I don’t want to diminish the importance of the other smart cities enablers. But truth be told, analytics is a super enabler. Analytics takes massive quantities of data and turns it into actionable intelligence that enhances liveability, workability and sustainability in very direct ways. For Australian cities and towns, we are yet to unlock the opportunity of data analytics.
Just consider last week’s discovery of the massive (yes, massive) inaccuracies in the estimated monthly passenger rail trip numbers in NSW, uncovered from an independent analysis of Opal Card data. So, what are the stages of realising the full power of analytics?
Achieve full situational awareness
It is important we gain full knowledge of what is going on throughout the city. This situational awareness can be delivered in many ways. From “dashboards” to visualisations to command and control centres and to alerts delivered to computers or phones. The exact method of delivery depends on the unique circumstances of the city.
In most, if not all Australian cities, this kind of awareness doesn’t happen. If we consider systems such as energy, water, traffic, policing and emergency response, you’ll recognise that today’s operators are often "flying blind." We may know general parameters, but don’t know precisely what is going on at every point throughout the system. One example is an electric utility that has not yet deployed smart meters or other sensing technologies across the electric grid. If power is out in a neighbourhood, the utility may not know it until a customer calls in. It’s the same story with transit operators, who may not get a heads-up that a bus has been disabled in an accident until the driver has a chance to make a call.
Achieve operational optimisation
We must then take steps to arrive at the best decisions (including financial decisions) for the overall city system. A simple definition is "the process of making something as good as possible." It implies balancing trade-offs to achieve the best results. Today, infrastructure and system optimization – if it occurs at all – happens without the ability to truly see the big picture. But in the smart city, optimisation will have data from many sensors and subsystems plus the computer power to analyse all that input to find the best path forward.
Achieve asset optimisation
Smart cities gain the maximum lifetime value from all their assets by applying advanced analytics to the data gathered from their instrumentation. In other words, city assets – roads, power poles, transformers, pumps and so on – are equipped with sensors and instrumentation that report their condition. Then asset management systems can analyse that data to optimize asset performance and maximize their lifetime value.
Pursue predictive analytics
As we’ve said, smart cities can pull data pieces together to analyse what is happening in real time and make operational decisions. But the value of that data doesn’t end there. Through predictive analytics cities can get a glimpse of what’s going to happen next – from where crime is most likely to occur to where streetlights are going to fail to where traffic congestion will stall the morning commute.
Creating real-time data platforms and enabling data analytics is a core responsibility of the smart city. Let’s explore this further, together.
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