Big Data is the Driver Behind Smart Cities

There’s no smart city without smart people.” : Stati Generali Dell’Innovazione (Italian Govt. agency tasked with innovating)

Have you glanced through the papers today – chances are you would have encountered at least 1 – 2 articles mentioning the term “Smart Cities”. Talk of Smart Cities is everywhere today, India has announced that over the next few years there are committed plans to develop 100 Smart Cities. The more developed world and even forward-looking countries in Latin America have serious initiatives underway. Almost a year ago Transparency Market Research published a report pegging the size of the worldwide Smart Cities market at an astronomical $ 1266 Billion in 2019 – a tremendous leap from $ 507 Billion in 2012. There is no doubt that this movement is here to stay – the more important question is what are cities trying to achieve and how?

Even before that – why focus on cities? The simple answer is that’s where the majority of the residents of the world live and therefore where all the action is. A Unicef data visualization (how’s that for an early Big Data / Analytics reference) recently showed that cities were responsible for 70% of the world’s GDP. As more people move to urban centers in population-heavy countries like India and China this number will tip even more towards the cities. This surge in the urban population centers brings with it a set of tremendous challenges. Infrastructure, Energy availability, transport – public as well as private, pollution and other environmental damage and the delivery of civic services are all under stress. This affects the quality of life and in many ways shows the city in poor light.

Eduardo Paes, Mayor of Rio de Janeiro very aptly said, “Smart cities are those who manage their resources efficiently. Traffic, public services and disaster response should be operated intelligently in order to minimize costs, reduce carbon emissions and increase performance.” It naturally follows that if improvements have to be made across all these areas the wannabe “Smart City” needs data, lots of it. What those who craft policy need is timely data and information that can help them make the decisions they have to. In today’s age of the smartphone, wearables and the internet of things acquiring this data is no longer a problem. In fact, it would be fair to say that the greater problem is the profusion of data. Where the Smart City scores is in facilitating a collaboration across these devices so the smart people operating the smart systems can examine the data in aggregate form to identify patterns and isolate those nuggets of information that can lead to appropriate corrective action. This is, in essence, a Big Data problem. To be able to fully leverage the data available the smart cities have to know how to separate the signal from the noise of all this data – which is the appropriate data? Then there is the question of organizing it in a fashion that it is readily accessible when required and also that it is amenable to be sliced and diced as required. This is imperative for the next step – analyzing the data to try & identify patterns and gather insights.

Consider the example of traffic management. Most cars today come equipped with GPS, the drivers all have smartphones and the traffic signals are usually networked, in the future RFID tags could easily be added into this system. The Smart city would gather the real-time data already being generated and feedback into the networked traffic management system to manage the location and duration each traffic signal stays green to try & ensure a smooth ride. Another example is managing ebbs and flows in the utility grid – it is easy enough to keep track of the pedestrian traffic in a public location and in response to pre-determined periods of low movement turn off the streetlights. Analysis of longer-term data regarding weather patterns and the incidence of seasonal ailments can allow the smart city to proactively put into action preventive measures.

The fact is for a city to be smart the decisions have to be smart – considered decisions can only be made based on a thorough analysis of the data available. That sounds like a job for Big Data.