Q&A with Amen Ra Mashariki, Urban Analytics Lead, ESRI

SCII: Tell us a little about yourself.
Mashariki: I am currently the head of Urban Analytics at ESRI. My B.S., M.S., and doctorate are in computer science, and using that background my first fore ray into the data driven world was through my work in Bioinformatics at the Johns Hopkins University Applied Physics Lab. When I was appointed by Barack Obama to become a White House Fellow in 2012 was when I began to use my technical background in the government space. After my time at the White House, I went to work as the Chief Analytics Officer in NYC. 

What was it like working for New York City?
It was very challenging, but also extremely rewarding. I think the biggest thing that I learned in my time in NYC, was how to effectively use data science to maximize impact on day to day city operations. I used to always say to me team: “We don’t write one single line of code unless we know the New Yorker it is going to impact”.

What do you tell communities that might not have a Chief Analytics/Data/Innovation Officer?
In my opinion, for the most part, Chief Analytics/Data/Innovation Officers usefulness comes when they can convene community members, city leadership and staff around using data, and data science, or some other innovative capability around solving an explicit challenge, and help to define quality standards, frameworks, and processes to get this work done. But, all of this can be done through a concerted effort by a community. Essentially, you can have one person who serves as the lead or drive the same effort through some sort of leadership body. But, either way, a community must be purposeful and explicit in an effort to drive impact through data and innovation to be successful.

Can you give us a few examples of ways communities can turn their data into problem-solving decisions?
I believe a good analytics solution is 80% finding the right data, and 20% a data science algorithm. To that extent, in order for data to be used to solve challenges it must have context. There must be clarity as to when it was collected, why it was collected, how it was collected, and detailed specifics as to what was collected, allows for the user to know how best to use that data.
In NYC we started building these exercises called “Data Drills”. These were exercises that brought multiple agencies together to solve practice scenarios by sharing data with each other that ended in integration and analysis of that data. This was meant to help us achieve excellence in collaboration and data sharing protocols.
Lastly, I would state that the single most important thing a community can do to turn data into problem-solving decisions is to successfully translate a city problem into an analytics question. This is a deceptively complex thing to do, but once done makes the rest of the work extremely straight forward. 

Amen Ra Mashariki, Urban Analytics Lead, ESRI
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Q&A with Amen Ra Mashariki, Urban Analytics Lead, ESRI
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