Biomedical Engineering Reference
In-Depth Information
Fig. 1
Relationship between data, information and knowledge in the bioprocess context [ 6 ]
from these would lead to faster and better decision making, since the final product
quality is a complex interaction of all these interdependent functional areas. For a
data-intensive industry such as pharmaceuticals, access to data and information
enables organizations to understand and streamline operational and business
processes. However, most organizations fail to leverage this information for better
decisions due to the lack of proper means of managing information. The next few
sections of this chapter explore the challenges and complexities associated with
capture, storage and retrieval of information and current best practices.
1.2 D-I-K Hierarchy
Data, information and knowledge are often referred to interchangeably, however in
practice they have different meanings [ 1 ] and follow a defined hierarchy as shown
in Fig. 1 . Data usually means raw numbers that have no context. To a bioprocess
professional, this means data entered into enterprise resource planning (ERP)
systems, datasheets or data captured in real time by sensors on process equipment.
Information provides meaning or context to data. To a bioprocess professional this
means the ERP reports, batch records and process execution reports and trends.
Knowledge is processed information that results in action. To a bioprocess
professional this means technical and investigation reports that contain process
knowledge, based on which process execution schemes, corrective and preventative
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