Yesterday saw City Hall host an ‘Urban Data Markets’ conference. The event — bringing together some of the best data and smart city brains from London, Berlin and beyond — was opened by London’s CDO, Theo Blackwell. Having launched the listening exercise for the Mayor’s forthcoming Smart London Plan this week, Theo emphasised the vital role data has to play across public services, the economy and society — from value-generating growth in new parts of the economy, to public health research and using it to drive innovation and collaboration in public services.

But data can be a little like Pandora’s Box, and a boring one at that. The jewels on the outside sure do attract attention. But upon opening it up, you can soon want to push the lid firmly back down again.

The issues associated with data management like privacy and security can quickly become fiendishly complex and stray into areas like governance and regulation.

And while artificial intelligence and machine learning are exciting, if not as new as some of the more evangelical pitches would have us believe, their various applications in areas like health soon stray into areas of ethical tension (the Prime Minister is in Davos today talking up our undoubted prowess in AI, and urging the safe and ethical use of it by industry).

Even the labels and using the word “market” rather than “exchange” can raise hackles — who owns this stuff and who has a right to trade it for a profit? When should companies pay for public data? And how do you balance this with a strong and accepted price tag for public value?

And let’s be honest, for all the buzzword bingo and general excitement, “data” — open, shared or closed, structured, unstructured, locked in a warehouse or swimming in a ‘data lake’ — can still be a topic that has family and friends sprinting for the nearest conversational exit.

So trying to keep to plain English, what did this Anglo-German data brain gathered yesterday in City Hall say?

Developing thinking set out in the recent independent review of Artificial Intelligence, we spoke about the potential for a “data trusts”. I am particularly keen to explore a “City Data Trust” to promote:

• proper stewardship of data by city governments, increasingly finding themselves dealing with technology and data propositions that blur traditional ‘industry’ or ‘service’ boundaries (energy and transport are the most obvious examples here);
• proper use by data consumers (e.g. businesses small and large, academia) using data for innovation, research and in short, building new businesses; and perhaps most importantly;
• trust among citizens (either as knowing or unknowing data donors), giving data as they interact with public services and private companies operating in the urban realm, in which there needs to be democratic accountability.

This issue of public trust soon gets complicated. Today, data ‘given’ in one place is already being re-used and re-purposed, often to very positive ends. Tomorrow’s uses for data are hard to discern in the here and now. Both make the traditional articulation of consent — based on who is collecting data, what they plan to do with it, who they might share it with and why — a tricky proposition.

Further, given that cities are places of increasingly intense experimentation with data, it seems to make sense to consider a Data Trust or body that can attempt to bridge the gap between the business of government — in this case, most obviously regulation — and the leaps and bounds of technology. It was clear from contributions though that we need not always reach for the comfort blanket of regulation. Voluntary codes and practice and principles-led collaboration are likely to be equally effective and more rapid courses of action.

There was plenty more under discussion — real nuts and bolts issues around management of data that require attention and that could underpin the City Data Trust approach. Echoing my own experiences of data science exercises, one participant pointed out that 95% of her business’ work can be about the “plumbing and the cleaning” of data. Another pointed to the need to increase the quality of data.

This observation and ‘ask’ raises a series of questions for me around the choking off of innovation and the balance of resources:

• how can City Hall use artificial intelligence algorithms to improve the disorderly state of our city’s public services data (where it is needed, in a priority order) so that human effort can be concentrated on analysis, insight and designing digital services around the data?
• linked to questions of ownership and value, whose job is it to clean this data, and who can do so most efficiently? Is it government’s task, or is it acceptable for this activity to sit within the business models for a range of companies operating in the data economy?

In closing this post, I want to loop back round to this issue of trust in data. Essentially, for city government is about balancing plain old-fashioned risk and reward, while listening attentively to and honing to its own needs, the technology and data pitch coming at it from a range of sources. The UK does not perform well in relation to, for example, the Nordics when considering societal trust in government to store and use data well (not a short read, but this New Yorker article is a useful in showing how Estonian society and government treat data and technology).

The listening exercise for the Mayor’s new Smart London Plan is now underway. Whether for civic participation, innovations like contactless payments or gathering data using WiFi networks to understand how millions of people navigate the tube network, or thinking about what forms of urban innovation 5G will give rise to, data will be at the heart of our thinking.

This is why ‘A New Deal for Data’ is one of the five themes around which we want to hear your views. In the wake of this event, we are currently considering how we take these complex issues into a more detailed discussion with Londoners. For now though, please do get stuck into the long read on the Smart London Plan and make your contribution to the discussion.