 I want to come at this from a particular perspective and talk about a particular potential use of data that I'm getting increasingly involved with and that's in connection with the City Deal. The Glasgow City Deal has associated with it a commission for economic growth and what we're been asked to do as an independent body chaired by the principle of the University of Glasgow, Anton Muscatelli, is to look at how the expenditure that's been allocated to Glasgow City Region, which is 1.13 billion, is going to deliver the outcomes that are being projected, which are 29,000 jobs and a particular level of what's called GVA growth that's a technical measure of growth. I want to talk about the role of data in that because it's a huge expenditure for everybody and increasingly I think policy is shifting from an approach where you took a decision and then hoped it would be alright and if it sounded good it would be okay, to a system of contract basically now where money is allocated on the basis that you deliver a particular return and the onus is on you to be able to demonstrate that you're able to deliver that return. There's some really interesting issues in my view about how you go about that and how data can actually feed into that which might affect a number of things across the public service looking ahead. The actual City Deal that's been struck in Glasgow is a deal with Glasgow and the Scottish Government and the UK Government. The parameters of it are really, as I said, quite narrow. There are about jobs growth and there are about additional GVA per pound spent. In that sense, the primary role of the commission is a technical task. It's to contribute to the design of a local evaluation framework that if everything was done properly in terms of specifying the projects and managing everything. That the City Deal will deliver what was promised. But the way in which these deals are constructed in Glasgow isn't unique was through a kind of, I suppose, a data modelling exercise where if you like, historical data was fed in about the performance of Glasgow City Region, the performance of other regions and what happened with our road structure. Quite a complicated process of essentially historical data and a model was produced out of that as if you took forward particular initiatives then in the end you would get certain kinds of results. Nobody can explain how that model works, not even the people who actually produced it. It's highly technical, it's mathematical, there's all kinds of statistical structures to it. But what we have to do, I suppose, is identify the logic chains. The things that kind of link together the inputs, the infrastructure investment with the outputs, which is, for example, the number of people using a particular transport facility and then the outcomes which are supposed to be additional jobs. So we need to be able to construct that. And that poses us questions about what data we need for validating the effects and also the attribution how these things are linked together. So those are the core tasks. Under the terms of the city deal, this kind of information is needed at different stages. We're not just doing a summative evaluation, saying by the end of the process we have delivered everything that was set. The deal actually involves a process of what's called gateway reviews. So money is released at different stages through the review based on where you are with that process. So it's very important to the city and its partners that we actually are able to design an evaluation that feeds back to the city deal cabinet at key points. And in that sense what we need to have is a continuing constructive relationship that allows the people who are actually, if you like, at risk in doing the projects to maximise the return for the investment. And that's not just in terms of growth, although that's the actual deal that's been struck, but they want to do other things with it. They want to address what's called inclusiveness issues, the wider inclusiveness agenda, which the council has, which the Scottish government has, less so the UK government. And the objectives are less tightly set. We've got very specific objectives in terms of economic growth, less specific objectives in terms of these other things. The city deal is intended to distribute benefits across the authorities and create more employment opportunities for people living in less affluent areas. We don't have aggregate targets for inclusionary outcomes or mechanisms really beyond the selection of projects for delivering the aspirations that have not yet been identified. So how do we go about this? I mean we're not quite clear what it is we're trying to do. We know we'd like to do some nice things. We know that there's added value to it. How do we do that? And one of the things we're thinking about doing is establishing a small set of carefully selected measures that have the potential to capture these inclusionary outcomes. But what we have to be aware of is that there may be complementarities between inclusionary outcomes and growth outcomes, but there may also be trade-offs. So it may actually in some circumstances, for example, if you put all the investment in Glasgow, then Glasgow might benefit more than let's say Inverclyde or wherever else. There has to be a distributional process. And what we might want to do as well is to identify particular groups that you'd want to support. So you might, for example, want to look at lone parents. How do they benefit from this? How do black and ethnic minority people benefit from this? How do young people benefit from this or some other targeted groups? So you develop a kind of a policy framework for what you're trying to do. And then what you're trying to do is to construct the data that allows you to measure and specify what it is that you're trying to do and inform the policy of how you achieve it. So, for example, you might make decisions about how you did your procurement link to this or whether you concentrated on reclaiming vacant and derelict land in areas that are immediately adjacent to poorer areas rather than more affluent areas. You might make discriminant choices about what you did in terms of the growth impact and the inclusionary impact. So what's going on here? Let me just try to draw some lessons out of this task. In the case of city deals, large sums of public money are being targeted on the delivery of specific outcomes through a deal in which success and failure and whether the next tranche of money is explicitly dependent on evidence. So evidence is going to drive what happens here and the councils and others, government, are actually going to have to be able to explain what they do and justify their policy based on evidence. There's a real focus on data in this process. The validation process for this is going to be rigorous. It's not just gathering data, it's going to be analyzed to death and you're really going to have to be able to show that your data captures or the way in which you analyze the data captures exactly what's going on and just justify it. And there'll be all kinds of peer review processes attached to that. You've got a question about what kinds of data might be of use. As I've said, the way in which the deals were constructed was based on, if you like, traditional economic data, so surveys, aggregate economic data of that kind. But what we've got available in Glasgow and in other cities is different kinds of data that maybe wasn't available before and hasn't been fed into the model. So you've got censored data, you've got administrative data that councils and others have. You've got, in Scotland, you've got input and output data that we've actually got that isn't available in the rest of the UK. And above all, you've got the capacity which, you know, vonna, I'm sure we'll talk about it, you know, with great enthusiasm. Possibility for using real time and synthetic modeling data to do this. So instead of actually, you know, looking at year end to see how many cars have gone along a road, you can actually do it minute by minute, almost second by second and do some really interesting patterns about that allow you to see before you've made a decision on infrastructure investment or even while you're making decisions, discriminant choices in ways that you simply weren't able to do before. You're able to look at who's using what piece of transport, you're able to look at a whole series of things that simply weren't possible before because of the nature of the data that's now being created. And that expands the quality, the logic chains that you're able to create. So instead of, you know, the black bots model, you're actually be able to produce very concrete information about what's going on at a fine level of granularity potentially. And that can feed into other things as well. It might not just be about the city deal, it might be, for example, about service design across a whole range of things. So the information that authorities are being obliged to gather for the city deal, they might be able to use for other purposes and use these techniques that have not been available before. And what that's going to create in Glasgow, and I think it's going to create it in Manchester and West Midlands and other places that got city deals, is the potential for data to move out of, for example, the silos of the people who collect it in the first place. So instead of data being defined by local authority boundaries or data being defined by departments within local structures, different kinds of data are going to be brought together and used to get a much more refined structure of information. So although the initial focus for this is actually about providing an evaluation for a city deal and see whether we can get the money from central government, the reality is it creates an opportunity for local government and local government's partners to think more carefully about what they know about what's going on in the area that they're trying to cover, to work together at a city region level and to make policy choices that actually they can do on the basis of data that they don't have to wait three years until they can assess, if I'm going to put it like that. The great advantage of the kind of big data we've got now is you don't have to wait this lag period until you can use it. And there's another dimension which I'm just going to end on, which is that it potentially this kind of data allows people to make much better or better informed policy choices. I mean I spent some time in politics and sometimes when you're making policy choices you are in a sense choosing in the dark, you don't actually know what the effect is of the choice you're going to make. Now potentially data and data analysis and the two have to go together, it's not just about data, it's about how you use the data and understanding how that data can be used is really important within this, might allow you to look at different futures or different options or different alternatives rather than testing them out and finding that you've made a mistake two years later or three years later, whatever. So there are all kinds of opportunities in data to actually think about how we run things better and how we govern things better and how we get more people involved in making the choices and in making the choices that we make more explicit, so all of these things can run together. And I think the other thing we can do is actually think seriously about the efficiency of delivery of service, so we all know that funding is tight for all kinds of things in the health service and local government and whatever. If we can actually get people's needs more clearly defined so that you can actually allocate or make service decisions that allow resources to be more appropriately applied and data can make a big impact on that, for example look at the interface between health service and social care, if we have better information about how we deliver social care to whom, we could actually probably reduce the level of demand on acute care services and that would be economically effective but it also would actually be better for the people who are using these services. If we can find ways in which data can actually move in that direction, then that's something that's really worth working towards. I think we're at the relatively early stages of this where there's a lot of cutting edge work being done in UBDC and in other places along these lines but it's actually getting that to the point where people are using it and people know how to use it is really where we need to go. But I think it's a very interesting time to be talking about the possibilities of this and the dynamics within the governance arrangements we've now got that are actually driving this forward. It's not just a group of people who are interested in data who are going to find that the world is changing and find that interesting. I think more and more policy makers, people who run things are actually going to find that they can use data to do things that they haven't been able to do before and that's what's really exciting about where we're headed.