Knowledge Management Implementation - The authors discuss the technology and guidance required to achieve good knowledge management in a biopharmaceutical company. - BioPharm International

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Knowledge Management Implementation
The authors discuss the technology and guidance required to achieve good knowledge management in a biopharmaceutical company.


BioPharm International
pp. 50-55

ABSTRACT

Knowledge Management (KM) is one of the most important systems for any biopharmaceutical company. KM is considered to be a vital connection between other management subsystems in an organization. This article focuses on the steps needed for successful implementation of KM in a biopharmaceutical company. The KM implementation discussed here enables new possibilities of effective usage and allows exploration of valuable information existing in a company. The article also emphasizes the use of an electronic document management system (EDMS) and the implementation of other such innovative information technolgy tools. Case studies from the biopharmaceutical industry are used to illustrate the KM implementation methodology.


(SOURCE: GETTY IMAGES)
The biopharmaceutical sector is a knowledge-intensive domain where the emphasis is on continuous product enhancement to meet the current market demand. Organizations are discovering that they need to do a better job of capturing, distributing, sharing, preserving, securing, and valuing their knowledge to stay ahead of their competition (1). The ability of companies to exploit their intangible assets has become far more decisive than their ability to invest and manage their physical assets (2). By managing its knowledge assets, an enterprise can improve its competitiveness and adaptability and increase its chances of success. With an increasing elderly population that consumes three times as many drugs as younger consumers, expansion into developing regions, and an overall increase in population and lifespans, the annual sales of the pharmaceutical industry have increased. Equally encouraging for drug companies is an evolving product pipeline. Process development of novel drugs, improved technology and laboratory research techniques, genomics, proteomics, and increasing R&D investments are shaping sophisticated research data systems. However, there are regulatory concerns, branding issues, impending patent expirations, escalating R&D and operations costs, and an increased complexity in research data that can result in information overload.

Opportunities in the pharmaceutical industry have never been brighter, but only if companies can harness their knowledge to make better decisions faster. Knowledge management (KM) is a crucial component of any life-science research company. Without an effective knowledge management strategy, it is difficult for a company to quickly respond to current market demand. KM assists in improving research methodologies, maintaining process flow, and ultimately cutting overall costs. This article focuses on the technology and guidance required to achieve good KM in a biopharmaceutical company.

KNOWLEDGE MANAGEMENT AND RELATED CONCEPTS

Knowledge


Figure 1: Identification of data and information in the company.
According to Davenport and Prusak, knowledge is located at the apex of the three-story pyramid (see Figure 1) (2). At the first level of pyramid is data, which expresses objective statements in terms of a transaction record (3). For example, the reading of a pressure gauge in a depth filtration process is a datum. The second level of pyramid is information, also called the message. To transmit a message, there must be a sender, a receiver, and a package of information created by the sender. For example, the reading of the pressure gauge can be converted into information by comparing it with standard values and pressure, and can be thus be attributed as high or low pressure. Knowledge is located at the third level of the pyramid. Obviously, it is more general than data or information, but still needs these two as a foundation. Knowledge stems from information just as information originates from data. For example, consistent high pressure above a certain value, for example, 3 psi, gives the user knowledge that a given depth filter will fail as soon as pressure reaches 3 psi.


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