We Have Fire Drills, Why Governments Need to Run “Data Drills” as Well

May 27, 2020 | By Amen Ra Mashariki

In September 2015, I was sitting in the NYC Office of Emergency Management’s (NYCEM) famed “war room”. It was packed. Literally standing room only. Yet somehow the steady influx of important looking people into the room continued. Was the crisis an impending storm, or a blackout? Neither. This was a “Tabletop,” a simulated emergency situation. In this exercise, the Commissioners of most NYC agencies and their senior staff, some state personnel, and private sector entities (i.e. gas/electric utilities) gathered to review and discuss the actions they would take in a particular emergency, testing their emergency plan in an informal, low-stress environment. This made it easy for everyone to calmly rehearse their roles, ask questions, and troubleshoot problem areas.

people sitting around a conference table with projection of data behind them
NYC Emergency Management “tabletop” exercise. Photo by Amen Ra Mashariki

After 9/11, 2012’s Hurricane Sandy and the Legionnaires outbreak, we knew very well that it’s the unknown unknowns that hurt you the most. This is when I along with a few colleagues created data drills. Data drills help a city baseline where they are with citywide data practices. They also help improve a city’s ability to identify, understand, and use data to solve a challenge when requested. Data drills help a municipal data team move faster and better, but it’s also a very important tool to understand exactly where the holes and problems in your data operations are.

Why was I, the Chief Analytics Officer and Director of the Mayor’s Office of Data Analytics (MODA) at a tabletop emergency management event? To understand, rewind to the beginning of summer 2015 and the outbreak of Legionnaires disease in the city.

Legionnaires—a type of pneumonia—was spreading in the Bronx and Manhattan through contaminated water in cooling towers sitting on top of buildings. My office was brought in to build a machine learning model to help find where every building with a cooling tower existed, and to count and track the registration and ultimately the cleaning of those towers. This was a citywide emergency effort, and because MODA played a key role in its successful conclusion, from that point on it was clear to city leadership that collecting data was key to emergency response efforts. Hence the invitation to the tabletop.

As I watched these agency heads work out their emergency response muscle so they could improve, I realized my former office, as well as the data teams or personnel in other agencies, should find a way to get better at finding, accessing, integrating, and sharing data during an emergency. Agency data leaders needed our own tabletop exercise, because when we weren’t thinking about using analytics to solve a particular problem, we needed to be thinking about data all the time.

We understood that there is data that we know we have, data that we know we don’t have, and data we know absolutely nothing about, including even the fact that it exists (Donald Rumsfeld’s famous “unknown unknowns”).

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In general, data drills are developed and conducted based on some operational challenge that involves data and will require multi-organizational cooperation to achieve a desired result. Drills can be designed for (but not limited to):

  • Specific scenario: hurricane flood zone, homeless counts, data center disruption
  • Capacity building: collecting data, learning how to operationalize a specific dataset
  • Operations development: down trees clean-up operations between two agencies
  • Testing Software: testing new features in a data sharing platform

Data drills help us take on that challenge by having organizations across the city surfacing, sharing and integrating data. A drill takes place with specified start and end times, forcing all participants to work within real life time constraints. Every data drill results in overall citywide-data I.Q. growing ever so slightly.

Data drill deliverables should be defined early in the planning phase. They may include (but not limited to):

  • Identification of data sets with metadata and data dictionaries
  • Organization-specific operational workflow relevant to data and use-case
  • Interagency workflow for operations, analysis and/or network infrastructure
  • List of organization contacts, roles and responsibilities
  • Documentation of activities and observations
  • Report with recommendations

NYC’s first interagency emergency data drill was conducted by MODA with assistance from NYCEM’s GIS and Training & Exercises divisions, and the 1st Deputy Mayor’s office, from October 14 – 16, 2015. It included an initial data call, assignments for agencies, and an in-person concluding session. Fourteen agencies were participants in the drill, and there were over 60 individual participants.

The scenario for drill play was an extended power outage in an area of downtown Brooklyn affecting 97,000 residents. Eleven agencies contributed data sets to test data sharing mechanisms and MODA’s data integration effort. Immediately after the completion of the drill, a post data drill review showed the drill successfully tested the capabilities it was designed to test.

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A key takeaway from this blog is that we built the concept of data drills in NYC up from a simple idea to a very complex, citywide, highly impactful undertaking. This wasn’t just because it was a good idea. Good ideas come a dime a dozen.  This was an idea that every agency in NYC government felt was overdue. This was something that we all knew needed to happen. Therefore, high participation and ultimately impact was inevitable. For every city, domestic or international, data drills should be a key part of their data strategy. These drills should constantly be running in the background at a cadence that keeps the city’s data ready to be put into action. Data drills make the city smarter about their data, and that is key to being able to use data and analytics to make a city safer, smarter, healthier, more efficient, resilient, sustainable, and equitable.

COMING SOON: Executing a Data Drill

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