How streaming data and real-time analytics can reduce operational costs
Vehicles sharing operators have gained some powerful momentum in the past three years, and they contribute to sharing mobility growth, reaching a market value of over 100 billion USD. However, they’re entering a critical new phase of development, where the goal is the delivery of real improvements against key metrics and priority outcomes. This development demands data to be embedded in service design to improve decision-making, support real-time operational control, increase service quality and efficiency, and improve engagement with customers, businesses, and other stakeholders.
Therefore, there is immense potential for the better use of data in this sector, even though companies need to address the common problems that analytics projects have, such as integrating and processing always-on data and make predictions on it. However, the use of new technologies and platforms able to reduce complexity in the management of streaming data can offer tremendous opportunities for this industry to solve key issues like the capability to acquire timely information about their fleet, considering the high throughput a large volume of data per second that needs to be processed
Here’s a case in point
It is certainly not new that a vehicles-sharing service is expensive and not very sustainable. The proof is that an ongoing discussion within the vehicles-sharing community about the opportunity and convenience of vehicles redistribution is still open and unsolved. Vehicles-sharing system becomes unbalanced during the day, with vehicles stuck in a cold spot while they would be needed in hot spots. A possible solution would be to move unused vehicles from cold spots to hot spots, but efforts and costs of relocation can be significant –especially for car-sharing– and it is crucial to perform it in an efficiently, to address this imbalance between supply and demand and offer customers a valuable service.
This is where data and real-time analytics come into play. Integrating and processing huge amounts of data on trips, vehicles, idle times, customers, transactions, weather, and traffic harnessing real-time, it’ possible to make predictions as this data comes, reducing dramatically operational costs and winning the redistribution imbalances, which constitutes, even today, the hardest challenge to the financial viability of vehicles-sharing systems’ business models.
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