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2019 Open Data Hackathon

On Thursday, May 16th, twelve teams came together in Toronto to share the incredible solutions they put together over the course of the 2019 Open Data Hackathon. The final event had around 100 people attending, to watch as our teams pitched, demonstrated and “sold” their products to our esteemed judging panel. Slalom leaders Stephen Roger, Hilary Feier, Gretchen Peri, Joe Berg and Steve Walintschek carefully evaluated the teams’ presentations and products, speaking to the teams one-on-one in our fast-paced four hour event!

The deliberations went into extra innings, but the judges concluded that team Detroit Hustles Smarter was to take home the trophy, held in Toronto by the winners of the Blockchain Hackathon since last year.

Congratulations to Team Detroit Hustles Smarter, as well as our runner-ups: teams Fire Department Slalom Consulting and InnoVate Public Health!

Thank you to all teams for sharing your projects. You can see the deck presented at the event here (if you're a Slalom employee!). Our informational booklet distributed at the final event can also be found here for a succinct breakdown of all of our finalist teams! Below are the project details from the winner and two runner-ups from the event.

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Slalom Hackathons


  • Tuesday, Jan 30th  – Hackathon Announced
  • Friday, March 29th – Stage 1 Submission Deadline
  • Tuesday, April 9th – Stage 1 Results Announced
  • Thursday, May 16th – Final Event in Toronto

timeline: announcement january 31st, social event February 8th, stage 1 submission March 29th, results announced April 9th, final event in Toronto May 16th

Detroit Hustles Smarter - Winner!

Project: Open Air Quality

Helping Detroit communities breathe easier

Peter Welch, Mark Holt, Jacob Scheatzle, Drew Triplett, Stephanie Wong

Why: The residents of the 48217 zip code of Detroit live in what is considered the most polluted zip code in Michigan. This fact was discovered by public health scholar-advocates concerned with environmental justice in this majority minority city. Many are children of the second great migration, and part of a very tight knit community that boasts a lingering spirit of 'real Southern values' of respect and warmth. They are also afflicted with high poverty rates, plummeting land values and some of the highest cancer and asthma rates in the state due to the ongoing industrial activity right outside their doorsteps. Slalom Detroit cares about the community we serve. We are geeks who recognize the virtue of turning data into intelligence. Despite an ever-increasing amount of environmental data, very little intelligence has been created on this topic. Creating this intelligence could be used to help the people of 48217 who are located less than 10 miles from our Detroit office.

What: In keeping with the theme of utilizing data to Improve Community and Government, our team set out to utilize existing air quality data sets to identify patterns and predict how air quality changes over time. During our investigation into existing data sets, we recognized a distinct lack of real-time Air Quality Index (AQI) information in the nearby 48217 area, known locally to have persistent complaints about the impact of industry. To solve this problem, we designed our own air quality sensors that can be installed outside community buildings to gather this valuable data and published into the same data sets we're trying to leverage for our air quality analysis.

Wow: Our cross-practice team of TE, D&A and XD consultants each brought their unique skills and passions to the project armed with a strong desire to help the people of 48217 as well as other local communities that might benefit from a better understanding of the environment they are raising their children in. The team identified publicly available data sets that could be leveraged to understand air quality patterns over time and ultimately help to predict how it is likely to change. However, in order to help 48217, we needed to enrich the existing information with data that could only be obtained through air quality sensors located within that community. The team decided to design a custom, 3D printed, air quality sensor powered by Arduino and publishing its data to the cloud via AWS IoT where the data can be used by any subscriber. We also built forecast and regression models using Prophet and Caret R as well as a Kibana dashboard for viewing real-time data and Tableau visualizations.

Partners Used: AWS, Tableau
Track: Improving Community & Government

FDSC (Fire Department Slalom Consulting) - 2nd Place

Project: FIRE SEER

Enhancing fire models through open source data to improve fire fighters ability to combat wildfires

Aaron Morgulis, Alex Trahan, April Zauri, Chris Grefe, Jeff Lutz, Jennifer Wheeler, Naomi Carrillo, Raquel Edwards, Ryan McNaught, Sean Jones, Sharadhra Sandur, Tim Stafford, Victoria Pindrik, Vivek Pai
Orange County / SD

Why: Wildfires occur worldwide and can negatively affect social, environmental, and economic aspects, including the loss of life, property, and natural resources. The cost of fighting wildfires and the damage inflicted continues to increase. In 2018, - 88 Lives were lost in California wildfires - $9,000,000,000 in damages were caused by California wildfires - 20,000 structures were damaged or destroyed by California wildfires - $2,900,000,000 were spent by the U.S. Forest Service along with other Department of the Interior agencies fighting wildfires 

What: Our Solution performs four functions: - Gather: Collect data from a combination of streaming and static sources into a Snowflake repository. Data includes current and historical satellite imagery, weather measurements, vegetation and canopy cover, building and structure characteristics, and air quality metrics. - Recognize: Identify fires, surrounding fuels, such as vegetation and canopy cover, homes and other structures, firebreaks and bottlenecks to fire spread using Amazon Rekognition. - Forecast: Predict probable paths the fire will take based on surrounding fuel, firebreaks, weather metrics (e.g., wind direction and speed), building burn rate, air quality, and historical data with Amazon Sagemaker. - Recommend: Run simulations to determine the best positions for fighting the fire and provide positioning and allocation recommendations. The out put of those steps will focus on three results: - Path Prediction - Damage Minimization - Fight Insight

Wow: This project is very personal to many members of our team due to the impact wildfires have had on them and their families. Most of the team was either directly affected or knew of someone that was impacted by wildfires. This is personal. Through the team's analysis of the open data sources available to use, we discovered some unique opportunities and capabilities to enhance existing fire models. An emerging need for firefighters is to understand the locations that have new construction versus older communities. Fire departments have to train differently to deal with wild fires impacting newer housing communities due to the difference in construction material (lighter woods, more particle boards and glued wood). Newer construction causes fires to burn in a faster and different way than how firefighters were traditionally trained. By incorporating Zillow's open source data and applying building ages to our fire model, we are able to provide additional insight for fire responders.

Track: Improving Community & Government

InnoVate Public Health - 3rd Place

Project: eVect Health

Fighting vector-borne diseases with the power of open data and machine learning

Diana Lin, Johnny Wang, Jeff Nelson, Peter Park, Rachel Hatanaka
Los Angeles

Why: Vector-borne diseases are human illnesses caused by parasites, viruses and bacteria that are transmitted through mosquitoes, ticks, mites, snails, lice and many others. They account for more than 17% of all infectious diseases globally, resulting in more than 700,000 deaths per year. The most well-known amongst these is malaria, which alone causes over 400,000 deaths per year, many of which occur in children under the age of 5. The World Health Organization recognizes "many of these diseases are preventable through informed protective measures." The open data set being utilized as part of our solution is critical as it forms the basis from which individuals, researchers and government & health agencies will receive and react to outbreak information as well as the appropriate treatment protocols. The open data set also serves as the foundation for machine learning as vector images and additional inputs are submitted from individuals.


What: The most crucial elements in fighting vector-borne diseases are education and awareness, expedient identification of vectors and behavioral change. There are numerous open sources of information online. But what if you had access to location-based alerts and information regarding vector outbreaks, the ability to identify vectors using just your phone's camera, and personalized preventative measures to help avoid such diseases? We believe eVect Health, addresses an unmet need in the market for people living in developing nations, travelers & clinical research settings. The application aggregates multiple data sources to inform users, through a mobile application interface, of vector-borne disease illnesses within a geographic radius. It also uses relevant information from WHO and vector-image data to provide users with the appropriate prevention and treatment protocols based on their specific profile. All of the data sources used in building the solution are open to the public.


Wow: We believe that eVect Health will not only addresses an unmet need in both developed and developing countries with a creative solution built on publicly available data, but also has the potential to make a meaningful impact on worldwide health outcomes. The impact on developing countries is particularly important, as the vast majority of vector-borne diseases are contracted in these areas. With over 60 percent of the world having access to mobile phones, we believe that eVect Health has the ability to obtain the penetration and adoption needed to make a meaningful reduction in the occurrence of vector-born diseases (potential of hundreds of millions of cases reduced assuming maximum adoption and efficacy). Open data sources are a critical input eVect Health, and this solution and potential positive impact could not be made possible without public resources from health agencies such as the World Health Organization, CDC as well as existing public health initiatives.

Partners Used: Google
Track: Improving Community & Government

Build Hackathon Team

The XM Hackathon series is sponsored by Mike Cowden, Ian Cook, and Greg Martin out of the XM Build Center in Seattle. The Hackathon program is lead by a committee of cross-functional members consisting of:

Jeremiah Dangler

Program Sponsor

Tristan Leonard

Program Lead

Chaffee Burke

Solution Owner

Gwen Zellers

Operations Lead

Jon Busby

Business Partner

Miheer Munjal

Local Lead

Dustin Schnaitman

Alliances & Partners

Jordan Denmark

Marketing & Branding
Chris Bueno

Experience Design

Simon Griffeth

Experience Design

In addition to the core team, we receive tremendous support from local representatives from every Build Center!


  • Ian Burns
  • Chris Freyer


  • Tim Knapp
  • Stephen Weinrich


  • Quinn MacKenzie
  • Dhana Viswanathan


  • MJ Alwajeeh
  • Pedro Melendez


  • Victor Aparicio
  • Kimberly Adams


  • Arthur Best
  • Joel Neubert


  • Carrick Carpenter
  • Chris Metzl

Our Mission


Slalom engages in this broad hackathons for a number of reasons. They build or reinforce our collaboration and teamwork muscle memory, not only within our delivery centers but throughout our entire company, across every discipline. They help get our name out there! Multiple relationships with non-profit organizations were easier to build simply because the point of contacts had heard of Slalom, our work, or even our previous hackathon efforts! They also give us an opportunity to learn something new, stay on top of emerging trends, and build expertise for upcoming client deliverables.


The 2018 Hack for Social Good inspired a record number of teams from six delivery centers and twelve local markets to form a relationship with a local non-profit, solve a problem, and make a positive impact on their communities. These teams presented their solutions at the final event in Denver, often alongside representatives from their non-profit. Previously, the Artificial Intelligence Hackathon challenged dozens of teams across a dozen offices. The 2017 HoloLens Hackathon inspired teams from all over the world to be spontaneous and creative. In 2016, the Amazon Hackathon gave the same opportunity to four delivery centers, where sixteen teams created over twenty Alexa skills.


The end results of our hackathons include:

  • Publishing skills and an open source tool to develop Alexa voice assets quickly
  • Utilizing engineering, design, and product expertise to create unique holographic applications
  • Using technologies from our top partners to build AI solutions to solve complex problems
  • Working with leaders of our communities to make earnest, real impacts on local non-profit organizations and our communities, our environment, our schools, and our healthcare systems
  • Exploring the capabilities of Blockchain, a core component of cryptocurrency solutions, but without applying it to cryptocurrency specifically. We explored the feasibility and desirability of Blockchain as a core component for solutions relating to fraud detection, healthcare and government applications

Change Log

  • 20190130 - Initial Version with intro, timeline, final event details, about section and program team -TLL
  • 20190131 - Added basic judging criteria, submission requirements, general guidelines, partner guidelines -TLL
  • 20190201 - Updated content -TLL
  • 20190313 - Expanded judging criteria, added travel guidance -TLL
  • 20190523 - Added winners, removed other content -TLL