Every step of the biopharmaceutical process generates data — from development to manufacture to distribution. Capturing and centralizing that data can help the industry create and deliver medicines and treatments more quickly and efficiently, ultimately improving patient care and outcomes. However, many biopharma companies still grapple with outdated data management practices, characterized by disconnected data silos and manual collection processes. This hinders their ability to extract meaningful insights and drive operational improvements that could positively impact patients’ lives. Recognizing this challenge, industry leaders in life science manufacturing are partnering with companies to offer their expertise and capabilities, to navigate the complexities of data infrastructure implementation and ensure they can fully leverage their data assets to achieve these critical goals.
In the process of leveraging their data assets to the fullest, forward-thinking pharmaceutical companies are turning away from disparate, siloed systems to modern, interconnected data infrastructure platforms. These platforms offer a comprehensive suite of tools and services to integrate, contextualize and analyze data from diverse sources, providing a single source of truth and a holistic view of operations. By connecting previously isolated data sources, companies enable data-driven decision-making at every level, from research and development to manufacturing and post-market surveillance.
In this article, Gerard O’Leary, Data Infrastructure and Intelligence Practice Lead at Cognizant and Thomas McCarthy, Industry Principal of Life Sciences at AVEVA, delve into the key considerations and best practices for establishing a successful data infrastructure program. They also discuss the crucial role that industry experts play in guiding companies through this transformative journey, ensuring their data infrastructure investments deliver optimal results to manufacturers and patients.
Overcoming the data silo challenge with interconnected infrastructure
Traditional data management approaches are increasingly inadequate for the demands of modern life science manufacturing. These methods often lead to data fragmentation, hindering comprehensive analysis and decision-making. The manual nature of data collection can also introduce errors and delays, impacting operational efficiency.
To overcome these challenges and enhance patient outcomes, a robust data infrastructure solution provides a centralized repository for data from across the organization. By providing a centralized, interconnected repository for data from across the organization, companies can maintain a single, trusted source of truth. The integration of disparate systems ensures interoperability and enables seamless analysis, providing a holistic view of operations and facilitating decision-making based on data. In turn, advanced analytics and visualization tools allow companies to identify trends, patterns and correlations that would be impossible to detect with siloed data. This newfound visibility empowers organizations to make informed decisions that can optimize processes, improve quality and enhance overall efficiency.
Establishing clear data integration and interoperability standards early in the process is crucial to ensuring smooth data aggregation and analysis. Companies can streamline their efforts by adopting electronic validation packages that can expedite system release and reduce paperwork, enabling faster adoption and utilization.
Creating sandbox environments connected to live equipment allows users to explore and experiment with the system’s capabilities without impacting production. This approach promotes faster user adoption and provides a safe space to identify use cases and refine processes before full-scale deployment.
To maximize the value of data and its impact on patient outcomes, companies should prioritize collecting data that aligns with specific use cases and desired business outcomes during the initial integration phase. This ensures that all captured data collected is not only relevant but also actionable, driving immediate value and demonstrating a clear return on investment. Such a strategic approach can fuel further investment in the data infrastructure by showcasing its potential to streamline processes, enhance staff capabilities through training and development, and ultimately drive agility and business growth.
While the benefits of an interconnected data infrastructure strategy are clear, successful implementation requires careful planning and a strategic approach. Factors such as comprehensive planning, adaptability, appropriate technology selection and effective team engagement are critical to project success.
Key considerations for a successful data infrastructure strategy
Establishing a solid data infrastructure program involves several key considerations:
1. Strategic alignment of an integrated approach:
A successful data infrastructure initiative starts with aligning an integrated data strategy with overarching business objectives. Rather than simply capturing data for data’s sake, companies must carefully consider which data points are most relevant to their specific goals. This involves identifying specific use cases, such as real-time monitoring of production line performance, predicting equipment maintenance needs or ensuring consistent product quality across batches. By clearly defining desired outcomes, like reduced downtime, improved yield or faster time-to-market, companies can prioritize investments and use their data to deliver tangible value that contributes directly to the bottom line and long-term success.
2. Data integration:
Gaining the maximum potential from a data infrastructure strategy requires the integration of data from diverse sources throughout the entire product life cycle. This includes data from research and development, manufacturing and post-manufacturing. Collaboration with IT and OT teams is crucial in this process, ensuring seamless integration of diverse data sources and formats, establishing naming conventions and tag creation rules ( leveraging established structures like ISA-95), and confirming licensing agreements and documentation availability. While some compromise may be necessary, prioritizing standardization and interoperability will create a cohesive and efficient data ecosystem.
This unified data platform provides a comprehensive understanding of products, processes and patients’ needs, leading to better decision-making, improved product quality and faster innovation. For instance, integrating data from R&D with manufacturing data can help identify quality issues early on, saving significant time and resources.
3. Real-time data access and usability:
Timeliness is critical in life science manufacturing. Real-time data access, made possible through interconnected systems, empowers decision-makers with up-to-the-minute information, enabling them to respond quickly to deviations, optimize processes and uphold product quality standards. Near real-time access to data can be used to monitor manufacturing processes, track product quality and ensure regulatory compliance. To maximize successful adoption and usability, it’s crucial to make the data easy for users to work with. This involves setting basic requirements for source systems early on and considering data compression and storage strategies when starting from scratch. Industry-leading platforms, honed over decades of experience in managing real-time, time-series data, offer purpose-built solutions that go beyond simple data collection. They enrich, visualize, and analyze data, providing critical context to users across the organization, thus driving informed decision-making and proactive actions.
4. Data governance:
Establishing robust data governance frameworks is essential to maintaining integrity, security and compliance with regulatory requirements. Clear roles and responsibilities, quality standards and access controls are crucial components of effective data governance, which ensures that data is accurate, reliable and used responsibly throughout the organization. In the highly regulated life science industry, this is a necessity to avoid costly penalties and reputational damage.
5. Scalability and flexibility:
The life science landscape is constantly evolving. Data solutions must be scalable to accommodate growing data volumes and flexible to adapt to changing business needs. As new technologies and regulations emerge, the data infrastructure approach should be able to evolve seamlessly to meet these challenges. The ability to standardize data collection across global sites, ensuring consistent and reliable data management, is crucial for scalability in the life sciences industry. Such standardization accelerates deployment, reduces costs, and enhances process visibility by collecting data from various sources and storing it in a central archive, which ultimately improves decision-making and supports regulatory compliance. A scalable and flexible data infrastructure strategy can future-proof a company’s investment and ensure it can continue to derive value from its data assets.
The path forward: Embracing a data-driven future
In an industry where data is paramount, a robust, interconnected data infrastructure platform is the cornerstone of innovation and operational excellence. Embracing a data-driven approach allows biopharmaceutical companies to harness the power of their data, driving improvements in process optimization, quality assurance, predictive maintenance and regulatory compliance.
By partnering with experienced technology providers specializing in life science manufacturing, companies can navigate the complexities of implementation and gain access to out-of-the-box functionality that significantly streamlines implementation. These partnerships offer a comprehensive approach to data integration, utilizing pre-built templates for equipment types and establishing a single integration point for manufacturing data. This not only simplifies deployment but also narrows the technology divide between IT and OT, ensuring a unified approach to data management.
The resulting clean, contextualized data becomes the bedrock for human analytics, providing a clear foundation for further analysis and insights. This, in turn, fuels the development of advanced analytics capabilities, enabling life science companies to unlock the full potential of their data, drive improvements in process optimization, quality assurance, predictive maintenance, and regulatory compliance. As the industry continues to evolve, a strong foundation of interconnected data is essential for meeting the market’s growing demands, delivering high-quality products and ultimately improving patient outcomes.
