Five Attributes of Successful Smart Cities
I attended the 2017 Smart City Expo World Congress. As in previous years, this was a great learning experience. Of all the interesting things I heard, the shiny new toys and the fancy demos that I saw, five attributes clearly stood out as key success factors for Smart Cities.
The first and most important was that smart cities strategies must be people centric. The value is not in the smart traffic infrastructure, autonomous cars or machine learning algorithms supporting digital services. The value is in driving outcomes for citizens, businesses, visitors and city employees in domains like governance, transport, economy and the environment. At an even more granular level, it is important that cities have clear goals in mind not only for citizens, but also for specific segments, like the elderly, that can benefit from integrated health and social care services, or for the teenagers who crave opportunities to develop their talents at school and in the community. For example, I had the opportunity to speak with a Spanish city that is honing in on the needs of tourists. The city is well aware that tourists are a major source of economic development and is trying to develop even more personalized services, by integrating data and coordinating processes from the public transit authority, local museums, local hotels, restaurants and retail. I also heard from a local council from Australia mention the need to optimize transportation for students for after school activities.
High-level people centric strategic goals are not enough. They must be contextualized by district or neighborhood. Cities generate economic and social outcomes at the neighborhood level or by interacting activities across nearby neighborhoods. Each neighborhood has its texture of businesses, schools, cultures, community, health centers, and infrastructure that determine the success of smart programs and projects. For instance, I talked with a US city that is trying to differentiate economic development policies by neighborhood to best leverage local capabilities, rather than force a one-size fits all approach. Another US city mentioned the importance of equality of opportunities for different income brackets within neighborhoods and across neighborhoods.
Whether it is at the whole-of-city level, or at the neighborhood level, city administrations cannot do it on their own. They need to collaborate with the ecosystem. Reducing traffic congestion and vehicle CO2 emissions cannot be achieved by putting autonomous vehicle on the road. It must be a concerted effort with the transit authority, car makers, railway operators, taxi operators, electric vehicle charging station operators and many others to offer true mobility as a service solutions. Only by integrating knowledge and processes across those domains can the city innovate. For instance, the City of Nanjing garners a detailed view of travel conditions and can provide planning recommendations through analysis of traffic movement patterns generated from sensors, and other data, such as travel behavior of individuals in conjunction with road conditions, area accessibility.
Cities and their ecosystem partners have the opportunity to create economic and social value for their citizens by leveraging data as a strategic asset. The data economy is not only about collecting data feeds from thousands of Internet of Things (IoT) devices, or applying machine learning, it is about business model innovation. Data can deliver the insights to policy decision makers or for service managers that must deliver personalized services. Data can become the currency for freemium services, whereby consumers get access to service for free, for instance wi-fi, but the service operator makes money through advertising or other means, or micro-transactions, whereby consumers pay a marginal fee for increased convenience of service, for instance to pay utility bills at a grocery store or collect social security payments at the local post office. One city I met during a panel at the conference, mentioned how they are piloting using water metering data, originally collected to optimize the operations of the water distribution network, to monitor the health conditions of elderly living alone, by detecting anomalies of excessive or scarce usage of water and then possibly triggering alerts for social workers. Cities that can scale these alternative business models, by investing in data science and business skills, and setting up the right commercial agreements with ecosystem partners, will deliver innovative services and increase sustainability of their finances.
To best leverage data across domains and ecosystems and to make sure that the insights from data can be injected into business processes to increase their efficiency and effectiveness, cities must take a platform approach to their technology architecture. It is only through platforms that can securely deliver data integration, data governance, data analysis, process orchestration, API services and collaborative tools that cities can realize the value of their investments. One of the cities that I met in Barcelona is leveraging its investment in a bike sharing fleet and the related technology to install sensors on the bikes, so that they can collect more granular, geo-located data on traffic, air, noise pollution, and road conditions. Cities that continue to conceive their technological roadmap in silos will not be able to scale pilots that they have launched in specific domains, such as smart bins, or bike sharing.
One may think that smart cities are about data and analytics, IoT and the digital twin, electric autonomous vehicles, blockchain and artificial intelligence. True, those five groups of technologies are opening up endless possibilities to innovate. But municipal leaders that want to make their cities smarter through innovation must focus first on: citizen centric outcomes, neighborhood context, ecosystem, alternative business models enabled by the data economy, and working with technology companies that can supply the right platforms.