Building Digital Architecture to Reach New Heights: Data and Knowledge Management for Pharma 4.0
Contributed Commentary by Jon Thompson, CAI
October 11, 2024 | In early 2024, it was revealed that contract research and manufacturing organization WuXi Apptec had allegedly shared intellectual property with the Chinese government without their client’s consent. This breach of trust generated widespread concern and backlash, bringing renewed attention to the importance of proper data management and data sharing in the biotech and pharmaceutical industries. Congress responded swiftly with legislation known as the BIOSECURE Act, which passed in the House of Representatives in September.
For the broader industry, it’s a powerful reminder of how important and sensitive biotech and pharmaceutical data can be. The increasing prevalence of personalized medicine requires companies to manage their data on a deeper level than ever before, protecting patient information and intellectual property (IP), while balancing the need to collaborate on a global scale to maximize supply chain efficiencies and coordinate research efforts. Complicating matters further, the ongoing adoption of AI and advanced analytics requires new data management systems and brings new challenges.
A Good Data Foundation
Historically, much of the pharma industry’s data has been “unstructured”. Unstructured data is data that exists in a variety of formats and can be qualitative in nature. There is a lot of valuable information that needs to be retained from this array of unstructured data, but it’s difficult to extract. As the pharmaceutical industry moves toward digitalization, companies have implemented data capturing systems that create structured data from the unstructured legacy systems. These quantitative and standardized forms of data are easily searched and analyzed by advanced analytics systems.
The integration and transition of unstructured data toward more structured data systems is an investment in both time and money, but it will ultimately provide a plethora of benefits. For instance, human error is one of the largest sources of process deviation in pharmaceutical workflows. A transition toward digitally captured, structured data allows systems to collect and communicate information previously recorded by hand, cutting down on costly mistakes. This is especially important as personalized therapies often require an even higher level of precision than generalized medicines.
Structured data is also more readily communicated between digital systems, which facilitates automation across the entire manufacturing plant and throughout the supply chain. This automation becomes possible in part because structured data allows important decision-making checkpoints to occur based on set and hard-coded criteria, which ultimately improves the efficiency and speed of a process.
Segregated Data is Secure Data
As companies begin to capture and collect large amounts of data across all their processes, it’s crucial that this data is appropriately categorized and organized. This separation of data based on set criteria is known as data segregation, and it is one of the cornerstones of good data security practices. For instance, data segregation can ensure that patient data or company IP receives the appropriate encryption and isn’t accidentally shared alongside supply chain logistics or external communications. Data segregation can also be an important risk mitigation strategy, as it limits the impact of any potential data leak or hack.
But data segregation isn’t just practical, it can also boost a company’s ability to perform advanced analysis. The data gleaned from creating a therapy for one individual is too limited to be informative in isolation, but with data segregation, informative data points from an individualized therapy can be digitally separated from a patient’s identifying information to allow widespread comparisons and analysis. Data segregation can also be a valuable tool for training AI, since an AI is only as good as the reference information you feed it. Data segregation ensures that an AI is only fed relevant and high-quality data, by filtering out extraneous information that might confuse the system.
Transform Data into Knowledge
Data segregation allows for fine-tuned management and filtering of data, but that does not mean that data should be decontextualized. In fact, the appropriate contextualization of data is what creates knowledge. While knowledge has many colloquial meanings, in the context of data management, it refers to the collection of information and personal understanding that is built on top of data by the individuals working with it. Knowledge can include the rationale behind prioritizing one process over another, insights into which therapies might work well in tandem, or other vital information.
Despite the inherent value of this knowledge, many organizations overlook the importance of good knowledge management. Creating organized and consistent systems for recording this knowledge can prevent it from being lost and improve efficiency by guiding future efforts away from past mistakes. If good data practices are the foundation, knowledge management is the scaffolding built on top that allows companies to reach new heights.
Good data and knowledge management are paramount for bringing pharmaceutical companies into the digitization era known as Pharma 4.0. The effort spent on implementing these systems will be rewarded with improved efficiency, stronger data protection, and a system for generating continuous, company-wide improvements.
Jon Thompson is Director of Product Management with CAI who has over 25 years of experience in the Life Sciences. He has worked in the Aseptic Pharmaceutical and Biotechnology space to optimize customers’ operational processes and implement digital solutions to meet regulatory requirements. Jon's mission is to deliver compliant digital solutions and expand digital and capabilities to solve his customers business problems and help lead them into higher levels of digital maturity. He can be reached at jonathon.thompson@cagents.com.