How to manage and analyse smart city data
Smart cities promise to give planners a vast amount of data about the urban environment, but making sense of all that information will require careful planning, write Abdulkader Lamaa and Sevag Papazian, of Strategy&
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Smart city initiatives in the Middle East continue to gain momentum. In the UAE, Dubai has taken the lead in the deployment of smart services as it prepares for World Expo 2020. Meanwhile, governments across the GCC are engaging in the innovation-powered transformation of cities. Already, Saudi Arabia has earmarked Jeddah, Makkah and Riyadh, with a combined population of over eight million, to be modernized as smart cities.
The aim of these initiatives is to improve the quality of life for citizens, stimulate business, and drive economic growth. Smart cities achieve this by using information and communication technologies (ICT) to enhance the quality, performance and interactivity of urban services. They reduce costs and resource consumption and improve the contact between citizens and government.
These smart city initiatives are vital because the world’s population continues to urbanize. According to the UN, by 2050 66% of the global population will live in urban areas, up from 54% in 2014. In particular, GCC countries are forecast to remain among the most urbanized in the world, rising from 85% in 2014 to 90% in 2050.
Urban planners, however, face a practical problem as these smart city initiatives start to take shape. How exactly will they manage the huge amounts of data related to government services, transport and traffic management, energy, health care, water, urban agriculture and waste management? The quantities of data, already large, could become overwhelming. IDC forecasts that by 2020, the world will contain more than 28 billion connected devices forming the ecosystem of the ‘Internet of Things,’ an internet of devices that communicate with each other, compared to a little over nine billion devices in 2013. All the apps and devices in the world by 2020 could create as much as 44 zettabytes of data (or 44 trillion gigabytes), up from 2.8 zettabytes produced in 2012 according to EMC.
In response to this data management challenge, the managers of smart cities should consider six practical actions that will allow them to collect, use, learn from this ocean of data. These actions put strategy before technology and ensure that the smart city is sustainable and able to cope with the inevitable advances in technology and data volumes.
Identify Relevant Data:
With all the smart devices being adopted and connected, urban planners need to identify what specific data they need to measure. With the ocean of data that they can potentially measure, they need to undertake an identification exercise that is based upon the smart city’s priorities and that potential decisions that taken. The task of identifying the types of data is independent of whether those decisions are automated through controllers or not. For instance, measuring temperature and light intensity will be valuable in most GCC cities, whereas precipitation levels will not be relevant for most of the year.
Smart cities will not capture all the potential data by default. Instead, acquiring the data will require the installation of specific sensors and the automation of processes that then generate the required data. For example, many urban transport systems around the world produce passenger and transit data by having all travellers pay by using either contactless smart cards or debit/credit cards. What this means is that the data design phase should be embedded in the earlier stages of smart city programs, because decisions on what data to collect can influence the design of the city or systems within the city. In addition to capturing the data, planners have to design storage to accommodate the large volumes that are generated, and they have to make decisions about how long to store each data point.
Define the Data Governance Model:
Managing smart city data is about more than technology. It is about putting an operating model in place with governance rules that define ownership, access, and maintenance of data. Ownership should be clearly stated to ensure that the relevant authorities, whether the municipal government or the transport system, properly maintains the data and reach the correct level of data quality in a sustainable way. In addition, planners will need to define data access rules and guidelines to accommodate privacy and confidentiality concerns. For example, people will not want their water supplier to sharing data on their water consumption with companies that will then try to sell them water-saving washing machines.
Create Feedback Loops:
Smart cities need to ‘learn’ continuously if data insights are to be put into action and to have an effect on improving citizens’ quality of life. To achieve this requires constantly analysing decisions and results and creating mechanisms to relay those analyses and conclusions back to enable better future decision making. A smart city is not just smart once, but always because it is learning from itself and improving. In addition, planners can use analytics to automatically control processes, which will allow analytics engines to send orders directly to Internet-connected devices that control urban services.
Define the Delivery Model:
A key decision is whether to in-source or outsource data capabilities. In many cases, finding the right data and analytics talent is difficult. In addition, developing the technology architecture that covers analytics engines and integration backbones while implementing security controls requires advanced capabilities that are hard to find and can be expensive. Some smart cities may decide to partner with specialized data and analytics companies to leverage these firms’ existing capabilities. Others will have concerns about data privacy and confidentiality and will have to rely on in-house capabilities, which they will need to develop and retain.
Adapt the strategy:
A smart city is not a destination, but a journey. Smart cities will change over time with rapid pace of technology improvements. Accordingly, urban managers will need to adapt their data and analytics approaches from the perspectives of decision-making and technology choices. Smart cities will need to consciously review their strategies on a periodic basis and potentially engage into big changes for longer-term sustainability.
Governments can move with confidence from the planning to the operational phase of smart cities by following these six steps to better manage smart city data. They can ensure that they deliver services within the city are truly smart, and that the city and its facilities constantly adapted to technological advances and the needs of citizens.
Abdulkader Lamaa and Sevag Papazian are principals at Strategy& (formerly Booz & Company), part of the PwC network.