dividing the workload among several computers.

Cloud computing and serverless architectures

Cloud computing uses remote servers hosted on the internet to store, manage, and process data instead of relying on local servers or personal computers. A form of this is serverless architecture, which allows developers to build and run applications without having to manage infrastructure or resources.

Containerization and orchestration tools

Containerization is the process of packaging an application, along with its dependencies and configuration files, into a lightweight “container” that can easily deploy across different environments. It abstracts away infrastructure details and provides consistency across different platforms.

Containerization also helps with reproducibility. Users can expect better performance if the computer is in close proximity to the data. It can also be optimized for longer-term data retention by moving data to a cheaper storage area when feasible.

Orchestration tools manage and automate the deployment, scaling, and monitoring of containerized applications. These tools provide a centralized interface for managing clusters of containers running on multiple hosts or cloud providers. They offer features like load balancing, auto-scaling, service discovery, health checks, and rolling updates to ensure high availability and reliability.

Caching and data storage optimization

We explore a variety of data optimization techniques, including compression, deduplication, and tiered storage, to speed up retrieval and processing. Caching also enables faster retrieval of data that is frequently used. It’s readily available in the cache memory instead of being pulled from the original source. This reduces response times and minimizes resource usage.

Best practices for data pipeline management in genomics

As genomics research becomes increasingly complex and capable of processing more and different types of data, it is essential to manage and optimize the data pipeline efficiently to create accurate and reproducible results. Here are some best practices for data pipeline management in genomics.

• Maintain proper documentation and version control. A data pipeline without proper documentation can be difficult to understand, reproduce, and maintain over time. When multiple versions of a pipeline exist with varying parameters or steps, it can be challenging to identify which pipeline version was used for a particular analysis. Documentation in genomics data pipelines should include detailed descriptions of each step and parameter used in the pipeline. This helps users understand how the pipeline works and provides context for interpreting the results obtained from it.
• Test and validate pipelines routinely. The sheer complexity of genomics data requires careful and ongoing testing and validation to ensure the accuracy of the data. This data is inherently noisy and may contain errors which will affect downstream processes. 
• Continuously integrate and deploy data. Data is only as good as its accessibility. Constantly integrating and deploying data ensures that more data is readily usable by research teams.
• Consider collaboration and communication among team members. The data pipeline architecture affects the way teams send, share, access, and contribute to data. Think about the user experience and seek ways to create intuitive controls that improve productivity. 

Start Building Your Genomics Data Pipeline with RCH Solutions

About 1 in 10 people (or 30 million) in the United States suffer from a rare disease, and in many cases, only special analyses can detect them and give patients the definitive answers they seek. These factors underscore the importance of genomics and the need to further streamline processes that can lead to significant breakthroughs and accelerated discovery. 

But implementing and optimizing data pipelines in genomics research shouldn’t be treated as an afterthought. Working with a reputable Bio-IT provider that specializes in  the complexities of Life Sciences gives Biopharmas the best path forward and can help build and manage a sound and extensible scientific computing environment, that supports your goals and objectives, now and into the future. RCH Solutions understands the unique requirements of data processing in the context of genomics and how to implement data pipelines today while optimizing them for future developments. 

Let’s move humanity forward together — get in touch with our team today.

Sources

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5580401/

https://aws.amazon.com/blogs/publicsector/building-resilient-scalable-clinical-genomics-pipeline-aws/

https://www.databricks.com/blog/2019/03/07/simplifying-genomics-pipelines-at-scale-with-databricks-delta.html

https://www.seagate.com/blog/what-is-nas-master-ti/

https://greatexpectations.io/blog/data-tests-failed-now-what

https://www.techopedia.com/definition/8296/memory-cache

Discover the differences between the two and pave the way toward improved efficiency.

Life sciences organizations process more data than the average company—and need to do so as quickly as possible. As the world becomes more digital, technology has given rise to two popular computing models: Cloud computing and edge computing. Both of these technologies have their unique strengths and weaknesses, and understanding the difference between them is crucial for optimizing your science IT infrastructure now and into the future.  

The Basics

Cloud computing refers to a model of delivering on-demand computing resources over the internet. The Cloud allows users to access data, applications, and services from anywhere in the world without expensive hardware or software investments. 

Edge computing, on the other hand, involves processing data at or near its source instead of sending it back to a centralized location, such as a Cloud server.

Now, let’s explore the differences between Cloud vs. edge computing as they apply to Life Sciences and how to use these learnings to formulate and better inform your computing strategy.

Performance and Speed

One of the major advantages of edge computing over Cloud computing is speed. With edge computing, data processing occurs locally on devices rather than being sent to remote servers for processing. This reduces latency issues significantly, as data doesn’t have to travel back and forth between devices and Cloud servers. The time taken to analyze critical data is quicker with edge computing since it occurs at or near its source without having to wait for it to be transmitted over distances. This can be critical in applications like real-time monitoring, autonomous vehicles, or robotics.

Cloud computing, on the other hand, offers greater processing power and scalability, which can be beneficial for large-scale data analysis and processing.  By providing on-demand access to shared resources, Cloud computing offers organizations greater processing power, scalability, and flexibility to run their applications and services. Cloud platforms offer virtually unlimited storage space and processing capabilities that can be easily scaled up or down based on demand. Businesses can run complex applications with high computing requirements without having to invest in expensive hardware or infrastructure. Also worth noting is that Cloud providers offer a range of tools and services for managing data storage, security, and analytics at scale—something edge devices cannot match.

Security and Privacy

With edge computing, there could be a greater risk of data loss if damage were to occur to local servers. Data loss is naturally less of a threat with Cloud storage, but there is a greater possibility of cybersecurity threats in the Cloud. Cloud computing is also under heavier scrutiny when it comes to collecting personal identifying information, such as patient data from clinical trials.

A top priority for security in both edge and Cloud computing is to protect sensitive information from unauthorized access or disclosure. One way to do this is to implement strong encryption techniques that ensure data is only accessible by authorized users. Role-based permissions and multi-factor authentication create strict access control measures, plus they can help achieve compliance with relevant regulations, such as GDPR or HIPAA. 

Organizations should carefully consider their specific use cases and implement appropriate security and privacy controls, regardless of their elected computing strategy.

Scalability and Flexibility

Scalability and flexibility are both critical considerations in relation to an organization’s short and long-term discovery goals and objectives.

The scalability of Cloud computing has been well documented. Data capacity can easily be scaled up or down on demand, depending on business needs. Organizations can quickly scale horizontally too, as adding new devices or resources as you grow takes very little configuration and leverages existing Cloud capacities.

While edge devices are becoming increasingly powerful, they still have limitations in terms of memory and processing power. Certain applications may struggle to run efficiently on edge devices, particularly those that require complex algorithms or high-speed data transfer.

Another challenge with scaling up edge computing is ensuring efficient communication between devices. As more and more devices are added to an edge network, it becomes increasingly difficult to manage traffic flow and ensure that each device receives the information it needs in a timely manner.

Cost-Effectiveness

Both edge and Cloud computing have unique cost management challenges—and opportunities— that require different approaches.

Edge computing can be cost-effective, particularly for environments where high-speed internet is unreliable or unavailable. Edge computing cost management requires careful planning and optimization of resources, including hardware, software, device and network maintenance, and network connectivity.

In general, it’s less expensive to set up a Cloud-based environment, especially for firms with multiple offices or locations. This way, all locations can share the same resources instead of setting up individual on-premise computing environments. However, Cloud computing requires careful and effective management of infrastructure costs, such as computing, storage, and network resources to maintain speed and uptime.

Decision Time: Edge Computing or Cloud Computing for Life Sciences?

Both Cloud and edge computing offer powerful, speedy options for Life Sciences, along with the capacity to process high volumes of data without losing productivity. Edge computing may hold an advantage over the Cloud in terms of speed and power since data doesn’t have to travel far, but the cost savings that come with the Cloud can help organizations do more with their resources.

As far as choosing a solution, it’s not always a matter of one being better than the other. Rather, it’s about leveraging the best qualities of each for an optimized environment, based on your firm’s unique short- and long-term goals and objectives. So, if you’re ready to review your current computing infrastructure or prepare for a transition, and need support from a specialized team of edge and Cloud computing experts, get in touch with our team today.

About RCH Solutions

RCH Solutions supports Global, Startup, and Emerging Biotech and Pharma organizations with edge and Cloud computing solutions that uniquely align to discovery goals and business objectives. 

Sources:

https://aws.amazon.com/what-is-cloud-computing/

https://www.ibm.com/topics/cloud-computing

https://www.ibm.com/cloud/what-is-edge-computing

https://www.techtarget.com/searchdatacenter/definition/edge-computing?Offer=abMeterCharCount_var1

https://thenewstack.io/edge-computing/edge-computing-vs-cloud-computing/

Unlocking Better Outcomes in Bio-IT: 3 Strategies to Drive Value and Mitigate Risk

Like many industries, Biopharma’s success hinges on speed for drug discovery, product development, testing, and bringing innovative solutions to market. Technology sets the pace for these events, which forces organizations to lean heavily on their IT infrastructure. But developing a technological ecosystem that supports deliverables while also managing the unique risks of Biopharma isn’t simple. 

Data security, costs, regulatory compliance, communication, and the ability to handle projects of varying complexities all factor into the risk/deliverable balance. For Biopharma companies to leverage their IT infrastructure to the fullest extent, they must be able to translate these requirements and challenges to their IT partners. 

Here’s how working with Bio-IT specialists can help unlock more value from your IT strategy. 

Understanding the Unique Requirements and Deliverables of Biopharma Organizations

When we talk about requirements and deliverables in the context of Biotech projects, we’re referring to the specifications within the project scope and the tangible devices, drugs, clinical trials, documents, or research that will be produced as a result of the project.

Biotech projects involve a range of sensitive data, including intellectual property, clinical trial data, and patient data. Ensuring the security of this data is critical to protect the company’s reputation and maintain compliance. 

However, this data plays a heavy role in producing the required deliverables — sample specification, number of samples, required analyses, quality control, and risk assessments, for example. Data needs to be readily available and accessible to the right parties. 

When designing an IT infrastructure that supports deliverables and risk management, there need to be clear and measurable requirements to ensure checks and balances. 

Developing Deliverables From the Requirements

Biopharma project requirements involve a number of moving parts, including data access, stakeholders, and alignment in goals. Everyone involved in the project should know what needs to be done, have the tools and resources at their disposal, and how to access, use, manage, and organize those resources. These requirements will define the deliverables, which is why good processes and functionality should be instilled early.

When developing IT to support the movement between requirements and deliverables, IT teams need to understand what those deliverables should look like and how they’re developed from the initial project requirements. 

Biopharma companies must be able to explain requirements and deliverables to IT project managers who may not share the same level of technical knowledge. Likewise, IT must be able to adapt its technology to the Biopharma company’s needs. This is where the value of working with Bio-IT partners and project managers becomes evident. With deeper industry experience, specialists like RCH can provide more insight, ask better questions, and lead to stronger outcomes compared to a generalist consultant.

Managing Multi-Faceted Risks Against Deliverables

Knowing the deliverables and their purposes allows Biopharma companies and Bio-IT consultants to manage risks effectively. For instance, knowing what resources need to be accessed and who is involved in a project allows users to gain role-based access to sensitive data. Project timelines can also contribute to a safer data environment, ensuring that certain data is only accessed for project purposes. Restricting data access can also save on computing requirements, ensuring the right information is quickly accessible.

The way in which data is labeled, organized, and stored within IT systems also contributes to risk management. This reduces the chance of unauthorized access while also ensuring related data is grouped together to provide a complete picture for end users.

These examples are just the tip of the iceberg. The more IT consultants know about the journey from requirements to deliverables and the risks along the way, the better they can develop systems that cater to these objectives.

Best Practices for Managing Risks Against Deliverables in Biopharma IT

Given the unique complexities of managing risks and maximizing value across the deliverables spectrum, Biopharma IT departments can follow these best practices to support critical projects:

• Set realistic timelines and expectations. Not setting milestones for projects could lead to missed steps, rushed processes, and unmet objectives.
• Establish clear communication channels. Keeping all stakeholders on the same page and capturing information in a consistent manner reduces missing details and sloppy work.
• Prioritize risks and develop contingency plans. Establishing checks and balances throughout the project helps compliance officers locate gaps, allowing them to intervene in a timely manner.
• Regularly review and update deliverables and risk management strategies. Continue updating processes, best practices, and pipelines to improve and iterate.

Driving Value and Mitigating Risk in Biopharma IT

The importance of managing risks against deliverables for the success of emerging Biotech and Pharma companies cannot be overstated. Creating an IT ecosystem that caters to your specific needs requires a deep understanding of your day-to-day operations, IT’s impact on your business and customers, and legal challenges and compliance needs. Ideally, this understanding comes from first-hand expertise, given the unique nuances of this field. Working with experienced consultants in Bio-IT gives you access to specialized expertise, meaning a lot of the hard work is already done for you the moment you begin a project. Companies can move forward with confidence knowing their specialized Bio-IT partners and project managers can help them circumvent avoidable mistakes while producing an environment that works the way you do. 

Get in touch with our team for more resources and information about managing risks against deliverables for emerging Biotech and Pharma organizations and how we can put our industry expertise to work for you.

Sources:

https://www.brightwork.com/blog/project-requirements

https://www.drugpatentwatch.com/blog/top-6-issues-facing-biotechnology-industry/

Cloud technologies remain a highly cost-effective solution for computing. In the early days, these technologies signaled the end of on-premise hardware, floor space and potentially staff. Now, the focus has shifted to properly optimizing the Cloud environment to continue reaping the cost benefits. This is particularly the case for Biotech and Pharma companies that require a great deal of computing power to streamline drug discovery and research. 

Managing costs related to your computing environment is critical for emerging Biotechs and Pharmas. As more data is collected, new compliance requirements emerge, and novel drugs are discovered and move into the next stages of development, your dependence on the Cloud will grow accordingly. It’s important to consider cost optimization strategies now and keep expenses under control. Optimizing your Cloud environment with the right tools, options, and scripts will help you get the most value and allow you to grow uninhibited.

Let’s explore some top cost containment tips that emerging Biotech and Pharma startups can implement.

Ensure Right-Size Solutions by Automating and Streamlining Processes

No one wants to pay for more than they need. However, when you’re an emerging company, your computing needs are likely to evolve quickly as you grow.

This is where it helps to understand instance types and apply them to specific workloads and use cases. For example, using a smaller instance type for development and testing environments can save costs compared to using larger instances meant for production workloads.

Spot instances are spare compute capacity offered by Cloud providers at a significant discount compared to on-demand instances. You can use these instances for workloads that can tolerate interruptions or for non-critical applications to save costs.

Another option is to choose an auto-scaling approach that will allow you to automatically adjust your computing based on the workload. This reduces costs by only paying for what you use and ensuring you don’t over-provision resources.

Establish Guardrails with Trusted Technologies

Guardrails are policies or rules companies can implement to optimize their Cloud computing environment. Examples of guardrails include:

• Setting cost limits and receiving alerts when you’re close to capacity
• Implementing cost allocation tags to track Cloud spend by team, project, or other criteria
• Setting up resource expirations to avoid paying for resources you’re not using
• Implementing approval workflows for new resource requests to prevent over-provisioning
• Tracking usage metrics to predict future needs

Working with solutions like AWS Control Tower or Turbot can help you set up these cost control guardrails and stick to a budget. Ask the provider what cost control options they offer, such as budgeting tools or usage tracking. From there, you can collaborate on an effective cost optimization strategy that aligns with your business goals. Your vendor may also work with you to implement these cost management strategies, as well as check in with you periodically to see what’s working and what needs to be adjusted.

Create Custom Scripting to Go Dormant When Not in Use

Similar to electronics consuming power when plugged in but not in use, your computing environment can suck up costs and resources even during downtime. One way to mitigate usage and save on costs is to create custom scripts that automatically turn off computing resources when not in use.

To start, identify which resources can be turned off (e.g., databases, storage resources). From there, you can review usage patterns and create a schedule for turning off those resources, such as after-hours or on weekends. 

Scripting languages such as Python or Bash can create scripts that will turn off these resources according to your strategy. Once implemented, test the scripts to ensure they’re correct and will produce the expected cost savings. 

Consider Funding Support Through Vendor Programs

Many vendors, including market-leader AWS, offer special programs to help new customers get acclimated to the Cloud environment. For instance, AWS Jumpstart helps customers accelerate their Cloud adoption journey by providing assistance and best practices. Workshops, quick-start help, and professional services are part of the program. They also offer funding and credits to help customers start using AWS in the form of free usage tiers, grants for nonprofit organizations, and funding for startups.

Other vendors may offer similar programs. It never hurts to ask what’s available.

Leverage Partners with Strong Vendor Relationships

Fast-tracking toward the Cloud starts with great relationships. Working with an established IT company like RCH that specializes in Biotechs and Pharmas and also has established relationships with Cloud providers, including as a Select Consulting Partner with AWS, as well as associated technologies gives you the best of both worlds. 

Let’s Build Your Optimal IT Environment Together

Cloud cost optimization strategies shouldn’t be treated as an afterthought or put off until you start growing.

It’s best practice to instill cost control guardrails now and think about how you can scale your Cloud computing in the future so that cost doesn’t become a growth inhibitor.

In an industry that moves at the speed of technology, RCH Solutions brings a wealth of specialized expertise to help you thrive. We apply our experience in working with BioPharma companies and startups to ensure your budget, computing capacity, and business goals align. 

We invite you to schedule a one-hour complimentary consultation with SA on Demand, available through the AWS Marketplace, to learn more about cost optimization strategies in the Cloud and how they support your business. Together, we can develop a Cloud computing environment that balances the best worlds of budget and breakthroughs.

Sources:

https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instance-types.html

The Importance of Value in an Evolving Business Climate

As signs of Spring start to boom around us, I can’t help but think of the exciting opportunities ahead especially after coming through a gloomy business cycle for the past several quarters.

Those opportunities only become recognized if we are willing to confront the sometimes brutal realities of the current business climate. 

And that reality is this: Businesses [across all industries] are looking closer at their budgets and questing spends. They’re asking hard questions and examining new projects and, quite frankly legacy partners, with a heightened level of scrutiny. 

While the accompanying uncertainty that looms as a result can certainly keep business leaders awake at night, I can’t help but think, for some very specific reasons—it’s about time.   

While cost cutting alone is sometimes a necessary reality, the larger thrust is really about driving more value, rather than simply lowering expenses. After all, work still needs to get done. 

Having led a business built on driving value for most of my career, here’s what “value” questions sound like, according to direct conversations I’ve had with many of our customers:

• Are your customers on the business side pleased with the outcomes? 
• Can you demonstrate a return to management?
• Has paying more yielded better results, other than convenience? 
• Has paying less yielded any results, other than savings?
• What is the return you are getting for the investment you’ve made?
• Are you reaching your goals? 

Let me be more specific, without naming names of course. I’m referring to the large professional services and consulting companies that work with many Biotech and large Pharma companies for strategic and then operational services.  Ok, let’s call them company A and company D. Then there are also the large multinational outsourcing companies that offer low-cost/low-value staff augmentation.  We will call them, well, there are too many to list. 

Please tell me the last time you said: “Wow, company A or company D did such a great job!  They finished on-time, under budget and actually did what they said they were going to do! Let’s give them more projects (and overspend more money next time)!”  

You can sense my sarcasm, of course. But the truth is, many providers in this space are doing Biotech and Pharma companies a disservice in the way they scope, execute, and hold themselves accountable for the outcomes of services that are mission critical to companies in the business of advancing science. In fact, a large pharma customer of ours recently shared, and I quote, “We only use D because A is much worse.”

Take some time to let that sink in. 

On the low-cost, outsourced side, we see much of the same. Poor service.  Inconsistency and turnover within the support team. Lack of accountability. And the inability (or worse, an unwillingness) to evolve and learn more about business in favor of following a dated and static runbook.   

I find myself asking, how much lower can the bar go?  And further, why do companies continue with these vendors for any critical scientific computing projects?

The Way Back to Better

I’ve spent a lot of time thinking about why companies continue a relationship with partners that either overcharge or underdeliver (or sadly, both). I’ve asked our customers as well. And what I’ve concluded is that it’s about mitigating risk—or rather, the perception of mitigating risk.

But the question then becomes, what happens if you stay with these providers?  Why would you expect the outcome to be different?  In fact, I wrote a piece last year on the inherent risk of doing what you’ve always done, and expecting different outcomes.  You know what they call that …. 

Of course, I have an answer.  My answer and solution is based not on what we believe at RCH but what our customers tell us and what they have done. 

Our customers are challenged with the market dynamics of having to do more with less—and they’re looking for greater value out of the support engaged to support them.

In fact, several of our large enterprise customers recently cut their spend on the large PS/Consulting companies and transitioned or are in discussions to transition those projects to RCH as their partner of choice.  Why? Because the bar has been elevated and these customers, now more than ever, recognize who has the skills, service model and specialization to rise to the occasion.  

For those that have already pulled the trigger, we continue to earn their approval and trust through results that speak for themselves.  

And for those who haven’t yet made that wise call?  Well, we’re here, we’re proven and we’re ready to add value where the others have not, whenever you’re ready. 

Building an Organization that Talent will Flock to and Thrive Within

As a business owner or leader, you quickly learn that employees are the backbone of your company. And this has never been more true with the exceptional talent we have and continually acquire at RCH. 

But in today’s competitive and challenging job market, it’s becoming increasingly difficult for many businesses to attract and retain top talent —or “A” players, as we often say.  Workers across all industries are voluntarily leaving their jobs due to burnout, a desire for a career change, and/or pursuing a more ideal work-life balance by going out on their own.

Call it the “Great Resignation”, a newfound  “Gig Economy” or something else, it’s more critical now than ever that your employee acquisition and retention strategy is a key focus, if it’s not already.

And Life Science organizations are not immune to this trend. We are all just experiencing its effects in different ways, given the unique skill sets and demands required to be a competitive leader in this space. I’m thankful to say, however, RCH has fared better than many. Here’s why. 

Our culture has been, and always will be, built on a people-first mentality. 

While attracting and retaining the right Bio-IT talent can be difficult, the flexible and balanced work structure we’ve followed since our company’s inception, combined with our incredibly high standards for our people and our outcomes have helped us mitigate these typical challenges.

And candidly, they set us apart and make RCH an employer and partner of choice.

In my experience, an organization’s ability to attract—and more importantly retain—the best specialists, goes hand-in-hand with the execution of truly unmatched scientific computing outcomes. 

Some of the reasons I think we’ve had success in this area, in no particular order, include: 

1. Our EE Training & Development Plan

At RCH, continuous learning and improvement is one of our core values. We invest in the success and expertise of our team and actively encourage and enable them to build their skills in meaningful ways—even if that means carving out work time to do so.

We aim to help improve employees’ existing competencies and cross functional skills while simultaneously developing newer ones to support the individual’s professional goals. We have unique and individualized training programs, relevant mentorship opportunities, and other career development and advancement strategies to support our team members.

2. Our Continuous Recruitment & People-First Approach

Our rolling recruitment strategy continuously accepts and reviews applications for job openings throughout the year, rather than waiting for a specific hiring season, role or deadline. With continuous recruitment, we build a pool of highly qualified, top talent candidates that will complement and/or add to the skills that exist within our deep bench of professionals, and we can effectively and quickly fill any vacancies with the right people—a key focus of ours.

Continuous recruitment also helps us plan for future workforce needs and stay competitive by having target candidates already identified and prequalified for future roles or project needs that may arise.

3. Our Focus on Hiring and Retaining ‘A’ players

In my career, I’ve seen far too many organizations with a Quantity over Quality strategy, simply throwing more people at problems. But a Quality over Quantity approach will win every time. The difference? The former will experience slow adoption which can stall outcomes with major impacts to short- and long-term objectives. The latter propels outcomes out of the gates, circumventing crippling mistakes along the way. For this reason and more, I’m a big believer in attracting and retaining only “A” talent.

The best talent and the top performers (quality) will always outshine and out deliver a bunch of average ones (quantity). Which is why acquiring and retaining only top talent that can effectively address specialized challenges should be your key focus if it isn’t already.

4. Our Access to Cutting-Edge Technology & Encouraging Creativity and Innovation

Bio-IT professionals thrive on innovation and new technology, so we always aim to provide ours with access to the latest tools and software, and encourage them to experiment with new technologies that could improve processes and workflows for our customers. We foster an environment that truly encourages creativity and innovation and provide our team members with the freedom to explore new ideas and take risks, while also setting clear goals and objectives to ensure alignment with organizational priorities.

This approach benefits both our customers and our team members by enabling the possibility for further accelerated breakthroughs, and satisfying their innate desire to leverage innovation to advance science and customer outcomes.

5. Our Core Values & Culture

Employees want to work for a company that values their contributions and creates an empowering and aspirational work environment. This can include things like recognizing employee achievements, providing opportunities for growth and development, and creating a sense of community and belonging within the workplace.

Our core values and culture do that and more, and unwaveringly represent the threads that weave together the fabric of our culture. And hiring the right people who share these core values, and building a culture around a team that embraces the RCH Solutions DNA is paramount. And more critical than ever.

6. Adhering to Our Unique Managed Services Model 

Unlike static workforce augmentation services provided by big-box consultants, our dynamic, science-centered Sci-T Managed Services model delivers specialized consulting and execution tailored to meet the unique Bio-IT needs of research and development teams, on-demand. This model gives our team diversity in their work and creates opportunities to take on new challenges and projects that not only excite them, but keep their skills and their day-to-day experiences dynamic.

It’s rewarding for our team, both personally and professionally, and from a learning and development perspective, to have the exposure to a wide range of customers and environments.

An Unwavering Commitment to Our People, Culture & Mission

Acquiring, retaining and empowering Bio-IT teams requires a commitment to creating a supportive and inclusive work environment, providing opportunities for growth and development and recognizing and rewarding accomplishments along the way.

While challenging at times, organizations that unwaveringly commit to their people, culture and mission will be able to attract and retain “A” talent, and foster an empowered work environment that will naturally drive innovation, advance the organization’s mission and propel customer outcomes.

Click below to get in touch with our team and learn more about our industry-leading Bio-IT team, our specialized approach and what sets us apart from other Bio-IT partners. 

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