Already operating at a deficit, many public transportation organizations are being tasked with achieving “more with less.” Now, as they strive to improve the quality and efficiency of the services they provide, they must also balance intense budgetary pressures with surging demand for public transport options. To put this in perspective, the American Public Transit Association (APTA) reports 10.2 billion trips were taken in 2008, up from 7.7 billion in 1995 . Against this backdrop, increasing numbers of transit authorities are turning to data analytics to provide them with critical strategic insights into passenger demand trends, scheduling, fare collection and underlying business efficiency. Held Back by Siloed Information Until recently, transit authorities have depended on manual methods for collecting, analyzing and reporting core information on passenger levels. With technologies and databases for each core function separately procured and individually managed, this was an inefficient, resource-intensive process. Just as important, the lack of interoperability between these siloed databases prevented organizations from leveraging the totality of the data at their disposal to optimize decision-making. ”Connecting” the Data To take their performance to the next level, leading organizations have focused on connecting this data, creating integrated information supply chains that can be mined for strategic insights. The key to their success lies in overlaying this “joined-up” data with predictive analytics capabilities that can optimize decisions. The good news? Predictive analytics capabilities are now readily available and widely used. Often building on pre-existing investments in ERP systems and integration solutions, these breakthrough capabilities connect key decision makers with targeted - often real-time - data and insights that transform operational effectiveness. Instead of using their data to answer “What happened?” predictive analytics moves decision makers to instead ask “What’s the best that can happen?” Enabling Rapid, Fact-Based Interventions Guesswork and anecdotal evidence become redundant. At any given time, management has instant access to detailed information on usage and performance metrics, including passenger numbers, failure rates and vehicle runtimes. Rapid fact-based interventions are a reality, with "What if?" scenarios being run at will to determine where new routes are needed or where service cutbacks make commercial sense. The recent example of one major metropolitan transportation authority highlights the scale of benefits available. This organization was facing a substantial budget deficit that made rapid cost-reduction a priority. By using analytics, it was able to identify significant savings. This analytics-focused approach included evaluating the existing operating project pipeline and securing valuable insights into procurement processes. In another example, a leading European rail transport organization used predictive analytics capabilities to establish its passenger branch as a profitable and preferred option for the travelling public. Needing to aggressively sell itself as a company, while still competing on price, this organization embedded analytics to provide critical insights into customer behaviors, pricing, products and services. As a result, the consistency, efficiency and flexibility of its overall service provision has significantly been boosted. Confronting the Data Challenge The scale of the data challenge facing public transit organizations should not be underestimated. In a 2009 Accenture survey of 600 U.K. and U.S. blue-chip companies, two-thirds of the respondents cited “getting their data in order” as an immediate priority. Around 40 percent said their current technological resources and systems highly hindered the effective use of enterprise-wide analytics. To ensure their organizations can seize the benefits of predictive analytics, management in public transit authorities should be taking the initiative with a couple of steps. First, setting the correct “tone from the top” by demonstrating their commitment to fact-based decision making. And second, by working with IT to enable seamless interoperability between systems, as well as embedding analytics into core decision-making processes. Technology is a vital catalyst, but organizations that succeed in this area are doing much more than buying and implementing technology. Crucially, they focus on making analytics integral to the way their people think, work and make decisions. Also, their IT organizations play an active role in the wider business, taking the time to understand what information is needed, before delivering the analytical tools and systems required to best leverage that information to improve business outcomes. And they nurture and develop analytical talent to ensure their investments in analytics capabilities generate sustainable, long-term strategic returns. The Power of Analytics Advances in technology and access to cloud computing mean predictive analytics capabilities are now well within reach for public transportation organizations across the United States. Their introduction can have truly transformational results. In addition to enabling urgently needed new opportunities for revenue generation, customer-centricity and service improvements, they can be used to introduce step-changes in passenger security. In the UK, for instance, smart surveillance uses complex algorithms to detect suspicious behavior in public places, automatically reporting any suspicious incidents to surveillance operators. With the United States’ transport infrastructure under unprecedented strain, public transport authorities must identify ways of facilitating mass transit, while driving efficiencies and responsiveness into the heart of their operations. Through the combination of data integration and analytics, these are realistic, affordable objectives. Brian Stein is the business development lead for public transit at Accenture.
Metropolitan Transportation Commission
