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New York City has nearly 1 million buildings, and each year, more than 3,000 of them experience a major fire. The Fire Department of the City of New York (FDNY) is adding BI analytics to its arsenal of firefighting equipment. It has created a database of over 60 different factors (e.g., building location, age of the building, whether it has electrical issues, the number and location of sprinklers) in an attempt to determine which buildings are more likely to have a fire than others. The values of these parameters for each building are fed into a BI analytics systemthat assigns each of the city’s 330,000 inspectable buildings a risk score. (FDNY doesn’t inspect single and two-family homes.) Fire inspectors then use these risk scores to prioritize which buildings to visit on their weekly inspections.²⁶

Review Questions

1. What kinds of BI analytics tools and techniques is the FDNY likely to use in sifting through all this data and determining a building’s risk score?

2. Identify three other parameters that ought to be taken into consideration when setting priorities for building inspections.

Critical Thinking Questions

1. While making investments in BI analytics seems like a good idea, FDNY is strongly challenged in measuring its success. Officials may be able to cite statistics showing a reduction in the number of fires, but demonstrating that BI analytics tools were the reason behind that decrease may be difficult because it involves proving a negative—that something didn’t happen because of its efforts. Go to the FDNY citywide statistics Web site at www.nyc.gov/html /fdny/html/stats/citywide.shtml. Use those statistics and a data visualization tool of your choice to see if you can discern any change in the number of fires since the BI analytics system was installed in 2014.

2. Can you identify other approaches that would be effective in demonstrating the value of BI analytics in reducing the impact of fires in New York City?