The City of the Future Needs a Data Culture that Sees the Visible and Invisible
by Poon King Wang
Last updated 20 July 2026
ETHOS Issue 24, August 2022
Data Matters
Technology and data have the capacity to make what is happening in a city more apparent, but they can also make things less visible. Very often, data can tell us that something is happening, but it may not tell us why. Even as data makes something more evident, it may also suggest other elements we have yet to uncover. For data to support the pursuit of more liveable and sustainable cities, we need to look at both sides: we need to be clear what is being made visible, and what is being made invisible.
This is where integration becomes important: bringing together big data, small data and thick data, across siloed boundaries. It is only in combining these that we can begin to see the fuller picture of things as a system and are better able to make the right decisions and set the right policies.
This also involves not just looking at outputs and outcomes, but also identifying and understanding the processes that are leading to them, and understanding what it all means in terms of how people live and the impact on their lives.
This is an aspirational ideal that everyone is striving for, but no city can be said to have fully mastered it. The relevant question is whether we are taking concrete steps towards it, and whether we have the prerequisites to do this well. These prerequisites include the will to organise data and to use it for good and being system-oriented in approach. In these regards, Singapore looks to be in a good position, given our history and track record. How can Singapore build on these strengths to embed and deepen a strong data culture?
Very often, data can tell us that something is happening, but it may not tell us why.
What Makes for A Healthy Data Culture
A healthy data culture is one which understands that data comes from all perspectives and directions. It is about pulling all kinds of data together—whether descriptive, predictive, modelling, quantitative or qualitative—and asking: “How do we decide what to weigh or prioritise? And given the totality of the insights, how do we make good analyses and decisions?” A good data culture has the capacity to understand what the right balance between all these considerations is.
It is also important to understand both the limitations as well as the possibilities of data. A healthy data culture also calls for the humility to say that the more the data tells us, the more we don’t know and need to find out. This may also mean what we know today is correct now, but it may not be correct tomorrow. To adapt a phrase from the Institute of the Future, we should have strong beliefs, weakly held. We need to have convictions about our analyses, but be open to changing our minds when new data comes in.
A vital part of data culture is having an iterative, feedback loop. This means that it is important to make a constant, daily effort to improve. One of the things we tell foreign delegations who come to Singapore is that the solutions and successes they see took us decades to get to. It was the outcome of people at all levels working conscientiously, assiduously, day in, day out, to just make things better; to make a city more liveable day by day. Sustaining a city is a long game.
This also means changing course, or policies, when the data calls for it.
A healthy data culture is about not judging people on the rightness of their decisions alone, but also how they react once they realise they have made a mistake. The latter tells you about their capacity to change, which means that even if they get things wrong, they will eventually get it right. Just as with driving, sometimes, you may need to make a U-turn because you realise you need to go the right way. And changing course is better than keeping to the wrong direction, or worse, crashing. Besides, going the wrong way initially can sometimes lead you to discover new places.
A healthy data culture is one which understands that data comes from all perspectives and directions.
Nurturing A Data Culture for Urban Sustainability
There are a number of ways to facilitate a conducive data culture. One is to encourage a willingness to experiment, with the capacity to accept that not all experiments will succeed: some will fail, but the important thing is to keep trying because there will be new data points, new technologies, new techniques, different ways of stacking the data, and different ways of interpreting the data. If we do not keep trying, we will not develop the muscles to be able to do better in future when something new arises.
Look at social media platforms: whenever new ones emerge, people try them out and come up with interesting ways of using them. This has to do with the nature of data and digital technology at large: it is general purpose, which means that it can be appropriated to achieve many different outcomes. Digital data is an intangible sort of capital, which means we must expect the unexpected, both positive and negative. If you don’t experiment and you think you can sit down and write out all the potential uses and outcomes, you will miss out on all the other ways it could be used. So, we need to leave room for experimentation, within the public sector as well as in the private and people sectors.
The more the data tells us, the more we don’t know and need to find out.
We also need to always ask ourselves, that given all the data we collect and the analyses we do: what does it mean for the individuals at the end of it? We need to always link back to something that benefits people, so that they feel it is meaningful to them. For instance, one of our industry fellows working in Shenzhen took the basic GPS data already available to officials—data that had been collected but not used—and, with a simple matching of supply and demand, was able to help taxi drivers increase their revenue. He also looked at the electric vehicle (EV) charging infrastructure and realised that instead of charging them for a few hours to get to 100%, EVs could be charged for just an hour with enough charge most of the time—which meant queues for charging points could be shortened, and the charging infrastructure could be optimised.
A vital part of data culture is having an iterative, feedback loop.
Lee Kuan Yew Centre for Innovative Cities adjunct fellow Dr Andy Zheng worked with a team of researchers along with PAIR CITY, a big data company, on a number of innovations to benefit taxi drivers in the city of Shenzhen, China.
Using Data to Improve Sustainability and Social Impact
Lee Kuan Yew Centre for Innovative Cities adjunct fellow Dr Andy Zheng1 worked with a team of researchers along with PAIR CITY, a big data company, on a number of innovations to benefit taxi drivers in the city of Shenzhen, China.
Matching Taxi Supply with Demand
Real-time supply-demand data (already collected by the city government) was provided to taxi drivers, improving their efficiency and earnings by 8% while also reducing passenger wait times in hotspots that often faced a taxi shortage.
EV Charging
EV taxi drivers, carrying over petrol car routines, used to charge their EVs to 100% so they could change shift with a full tank. This led to long queues at charging stations near shift-changing locations. The researchers and PAIR CITY were able to show that most of the time, only 70% of the charge was needed for the night shift. EV taxi drivers therefore only needed to charge their taxis for less time, at any station wherever and whenever was convenient (e.g., during their lunch break). This greatly reduced queues and saved the drivers time which could be used to increase earnings or to rest. It also improved the overall use of the charging network without requiring more infrastructure to be built.
Rows of faster chargers (total 637) inside China’s largest EV charging station as of 2019 (near Shenzhen North Railway station)
Source: PAIR CITY
Data mining showing where Shenzhen EV taxis go for charging
Source: PAIR CITY
Making a Material Difference with Data
Another aspect of building a data culture is to develop the right talent: we need enough people who can do what is needed to get involved. Our research has found that in the digital age, one’s ability to master something and to be good at it depends on the interactions that we have.
Nobody has all the expertise needed, so you must complement your own skills with those of people from other disciplines, as well as those from other backgrounds and generations, because they have different perspectives and understand the system differently. For instance, in a power plant or large infrastructure facility, someone who’s only been in the control room looking at data will have a very different view from someone who has had to walk the facility, knock the pipes and smell the place.
We need to always link back to something that benefits people, so that they feel it is meaningful to them.
Data culture is not just about whether you understand the statistics, but whether you can relate the numbers to what is happening on the ground. In the same vein, the nursing schools have said that it is easier to get someone with a nursing background to pick up health informatics and know what the data means, than it is to get a data scientist to understand what nursing is about.
The design of these technologies, down to what kind of gauge or display is used and how people interact with them, also matters. Is your data infrastructure in fact helping you to understand what is going on? We need people who not only understand the technology and the data, but also how all these line up at the systemic level, and then what it means at a material level.
After all, a city is a cyber-physical setting. It is not just made up of digital data or technologies. It is made up of people, infrastructures, cars, roads, trees and so on. The ability to operate at the interface between the cyber and the physical, and to traverse them, becomes quite critical.
The risk is that sometimes we want something to happen, but it is not felt on the ground. And when it isn’t felt, it becomes harder to convince people to change. It is already inherently challenging on issues of climate change and sustainability, where what we need to do today will only show benefits in the future. It’s a long game. But all the more, it becomes important to find those gradations and small improvements that people can see and feel on a regular basis.
This is where Singapore can take the lead. Never in history have we been able to gather, store and analyse data like we are able to today. And if there’s any place that can pull all the data at different levels together in a meaningful way, it is Singapore. To do this well requires a certain administrative ability as well as the will to organise towards policy outcomes and public benefits.
Our public sector can be a role model for the data culture that we want to see in the rest of Singapore. It can show that it is open to experimentation; that it trusts but verifies data; that it has strong beliefs but is prepared to let go of them once the data shows otherwise, and that it can do this in a way that is mature and sophisticated. We should remember that at the end of every data point is a human being. The task is to try and make that human being’s life a little bit better all the time.
Our public sector can be a role model for the data culture that we want to see in the rest of Singapore.
ABOUT THE AUTHOR
Poon King Wang is Director of the Lee Kuan Yew Centre for Innovative Cities at the Singapore University of Technology and Design (SUTD). He is concurrently SUTD's Senior Director of Strategic Planning. His teams’ research is recognised in Singapore’s National AI Strategy for contributing to a trusted and progressive AI environment. Collaborating with IMDA/PDPC, they launched Singapore's first human-centred industry-agnostic guide to Job Redesign in the Age of AI. He received the Public Administration Medal (Silver) in 2021.
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