Recently, the world has witnessed several large scale natural disasters: the Tsunami that devastated many of the countries around the rim of the Indian Ocean in December 2004, extensive flooding in many parts of Europe in August 2005, hurricane Katrina in September 2005, the outbreak of Severe Acute Respiratory Syndrome (SARS) in many regions of Asia and Canada in 2003, and the earthquake disaster in Pakistan towards the end of 2005 . These emergency and disaster situations (E&DS) serve to underscore the utter chaos that ensues in the aftermath of such events, the many casualties and loss of life, not to mention the devastation and destruction that is left behind. One recurring theme that is apparent in all these situations is that irrespective of the warnings of the imminent threats, countries have not been prepared and ready to exhibit effective and efficient crisis management. This paper examines the application of the tools, techniques, and processes of the knowledge economy to develop a prescriptive model that will support superior decision making in E&DS and thereby enable effective and efficient crisis management.
The Intelligence Continuum consists of a collection of key tools, techniques, and processes of the knowledge economy; that is, including data mining, business intelligence/analytics and knowledge management which are applied to a generic system of people, process and technology in a systematic and ordered fashion (Wickramasinghe and Schaffer, 2005). Taken together, they represent a very powerful instrument for refining the data raw material stored in data marts and/or data warehouses and thereby maximizing the value and utility of these data assets. As depicted in Figure 1, the intelligence continuum is applied to the output of the generic information system. Once applied, the results become part of the data set that are reintroduced into the system and combined with the other inputs of people, processes, and technology to develop an improvement continuum. Thus, the intelligence continuum includes the generation of data, the analysis of these data to provide a “diagnosis,” and the reintroduction into the cycle as a “prescriptive” solution. In this way, continuous learning is invoked and the future state always builds on the lessons of the current state.
Key Terms in this Chapter
Data Mining and KDD Process: Knowledge discovery in databases (KDD) (and more specifically data mining) approaches knowledge creation from a primarily technology driven perspective. In particular, the KDD process focuses on how data is transformed into knowledge by identifying valid, novel, potentially useful, and ultimately understandable patterns in data (Fayyad, et al., 1996 AU35: The in-text citation "Fayyad, et al., 1996" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ). From an application perspective, data mining and KDD are often used interchangeably.
Germane Knowledge: The sum total of all information plus the ability to implement it constructively and purposefully in the dynamic and unstable environment.
Readiness: The instantaneous ability to respond to a suddenly arising major crisis (e.g. sudden slow-down in the manufacturing parts supply chain) that is based on the instantaneously and locally available/ un-prepositioned and un-mobilized countermeasure resources.
Tacit Knowledge: Or experiential knowledge, that is, “know how” represents knowledge that is gained through experience and through doing.
Explicit Knowledge: Or factual knowledge, that is, “know what”, represents knowledge that is well established and documented.
Pertinent Information: Information structured data, grouped into coherent categories that are easily perceptible and understood.
Preparedness: The availability (prepositioning) of all resources, both human and physical, necessary for the management of, or the consequences of, a specific event or event complex .