Disruption, big data, sensors, the Internet of Things – these are the topics impacting us all and being discussed at smart city forums across the globe.

They are the how of solving our cities’ challenges through the use of technology. But first, we need to identify and understand what the challenges are. 

Technology for technology’s sake won’t create resilient and more efficient cities to support our growing population. But by understanding the challenges cities face, we can confidently identify the technology needed to help solve them. 

Technology allows us to collect enormous amounts of data. Deloitte’s 2015 Mobile Consumer Survey found that almost 80% of all Australians now own a smartphone. Most, if not all, of these devices collect and share data to some degree. Federal, state and local authorities collect and share all types of data, with the list expanding on a daily basis. Add private operators like Cisco or IBM to this and we get a hint of the sheer volume of data that exists. 

If we already have all of this data and we know that data is critical to unlocking smart cities, why haven’t we solved the challenges our cities face?

Data Dilemmas - Access for All 

Sensitive data must be protected and should not be made publicly available. But unwarranted withholding of data stifles innovation in the smart city space. Private enterprises attempt to control and restrict certain data for commercial gain, which can prevent data from being openly accessible and free to use. 

How do we go about addressing this issue? If the ultimate purpose of smart cities is to improve quality of life, we need to adopt a series of guidelines around how we collect, share and use data. 

Sensitive aspects of data can easily be scrubbed to alleviate security and privacy concerns and make it fit for public consumption. But just as architects, engineers, designers and developers accept planning systems as essential to achieving good city planning outcomes, our data collection also requires a smart city framework. 

Policy planners can play a central role in defining this framework, with companies and individuals wanting to collect and use data in our cities potentially being required to operate within the framework. As with our planning systems, guidelines would be adapted and refined over time. 

Frameworks can also be tailored to specific cities or geographies so that, rather than impeding our smart city aspirations, they have the flexibility to support innovation and create infrastructures designed for resilience.

Connected cities in Australia are in their infancy and we are still learning what all of this might mean for us. One of the most effective ways to quality check our data is to put it to the ‘people’ test. Giving the general public access to data is a time and cost-effective way to scrutinise data. It allows communities to decide whether data is sufficiently accessible, accurate and usable. 

A number of city councils have released their data to the community, regardless of concerns around quality. These councils are now actively working to refine how and where they collect information, with many updating their data acquisition infrastructure based on the feedback received. 

This is crowd-sourced data auditing at its very best. Once we understand the quality of our data, how we collect it, and how it can be used to meaningfully improve the quality of life in our cities, we can refine how it is gathered and shared. 

The ‘people’ test is a learning process. But the more accessible the data is the quicker the transition to meaningful data collection and distribution – data that will help solve city challenges and support more resilient communities.

Tapping into Meaningful Data 

So what can data help us achieve? The Dunedin Study follows the lives of over 1,000 babies born in the early 1970s in a hospital in Dunedin, New Zealand. 

The study is now in its fifth decade and has supported over 1,150 publications and reports, identifying links between issues like heavy alcohol use and poor reproductive health, and poor credit ratings and cardiovascular health. This empirical data continues to influence policymakers and medical professionals both here and overseas. 

Imagine what we could uncover tapping into similar data at a city, state or national level? We could learn more about how we interact with each other and with our cities – how we live, travel, relax and socialise, and track how this changes over time. 

Understanding our relationship with the urban environment, and our interaction as communities, is an imperative. According to a 2014 World Bank report, cities are predicted to hold more than two thirds of the global population by 2050.

In Australia, urbanisation is expected to be even more pronounced. The Department of Infrastructure and Regional Development predicts that by 2061, 80% of Australia’s population will be living in cities. 

If we’re to better respond to our needs, meaningful data is essential to planning, building and operating our cities. So how do we decipher all this data and analyse it usefully? It’s no easy task. 

Einstein once said that the definition of genius is taking the complex and making it simple. With this in mind, the real question for smart cities is how do we isolate quality data sets and correlate them in a meaningful way to solve our city challenges? 

Artificial Intelligence (AI) is one area beginning to support us in this quest. It will have a significant impact on data analytics and the speed with which we can intelligently assess and correlate data. 

We are at an early stage in our data-rich evolution. There will be growing pains, but there is no question that these are exciting times.