This week, IBM hosted its Pulse 2013 infrastructure computing conference in Las Vegas.
One of the most fascinating use cases involved Yarra Trams from Melbourne, Australia. The company runs 500 trams throughout the city of Melbourne, and handles an annual capacity of 185 million passengers.
Yarra is a KDR company (Kiolis Downer Rail) that combines the engineering infrastructure expertise of Keolis with state-of-the-art tram cars from Downer EDI. Yarra’s CIO and Director of Information and Communications Technology (ICT) Neil Roberts described his company’s core values as: zero harm (i.e., safety first), thinking like passengers, and continuous improvement.
Yarra is one of a growing number of companies that are meeting their end business objectives by marshalling big data analytics into their operations in order to improve company workflows and workers, and also to benefit customers.
Noting that Melbourne’s weather can be unpredictable at times, Roberts presented a use study in which weather conditions rapidly changed, generating flooding and the stranding of trams.
In a case like this, the workflow starts with the tram operator calling for help to a dispatch or control center. While the problem is being located and addressed, would-be passengers are also alerted via Twitter over their mobile devices that there is a problem with a particular route, or that an area of the city is impacted. They are also given (again via Twitter over mobile) updates on the situation every 15 minutes, along with alternate routes that they can take in order to avoid difficulties.
Meanwhile, the control center issues work orders, requisitions materials and dispatches work crews to remedy the problems and to report on field status via their own mobile devices. This data is streamed in real time and later moved into an analytics system for purposes of research and continuous operational improvement.
Of course, ensuring that customers have a pleasant experience with the tram doesn’t stop there.
Trams additionally carry sensors that monitor the conditions of the rails that they run on. Over IP (Internet protocol), these sensors detect areas of track that might soon be in need of repair. This data is collected, processed and evaluated before a rail maintenance incident ever occurs. Consequently, if there is a problem, it is entirely transparent to tram customers because it is intercepted and solved before they ever experience it. This is big data at work-and it is paying off big in the area of customer satisfaction.
For Yarra, a key success factor rested in its ability to harness the big data that it collected to best advantage. Much of this data comes from sensors and machine interfaces (i.e., the Internet of things) that are capable of capturing the physical condition of the track on which trams were operating.
The company built an information strategy on this intelligence by connecting the results of its big data analysis to its own customers in the form of results and conditions for travel. In many cases, the advance information was instrumental in preempting poor track conditions.
This didn’t mean that Yarra would never again experience a problem in its rail system-but it did a lot to further customer relationships by giving customers greater visibility of existing conditions, accompanied with a set of options whenever these conditions impacted travel. From a customer standpoint, this visibility fostered trust-which leads to customer satisfaction and loyalty.
Today governments at all levels, financial services companies, healthcare providers and utility and telecommunications companies are all starting to get the message about the importance of mission when it comes to big data. They are beginning to implement big data strategies for building customer loyalty. As part of the effort, they are also developing near real-time systems that give their customers insights into how a particular situation or transaction is progressing.
Although these organizations span a variety of industry sectors, they all share a common understanding: that we are fast entering the “Age of the Customer”- where competition for customer loyalty will be fierce. In this setting, the ability to preempt unpleasant customer experiences and to provide good ones could well rest on how well an organization marshals its big data into productive use for the right kinds of missions.