NYC improves firefighting operations with data analytics
One of the reasons that big data and Hadoop analytics tools have garnered such widespread praise both within technology and business communities is the fact that these resources have near limitless applications. While there are numerous opportunities to integrate these powerful programs within a business environment and improve various aspects of operations including consumer engagement, identifying burgeoning market trends and reducing expenditures, there are just as many ways for government, non-profit and scientific communities to successfully deploy data analytics solutions. For instance, authorities with the New York Fire Department have leveraged analytics software to enhance their ability to identify buildings that may be at risk for the ignition of raging infernos.
According to city records, New York witnessed more than 25,000 structural fires in 2012 alone. Of those cases, 2,686 were categorized as serious incidents, meaning they required at least four fire units to be deployed in order to properly address the threat. In addition to the high number of fires that occur in New York City each year, authorities have also had to contend with 911 dispatch issues in recent months. The New York Daily News reported that the city's Emergency Medical Service dispatch system crashed multiple times over the course of three days, at times requiring operators to take down 911 caller information with pen and paper. In one incident a 96-year-old woman was denied emergency care for more than an hour after falling and injuring herself.
Addressing fire threats with big data
With these myriad challenges, firefighters need every advantage they can get to mitigate the amount of the damage caused by blazes. Building inspections have been immeasurably helpful in that regard over the years. By identifying structures that are more at risk for fires to erupt, officials can take precautionary measures to secure those buildings and allocate their resources more effectively. According to GCN, the department inspects approximately 50,000 buildings each year, although there are 300,000 structures that fall under its watch. In order to prioritize buildings that presented conditions more susceptible for causing fires, authorities built a data warehouse to store relevant threat information.
Integrated with other governmental databanks, including those operated by the City Planning, Environmental Protection and Finance departments, the system has access to a wide range of information including past inspections, incidents of illegal dumping on the premises and the building's age and construction material. The program then uses that data to run a threat assessment of each structure, prioritizing those that are found to be most at-risk for a fire and schedules an inspection appropriately.
Officials leveraged the SAS suite of analytics tools to create their threat identification program. In recent years, this software has been integrated with the powerful Apache Hadoop platform. With this increased functionality, organizations can custom-build their analytics applications to match their exact needs. As more institutions look to big data to improve their operations, using these resources to individualize their analytics solutions will become more imperative.