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Decision streams utilize numerous components of business intelligence to aid business leaders and decision makers in analyzing data and ultimately making better operational decisions. The principles and practice of using decision streams adds a dynamic component to processes in crisis management that have been historically static. For crisis managers, decision streams’ greatest benefit is the integration of decision support, decision evaluation, and impact analysis to determine the full and total implications of every crisis and associated actions. The use of streams is unique because users are able to quantify data previously in a qualitative format and attach a fiscal component to actions previously perceived to be nonfiscal in nature.

Decision Evaluation

Crisis managers using business or operational intelligence tools to evaluate decisions and action interactions are presented with graphical and textual outputs through tools such as Sync Matrix and similar systems. Sync Matrix was originally pioneered by Argonne National Laboratory, in support of the Federal Emergency Management Agency (FEMA) and the Department of Defense (DoD), to evaluate several complex emergency response programs, most notably the Chemical Stockpile Emergency Preparedness Program. Tools, such as Sync Matrix, allow crisis managers to view the complex interactions between decisions, actions, and reactions from the operational environment in a variety of graphical and data forms. These outputs then drive a data-based after-action or performance review process that has historically been qualitative in nature. The data-based reviews also assist crisis managers with the process of separating perceived gaps in performance from actual gaps in performance. Furthermore, when decisions are charted in real time and compared with predetermined benchmarks, crisis managers have the ability to trend responses and actions over a period of years. Multiyear trending allows managers to separate effective and ineffective initiatives over a prolonged period of performance.

Integrated Data Flow

Data generated during a crisis and reviewed following a crisis response can also be channeled back into an integrated hazard vulnerability analysis (HVA) and associated business impact analysis components to challenge current ratings or profiles for specific hazards or threats. Although a single outlying data set is not sufficient to change all planning assumptions, it does, however, provide a point of reference for a response outside the assumed norm. These points of reference, over time, can be used to establish outer edges, or sigmas, of a response or action curve. In the case of crisis management and emergency response, these points represent the best- and worst-case scenarios. Periodic evaluations and statistical analysis are required of data sets to ensure the data inside these sigmas remain statistically significant and valid. As the results of an integrated HVA start to ebb and flow, so does the decision stream for certain hazards or risks. As a particular hazard increases or decreases in threat or impact, crisis managers can adjust the benchmarks accordingly.

Case Study: Decision Stream at the University of North Carolina Health Care System

In 2006, the University of North Carolina health care system started aggregating and trending threat data through a database with the intention of utilizing decision streams as a decision support and planning tool. In 2011, following the collection of five years of data, response benchmarks and profiles were compiled for six categories of frequently occurring emergencies. These profiles measured and benchmarked the following components: (1) productivity loss during emergency response; (2) time lapse between warning receipts and message dissemination; (3) time spent conducting pre-planning, response, and recovery operations; and (4) loss of revenue/increase in expenses during the crisis.

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