If London wants to be a smart city it must first learn how to use data

by Eddie Copeland

Here I explain the key messages behind my first report for the Capital City Foundation: Big Data in the Big Apple: The lessons London can learn from New York’s data-driven approach to smart cities.

This article first appeared in Computer Weekly on 9 June.

One thing baffles me about the UK’s approach to smart cities: why are local authorities rushing to add new 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?

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 DWP.

As The Economist recently observed, to a large extent London therefore 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 upon 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 councils have still not woken up to the fact that they can do more in collaboration than alone.

It would be easy to conclude that overcoming these barriers is a non-starter. Today – in a report for the Capital City Foundation – I argue that’s false. Instead, I believe the answer lies in a model found three and a half thousand miles away.

Why London needs a Mayor’s Office of Data Analytics

In “Big Data in the Big Apple” I argue that London should establish a Mayor’s Office of Data Analytics (MODA), inspired by a team of the same name created under Mayor Bloomberg in New York City.

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’s 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 proven 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. In the report, I explain how a London MODA could use data to tackle ‘beds in sheds’ (illegally converted outbuildings); improve food safety inspections; identify empty homes; help new 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 their decision making. This would be based on a strict principle of reciprocity: organisations could access MODA’s data on condition that they first shared their own. To those who claim such data sharing is not permitted, I’d point out that if it can be done in the most litigious society in the world, it can work here, too. It just takes the political will and leadership to do it.

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 (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 each council (33 in total). It would also enable City Hall to have local data from across the whole city for the first time.

Third, as in New York, MODA would be a catalyst for spreading much-needed data skills throughout the public sector. After they created a model that helped New York fire fighters predict which buildings were at greatest risk of fire, MODA delegated the data model to FDNY, 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 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 that is already collected. It does not involve fundamentally changing the nature of activities conducted by front-line staff, but intelligently re-prioritising their work. It does not entail gambling on a radical new ‘smart city’ business model, but on testing and scaling ideas that each provide a proven return on investment. Is 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, I honestly can’t think of a better place to start.

Find me on Twitter

You may also like