Knowledge management (KM) in pharma goes beyond storing files. It requires a connected approach that brings together tacit and explicit knowledge, unifies structured and unstructured data, and makes that information usable across development, manufacturing, submissions, inspections, and ongoing lifecycle management.
The following FAQs address the core concepts, system requirements, and operating principles behind an effective knowledge management strategy—offering practical clarity on how organizations can turn data into usable, lifecycle-ready knowledge.
Foundations of Knowledge Management in Pharma
What does knowledge management mean?
Knowledge management is the disciplined way to collect, store, share, analyze, and reuse product and process knowledge across the lifecycle. The end goal is a clear view of the process and product over time.
What kinds of knowledge need to be managed?
Knowledge can be separated into tacit knowledge and explicit knowledge. Tacit knowledge comes from the people working on the product and process every day, while explicit knowledge comes from process-generated data.
What data types are involved in that knowledge base?
Companies need to manage both structured data and unstructured data. Structured data includes tabular or relational data, while unstructured data includes items such as images, logs, shop-floor communications, and notes.
Why should I connect structured and unstructured data?
Connecting them improves product and process understanding. It also makes it easier to query the full picture instead of working from isolated fragments of information.
Enabling Knowledge Management
Why has knowledge management lagged behind quality risk management (QRM) in pharma?
Many companies have mature QRM systems, with risk management applied across the product lifecycle, but knowledge management remains underdeveloped. The main barrier is people and culture, not technology.
Is technology the main obstacle to knowledge management?
No. The bigger barrier is whether people recognize product and process knowledge as a valuable asset. Without that culture, digital knowledge management tools will not deliver meaningful value.
What kind of culture does a company need before KM will work well?
Organizations need a culture that values knowledge and treats it as an asset. That culture creates the conditions for the right processes and technologies to work.
What should a digital KM solution actually do?
A digital KM solution should collect and accumulate knowledge, store and archive it, and make it easy to share and analyze. The purpose is to create a clear process and product view across the lifecycle.
Which technologies are mentioned as KM enablers?
The technologies mentioned are advanced analytics, artificial intelligence (AI), GxP-regulated cloud computing, virtual and augmented reality, Internet of Things (IoT), and blockchain.
What does the life sciences industry need in place to apply KASA-style knowledge management?
The industry needs digitalization, structure, infrastructure, and approach. In plain terms, that means digitized data, mapped and connected information, a centralized environment, and workflows that guide how data become decisions.
What does structure mean in this context?
Structure means mapping digital information around the process itself so variables and records stay connected. For unstructured data, it also means maintaining a clear link to a time stamp, a place, and a responsible person.
Why is centralized infrastructure important for knowledge management?
Different data types need to reside in a centralized environment with their interconnections defined. This makes it possible to query the full spectrum of information instead of only one slice of it.
Why are workflows so important in KM?
Data alone do not become knowledge by themselves. Teams need workflows that guide acquisition, storage, analysis, and decision-making so the method remains consistent over time.
KASA and Lifecycle Knowledge Application
What is KASA?
KASA stands for Knowledge-Aided Assessment and Structured Application. It is an FDA initiative launched in 2019 to define what a computerized platform should support for regulatory assessment and for connecting similar processes and products.
What are the main goals of KASA?
KASA's main goals are to capture and manage information across the product lifecycle, establish rules and algorithms for risk assessment and control, and perform computer-aided analyses that compare standards and quality risks across applications and facilities.
How does KASA turn data into usable knowledge?
KASA provides a structured path from a knowledge base to knowledge-aided assessment. It relies on the structured application of knowledge management rather than free-text summaries alone.
What are the three pillars of KASA?
The three pillars of KASA are risk assessment, risk control through product design and quality standards, and risk control through manufacturing, facilities, and inspections.
Can the industry apply KASA ideas, or is KASA only for FDA use?
KASA is an FDA internal initiative, but its principles can also be applied more broadly across the pharmaceutical industry and adapted by other regulatory bodies.
How does stronger KM help with regulatory submissions and inspections?
A unified, systematic approach to gathering and analyzing process and product information makes it easier to provide what regulators need during submissions and inspections, both before and after approval.
How does KM support horizontal and vertical integration?
Complete, structured access to information supports horizontal integration through end-to-end process mapping and analysis, and vertical integration through historical data aggregation across the product lifecycle.
How does KM support reuse across sites, facilities, and products?
The approach becomes more powerful when the same information can be gathered and applied across a product portfolio, multiple facilities, or even multiple organizations. This is how knowledge begins to scale rather than remain siloed.
What happens when QbD knowledge is stored only in files and spreadsheets?
Knowledge becomes static, decision rationale is harder to defend, and teams struggle to reuse insights across tech transfer, validation, and commercial operations.
How should knowledge be managed to justify future changes?
Knowledge should be managed with two goals in mind: reuse and the ability to justify future changes. This makes later decisions easier to explain, support, and defend.
As knowledge management matures, the focus is shifting from data collection to lifecycle usability. Organizations that structure, connect, and operationalize knowledge effectively are better positioned to support development decisions, regulatory interactions, and continuous improvement.
See how knowledge-driven risk management works in practice. Watch the video.
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