Foundations for a data-driven future

Data-centric architectures must operate in real time in order to boost the analytics that power AI applications.

If we chart the evolution of transport we can identify a consistent trend — it has been driven by technological innovation. The use of donkeys, horses and camels dates as far back as 4000 BCE. In 1906, the first car was developed with an internal combustion engine. Today’s conversations about transport revolve around autonomous vehicles and space tourism. What’s fuelling the latest innovation? Data. Its application is driving the breakneck speed of innovation, with researchers unearthing new ways to increase transport efficiency and reduce costs.

A data-driven revolution

Last year, Transport for NSW (TfNSW) announced it will roll out a new transport management system that holds the promise of busting Sydney’s congestion through better real-time data and predictive technology. It is being described as the world’s most advanced transport management system. By leveraging real-time data and artificial intelligence it will reduce traffic congestion by predicting problems before they occur. Unsure about how bad congestion really is in a city such as Sydney? Consider this: the average speed of Sydney’s road network, the slowest in the country, is even worse than New York, a city with over 50% more residents than Sydney.

TfNSW’s new platform aims to provide a complete view of the transport network by 2020 in order to “make faster, more informed decisions making passenger journey more reliable and reducing the cost of congestion”.

Australian organisations have a healthy appetite to explore AI and real-time data initiatives. What will it take to ensure projects such as these get off the ground? The answer lies in data. The raw material for AI applications is data and it is required in vast quantities. Enterprises gather 44 TB of data daily, and that’s set to increase by 40 times over the next decade. The data which will feed into the TfNSW platform for analysis will arrive from a variety of sources, such as footage from traffic management cameras installed at most major intersections, Census data and weather data. Another rich source of data will come from the Opal card, the contactless fare collection system for public transport. TfNSW has made the data available so researchers and developers can access it to innovate and gain insights into commuters’ travel patterns.

To process all of this data in real time requires powerful compute capabilities but it also requires a shift of mindset. To get the most out of their data, organisations need to rethink how they are capturing, consuming and storing it. The systems built to share data are fundamentally different than those built to simply store it. Organisations need to put their data to work and unify and share it for real impact. A data-centric architecture must operate in real time, to boost the analytics that power AI applications.

Storage: an on-ramp to AI

In all industries across the globe, organisations of all shapes and sizes are starting to understand that future growth hinges on intelligent use of data. Initiatives such as those announced by TfNSW are much welcomed. Australia must turn to technologies such as AI to solve issues such as congestion to increase productivity to secure the country’s economic future. Transportation is about to get a technology-driven boost — its future will be connected, data-driven and intelligent. An optimised approach in compute and storage is enabling forward-thinking organisations such as TfNSW to tap into the power of AI to turn their data into a revolutionary service.

Image credit: ©stock.adobe.com/au/nosopyrik

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