AbstractRecognized limitations to data in disaster management have led to dozens of initiatives to strengthen data gathering and decision-making during disasters. These initiatives are complicated by fundamental problems of definitions of terms, ambiguity of concepts, lack of standardization in methods of data collection, and inadequate attempts to strengthen the analytic capability of field organizations. Cross-cutting issues in needs assessment, coordination, and evaluation illustrate additional recurring challenges in dealing with evidence in humanitarian assistance. These challenges include lack of agency expertise, dyscoordination at the field level, inappropriate reliance on indicators that measure process rather than outcome, flawed scientific inference, and erosion of the concept of minimum standards.Decision-making in disaster management currently places a premium on expert or eminence-based decisions. By contrast, scientific advances in disaster medicine call for evidence-based decisions whose strength of evidence is established by the methods of data acquisition. At present, disaster relief operations may be data driven, but that does not mean that they are soundly evidence-based.Options for strengthening evidence-based activities include rigorously adhering to evidenced-based interventions, using evidence-based tools to identify new approaches to problems of concern, studying model programs as well as failed ones to identify approaches that deserve replication, and improving standards for evidence of effectiveness in disaster science and services.
An Integrative Knowledge Management Framework to Support ERP Implementation for Improved Management Decision Making in Industry
