Smarter cities with smarter data

The Smart City is no longer just a vision for the future. The administrations of leading cities around the world are busy installing and linking the networks of sensors, cameras, lights and other devices needed to deliver the promised benefits of a digitally connected urban environment.

Singapore – The world’s top smart city

Leading the pack is Singapore. A study released in March this year[1] shows the island state topping the index in the four areas covered - mobility, health, safety and productivity.

The study examined progress in 20 cities, including major cities like San Francisco, Seoul, London and New York.

It had particular praise for Singapore’s achievements in providing smart connected solutions in mobility, healthcare management and public safety. It also noted the country’s leadership in providing citizens access to digital services and information.

However, Singapore cannot afford to sit back and rest on its laurels. The exponential expansion of mobility and the growth in the number of Internet of Things (IoT) devices present city planners and administrators with huge opportunities, but at the same time enormous challenges.

Growing numbers of mobile and IoT devices

Singapore has among the highest smartphone penetration rates in the world, with the number of users projected to rise from 4.27 million in 2017 to 4.82 million in 2022.[2] On top of that, data from Analysys Mason states that total IoT revenue in Singapore is projected to be S$714 million in 2025.[3]

What these numbers mean is a deluge of data that, if used wisely, can improve the quality of citizens’ lives, improve the use of resources and save costs. The challenge comes in actually being able to analyse and act upon the data in real time, in order to create actionable intelligence. This data also needs to be stored, to allow smart cities to derive historical insights and predict future events using machine learning.[4]

Big data can bring big problems

Planners are realising that public sector organisations responsible for everything from transport and healthcare to public safety and homeland security are going to run into problems that derive from the massive and growing amounts of data from a variety of sources.

For example, transport authorities have traditionally used ERP (Enterprise Resource Planning) systems to manage infrastructure such as traffic lights, parking and emergency vehicles. Singapore has the ambition to go further and integrate all transport data to deliver a comprehensive system that also covers buses and private vehicles, making journeys swifter and the streets safer. However, ERP systems are not capable of handling such large quantities of data, neither are they scalable or flexible enough to provide real time insights on the data collected.

In the area of public health, the SARS outbreak in 2003 was well managed with public information campaigns. But should there be such a disease outbreak today, there would be a huge and dangerous surge in social media rumours and unrest, which would be impossible to control without the ability to collect and mediate all the data.

Traditional solutions don't work

Traditional enterprise architectures and data warehouses would be unable to scale to the volumes necessitated by IoT and mobile devices. Without machine learning, it would be extremely difficult to predict and prevent the cybersecurity threats posed by the sheer number of connected devices, and if the floods of generated data are pouring into public agency silos, a holistic view of the data and associated relationships becomes impossible. Visualisation of data at scale across an organisation, to make timely and efficient decisions, would likely be impossible with traditional platforms.

The generation of unprecedented volumes of data is going to happen - the challenge is how to control it and put it to effective use.

The solution lies in helping the public agencies that manage cities build data analytics platforms that are enterprise-grade, highly scalable and available to multiple users.

Such platforms must be able to store petabytes of data without compromising performance, and at the same time, provide centralised governance and responsive security. Since public funds are involved, it is also essential that the software operates on low cost commodity hardware, thus helping to keep costs under control.

Open source solutions

A further important option in managing the costs of an effective Smart City solution is to build on open source and open community software. Open source should, however, not be seen as an automatic benefit; it is always wise to choose best of breed.

City authorities and ICT departments should look for scalable software systems capable of collecting, processing and storing data from, for example, sensors, public vehicle fleets, telemetry from roadways and carparks, and footage from security CCTV cameras and even public safety officers’ bodycams.

The preferred system should bean end-to-end solution offering flexibility of use in running either on-premise or in cloud, integration with existing directory access protocols to enforce authentication and authorisation, and in-built state of the art security.

Smart cities are for people

At the end of the day, a Smart City is what its inhabitants perceive it to be. The network of sensors, meters, lights and cameras must deliver municipal services that recognisably improve public infrastructure and services. It is the human-scale benefits of connected communities that will make a smart city a more efficient and more pleasant place to live.

Singapore’s Smart Nation initiative has put the country well on the way to achieve this vision, making it an example for others to follow. If the country moves forward with the right choice of big data analysis systems, Singaporeans can truly look forward to living in the world’s leading smart city.

- Kamal Brar, Vice President & General Manager, Asia Pacific/Middle East, Hortonworks -





[4] Big_Data_in_Smart_Cities_White_Paper_v1