I was lucky enough to participate in a recent POLITICO panel entitled, “Big Data and the Policy Behind Big Ideas,” for the well-known politics and government publication’s Outside, In program. The series is a yearlong conversation bringing innovators who have used real-world solutions to enhance government efficiencies and solve complex problems together.
During this big data-focused panel, I was not only able to discuss the evolution and current state of big data with other experts in the space but had the chance to share our new data science methodology and analytics process with both a live and virtual audience as well.
Our company is a new big data and predictive analytics subsidiary of Cubic Transportation Systems, working to achieve greater efficiency by optimizing the allocation of resources deployed in urban transportation networks around the world. The end goal is to improve and maximize the quality of service for these networks’ travelers.
As the director of Analytics for this emerging company, I’m responsible for advanced analytics solutions and program management activities supporting the firm’s consulting services and technology offerings. During the conversation, I was joined by Tom Schenk Jr., director of analytics and performance, City of Chicago; Evan Burfield, co-founder, 1776; Tyson Gersh, president and co-founder, The Michigan Urban Farming Initiative and Naseer Hashim, CEO, Imaging Advantage, to discuss this prominent technology topic.
Throughout the panel, the participants and I examined how government and private companies are using big data to inform decisions, shape policy and affect communities—something we know well here at Urban Insights. I provided comment, specifically, in the context of the transportation industry.
I was sure to note that agencies have many sensors and platforms collecting data already, but most importantly, that these agencies need to use this data as a service to the customer. What I mean by this is that public transportation as an industry has a prominent issue: all of its gathered data is being housed in separate, disparate silos. By fusing these independent data sources together, transportation authorities can see a fuller picture of the factors and dynamics that contribute to the success, effectiveness and efficiency of the network, which ultimately leads to more satisfied travelers.
The San Diego Metropolitan Transportation System (MTS ) is a prime example of this predictive analytics approach, which I referenced during the discussion. The transportation agency is provisioning public transit services to meet its riders’ demands. It seems obvious, but since systems track how they’re actually used through different systems, journeys and network planning become complicated. Creating a view through the analytics process to understand how people are making connections “on the whole” so that the agency can help travelers consume the services in the most the efficient way is the ideal approach for transportation authorities using big data.
The panel took place in Washington, D.C., so it only made sense to illustrate this point using their transit system as well. If Washington Metropolitan Area Transit Authority (WMATA) used a big data and predictive analytics approach like our own to maximize its network efficiency, it could save a significant amount of money per trip for its, on average, 350 million trips per year. This could ultimately result in nearly a $100 million productivity gain.
This is where big data in transportation stands now, but what will it look like in 20 years? This was the final question of the engaging conversation. My response was that I would like to see us, as consumers, be more directly engaged in the process of delivering the services and infrastructure and access that we need – with a direct line of communication between agencies. Transportation authorities need to stretch public resources so that the consumer is an active partner in the process and is incentivized to relieve strain in one part of the network and consume, at times, where there is more capacity. For example, D.C. leveraged its bike share program around town to give consumers an alternative to reduce network congestion. As for incentives, new mobile technologies can reward consumers for making choices like this one in the future, leading to an optimized network and happier riders.
In the meantime, though, the more we talk about big data, the better. As Hashim of Imaging Advantage noted, educating the public on what the phrase ‘big data’ actually means could help solve America’s hesitation toward the subject. And I think this panel was a great place to start.
To view the complete panel on-demand and learn more about big data in government, visit politi.co/1sUVtQv.