More so than with other industries, developing an effective data governance strategy is critical for Pharma and biotech companies. It’s imperative that data is accurate, complete, reusable, and secure.

Without proper governance,

• Compliance could be at risk with agency regulations.
• Data quality, accuracy, user access, reuse, and reliability may be jeopardized, and this could impact the organization’s data-driven decisions across drug development, clinical operations, and commercial. Organizations need to ensure that high data quality and non-duplicative data exists throughout the complete R&D lifecycle of the data, and data controls are implemented that support business objectives.

Several Key Steps to a Successful Data Governance Strategy

A successful Data Governance strategy involves change and requires understanding how R&D users define data, access, analyze, and share data along with policies and procedures covering ownership, accountability, and security. With data governance in place, data ceases to be a byproduct of your applications and instead becomes a crucial corporate asset.

Given Astrix’s experience in assisting clients with their data governance challenges, we highlight in this two-part series several of the key elements for a successful data governance strategy and some important considerations as well as key technologies.

There is no one-size-fits-all approach to data governance. Each organization will have their own unique requirements based on their size, organizational structure, products, and other factors. Ensuring a successful program will require experienced internal and external resources. Although each organization is different, there are some key elements that are critical to success, and they are:

• Define Data Governance goals and objectives: The first step in developing a data governance strategy is to define the goals and objectives of the program. This involves identifying the key stakeholders, defining the scope of the program, and establishing the desired outcomes.

Questions to ask

• • • What does the organization hope to achieve with data governance?
    • What are the main drivers for implementing data governance in your organization?
    • Are the goals aligned with the overall business strategy and the needs of the stakeholders.

• Establish Data Governance policies and procedures: Governance policies enable everyone in the organization to follow the same standards and procedures for access, use, security, standards, and quality. These policies should be aligned with regulatory requirements and industry best practices.

The policies typically cover:

• • Policy purpose: The statement of purpose describes the reason the policy exists and how it supports the company’s objectives.
  • Policy scope: The scope explains who is affected by the data governance policy, organized in a measurable, timely, compliant, and repeatable manner to realize full value of enterprise data assets.
  • Policy rules: This outlines the rules guiding data usage and access, as well as privacy, security, compliance/integrity and integrations. It enables R&D data governance as an integrated part of an enterprise-level Framework.
  • Data governance structure: This entails the roles and responsibilities of individuals and groups, which ranges from the data governance body-council enterprise teams, data owners, data stewards and data users (subject matter experts). It reflects the strong Business and IT involvement, with the ultimate accountability for Governance lying with the business owners. Governance structure should be repeatable and adaptable to other domains and attributes on the basis of sustainability after implementation.
  • Definitions: A glossary of common terminology referenced in the policy i.e., roles, functional areas, data sources, metrics, standards (FAIR), key Data Governance terms, i.e., Master Data.
  • Review process: Describes how the data governance policy is established, reviewed, and updated. As data volumes grow, new data streams emerge, and new access points emerge, you’ll need a policy for periodic reviews of your data governance structure – essentially ‘governance of the data governance process’.
  • Resources: Any related documents, other policies (access, security etc.), training, or regulations (GxP) that are referenced.

Key Factors to Consider in Ensuring a Successful Data Governance Strategy

•  Define data ownership and accountability: Pharma and biotech companies generate and manage large volumes of data, and it is essential to establish clear ownership and accountability for the data. This involves defining the roles and responsibilities of data stewards, data owners, and data custodians.
• Establish data quality standards: Data quality is critical for the effectiveness of data governance, and it is essential to establish data quality standards that define the criteria for data accuracy, completeness, and consistency. These standards should be regularly monitored and enforced.
• Develop a data classification framework: Pharma and biotech companies handle sensitive and confidential data, and it is essential to develop a data classification framework that categorizes data based on its sensitivity and criticality. This framework can help to ensure that the appropriate security and access controls are applied to different types of data.
• Implement data security and privacy controls: Data security and privacy are critical for Pharma and biotech companies, and it is essential to implement robust security and privacy controls to protect data from unauthorized access, theft, or loss. This includes implementing access controls, encryption, and data masking techniques.
• Develop a data governance roadmap: It is essential to develop a data governance roadmap that outlines the steps to be taken to implement the data governance strategy. This roadmap should identify the key milestones, timelines, and resource requirements for the program.
• Establishing cross-functional data governance teams: Data governance is not just the responsibility of IT departments; it requires cross-functional collaboration across various departments within a Pharma company. Establishing cross-functional data governance teams can help ensure that all stakeholders are involved in the data management process.
• Adopting a risk-based approach to data governance: Pharma companies can adopt a risk-based approach to data governance, where they focus their efforts on managing the data that poses the greatest risk to the organization. This can help them prioritize their resources and ensure that they are focusing their efforts where they are most needed.

Key Technologies that Assist in Building a Successful Data Governance Strategy

• Implementing advanced data analytics and machine learning: Pharma companies can utilize advanced analytics and machine learning to analyze large volumes of data more effectively. This can help them identify patterns and insights that may be difficult to detect using traditional methods.
• Leveraging cloud computing: Cloud computing can help Pharma companies store and access data more securely and efficiently. It also allows them to scale their data storage and processing capabilities more easily as their needs evolve.
• Using blockchain technology: Blockchain technology can help Pharma companies improve the security and transparency of their data management processes. It can provide an immutable record of all data transactions, making it more difficult for unauthorized users to modify or corrupt the data.

Summary

It is critically important when building out a data governance strategy and plan to have the right experienced internal staff and external organizations involved to assist in the process. Formulating the appropriate approach along with the proper processes and technology is imperative.

Astrix’s team of professionals in our Strategic Consulting Service Practice have worked with many of the top life science organizations and leaders to assist them with respect to their business needs in these areas. As a technology-agnostic partner, without a preconceived preference for a specific supplier or product, we work closely with your team to ensure solutions are reviewed and incorporated into your business so that you succeed in realizing your vision and achieving your organizational goals.

In this blog we discussed.

• Several key elements for a successful data governance strategy
• Other important considerations in building a successful Data Governance Strategy
• Technology considerations that can assist in building a Data Governance Strategy

Other Related Material

Case Study: Data Governance – Disorder to Alignment on a Common Framework

Essential Components of Data Governance – Part 1

Essential Components of Data Governance – Part 2

DAMA DMBOK and Data Governance

About Astrix

Astrix is the unrivaled market-leader in creating & delivering innovative strategies, solutions, and people to the life science community.  Through world class people, process, and technology, Astrix works with clients to fundamentally improve business & scientific outcomes and the quality of life everywhere. Founded by scientists to solve the unique challenges of the life science community, Astrix offers a growing array of strategic, technical, and staffing services designed to deliver value to clients across their organizations.

The post How to Develop an Effective Data Governance Strategy in the Pharma and Biotech Industry – Part 1 appeared first on Astrix.

Managing a data migration project typically involves both manual processes (e.g., developing custom software, manual data entry, and file relocation) and automated programmable tools. Given the time and labor involved in manual data migration, it is essential to leverage automated tools to their fullest.

• Migrate data early in the implementation. When migrating data to the new system, static legacy data (and sometimes dynamic data as well) must be extracted, translated, and loaded into a new location.
• Before the migration begins, examine the data structure of both the source and target. The data migration plan should define which data will be migrated, why, and in what manner, based on the needs of all the different stakeholders involved.
• Validate data both during and after This step is especially important for life sciences data given its significance in research, development, and manufacturing. A data migration plan should detail the ways in which the data transformations performed during the migration (prior to loading them into the final system) will be validated to ensure correctness. The plan should also detail the methods used to verify that the data is correct after the final loading into the new system.
• Choose a commercial ETL tool. In general, the best approach maximizes use of automated programmable tools (ETL) for data migration, supplemented by manual methods when needed. While there are a variety of commercial and open-source ETL tools available, in our experience choosing a commercial tool is usually the better option because they are less buggy.
• Back up and archive all raw data acquired in the extraction phase. A good ETL tool has a staging area that enables storage of an intermediate version of the extracted data. This enables validation activities to occur before the transformation stage, to confirm that the extracted data has the expected values. Any data failing validation is rejected entirely or in part and held back for analysis to discover where the problem occurred. A staging area also avoids the necessity of re-extraction in later phases of the migration.
• Cleanse and validate data in the transformation While some data (AKA pass-through data) may not require transformation and can be loaded directly into the target system, most data will need some form of pre-load transformation. A good ETL tool enables complex processes and extends a tool library to add custom functions.
• Ensure your ETL tool will process multiple terabytes per hour or one gigabyte per second. This is essential since the loading phase is usually the bottleneck with high-volume migrations. If you need to move high volumes of data through your ETL tool, it should be using servers with multiple CPUs, hard drives, gigabit-network connections, and plenty of memory.
• An experienced subject matter expert, leveraging a good ETL tool, will reduce risk and expense.. Regardless of the ETL tool utilized, facilitating a successful data migration requires a thorough understanding of data structures and definitions, as well as how the data will be used. An experienced migration manager can reduce, or eliminate, the need for developers to fix problems or build patches later in the process.

 About Astrix

Astrix is the unrivaled market-leader in creating & delivering innovative strategies, solutions, and people to the life science community.  Through world class people, process, and technology, Astrix works with clients to fundamentally improve business & scientific outcomes and the quality of life everywhere. Founded by scientists to solve the unique challenges of the life science community, Astrix offers a growing array of strategic, technical, and staffing services designed to deliver value to clients across their organizations.

The post Best Practices Advisory: Succeeding in Data Migration with ETL Tools In Life Sciences appeared first on Astrix.

Astrix recently hosted a roundtable discussion for senior executives of several leading pharmaceutical and biotech companies. In this second of two articles, we share additional perspectives and practices for enhancing their organizations’ use of critical information via the production of data fabric.

The adage, “garbage in, garbage out” is a fundamental concern in the planning and organization of an organization’s data fabric but it is no more crucial than its operational design. This is an essential yet sometimes overlooked issue that can render a design inadequate in terms of efficient and reliable data extraction. And, when this occurs, researchers and regulatory authorities will be unhappy.

Business users want data accessible in real-time, for early looks, exploration, insights, etc., however, when organizations have legacy infrastructure such as multiple data warehouses and/or data lakes, it can pose a considerable challenge to overall data management and architecture strategies.  This can be an especially acute problem when data is derived from external sources.

Here are five of the key insights on which our expert client panel agreed:

1. To prevent data’s physical location from restricting or impeding its findability, the architectural design needs to focus on bringing all the data generated into a single data lake with which to build an intelligent fabric, data backbone, or virtual data layer.
2. To facilitate teams in the construction of infrastructures capable of focusing on users’ data needs and providing rapid access (while mapping and managing data patterns behind the scenes), it is essential to recognize and enable different patterns of data ingestion and use.
3. To build an efficient infrastructure, organizations are well advised to bear in mind that the understanding and value—and ways in which data is used—evolve. There are use cases where data and data products are well-defined and reusable but, in other cases, data use may yet be fully realized.
4. To enable systems to scale in the future, it is important to fully consider the technology solutions and their ability to support performant data use at high volumes. This is especially relevant if the organization is contemplating a search for (or acquisition of) a large volume of experimental instrumentation data, or other data science use cases, which may rely on large data sets and compute-intensive resources.
5. To help support tracking data usage and provide the entryway to data exploration, a Data Catalog is a critical data architecture element. Effective data catalogs are used across organizations to help data governance to succeed because of their ability to ensure that data meets specific quality criteria for inclusion.

Capturing metrics on data usage trends informs plans and decisions on access and availability.

• Having metrics on data use and reuse (active and augmented metadata) can aid an organization’s understanding of both how it’s being leveraged and how often.
• The insights and perspectives on what data is most important to users enable IT teams to prioritize their efforts toward ensuring specific data’s quality, access, and availability throughout the entire enterprise.
• When selecting, or working with, third-party vendor partners it is crucial to confirm that:
  • Their technological capabilities are sufficient to meet user needs, and
  • They can align with the real-time data strategy.

Summary

It is critically important, when looking to incorporate new emerging technologies like Data Fabric into your business, that you ensure you have the right external organizations involved who can assist you. Formulating the appropriate strategy along with the proper processes, and technology is imperative. Astrix’s team of professionals have worked with many of the top life science organizations to assist them with respect to their business needs in these areas. As a partner without a preconceived preference for a specific technology, we work closely with your team to ensure solutions are reviewed and incorporated into your business that will help you succeed.

About Astrix

Astrix is the unrivaled market-leader in creating & delivering innovative strategies, solutions, and people to the life science community.  Through world class people, process, and technology, Astrix works with clients to fundamentally improve business & scientific outcomes and the quality of life everywhere. Founded by scientists to solve the unique challenges of the life science community, Astrix offers a growing array of strategic, technical, and staffing services designed to deliver value to clients across their organizations.

The post Roundtable Discussion – Four Key Insights to Enable Data Fabric to Achieve Business Objectives – Part 2 of 2 appeared first on Astrix.

Astrix recently hosted a roundtable discussion for senior executives of several leading pharmaceutical and biotech companies. In this first of two articles, we share their perspectives and practices for enhancing their organizations’ use of critical information via the production of data fabric.

One of the first points of agreement during the discussion was that to maximize the outcomes of data analysis, enterprise leaders must collaborate with their internal teams in determining the priority of what data and metadata are needed.  Our panel members agreed that this requires both ownership and leadership from a business perspective as well as support and resources that get the data right in the first place.

This is easier said than done, so let’s highlight four actionable insights from our panelists:

1. Upfront ownership is very important, so data stewards play a crucial role. With ownership comes responsibility for data quality, which is key for business data stewards whose roles are critical for maintaining the quality of the data that the business organization seeks to leverage. Ensure that your data quality stewards are empowered and authorized to monitor and act to preserve data quality.
2. IT and Business groups both need to support new ways of managing data. The journey starts with data-centric business behavior and thought leadership. This needs to be developed intentionally, with the objective of structuring both clear goals and rules.  Once these new goals and rules are finalized, they must be distributed to everyone involved in data production, collection, and storage management.
3. Assessment and tracking are twin engines of data quality. Since data quality is essential, processes must be completely in place to ensure accurate attribution and effective metadata tracking.
4. Enhancing metadata isn’t necessarily a technology stack or capability problem, but challenges to implementing a comprehensive data strategy and reliable data quality system must be fully addressed. To accomplish this, an enterprise needs the technological capability to support metadata management—which may require system augmentation.

Too often, the executive panel members agreed, the emphasis appears to be on loading data into systems, rather than properly focusing on:

• How the data will be used,
• Who the target users are—or will be,
• Who will steward the data, and
• What data products are destined to become.

Shifting the focus to address strategies for how data will be used and governed, after loading, will better support the organization’s needs for data use and reuse.

Summary

It is critically important, when looking to incorporate new emerging technologies like Data Fabric into your business, that you ensure you have the right external organizations involved who can assist you. Formulating the appropriate strategy along with the proper processes, and technology is imperative. Astrix’s team of professionals have worked with many of the top life science organizations to assist them with respect to their business needs in these areas. As a partner without a preconceived preference for a specific technology, we work closely with your team to ensure solutions are reviewed and incorporated into your business that will help you succeed.

About Astrix

Astrix is the unrivaled market-leader in creating & delivering innovative strategies, solutions, and people to the life science community.  Through world class people, process, and technology, Astrix works with clients to fundamentally improve business & scientific outcomes and the quality of life everywhere. Founded by scientists to solve the unique challenges of the life science community, Astrix offers a growing array of strategic, technical, and staffing services designed to deliver value to clients across their organizations.

The post Roundtable Discussion – Four Key Insights to Enable Data Fabric to Achieve Business Objectives – Part 1 of 2 appeared first on Astrix.

High-performance computing is a fundamental enabler for Big Data, AI, and Advanced Simulations and non-classical computing architectures use parallelism, in-memory processing, and other mechanisms to dramatically increase computing throughput potential.

Non-Classical computing platforms, such as quantum computing or large-scale (i.e., exascale) computing platforms, are making it possible to run previously impossible algorithms to compute in a reasonable time. The development of processes and architectures for highly specialized purposes, use of Graphical Processing Units or GPUs (formerly limited to video programming), and Grid-based cloud computing to enable scalable computing through elasticity enable businesses to run algorithms currently not solvable on digital computers today. As interest and investment continue to grow and these technologies mature, the potential for dramatic increases and scalability in processing power opens new modeling, simulation, and permutation calculations that can help inform Life Sciences R&D.

A Few Industry Trends Associated with Non-classical Computing

• Enabling on-demand compute environments, fully configured for analytics and data sciences use cases including ready access to the data required
• Establishment of statistical and/or data sciences compute environments that support model and code sharing, auto-code technology, and “electronic notebook” capabilities linking code versions to data set versions, enabling sharing, and offering the ability to revisit algorithms with new data sets and verify results on decision data sets with new algorithms
• Drug research and development areas are prime candidates for use of Quantum Computing; potential use cases include hit generation and identification, lead generation and optimization, predictive analysis, and simulations for protein folding, ADME, dosing and solubility prediction, among others
• Similar to the pharmaceutical industry, the chemical industry is also benefiting from development of chemical molecules

Implications for Life Sciences Organizations

High performance computing requires specialized computing platforms, environments, and services to bring them to scale, including collaboration conditions. Life Sciences companies can facilitate the orchestration and elasticity for rapid access to need-specific computing power on demand, coupled with new integrated in silico working environments and services. A strategy based on archetypes will accelerate value in the short term, enabling maximized attention to ensure mid- and long-term solutions.

The following are considerations when looking to leverage non-classical computing:

Data Sciences Ecosystem

• Algorithmically driven science, clinical and operations will require new collaborative environments that marry algorithm code, data, and results. Companies must conceive integrated fabrics using technologies that overcome cumbersome silos associated with today’s systems

Computing Infrastructure

• There is a need to enable an evolutionary ecosystem of different computing resources suitable to the breadth needed in life sciences. There are clear compute strategies needed for each of the advanced technologies such as big data, AI, advanced simulation and blockchain.
• Preferred partnerships are also needed to cover the breadth of R&D needs, from contract service providers to academic and start up participants who will lead the run up to exascale and quantum computing

Intelligent Orchestration 

• In order to understand patterns and workflows in scientific, exploratory, translational, and statistical computing, you need to provide an ecosystem of intelligent automated solutions leveraging virtualized services (marketplace) and distributed computing architectures to optimize computational resources wherever data may exist. These solutions need to be on-demand and effectively delivered to end user communities to maximize their usage.

The Importance of the Right Strategy, Processes, and Technology

It is critically important, when looking to incorporate new emerging technologies like non-classical computing into the business, that you ensure you have the right external organizations involved who can assist you. Formulating the appropriate strategy along with the proper processes, and technology is imperative. Astrix’s team of professionals have worked with many of the top life science organizations to assist them with respect to their business needs in these areas. As a partner without a preconceived preference for a specific technology, we work closely with your team to ensure solutions are reviewed and incorporated into your business that will help you succeed.

Why it Matters to You

As the demands of Life Science R&D organizations grow to find new solutions in healthcare, the need to utilize new emerging technologies like non-classical computing will also rise.

In this blog we discussed:

• What non-classical computing is.
• How it impacts the Life Sciences industry.
• Some considerations focused on non-classical computing.

About Astrix

Astrix is the unrivaled market-leader in creating & delivering innovative strategies, solutions, and people to the life science community.  Through world class people, process, and technology, Astrix works with clients to fundamentally improve business & scientific outcomes and the quality of life everywhere. Founded by scientists to solve the unique challenges of the life science community, Astrix offers a growing array of strategic, technical, and staffing services designed to deliver value to clients across their organizations.

The post Non-Classical Computing is Impacting the Digital Transformation of Life Sciences appeared first on Astrix.

From the switch to paperless digital lab technologies such as electronic laboratory notebooks (ELN) and the integration of instruments to connect disparate systems, the path towards digital transformation is complex and the endpoints can change based on an organization’s requirements. The road to fully automated experiments, cloud-based data storage, and seamless connectivity between collaborators and data systems is long and requires a methodical approach to managing change in the laboratory.

The steps toward achieving a Lab of the Future (LoF) are numerous and involve changes at both the organizational and individual levels that can take many months or even years to complete. But the investment of both time and resources can deliver substantial gains leading to impactful innovation.

Laboratory informatics software services provider Astrix’s annual market research report has delivered a wide array of statistics that help to shed some light on current digital transformation activities. The report also highlights what it is laboratory managers are looking for when adopting digital technologies.

The data is based on a survey conducted by Astrix in March 2022. The survey was completed by laboratory professionals from a wide cross-section of science-based industries including professionals from at least 20 sectors, representing business, government, and academic laboratories.

Through the process of evaluating survey responses, Astrix has established that the movement towards the LoF is evolving in a non-linear path. The company has determined several steps that are key to the digital transformation journey; Awareness, Interest, Consideration, Investigation, Information Gathering (Shopping and Selection), Acquisition (Purchase), Installation, Training, Adoption/ Conversion, Implementation, and Prevalence. These stages describe the process of modernizing an operation and, irrespective of size, appear to be common to laboratories across the spectrum of type, function, and setting represented in this snapshot.

The report notes that early adopters of LoF technologies, sometimes driven by economic necessity, are predominantly enjoying real and positive impacts on their operations thanks to continuous technological innovation.

However, even professionals in the most sophisticated and well-funded laboratories are aware that progress is needed to fully capitalize on the breadth of capabilities afforded by tools designed to deliver on the promise of a LoF.

In an aim to streamline the process of digital transformation, laboratory informatics providers are developing integration software and making it easier for scientists to connect their instruments and data systems. However, there are still differences in these platforms and so the right choice for one organization may not be the same for others.

Integration of digital systems

In a blog post written by Matt Hazlewood, Senior Director, Global Chromatography Data Systems at Thermo Fisher Scientific, he noted the idea of eliminating data silos through seamless integration data standardization.

‘Data standardization has long been a challenge for pharmaceutical companies, resulting from a historic lack of standardized data formats used across the informatics industry,’ states Hazlewood.

Astrix survey results have helped to quantify some of the drivers that are most cited by the respondents. They found that organizational priorities have changed, or requirements increased, for achieving or maintaining compliance.

For example, data integrity was highlighted by 63% of respondents. This reflects the need to initially obtain and record error-free, reliable data for use in the automation and digitization of key functional and reporting processes.

Good Laboratory Practice (GLP) guidelines (44%), Regulatory Requirements (38%), and Good Manufacturing Practice (GMP) guidelines (35%) represent the second tranche of organizational priorities – by prevalence – that survey participants report having changed. Astrix also found that of its respondents actively engaged in LoF initiatives – which accounts for 76% of total survey respondents – IoT and Artificial Intelligence (AI) or Machine Learning (ML) were the most prevalent investment activities. Smart technology is the most popular area of investment among 62% of respondents. Although fewer survey participants report investment in AI/ML (48%), it represents a rapidly growing field among lab professionals.

The post Astrix Article in Scientific Computing World Managing Digital Transformation appeared first on Astrix.

Composable Architecture shifts the focus from monolithic application suites and hosting to use of best of breed solutions as part of a unified platform-ecosystem.  By leveraging business architecture, technologies, and agile thinking, organizations can speed discovery, gain greater agility, and enable flexibility to handle future changes. Many organizations find that the fundamental components to enable Composable Architecture are already in place.

Composable architecture offers a fresh perspective on how to align the business with technology by utilizing components that work together, thus improving the way organizations can use existing competencies and making it easier to build complex fit for function solutions without traditional point to point integrations and coding. By breaking down traditional organizational silos and introducing API-centric models, developers are able to more efficiently access data and drive workflows cross-functionally and drive digital disruption. ¹ 

Benefit to Life Sciences

The value of composable architecture is that Life Sciences R&D needs to deliver innovation and adapt quickly to industry needs. This requires scalable technologies which can be implemented efficiently, agilely and in less time. Composable architecture fits the bill and makes it feasible to deliver technologies which can be assembled quickly, reassembled if needed and extended in the future.

Developing a platform centric composable architecture approach for capabilities across R&D:

• Reduces redundant capabilities
• Facilitates and accelerates AI adaptation
• Enables faster digital transformation efforts
• Unifies the user experience
• Supports workflow orchestration

Considerations when implementing Composable Architecture

Here are several key considerations for organizations moving towards Composable Architecture:

Composable Architecture and Organization Readiness

• Establish powerful integration platform (API management) strategy to enable democratized, self-service integration and reduce point to point integrations
• Enable business-user focused applications and modernize existing solutions (monolithic applications). to adapt support and expand a modular architecture ecosystem, expand modular architecture around the same,
• Maintain traditional or SaaS solutions when they fulfill needs easily, but combine with other solutions to more fully meet the needs of the business and support other capabilities more seamlessly.

Partnerships and Vendor Management

A few key considerations relative to internal and external collaboration and partnering:

• Support deeper collaboration between internal stakeholders (IT and business) as well as external partners
• Establish a sourcing strategy around technology partners/vendors who have capabilities around modular components
• Investigate vendors offering packaged business capabilities, which can fulfill current and future needs

It is critically important to work with partners who can assist your organization to understand the value of composable architecture and other technologies that can be leveraged within your organization. Astrix’s team of professionals have worked with many of the top life science organizations to assist them with respect to their strategies and approaches relative to business processes and technologies. As a partner without a preconceived preference for a specific technology, we ensure solutions are reviewed and incorporated into your business to help you succeed.

Why It Matters to You

The Life Sciences industry needs to quickly adapt to business needs while also delivering innovation. To accomplish this, it requires scalable technologies which can be implemented efficiently, nimbly, and in less time than current methods. This can be accomplished by leveraging Composable Architecture which makes it feasible to deliver technologies that can be constructed quickly and can extend into the future.

In this blog we discussed:

• What Composable Architecture is.
• The benefits to the Life Sciences Industry.
• Considerations when implementing a Composable Architecture.

About Astrix

Astrix is the unrivaled market-leader in creating & delivering innovative strategies, solutions, and people to the life science community.  Through world class people, process, and technology, Astrix works with clients to fundamentally improve business & scientific outcomes and the quality of life everywhere. Founded by scientists to solve the unique challenges of the life science community, Astrix offers a growing array of strategic, technical, and staffing services designed to deliver value to clients across their organizations.

The post Composable Architecture’s Impact on a Digital Transformation of Life Sciences appeared first on Astrix.

With greater emphasis on patient centricity, it’s increasingly important to engage with them in the digital landscape. Finding ways to meet patients where they are to better engage with and educated them through their entire care journey.

Is digital data capture tied to patient-centric approaches for the design and execution of clinical trials. A DPE platform is an intuitive digital application that can be utilized by healthcare organizations and patients as a means to monitor pre- and post-operative care. This platform should be incorporated into the patient engagement strategy.¹

Digital patient engagement brings together multiple technologies (remote data capture via devices, use of eSource, etc.). This supports the emphasis on patient-centric trial approaches to enroll, educate, and to more quickly analyze and use results in trials across the ecosystem.

Benefits of Digital Patient Engagement

There are several benefits of leveraging Digital Patient Engagement.

• For the patient, this patient-centric approach to clinical trial design and conduct addresses patient needs that go unmet in traditional trial methods. Technology approaches using telehealth, total experience (TX), digital communications and engagement help to support the entire patient experience. These technologies are being used to support patient reported outcomes, digital biomarkers and endpoints, remote patient monitoring and testing, patient education, and electronic informed consent
• For the Sponsor the benefits include:
  • Accelerating clinical development
  • Enabling more representative patient access, gathering data directly from patients that would not otherwise be captured
  • Developing a more robust evidence package than traditional trials

Industry Trends

• Decentralized Trial (DCT) approaches are rapidly gaining traction. This is partially driven by COVID, making patient-centric and site-centric approaches, including direct to patient supplies, remote assessments, telemedicine, at-home sample collection, point of care assays and other means of collecting patient data faster.
• Digital patient engagement is increasing. This is happening through linking to social media and patient communities for improved demographics and enrollment, to the use of telehealth to connect with and educate patients and physicians, to supporting tracking, monitoring, to receiving data sooner and more reliable data collection and ingestion.
• Move towards rapid collection of more granular and rich data. Leveraging digital biomarker and endpoints for faster medical and safety assessments, adaptive trial design, and exploratory and translational research

Considerations with DPE

The ability to collect patient information and results more rapidly, and with richer datasets, enables better inputs for clinical, translational and exploratory research using data that extends from “bench to bedside”. There are however several factors to consider with DPE.

Requirement for a Technical Framework to collect patient data

• The need to establish technical capabilities (e.g., devices, apps, data transfer, etc.) keeping user experience in mind to enable patient research to collect patient data and endpoints. This in support of data driven innovation (in clinical, translational and exploratory) while assuring secure interactions, including identifying confirmation and protection.
• Establishment of a core common product platform and the continual assessment of changes in the marketplace to ensure cutting edge technologies are being leveraged.
• Introduction of core product integrations where relevant including advanced technologies.
• Different technologies may be used separately or combined to deliver capabilities in the Digital patient experience space such as:
  • Semantic and context sensitive graphical interfaces for patient facing systems
  • Touch interfaces on devices, wearables, data collection applications, etc.
  • Voice interfaces, chatbots and personal AI-assistants

Data Governance and Regulation

• The need to enable Dynamic Data Masking (DDM)¹ to mask any patient sensitive data based on HIPAA and other regulations.
• Incorporation of verification method for the availability of patient consent and expiration date if required.
• Deepen understanding of mechanisms of action through digital twins for the design of precision medicine.
• The need to utilize social media and mHealth technologies to improve recruiting, screening, enrollment and retention of patients in clinical trials.
• Enablement of clinical and translational to rapidly access and use relevant clinical data in work.

Why It Matters to You

An increased emphasis is now being placed on the Life Science industry to be patient centric. The intention is to find ways to meet patients where they are; making it easy for patients to engage in the process and educate them through their entire care journey. Life Science organizations are turning to Digital Patient Engagement Platforms to engage with patients digitally.

In this blog post we discussed:

• What is Digital Patient Engagement.
• Benefits of Digital Patient Engagement.
• Industry Trends regarding this area.
• Key Considerations when looking to deploy a strategy

About Astrix

Astrix is the unrivaled market-leader in creating & delivering innovative strategies, solutions, and people to the life science community.  Through world class people, process, and technology, Astrix works with clients to fundamentally improve business & scientific outcomes and the quality of life everywhere. Founded by scientists to solve the unique challenges of the life science community, Astrix offers a growing array of strategic, technical, and staffing services designed to deliver value to clients across their organizations

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Voice Command and Natural Language Processing is the computer’s understanding of spoken and written language. The ability of voice assistants like Google, Siri, and Alexa to detect human speech, react to it, and carry out voice-based requests is made possible through a process called natural language processing (NLP). NLP is the technology that makes it possible for machines to comprehend and communicate with human speech, however it is not just used for voice interactions. ¹

Natural Language Generation (NLG) is another important term. While NLP parses information, NLG brings data together to create language (e.g., content for submission to health authorities).

Industry Trends for This Technology in Life Sciences

More organizations are moving towards the implementation of technology supporting key capabilities such as part-of-speech tagging, parsing, text conversion, pattern matching, etc. These technologies are providing major efficiencies in R&D. Some of the bigger use cases for voice command and Natural Language Processing (NLP) are in the R&D efficiency area with the potential to assist with improving research, lab notebooks, development and regulatory documentation, and the processing of unstructured repositories and text as we standardize, analyze, translate, and/or predict outcomes.

For example:

• Smart speakers/apps, sensors, chatbots, etc., enhance the patient experience

• IoT speech recognition and voice commands dynamically capture lab observations into Electronic Lab Notebooks
• Machine-reading for large amounts of unstructured content helps to support regulatory intelligence and publication search
• Automated document generation, translation, and redaction  through neural machine language technologies (NLG) can support regulatory authoring and submissions.
• In the lab, the implementation of a digital lab assistant is eliminating the need for paper/typing notetaking and NLP combined with machine learning capabilities is creating narratives from unstructured content.

Establishing archetypes for these technologies will streamline strategies and accelerate outcomes.

Some Considerations with this Technology for Relevant Technology Selection and NLP Training

• Toolkits to support the implementation of NLP are available from a variety of open source or commercial locations
• Neural networks and AI are required to work in conjunction with NLP capabilities to train recognition of speech patterns and common phrases enabling analytics and predictions.
• Leveraging AI trained vernacular when asking and answering questions enablesend users to sort, filter, and build on previously asked questions utilizing simple, common commands.
• A plethora of options are currently available for cloud-enabled listening devices, requiring decisions to be made on which devices best fit your organizations needs for voice command capabilities.
• Consideration should be given to devices based on features like security, privacy, mobility, underlying architecture, NLP capabilities, and connectivity.

Why It Matters to You

In the life sciences industry, voice command and NLP/NLG will begin to play a more significant role in optimizing operations. It is therefore imperative to ensure that the organization understands the possible uses and challenges associated with leveraging this technology.

In this blog we discussed:

• What voice command and Nature Language Processing is.
• Some trends with this technology
• How organizations are leveraging it today and will evolve further into the future

About Astrix

Astrix is the unrivaled market-leader in creating & delivering innovative strategies, solutions, and people to the life science community.  Through world class people, process, and technology, Astrix works with clients to fundamentally improve business & scientific outcomes and the quality of life everywhere. Founded by scientists to solve the unique challenges of the life science community, Astrix offers a growing array of strategic, technical, and staffing services designed to deliver value to clients across their organizations

The post Voice Command and Natural Language Processing’s Impact on a Digital Transformation of Life Sciences appeared first on Astrix.

Structured Content Authoring (SCA) is a standardized method where technical content is controlled by pre-defined guidelines. The technical writer marks up the content according to what it semantically represents and agreed guidelines. The writer is not concerned with editing styling or formatting. The idea is to develop discoverable, reusable, adaptable, reconfigurable templates for storing, tagging, standardizing, and translating critical content. The content should be auditable, publishable, individually authored, and approved. Moreover, it can also be used to automatically generate standardized documents from digital records and approved, reusable content.

The Value of Structured Content Authoring

By creating and managing standardized and approved content entities that are ascertainable, compact, and reusable across the organization, it enables:

• Single source of truth – one standard copy of the information to be used across the organization
• Increased compliance and visibility/traceability of where the content is being used (even slightly different versions)
• Improved organization of content
• Decreased transcription errors through automated document generation and translation.
• Content Libraries and templates that can speed authoring through supporting technology such as:
  • Component Authoring tools
  • Smart contracts
  • Natural Language Generation
• Machine Learning closed-loop feedback to continually improve documentation, submissions, communications, and code – creating a self-improving ecosystem.

Structured Content Authoring Considerations

There is a shift from using documents to record scientific insights to viewing the content itself (layered intelligence, context, and knowledge within) as being the critical component. This is driving a move from document creation and management to development of core content. This allows for the assembly of content across sources to construct documents, publications, dossiers, and reports.

Emerging standards and open architectures,

• Darwin Information Typing Architecture (DITA), enables organizations to deploy tools to rapidly author and organize information into hierarchical, modular, and reusable objects. This allows for better compliance and traceability of approved and standardized information

Federated Content Management

• Gartner, and other tech consulting companies, advocate for content management strategies in recognition of the diversity of data resources in an enterprise ecosystem.

Document Repository Technologies

• Emerging technology brings emerging standards. Implementation of industry standards, such as Darwin Information Typing Architecture (DITA) will require a strategic position and evaluation of tools, either open source through DITA-OT or other commercially available solutions, to support these protocols.
• Content Management Systems (CMS) enable curation and storage of controlled documents and other standardized content. CMS software includes audit trails and workflow automation in order to enable template management, collaborative authoring, review steps, facilitate the approval of new content, maintain traceability, and uphold compliance with regulatory requirements.
• This requires environments to enable autonomous determination of relevant, reusable content, leading to intelligent authoring of documents, publications, and presentations.

Content Object Governance

• Disparity in lexicon must be alleviated by determining standard ontologies and establishing agreed dictionaries, standard terms, and templates.
• A single source of truth in current content must be determined to enable simplification and consolidation of information that will facilitate future use. This approved content should consist of the smallest, reusable pieces to enable rapid impact assessment and update of new documents.
  • Implementing new capabilities for tracking review and approval progress, as well as content object circulation.
  • Enact updates centrally (distributing changes as appropriate to all versions by embedded logic), based on a persistent review of outcomes and content effectiveness, of content and code.

Why It Matters To You

Emerging Technologies will play a major impact on the life sciences industry. One of those technologies is Structured Content Authoring (SCA).

In this blog we discussed:

• What Structured Content Authoring is.
• The value it provides to life science organizations.
• Some key considerations relative to leveraging SCA

The post Structured Content Authoring and its Impact on a Digital Transformation of Life Sciences appeared first on Astrix.

By 2022, the Hyperautomation market, according to Gartner, will be worth $596 billion^[1] and by 2025, 70% of organizations will implement operationalized AI architecture^[2]

Hyperautomation involves the automation of processes both within and across functional systems using traditional automation tools such as BPM (business process management), BPA (business process automation), RPA (robotic process automation), and low code/no code development tools to automate and orchestrate end to end processes. These traditional tools are augmented with AI and ML tools that enable capabilities such as natural language processing and generation, machine vision, and intelligent assistants at the data processing end of the spectrum to the use of predictive analytics and probabilistic logic that may suggest next best steps, at the decision making end of the spectrum.

In essence, Hyperautomation assists organizations in systematizing processes across the business, increases agility and optimizes workflows. It enables life science organizations to produce products more rapidly by leveraging intelligent insights, improving consistency, and improved human decision making. It also reduces costs by replacing manual tasks with automation.^[3]

The top business challenges that organizations face today that Hyperautomation can assist in fixing, are:

• Decision making is often made with partial information.
• Siloed legacy systems with internal workflows that are primarily manual in nature.
• Non-transparent processes for patients or HCPs, and non-compliance with the Audit Trail.
• Grueling, repetitive work that requires many worker hours and raises operating costs
• Manual processes that are prone to error that present an ongoing operational and regulatory problem.^[4]

With Hyperautomation, the AI/ML needs to be definable and trainable by end users. Automation is universally applied through workflow orchestration and process/task automation tools. The intelligent automation is adaptive to user role and task context. It leverages probability and logic to deal with uncertain situations.

Summary

Hyperautomation is an emerging technology that will have a major impact on the life sciences industry. Utilizing AI tools with workflow orchestration and process and task automation tools facilitates automating almost any repetitive process executed by business users. This connects siloed activities, processes, and data. It makes processes more transparent. It automates repetitive tasks. This technology trend will have a significant impact on all aspects of life sciences including healthcare operations, patient data management, claims processing, customer service, and patient-HCP interaction.

Why It Matters to You

To remain competitive, organizations need to evaluate the newest emerging technologies and trends in the industry. One of these trends is Hyperautomation.

In this blog, we discussed:

• What Hyperautomation is.
• How it impacts the Life Sciences industry.
• How it will eliminate some key challenges that organizations face.

The post Hyperautomation’s Impact on a Digital Transformation of Life Sciences appeared first on Astrix.

Data integrity must be maintained over its entire life-cycle and is an important aspect in the design, implementation and usage of any system that stores, processes or retrieves data. The overall objective is to ensure that data is recorded as intended, preserved, and remains intact throughout the process, preventing unintentional changes to the information as it was originally recorded. When the integrity of data is secure, the information stored in a database will remain complete, accurate, and reliable regardless of how long it’s stored or how often it’s accessed.

Whenever data is replicated or transferred, the opportunity exists for data integrity to be compromised. Error checking protocols and validation procedures are typically utilized to ensure the integrity of data that is transferred or reproduced to verify that it remained intact and unaltered throughout the process. But what about data ‘in flight’? With digital transformation being a key initiative in nearly all scientific laboratories today, maintaining data integrity for data in flight is quickly becoming an area of focus for regulatory compliance measures.

Event Stream Monitoring and Digital Compliance

The development and widespread use of novel biological therapeutics and vaccines is emerging as the new standard for precision medicine and the treatment or prevention of disease. With the mainstream production of biotherapeutics, it is imperative to establish a well-defined regulatory framework throughout the biopharmaceutical development life cycle. The traditional scientific laboratory has implemented a wide range of rules and processes that govern how data is entered, stored, transferred and validated. With the current drive towards biological based processes, the automation of workflows, and the need for immediate access to real time data, the issue of data integrity for data in flight is rapidly emerging as a top area of concern surrounding regulatory compliance.

To facilitate data integrity, a digital trail must be captured and documented throughout the entire data stream. Within the biological development platform in particular, there are numerous stages of the process that necessitate interim intervention or decision points that require accessing and possibly “re-shaping” data or events in flight. Legacy or not ‘fit-for-purpose’ informatics enterprises often do not have the underlying system architecture for efficiently tracking the resulting data changes..

The Scitara Digital Lab Exchange (DLX) cloud platform brings this process into the 21st century, addressing the unique needs of the biopharmaceutical laboratory. From cell line development through upstream and downstream processing, Scitara’s DLX event driven Orchestrations maximize process integration, automation, and workflow efficiencies. Data in flight between instruments, applications or repositories are managed by Scitara DLX Orchestrations and become part of the event stream.

Connecting to streaming data systems as well as traditional analytical systems to apply calculations and logic for data in flight is now possible with Scitara DLX. This innovative platform allows you to create automated workflows that have gates for review and critical stage decision points while the data is on its way to its ultimate destination. All of this transactional information that flows through the system is captured and can be reconstructed at any time, providing a strong regulatory and data integrity position for data in flight.

Scitara DLX Orchestration and Event Stream Monitoring allows you to:

• Construct event driven lab workflows and multi-destination data flows
• Configure event driven data exchange between any connections
• Enable event driven notifications informing you of steps needing review or action by the lab team
• Connect to and interact with streaming data systems and non PC based instruments
• Add notes and a picture or video of the activity throughout the workflow
• Apply business logic and processing for data in flight
• Capture a complete digital transactional record in the Scitara DLX Event Stream that can be reconstructed as needed

Scitara DLX Orchestrated activities are tracked and logged in the Scitara DLX Event Stream, establishing a clear chain of custody as data is moved, merged or transformed. Data aggregation support is provided throughout the entire bioprocess. This allows reconstruction of complete workflows weeks, months or years after the fact and extends support for GxP compliant processes to data in flight.

Conclusion

The digital landscape of the biopharmaceutical laboratory is changing dramatically with the introduction of new technologies, applications and advanced AI/ML solutions. The resulting increasing complexity of data and regulatory compliance requirements challenges the status quo of workflow automation, data management and data integrity standards.

Digital transformation initiatives are bringing together siloed and disparate systems, allowing for the seamless connectivity of instrumentation and data, enabling workflow automation. The need for real time process interaction and monitoring in the biopharmaceutical laboratory presents unique challenges within the automation workflow. Innovative technologies such as the Scitara DLX platform provides the necessary tools and technology to enable this level of interaction with in-flight data.

“By providing, for the first time, a cloud-native platform for seamless data access and connectivity and easily configurable routine and complex workflows in a scientific laboratory, scientists can not only free up more time for science, but also leverage AI/ML techniques, data analytics, and decision-making tools, to more effectively develop life-saving therapies, novel biologics and vaccines.” – Ajit Nagral, CEO of Scitara.

Success in laboratory informatics projects can be difficult to achieve largely due to the complex processes and technologies utilized in laboratories, and the many different aspects of the workflow across the enterprise. With over 25 years of experience in laboratory informatics professional services, Astrix is uniquely positioned to drive the success of your digital transformation project and solve challenges in your lab.

Why It Matters for You

Combining the benefits of modern cloud-based architecture with a vendor-neutral peer-to-peer platform with robust security, compliance, and laboratory-specific functionality provides unprecedented insights into laboratory and scientific operations. Scitara DLX’s monitoring system further ensures every data transaction in the lab is captured, creating a trusted digital trail available for further analysis and decision making.

• Implementing Scitara Digital Lab Exchange (DLX) platform within your informatics enterprise will allow you to connect to streaming data systems to apply calculations and logic for data in flight while capturing that information in an audit trail.
• Eliminating manual transcription steps establishes an auditable chain of custody for simple instrument outputs through the use of Scitara DLX IoT enabled Orchestrations.
• Creating automated workflows through Scitara DLX provides secure in-process review gates and critical stage decision points while the data is on its way to its ultimate destination.
• Capturing transactional information that is flowing through the system allows it to be reconstructed at any time, providing a very strong regulatory and data integrity position.

About Astrix

Astrix partners with many of the industry leaders in the informatics space to offer state of the art solutions for all of your laboratory informatics needs.  With over 25 years of industry proven experience, Astrix has the informatics specialists and business process analysis tools required to develop and implement the solution that works best for your enterprise. Our domain experts have helped hundreds of companies globally effectively navigate their digital transformation journey, connecting people, processes and systems to accelerate the advancement of science and medicine.

References

¹Data Integrity and Compliance With Drug CGMP – Guidance for Industry, December 2018, www.fda.gov, accessed Mar 19, 2021.

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