As the discipline has evolved, data and quantitative analytics are becoming a bigger part of emergency management. This trend is likely to continue as technology and data become more available. Current and future emergency managers need to understand data and how it can be used to support all phases of emergency management.
Emergency managers need a variety of “soft skills” to lead and coordinate diverse stakeholder groups and the associated personalities. Leadership, teamwork, problem solving, and communication are just a few of the many skills emergency managers must employ on a regular basis. However, as technology and data have become more prevalent, emergency managers must also develop at least a working knowledge of data and data analytics as these concepts are becoming more ingrained in all phases of emergency management.
Data for Preparedness
Preparedness is a notoriously challenging concept to quantify, but quantitative measures are being used to assess the capabilities necessary to prepare for disasters and other emergency situations. For example, FEMA now requires the identification (and associated assessment) of specific data points as part of the annual Threat and Hazard Identification and Risk Assessment (THIRA) process. Jurisdictions that complete the THIRA must set capability targets by identifying a series of key metrics related to each capability, and then annually assess the jurisdiction’s ability to meet the targets. The net result of this process is a much greater emphasis on the ability to identify, gather, and analyze data.
States are also advancing more data-informed preparedness efforts as well. For example, the New York State Division of Homeland Security and Emergency Services (DHSES) worked in concert with local emergency management stakeholders to create the County Emergency Preparedness Assessment (CEPA) program. As part of the CEPA process, DHSES captures both quantitative and qualitative information related to local (i.e., county) risk and capability levels. That insight is then used to help inform decision making. DHSES also employs data visualization techniques to better identify CEPA trends and analyze the information against other open-source data, to include census data and FEMA’s disaster declaration data set. The DHSES data visualization process allows different data points to be mapped and overlaid simultaneously so that trends and data correlations can be quickly spotted and analyzed. The agency has received accolades for their forward-thinking approach to data analytics, but many other jurisdictions are placing an increased emphasis on data and technology as well. In fact, some jurisdictions are even hiring Chief Data Officers.
Data for Response & Recovery
Due to the nature of the work, the response phase of emergency management is also ripe with available data. Power outage numbers, road closures, precipitation amounts, shelter totals, and the number of resource requests are just a few of the many types of data points emergency managers often covet. Collectively this information can be used to enhance situational awareness and inform response activities. Emergency management agencies, to include the Arizona Department of Emergency and Military Affairs, use this type of data to create operational dashboards and other situational reports. FEMA also relies on a great deal of data for their Daily Operations Brief. In addition, their new community lifeline toolkit contains resources to help jurisdictions quantify and visualize the impacts to community lifelines. Instead of data availability, data overload can often be a bigger challenge during the response phase, which speaks to the need for emergency managers to package and present information in a way that decision makers can understand and use.
There are a variety of key indicators and data points in the recovery phase as well, to include economic impact data, unemployment rates, housing numbers, and several other indicators that are used to understand how a community is recovering from disasters. Additionally, the number of infrastructure projects, and the associated funding and expenditure rates are also often scrutinized. The process to obtain a federal disaster declaration relies heavily on data, as states must meet (and quantify) per capita damage thresholds to obtain a declaration. FEMA’s implementation of the Disaster Recovery Reform Act (DRRA) will result in an increased emphasis on the ability to quantify a variety of information, to include new factors and criteria for the Individual Assistance program, such as state fiscal capacity, insurance data, disaster related unemployment statistics, and other indicators. Essentially, the process to obtain a federal disaster declaration (and the associated recovery funding) has become more data dependent. This further reinforces the need for emergency managers to be able to understand and work with data.
Data for Mitigation
Studies have shown that every dollar invested in mitigation can save up to six dollars in disaster-related impacts. As such, it is easy to see how the mitigation phase of emergency management may also rely on data to demonstrate a return on investment. Additionally, the mitigation planning process requires a great deal of quantitative analytics when it comes to understanding the risks posed by the various hazards facing state and local communities. Natural hazards lend themselves to trend analysis and other quantitative measures to understand their potential threats, vulnerabilities, and consequences.
NYS DHSES is again leading the way when it comes to the use of technology and data for mitigation planning. DHSES strategically partnered with the Albany Visualization and Informatics Lab (AVAIL), a data science and planning laboratory at the University at Albany to transform the state’s hazard mitigation plan from a static, linear, 2,000-page document into a living, data-forward, nonproprietary, web-based planning platform called MitigateNY. MitigateNY serves as a foundation of centralized data to maximize efficiency and reduce the burden and cost of data processing required for local planning. This data-driven planning process will transform the way state and local emergency managers think about mitigation planning in New York State.
Tools & Skills Needed for All Emergency Managers
The increasingly ubiquitous nature of data and technology means that emergency managers must develop at least a working knowledge of how to identify, gather, and analyze data. Emergency managers should also explore data visualization tools and other technologies to help process and display data for decision making purposes. However, to be truly effective in today’s dynamic environment, emergency managers cannot neglect the soft skills as well, because emergency management is still about working with people at the end of the day. However, emergency managers that can incorporate more scientific and data-driven approaches will be better positioned to tackle the current and future challenges facing their jurisdictions. Academic institutions that offer emergency management related degree programs should also keep this mind and ensure their curriculum includes at least some basic level coursework in data and data analytics. In doing so, they can help to ensure the next generation of emergency managers have the necessary skills to succeed.