One thing about the UK’s approach to smart cities is baffling – why are local authorities rushing to add technology to give them additional data, when most haven’t even put in place the most basic mechanisms to use the data they already have?
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Take London, for instance. City Hall has done great things for open data with the London DataStore. Yet remarkably, it does not systematically collect data from London boroughs, other than that required for statutory purposes, such as population and school place statistics. The information used to shape decisions affecting the capital is therefore largely based on data collected by central government departments, such as the Department for Work and Pensions.
As The Economist recently observed, to a large extent London operates as 33 separate islands. Add to that the many public sector organisations that serve the capital, from the Metropolitan Police to the London Fire Brigade, and there are simply dozens of organisations holding their own siloed data.
Like a jigsaw that has never been put together, London has all the pieces, but no one can see the big picture.
This is no trivial problem. London must serve a rapidly expanding population. The city surpassed its 1939 peak of 8.6 million residents earlier this year, placing unprecedented demands on infrastructure and public services. Significantly, many of the obvious ways of responding to that demand – shared services, predicting where problems will occur to intervene early, and carefully targeting resources – all require joining up, analysing and acting on data.
The barriers to doing so are well known. There are technical hurdles in the form of different data standards and IT. There are legal obstacles, with each public sector body commissioning its own legal advice to come up with different interpretations of the same laws. There are skills barriers, as councils struggle to recruit data analysts. There are also cultural challenges – quite simply, some local authorities have not woken up to the fact they can do more in collaboration than alone.
It would be easy to conclude overcoming these barriers is a non-starter, but my report for the Capital City Foundation argues that’s false.
Instead, the answer lies in a model found 3,500 miles away.
Why London needs a Mayor’s Office of Data Analytics
London should establish a Mayor’s Office of Data Analytics (Moda), inspired by a team of the same name created under mayor Michael Bloomberg in New York City (NYC).
Moda would be a small group of data analysts, based in City Hall, who could combine, analyse and seek insights from (non-personal) datasets sourced from all London boroughs and public sector organisations. The team would be led by a chief analytics officer, reporting directly to the Mayor of London.
As has been consistently proved in New York, by overlaying data from multiple different sources, analysing past data trends and seeking patterns and correlations, Moda would be able to help improve areas as diverse as public service delivery, emergency response times, economic development, tax enforcement and education.
A London Moda could use data to tackle “beds in sheds” (illegally converted outbuildings) and improve food safety inspections, identify empty homes, help businesses decide where to set up shop, and fight tax and benefits fraud. The list of potential applications is essentially limitless.
The data that Moda collected would also be made available to London’s boroughs and public sector bodies, enabling them to combine it with their own department’s data to improve decision-making.
This would be based on a strict principle of reciprocity – organisations could access Moda’s data on the condition they first shared their own. To those who claim such data sharing is not permitted – if it can be done in the most litigious society in the world in the US, it can work here, too. It just takes the political will and leadership to do it.
Why the Moda model works for London
Why would such a model be desirable?
First, Moda would have the time, expertise and technical resources to translate each organisation’s records so they could be joined together, as many different data formats are used across the capital. This would enable boroughs to see beyond their boundaries, helping spot potential for further collaboration with neighbours.
Second, it would create the most efficient way to enable data sharing across the capital. If each London borough tried to negotiate individually with the 32 other councils to share their data, it would require setting up 528 one-to-one connections. By contrast, Moda could set up a single data exchange with all 33 councils. It would also enable City Hall to have local data from across the whole city for the first time.
Read more about smart cities
- Groningen in the Netherlands is working with Huawei to develop a smart city infrastructure.
- NEC, Bristol Council and the University of Bristol create an open, programmable city for smarter transport, environment and health services.
- Juniper Research names its top-ranking smart cities and points to a number of areas of concern to be addressed.
Third, as in New York, Moda would be a catalyst for spreading much-needed data skills throughout the public sector. After NYC's Moda created a model that helped New York fire fighters predict which buildings were at greatest risk of fire, the team delegated the data model to the New York Fire Department, training them to run and update it for themselves.
Fourth, it would make the provision of open data financially sustainable. Moda would support public sector bodies to derive real value and savings from their own data, incentivising them to invest in its quality. As in NYC, a subset of that data could then be released as open data.
For anyone interested in how data can deliver real public sector reform or create a smart city, the Moda model is one of the most impressive around. My report for the Capital City Foundation explains in detail exactly how it works and how it could be converted for London.
Significantly, it does not require extensive new technology or placing sensors on every street, but on making better use of data already collected.
It does not involve fundamentally changing the nature of activities conducted by frontline staff, but intelligently re-prioritising their work.
It does not entail gambling on a radical smart city business model, but on testing and scaling ideas that each provide a proven return on investment.
It’s not about preparing for some distant vision of future urban intelligence, but instead taking simple but concrete steps that could start tomorrow.
If London is serious about meeting its challenges and becoming a world-leading smart city, there is no better place to start.