The 2019 Queen City Hackathon: A Personal Account
“If you don’t have a team, you can come make one over here,” the director said from the podium, gesturing to a corner of the room. A URL was projected behind him, which was being furiously retyped by more than 150 keyboards in the RV Arena, by fingers unwilling to waste even a second. I watched as the heads around the room started to crowd around their circular tables and whispers of eager excitement managed to fill the enormous space. The datasets had been released, and the 2019 Queen City Hackathon was underway. Teams of professionals, students, and tech enthusiasts of all sorts had gathered at the Red Ventures campus to take part in the all-night affair, and a $25,000 prize pool awaited the winners.
As I surveyed the room, there were those who had clearly done this before. Some, like myself, had brought only their laptops (a minimum requirement). Others had brought their favorite coding fuel, be it Clif Bars, Pringles, or Red Bull, and often a stack of textbooks for inspiration. Some had prepared for the elements: bringing toiletries, pillows, blankets, and sleeping bags. As the pre-formed squads began buzzing early ideas around the campfire glow of their collective screens, I made my way to the designated corner of the room for those riding solo. There were only a few of us.
For those unfamiliar with the structure of a Hackathon, let me begin by dispelling one glaring misconception (and by glaring I mean the one my parents had). A Hackathon does not involve hacking into a system like we see in the movies, and the winners are not just those most adept at stealing our personal information and selling it to the Soviets (I promise, Dad). This kind of event is about gathering a group of computer scientists in a dedicated environment and sprinting to a solution to a problem, those problems varying in scope depending on the event. Today’s event focused on using data for social good. More specifically, to help serve and understand two subsets of our community: those suffering from Opioid Addiction, and those staying in our area’s homeless shelters. Local non-profits volunteered their data — to them a rat’s nest of rows and columns — with the hope that the tech community could provide answers to questions they may have not even thought to ask. When I started my foray into the data world, these were exactly the kind of issues I was hoping to eventually work on. I didn’t imagine, however, that working at Red Ventures would expose me to them.
A motley crew, Shane, Steve, Larz, and I made our way to an empty table as the last-formed team of the event. We had all shown up unsure of where the experience would lead, and that proved enough of a common ground to band together. We had one veteran — an intern for an engineering company — one recent coding bootcamp graduate with a background in real estate, a developer for a non-profit consulting agency, and me — a data analyst. For some, this was the first time seeing real life data-in-action, but absolutely none of us had done a Hackathon before, and it showed in our lack of toiletries. We found that we needn’t worry about the coding fuel, there were plenty of tacos and coffee. Whether we would miss the pillows and sleeping bags remained to be seen.
I did not come into this event without my own doubts. Having been on the Analytics team at RV for about six months, I wasn’t entirely sure how my skills of visualization and eye for pattern matching would translate to this environment. Most teams were excited about the opportunity to do some predictive modeling, something outside my area of expertise. However, I did have experience working with the homeless population in my hometown of South Bend, Indiana, and I thought that I could use that experience to add color to an analysis of the homelessness dataset.
My teammates were agreeable, and the four of us began trying to piece together the 23 tables that comprised the set. This, unsurprisingly, was no trivial task. After an initial 30 minutes of digesting the data separately, we came together to focus our efforts. We didn’t want to spend any time stuck down rabbit holes of analysis, something my tenure at RV had already made abundantly clear to me. We had an opportunity to speak with one of the community members who had helped aggregate the dataset, who gave us some background information on how and why the data was being collected. We learned that in order to access the data, you had to attend a multi-day training — not for privacy reasons, but simply to understand the application responsible for presenting the data. We spoke with mentors who were walking from table to table, who were always able to provide valuable insight. We kept our conversation lively, always keeping in mind the people behind the numbers.
In these first few hours, a goal arose that we felt would be tractable and valuable. We would create a simple tool that would allow a provider to easily gather a snapshot of a client’s homelessness experience as soon as they came through the door. To determine what fields to include, we incorporated a mix of analysis, professional insight, and personal experience. We spent time thinking and analyzing the questions that we would want answers to, which laid a solid foundation for the legwork that would have to come in the witching hours of the night.
By about 2:30, several LED campfires had been permanently extinguished. The once full and lively arena was noticeably emptier and quieter. The silence of those who remained, however, was not one of exhaustion or frustration. There seemed to be, exuding from the clicks of keys, scratching of pens, and crunching of chips, a palpable sense of a shared flow — we had figured out what we wanted to do, and we were doing it. A few times, I took a break for a stretch and a couple of deep breaths, only to be overcome with an awareness of how rare this kind of thing was. There I was, working intimately with people I had never met six hours previously, using skills I had gained at a well-paying job, in order to help a part of my community that I had cared deeply for — ever since I saw them from my car seat through the windows of my family’s 1990 Ford Taurus (the ones with the seats which faced backwards). I couldn’t help but feel grateful. Going without sleep seemed a relatively low cost of admission.
As sappy as I may have been feeling, there was still much work to do. And as much as time seemed be an afterthought, it was nonetheless slipping away. I found that in creating the functions of the tool incrementally, I was able to also inform further analyses which wouldn’t have occurred to me otherwise. Functions to our tool and insights from our data were created in parallel. We made demographic and behavioral breakdown visualizations which wound up supplementing our presentation, and ultimately led to the biggest ah-ha moment of our efforts. We were able to show that over 70% of clients were staying in Emergency Shelters, and that the data collected on these clients was not only inconsistent but was possibly leading to a greater number of shelter visits per client. This highlighted the need to collect exit data from homeless clients, to better understand their journey.
When it was time to present, we had created a tool which we felt could help shelters provide better care, and a series of visualizations which would help them better understand their clients. Our numbers, due to a family emergency, had shrunk to three. We turned in our work about 10 seconds before the deadline. We practiced presenting for about that length of time and may have left a line of ‘Lorem Ipsum’ in one of our slides. We came into a room of bright, well-rested faces, and talked about what we had made. They asked lots of follow-up questions, and we were ready for them. They applauded, and we high-fived (outside the room). Shortly thereafter, that same director who had guided us to that lonely corner of the room was announcing the winners.
“Just One Team!”
That was our name.
In case you were wondering, the answer is yes. Holding up one of those big checks feels awesome. But if that were the real end to this story, what was the point? Writing this, I still don’t quite know. I spent time talking with city officials, workers from the local shelters, and fellow competitors after the Hackathon about what comes next. What does it mean that the data from the emergency shelters is inconsistent, and what would tying it together uncover? What energy will this tool free up for those providing care? I’m meeting with some of these people to keep the conversation going and try to find the answers. One possibility is that it will allow us identify and engage those regular visitors to the shelter and properly direct them to other programs, so they can begin to rebuild what they’ve lost. Another possibility is the elimination of redundancy in the data collection process, paving the way for new information to be added to a more holistic picture of an individual. Whatever the case may be, I hope to see the product of our work be put into action, and that it can inform future efforts using data for the greater good.
