[playing video - Web viewSo we kept the navigational method for going ... the Federal Data Policy Statement, ... I think it was the Chinese were blamed on the night for hacking into

Ian and James talks 021216

Ian McLoughlinWe felt that whilst there have been recurrent phases of innovation, especially technology-driven innovation and government, we were kind of moving into a different situation than we may have had before and there are a lot more unknowns and a lot more things that were less than well understood in this particular way and this particular area of data-driven innovation. So we kept the navigational method for going to this particular presentation as you will notice. Although when I saw Into Unchartered Waters at the lift entrance this morning, it looked more like abandon hope all ye who enter here [00:42]. Anyway, we’re going to start in the time honoured way with a quiz and I just wondered whether, we’ve got big money riding on this, anyone can name these four people, is the first part of the question.

SpeakerShould we clue people that we took David Bowie off?

Ian McLoughlinWe took David Bowie off to make it harder.

SpeakerThat’s a shame.

Ian McLoughlinOkay, I’ll start to put you out of your misery. The only political scientist in the room, and everyone will kick themselves, that’s Salvador Allende in the top right hand corner there, as you look, the top left hand corner. The gentleman on the bottom any technical people in here will kick themselves, that’s Stafford Beer, one of the founding fathers of cybernetics. And I can see a few people in my kind of age group they must have a knowledge of 70s art glam rock in the room I would’ve thought, so we’ve got Brian Eno and Robert Fripp. So second question you’ve got no hope on, which is what’s the connection between the four of them. Well the connection is this, it’s called ‘Project Cybersyn’ and this was written up in a book called Cybernetic Revolutionaries three or four years ago; and there’s a little bit of commentary in some of the popular pressure times. And Cybersyn might now be called one of the first attempts to do big data to inform government policy making and planning and to start to get more predictive analytics into policy making. What happened was that Allende’s government hired Stafford Beer in the early 70s to come and look at how they could use computer technologies, as it was then to try and develop, to grow to socialism, how they wanted to develop in Chile. And they focussed on state-owned factories and tried to gather real time data on production issues and real time from factories deliberately involving workers as the source of information to make this very participative system and on the ground they probably knew more about what was going on than what the management did.

And all this data is gathered, this was in pre-internet days so the Telex was actually the means by which this information was communicated and was drawn together into this control room where policy makers and

Ian and James talks 021216ARC

1

decision makers would sit. Stafford Beer had a custom designed chair, he smoked cigars and drank whisky all the time, so there was a drinks holder and somewhere for his cigars at the same time. And they used to plan production in this room as best they could in real time – that was the idea. There were no graphical user interfaces and HD screens and all the rest of it in those days. So it was an army of graphic designers who created visualisations of this data, you can see some of those coming up. It was a great time to be a graphic designer in Chile, they were in much demand to draw the visualisations of the data to enable the policy makers. And the seats were deliberately set out like that to allow conversation and interaction and not have fragmented silo thinking. So everyone faced each other and exchanged their views and observations in the decision making process. So that’s the connection between Stafford Beer and Allende.

The other two are quite interesting in that Brian Eno became very interested in this project and in Beer’s work altogether. In fact he wrote performance to several of Stafford Beer’s books published in the mid-70s onwards, and Robert Fripp who worked with Eno and indeed with David Bowie as we mentioned earlier, also took a keen interest and they had a correspondence with Beer over this experiment. And the David Bowie link, we haven’t been able to establish it, it’s alleged in this top 100 books his brain of the factory, this one of Beer’s, although I have looked online at Bowie’s top 100, even his desert island top 100, it’s not there so I’m not sure. But Fripp definitely is in the frame. So there you go. So we won a lot of money there because we didn’t think anyone would get any of that.

So what we’re going to do in the presentation is I’ll say a little bit more about the nature of these unchartered waters, some of the opportunities, challenges, problems and issues that we can find at least in current research that’s been done in this area. Start to think a little bit about how we might chart the waters a bit more effectively so I can attempt to understand them a little bit more effectively and I’ll introduce a term that gets talked a lot here on new information, ecology, data ecosystem, civic technology ecosystem are the words you can find in some of the literature now. But also some of the other ways that we try to understand the issues of bringing that transformational change in government and public services. And then we’ll talk about what we’ve been finding in terms of our research, in terms of testing the waters in new information ecologies as they’ve been… find them in New Zealand and Australia. When I say Australia, we’ve mainly focussed on Victoria, so I apologise the Victorian-centric nature but we only had a year, other states may be later.


2

And as I say, some things at the end I’m speculative about navigation in these waters and an idea that comes from good tools of government which was updated a few years ago and called the digital tools of government. And one of the arguments in there about the implications of digitalisation for the nodality of government as the centre of social and informational networks in society and the challenges that this new ecosystem that’s emerging may be posed in terms of that modality into the future.

Okay, we’ve introduced everybody there so I think I would acknowledge the work that Yolande and Chi Wei have done in their research assistant time on the project but everybody else you know. So a little bit more about the project first of all, funded by ANZSOG Research Committee, really interested in the implications of attempts to exchange, link, share data and focussing broadly on the social sector, which I’ll say something more about in a moment and the issues of, in particular, governance that may have been involved with this increased potential of the actuality of data sharing to do things such as open data and big data. Comparative, so looking at New Zealand and Australia in particular, but also importantly with a benchmark, if you like, from experience in the UK and James will say something more about that benchmark in his observations. And this event, we always planned to do a validation workshop, it feels more like a trepidation workshop to me at the moment. But really to try and feed back to a knowledgeable policy makers and practitioners and academics and see whether what we’re saying is resonating and where we may be a little bit off-key or whether we’re tuned in pretty well.

And just to say something about the outputs, we’re producing an ANZSOG teaching case based on part of the work we’ve done, we aim to do next year an evidence-based review if we can get that accepted into the ANZSOG journal, and as part of the research requirement that’s if they fund it, so I’m [08:24] we have to write academic articles and we’re busy doing that as well in the near future. So there will be some written output from the project and in terms of today, the recording if it comes out okay and the slide deck we’ll make available to everyone who attended and please share them with interested colleagues if you wish to do so.

Now it’s a very big topic and our strategy for trying to make it doable in the time available to us was to say, “Well let’s try and take a slice through the whole onion, if you like, of the topic and not focus on one layer”. So we’re not focussed strictly at the government, just only at the government level, not focussed necessarily at the user, the citizen level or the community of service providers level. But try and take a slice through the whole onion. But in order to do that we had to really say we focussed on broadly the social sector, in practice we’ve focussed on the care sector and in practice we’ve focussed on the social side of the care sector. So we haven’t said too much about health, focussed too much upon health on deliberations. This is really, it’s not because things aren’t interesting, it’s just to make the project, give some focus to the project to make it doable in the time available. Critically to try and look at the thing in the different layers and not focus on one layer only, it’s not just look at this as what this government’s problem.


3

But try and look at the problem and issues and opportunities from the point of view of users from public agencies, non-government organisations, intermediary organisations who are coming into this space and in the user space distinct providers of care that was so new entrance into this space, social enterprises and social entrepreneurs. So try and take soundings, if you like, across the unchartered waters in a number of different places and to do that in Australia and New Zealand.

Okay, while on the social sector, I think this a great quote that kind of summarises it, although arguably applies to many other sectors as well, fragmentation, interests, parochial, silo-based, things never join up, disparate advice even at cabinet level, people are presenting their own data to support their own interests. There isn’t any genuine visitors through the system as a whole. There’s no understanding of the whole customer of the pathway they take through the system. There isn’t any ability really to know what’s going on, no one really knows what works, what doesn’t work and why in both cases and there’s little social learning that occurs across the system to enable sustainable change to take place. We can say that about health, we can say it about other sectors as well. It definitely applies in the social sector. James Mansell is a very influential thinker in New Zealand and this comes from his report to the New Zealand Productivity Commission on how to improve social services, and it pretty much nails on the head why the social sector broadly and [11:33] are worthy of focus, they certainly lag behind other sectors relatively speaking, and the take up of digitalisation, even basic computer infrastructure and so forth, has not spread and diffused, is not used as much in other sectors, the wickedness of the problems that are dealt with are obviously acute and quite different issues for policy and practice in many other sectors. There are well known constraints which are captured, in part, in James’ observations there in relation to fragmentation and overall system visibility and so forth and there are some really unique challenges around sharing and linking of data because of the nature of the information data that’s collected in relation to personal, social problems and so on and so forth. So our rationale for focussing on this sector is whatever the opportunities, challenges and problems and issues might be, they’re going to be quite amplified in this sector.

So what are some of these opportunities and challenges? This is a quick flick through some of the existing literature and this definitional problem with different types of data, this is derived from a number of resources Open Data Institute and a few other things. The notion of closed data, the stuff the government collects itself for its own purposes, it’s not quite clear what it collects, what it does with it and why but it’s obviously to do with things like national security and border protection, fighting crime and counterterrorism and so forth. And from time to time it’s a great concern, the Wiki leaks issue, for example, the idea that Obama used to be able to listen to everything you said which was current a couple of years ago. All things in this world are closed data, we haven’t really looked at that area that hasn’t been in the scope for us. What has been is broad world of the way government shares the data and collects on a routine basis, or by and large, doesn’t share very well the data it collects on a routine basis.


4

A few years ago that was seen as an information sharing problem and James and I were involved in certain projects in Europe about the difficulties of increasing the sharing of information in government.

Then we have got this relatively new phenomenon now of open data, which in the last four or five years has become a key trend to be involved in from a government point of view, and interestingly I need to get my notes for these scripts, I can’t remember, but one of the better definitions of open data I’ve come across is this one, which says to be open data it must exist, it must be indexable or [14:26]. So I quite like the idea that data just doesn’t exist, it must exist in some sense, you must be able to index it in some way. You must be able to engage with it, so it must be in an open and machine readable format, and thirdly it must empower, there must be a legal framework, a governance framework that allows it to be used and repurposed. So I think one of the better definitions exist, engage and empower. And then in this world of peer to peer data which is a very difficult one to pin down, some of the interest in the semantic way of a web too that was talked about in government circles is related to this and it’s to do with social media. I think the key thing is that an enormous amount of this data isn’t generated by government but it’s about stuff that’s with interest to what government does and we’re going to touch on that in some of the things that we look at.

So we’re really interested in this area of shared data, open data and peer-to-peer data and of course the confounding thing that sits on top of it, big data. There are a number of definitions in relation to this, and this definition from Helen Margetts about whole populations of things is diverse, it’s unstructured and she says it can’t be captured, managed or analysed on a desktop computer, so I don’t know whether that’s a good differentiation or not. The other definition that we quite like is that ‘the ambiguity of what big data means doubles each year’. But clearly people have started to latch onto this term now and in some ways it captures elements from all of these areas where we talk about big data. Okay, some of the reasons why all of these things have become so significant, these are some of the drivers, if you like of the interest, it’s very much … [16:27].

So as part of the process of open government more generally, of increasing civic engagement, increasing focus on citizens, increasing the transparency of government activities, it clearly also offers significant opportunities and advantages which we certainly found in our research in terms of more effective design of policy and the impact and potential impact of those policies, improvements just in the efficiency of government operations, perhaps that may turn out in practice the researchers have found to be a primary focus of trying to make the existing way of doing things more efficient, effective. Very interesting area of crowd sourcing new ideas and thinking about how to co-design and co-produce services. So engaging in a much broader set of stakeholders would normally be the case and of course the generation of economic, social and environmental value.

And there was a really interesting report published quite recently, if I can find, which came up for Australia with, this is by Gruen et al suggesting that $25 billion a year could be generated by the use of open data in


5

Australia which is 1.5% of GDP and I think the same kind of money that Twitter makes. So these economic testaments are debate in themselves, and as we found in the research we did on digital health a while back the benefits of electronic health records have big figures attached to them that have never ever been realised, we have to be cautious about these things. But nevertheless there’s a belief that significant economic value to be created as well as social and environmental benefits from better use of data to evidence of policy and practice.

Of course this is problematic. Often the data that government and other agencies has is not complete, it’s poorly configured, it’s not ready, off the shelf to be used and knowing what’s value and what isn’t, isn’t necessarily self-evident. It points to the adequacy of putting infrastructure in place to be able to use and analyse big and other forms of data. There’s some interesting issues around third parties reusing data which came out very much in the open data area, is it just a question of publishing data, is it a question of supporting those who are trying to reuse it in some ways, where does the responsibility of government, for example, begin and end in relation to those things. And as we all know there are some issues of trust and consent that loom large of making sure that our data is shared in a safe and appropriate manner with consents appropriate to the situation. And of course there are concerns about protecting identity and that we having appropriate privacy legislation and frameworks to enable that to happen.

And then there’s another set in the literature of more critical commentary that may be, I was asked a question whether this was a good thing or not, so the first argument is really… it’s like giving Kalashnikovs to the kids, why would you trust government with this and they’re pretty bad at doing large scale information technology projects and they’ve got bit of form on losing your data and we collect almost on a more than weekly basis instances of data breach and security breaches and breaches of regulations around the world. So a lot of this stuff gets lost so we’re going to increase the velocity and the volume of sharing information, for example, is it really a good thing because of these risks. The affordance is that big data, in particular, seems to be bringing in terms of focussing policy on particular problems and particular groups, some people refer to it as social sorting of really identifying problem families, which is happening in the UK, for example, and New Zealand. We also have problems in indigenous communities. And once the data can highlight problems and make convictions about the kinds of interventions that might address those problems, there are justice and equities about the way particular groups are singled out. In the UK there’s been a number of things, we wrote the manual version of this many years ago, when there were a number of riots. In past years the police had a tactic called Sus which basically meant they could stop you in the street and if they thought you were likely to be of a criminal bent and unsurprisingly they stopped people who they thought looked like criminals.

So if you were Afro Caribbean with a rasta haircut at the time wearing a Bob Marley hat your chances of being stopped were much greater than if you were say dressed like me. Although I’m not sure if they would stop you being dressed like this, I don’t know.


6

So this is a digital version of Sus, the idea that the prejudices, ideologically driven things get driven into the categories that get socially sorted. So there’s been quite a bit of comment in the literature about that. And finally what James Mansell has mentioned already is referred to as the tragedy of the non-commons, the fact that much of this debate is framed around who owns the data and what rights have they got to use it and it’s made a big argument which I’ll touch on in a few moments about the need for a social conscience, the idea that data is owned by all of us and that the issues are to do with how we work out and what circumstances it can be used and what consents are necessary in order to enable that to happen.

And finally we can’t do anything on innovation these days without talking about the D word, so we have the disruption word and in the literature there’s been a smaller growing discussion of the Uberisation of care and the idea that an Uber-type platform business models are starting to be introduced to various… sometimes by existing and incumbent entities that were also interested in by new entrance into the care market place, if you like, where those market places are emerging. And so we get this interesting phenomenon which we put very much in scare quotes of the ‘Uberisation of care’ and the idea of disruptive forms of data-driven innovation.

Very quickly in terms of charting the waters, two ideas that have driven our thinking, one thing that’s quite important, and James is going to say a lot more about this, is that data is often, it seems, a taken for granted thing, it’s a fact. But to be useful it has to be turned into information, which means someone has to make some sense of it, give it some meaning or interpret it. And how that meaning and sense making occurs is through a very important aspect of how data is used and for what purposes and for what purposes and what effects it might have. And a lot of scholars have started to refer to the, use the idea of ecosystem, the idea that this interpretation and meaning is given in ecosystems that emerge around data-driven innovation and these are the places where this interpretation takes place and in principal in systems models where feedback that will enable learning takes place as well. So if government makes data open, what processes and other means does is the disposal to get feedback from the innovation that’s done elsewhere in this ecosystem with that later to help improve government practice and thinking in the future. One of the criticisms of open data in terms of its impact on effective data is there’s an absence of such feedback so the data goes over the wall, the box is ticked, we published data and leave it open. But there aren’t feedback loops which enable the system to occur.


7

There is also an interesting issue here, because ecosystems work best in the self-regulating way and yet government hasn’t necessarily been used to more bottom-up self-regulation but has a more top-down control orientation. So it really asks questions about the government roles and the control of information but does it have to move to a more stewarding kind of model of what the development role should be.

The other thing we know from the literature is the change in public organisations in general, government organisations in particular, does not come about a naturally occurring phenomenon. In fact whenever there are new ways of innovation and studies from the early computerisation of the 1950s onwards have shown this, is that exactly what happens is strongly mediated by distinct organisational institutional arrangements or internal logics and tendencies as Jane Fountain said in her book on Building The Virtual State. And that book studied some of the first attempts to publish website data by American Federal Agencies at the end of the 1990s. She was interesting how far this would be to a transformation in the nature and effect on government. In fact all it did was sure up the existing silos of Federal Government and that’s been a recurring theme that the existing prevailing imbedded logics and cultures eventually win out and that new technologies data innovation, may be the latest one, eventually are forced to succumb to the status enquiry. So that’s the ‘abandon hope all ye who enter here’ message. With those two things in mind we’ve had a number of goes at drawing information ecologies which we found in that one as well, and so the litereature that has talked about this focuses very much on open data but we’ve tried to think a little bit more broadly.

So there are three areas and one is the joining up of agenda around big data, primarily within government at the moment. The second one is the open government sector here, it’s very much government outwards to other communities, social entrepreneurs, private and third sector, for example. And then there’s other world which we think has been so far not really looked at in any detail which is the Uberisation world, a peer-to-peer innovation where you can see government doesn’t necessarily map into it directly. So I’m going to say some things about that now and I’m going to talk to you about some of the things, impressions we are forming from the interviews that we’ve done. For the research we interviewed people in various parts of government in the care area, we’ve talked to people involved in the delivery of services, we’ve talked to social entrepreneurs, we’ve talked to intermediary organisations trying to join data company solutions up with care providers, so on and so forth. As I said earlier, we’ve tried to take a slice and take samples at different points as to what the perceptions of some of these issues might be. And the backcloth to you, some of you will know this very well, in Australia a whole host of things in relation to digital data policy in general, in relation to open data and the most recent statements, I think, the Federal Data Policy Statement, which is quite structured to just read the first sentence, so it’s signed by Malcolm Turnbull as Premier: “Australia’s capacity to remain competitive in the digital economy is contingent upon its ability to harness the value of data”.

A couple of months later the Productivity Commission published its Data Availability and Use Issues paper and Table 2 in there is a big sigh, here


8

we go again, and it summarises the findings of all the previous enquiries over the past six years on topics related to information and data sharing and why it doesn’t work in Australia. So there’s insufficient data sharing between agencies, insufficient data set linkage, little public access to administrative data, little research or access to administrative data, non-standardised data sets and a load of missed opportunities for evidence-based policy making. So the aspiration in the policy statement is yet to be fulfilled in the experience to date, and that’s not unusual in singling out Australia in particular at all. At the same time there are all kinds of pressures to address these problems.

We’ve been particularly interested by the implications of the individualisation and personalisation of care and giving consumers of care services, especially disability services in the case of the NDIA and [NDI]S, more choice of control. This aspiration, very laudable as it is, opens up informational gaps for consumers, how do we get the information to exercise that choice and control [29:43]. There are also system failure issues which I pointed in Victoria, the Royal Commission on Family Violence did a whole big chapter on data and how that needed to be done more effectively to addressing this social policy area and the Duckett Review highlighted the issues around getting better analysis of data, sport the issues and problems in the healthcare system in a way that didn’t happen in the events investigated on or reported on by Duckett. So there have been system failures which have highlighted this issue of getting better use of data and the sharing of data. I won’t dwell on the New Zealand situation at the moment, but similar a thing is happening in New Zealand, in particular the equivalent of, I guess, the NDIS there is the Social Investment Policy which is really highly attune to harnessing the importance of big data and some very interesting infrastructure developments around integrated data infrastructure Statistics New Zealand social investment unit which is designed to buy the data exchange highway but also tools and resources to help agencies work towards a social investment model, and a very interesting debate around social commons, who owns the data. Something called the Data Futures Forum has been trying to lead the debate in New Zealand about this issue. So again some very interesting developments there.

So what have we kind of got from the people we’ve interviewed, and here are some of our impressions to date. Not only is there a navigational metaphor, and of course you’ll notice the musical one as well, so Knowing Me Knowing You. Now one of the arguments about better evidence-based data driven policy in government is that enables the government to learn the systems better but it also allows government to know itself better. And certainly we found in the people we talked to in the social investment area, the idea that it improved the line of sight from the level of policy making to what was happening on the ground was an important one and really convincing people of the birth of social investment probably was in showing how that line of sight was improved.

I mean clearly also it allows a much better understanding of the needs of service users and talking to people in New Zealand they gave us some really interesting examples about how they are able to better target and understand needs and make interventions now rather than in 20 years’


9

time when those needs have escalated into much bigger problems from a social point of view.

We found some interesting examples of government reflecting to some degree on how this allowed them to better understand their own issues. A lot if it was, one of our response was how crap our data is and a whole load of getting our own house in order issues that will have to be faced before the advantages and opportunities could really be explored. But also really interesting in fact, and it really came out strongly in Victoria, and Stephen I hope you don’t mind me mentioning your graph, but we found that quite striking, a graph of expenditure on a large scale of IT projects which is kind of up here and then a couple of years ago it just went down like that.

SpeakerA few years ago [33:13].

Ian McLoughlinI hope evidence of real reflection and learning that large-scale IT projects don’t necessarily solve the problem and certainly James and I know that to be the case in the UK with the National Program for IT, for example – and I won’t mention My Health Record. The second thing we found which is quite interesting, and two days ago Nesta in the UK, the National Endowment for Science and Technology.

James CornfordAnd the Arts.

Ian McLoughlinAnd the Arts, published a report on how local councils were using data in a different [33:52] way. And one of the headline recommendations to other local authorities was that they should think about appointing data scientists to work on the frontline, the one side people do get policy and practice problems. And that was quite interesting because what we got in our data, and I think it speaks to the Jane Fountain model of how difficult it is for existing embedded norms and values to be overcome by new thinking. There was a lot of indications of stress and tension between this new data based approach to policy making so we were told anecdotes of stories about struggles between policy advisors who were gatekeepers to ministers and data scientists were trying to get the ear of the Ministers, for example. We found the crowd sourcing fraternity were stereotyped as, ‘kids in hoodies’ and there was clearly a cultural mismatch, if you like, both within government and when it’s looking out to the innovation community, hackathons and so on, they’re two different worlds. And there’s a process of accommodation and assimilation to be done. And so if you put a data scientist next to somebody working on the frontline, a classic street level bureaucrat our, kind of, assumption will be that may not work out too well in the first instance as a recommendation.

We also found looking back the other way, to look at the social entrepreneurs, that they found it difficult dealing with bureaucrats and they have their own stereotypes of dealing with government and in fact went out of their way to avoid it in some cases, so that’s what we found here. And they come and said, “Well we’re cool and the government isn’t,

Ian and James talks 021216ARC10

so we won’t spoil an image by…” I don’t know who came; I assumed they’d been here before. We’re also looking specifically at open data; we formed an impression both in New Zealand and Australia that the low hanging fruit stuff had been done, the land… Registry of Information in New Zealand, for example, was as big success story and that was pretty easy to do. And when it comes to the stuff with real value it’s yet to be exploited and it’s there for the barriers and both cultural and organisational to getting people to share data, they perceive they own – this idea that data ownership and information is power. And we have told many stories about the difficulties in getting traction to get the different government departments to share data with each other and to make data open. So, and I think James will say something in terms of the broader picture but it may be that the initial success period of open data is now running into a more difficult phase where you have to actually start to crack some of the more imbedded issues and problems around getting data sets of real value [36:52].

And then finally this debate on the non-commons, and whether it’s possible to have some kind of consensus about rethinking ownership of data, which seems to be quite a critical mind shift in thinking about these things, if some of the opportunities and benefits of greater data linkage and sharing can be achieved. Okay, so I need to move quickly.

I just want to show you one thing, this is the ‘cool like Kanye’ stuff. Now the video is a little bit jumpy but I think it should show. So what does Uberisation look like? What’s the equivalent of Stafford Beer and President Allende look like today. Looks like this.

[playing video: Clickability in 60 seconds. Available at: https://clickability.com.au/]

So Stats for Nerds, that could be the topic.

[all talking]

So Clickability is the focus of the ANZSOG case study that we’re preparing and what we found really interesting was that it wasn’t actually a data-driven enterprise, it’s actually a relationship-driven building enterprise. And if you can imagine in trying to do TripAdvisor for services and disability sector, people with disabilities don’t normally get asked what they think about things, they certainly don’t know how to do ratings and ranks per se. So a lot of the work that our social entrepreneurs were doing was in really making people capable of participating in creating that kind of information through social media and exchanging an understanding. So one of the things they do is they gather people with disabilities together and they do a dance, and it’s a pretty crap dance, and then they say, “How many stars would you give that and what would you say?”

And they might swear and they say, “No, no, you can’t swear when you’re doing a review”. Then they do another dance and it’s a bit better. As I said, that level of capability building in this part of the ecosystem is a really interesting way they define what their role was in building a capability to interpret and give meaning to data. We had similar issues with care providing organisations who first were quite hostile to the idea


that this method of visibility would be put onto their operations in much the same ways that hotels were a bit hostile to the idea of TripAdvisor. But gradually it seems that some of these care providing organisations are coming around to the idea about how this could be used to their benefit and they might learn from that kind of data. So we think it’s a really interesting example of what’s going on in the ecosystem which is quite a long way away from what government maybe sees a lot of the time and how that might… some of the issues it might involve. Steve, you look like you’re about to say something.

StephenYeah, the interesting thing is exactly that system was created by the South Australian State Government about four years ago so they’re doing this thing if you look at the case study what happens when government tries to do it.

Ian McLoughlinExactly, James is going to do exactly that with similar things in the UK and we thought for a discussion it might be quite an interesting discussion as to why, what the differences are and what the prospects for the interim might be. So thanks for that, it’s a really good point. We’re running out of time, very quickly on the internet nobody knows you’re a dog but they do know everything else about you. Final point, just very quickly, I mentioned the nodality idea, if you’re into the political science literature this is of interest, but we think there’s some really interesting questions here about one of the tools of government, in particular, that Hood, and Hood and Margetts identify in their quite influential book. Governments have the [43:39] property of being at the centre of social information networks. Historically governments have had the monopoly in that regard and it has enabled them to use effectively its other tools of authority, ‘treasure’, and ‘organisation’, which I won’t go into now. But nodality equips government with a strategic position from which to dispense information and we think one of the interesting things that’s happened is that the emergence of these new kinds of ecosystems to interpret and give meaning to information takes some of that monopoly away from government and it really attenuates the noble position.

So if you just look at digital visible, and Helen Margetts gives a very good example of the reward, I think, that the FBI offered to capture Osama Bin Laden. But if you do search on Google, you’ll find if you do a search on Google to find this, you’ll find it on every website bar the FBI one. They haven’t worked out how to get their message to the forefront of things. And there’s quite a lot of evidence now that’s… obviously a lot of investment to giving government presence online, but much less evidence of it being where users are going to interact with government. The Australian Government used to do in that annual usage survey.

They stopped doing it maybe three or four years ago but it showed a platform, a threshold had been reached of people being compared to use the web, the main way of interacting with government, face-to-face and the telephone is still significant, especially amongst certain age groups. So there’s a problem of visibility, there’s a problem of competition, of other sources of the same information. The more data that’s made open, the more other things happen in the ecosystem, like Clickability that


becomes a trusted source of information, the governments start to lose out online competition in terms of the strategic position to dispense information. And then finally there’s the problem with the illegal competition, from other governments, from hackers, from cybercrime, sometimes they’re the same thing it seems. And just the example of the Australian Census debacle earlier in the year when, in an attempt to, I guess, increase visibility of government by putting the survey online became very controversial, a lot of people didn’t want to do it that way, they wanted to use the old methods of resisting this idea of digital disability. It raised lots of issues about why this was being done, was it about the competitive strategies for commercialised ABS data, sure up its relevance today. And then of course, I think it was the Chinese were blamed on the night for hacking into the system and making the whole thing crash so people couldn’t do their return. So I think it was a good case study of the problems of nodality in a data driven world and with that I’ll be quiet and give the floor to James, thank you for listening.

James CornfordSo hello, my name’s James Cornford. I was going to say I’m here from England and it’s really good to be in Australia. But I’m going to say I come from Plant Windows and it’s really good to be in this land of the Mac, which sometimes is a bigger gap for international gap. I actually work at this place called the University of East Anglia where we sometimes say we’re the best university that no one’s ever heard of and we often have a problem with our nodality on the web, that people searching for us say, “But we keep coming up with this stuff about the United Arab Emirates”. That’s our major problem. In fact it UEA doesn’t stand for the University of East Anglia, it stands for the University of Endless Acronyms is the local joke. I also work for this thing, the Economic and Social Research Council’s Business and Local Government Data Research Centre and we’ve been working around the practical issues of trying to extract data from local government to work with it, to repurpose it, to feed it back into academic research, to feed it back to some of the people who have collected it, and to feed it back to some of the original users of data. So that’s my context.

Why are we talking about the UK? Why is the UK a good comparator, a good benchmark in terms of thinking about Australia and New Zealand? Well there’s a minor industry in terms of creating lead tables for open data and data driven innovation in public services. The two leading players in this are, I think the Open Data Institute who work with WW – sorry about the acronyms, I do come from UEA – WWF is the World Wide Web Federation and the joint point that links those two organisations together is Tim Berners-Lee who’s on the board of both of them.

They have been doing these rankings now for a little while, so 2013 they looked at readiness implementation and impact, scored countries and surprisingly given that they’re based in the UK they came up with the idea that the UK was the leading country. I’m sure if the Americans did it they would find that America was the leading country and so on. But I think what’s interesting about these is there seems to be a fairly stable group of leading or advanced countries that have been reasonably successful in terms of at least engaging with these kind of agendas. So developing readiness, beginning to do some implementation and perhaps being able


to see a little bit of impact. The 2015 example, you can New Zealand has slipped down to number six and so on.

Interestingly Australia was tenth in 2015, and its main weakness according to the ODI wasn’t particularly about readiness and implementation, it was about impact, it was about an inability to actually present impact. But when you read the reports carefully they often say impact is the most difficult thing to measure, it’s the thing they’re most unsure about and so on.

I think another point that’s interesting from looking at these comparative statistics is that quite a lot of them talk about there having been a big period of development and progress, in particular readiness and making data available in signing up and developing policy in the area. Lots of governments signed up to the G8, Open Government Agreement. But more recently there’s been a stalling of progress, so Ian kind of alluded to this. If you think that the Open Data Institute – I think it was founded in 2002 – you can see the open data in government is entering into its difficult teenage years at the moment. A lot of the easy and early stuff has been done so we’ve got this stalling of progress. I think another thing I quite like is that geography definitely makes a difference. So if you look at the OECD version of this, OECD is one of the other organisations that have done these comparative statistics, it puts South Korea at the front. But given that the OECD is based in Paris and surprisingly France is considerably higher up the pecking order.

But just to make the case, the UK, Australia and New Zealand they tend to be in the more advanced group, they make a good comparison, they’ve got comparable traditions of government and so on. I think when you look at the development of big and open data in the UK it has a long and tangled set of roots, open data in particular. And I think two things happened in the early 1980s that are really quite important, two sides of the open data movement emerged in the early 80s; and these are two symbolic moments. The first one is this thing called the Cabinet Office Information Technology Advisory Panel that Mrs Thatcher set up to advise her about Britain’s development in an information society environment. It was her nod towards the White paper on [52:38] technology earlier version. But it was Mrs Thatcher so it was very definitely making a business in information and that really I think was the first time government had started to see information data, including government information data, as a viable economic resource. So the metaphor that people use now is that data is the new oil.

It’s the commodity that goes into every single part of the economy that feeds everything, that has the same kind of implications that oil has, sometimes they’re good, sometimes they’re bad, and I’ll come back to that oil metaphor at the end. So that was one part of it.

The other thing that happened in the 80s, just a bit later, was the establishment of the Freedom of Information, campaign for Freedom of Information, Maurice Frankel and actually another person called James Cornford was very important in that, not me. So that was founded in 1984 and that also permeated through government leading up to the Freedom of Information Act in the UK in 2000. So open data emerged on two sides,


on one side it had an economic understanding of data and on the other side a much more political or civic understanding of data. And the open data movement has always been a bit of an uneasy alliance between these two kinds of perspectives, these two things work together.

So how’s it subsequently been pulled about? Well it’s been pulled about by a number of other kinds of developments in the way in which we think about care. So the development of new public management, another thing that’s been influential in New Zealand and also in Australia, and concepts about performance management which have pushed the idea of data as being an important part of how government manages to control activities. The development of the evidence based policy movement which has kind of grown out of the use of data and randomised controlled trials of gold standard models in medicine, but has now been applied to a very much wider range of areas. So probably in the 90s those were the two dominant things. Then from the mid-2000s the personalisation agenda and personal budgets had started to open up these kinds of issues that Ian talked about of starting to create a new market and requiring new kinds of information and data to support those markets, to create informed choice. And you can see, probably as I say, the open data mission, I’m dating it real birth rather than its pre-birth, to the development of the Open Data Institute and the growing interest from Nesta, National Data for Science and Technology in the Arts. Really from about 2012 Nesta started open data. They have a series of programs they talk about datavores, like carnivores and pescavores; but datavores. So it’s got deep and tangled root so it’s bound up in a range of other things that have been going on in the UK.

So if you have look at the recent timeline, we had 2011 coalition government, we had white paper on open public services, white paper on open data, there was a lot of policy action, a lot of the creation of statements and intent and so on, Shakespeare review of public sector information which almost it was like reading the ITAC Report over again, it’s very much the same making business out of information stuff. We signed up to the G8 Charter, the principals of the G8 Charter as did Australia, as did New Zealand. We’ve created our national information infrastructure and then around a couple of years ago, the focus of it seemed to move away from open data, at least in terms of the legislative agenda, and there’s much more interest now in big data and what we can do with big data. Although that’s still really, I think, in its infancy in terms of actual projects.

So what’s going on in the UK has to be seen in that kind of historical context, but also in the context of some of the other realities. So our banks were particularly problematic and they cost an awful lot to rescue. So we’ve had an austerity program that although we keep saying that we’re going to get rid of it, it’s now so hard-wired into the future spending plans that we’re going to have I think probably another ten years of pseudo austerity. Now we’ve had austerity from 2010, very tight, focus on efficiency, demand management and in particular in the care sector. So some parts of government have been protected including overseas aide, including very importantly a health area and education. But that means that other parts of government, and in the UK the care sector is run


outside of health, it’s in a separate policy side of it, have been extremely hardly hit by austerity.

We’ve had a very powerful debate about health and social care integration in the UK and a lot of work around that. I don’t know if anybody knows Leutz’s Five Laws of Health and Social Care Integration? I highly recommend it to you. But basically Leutz’s looked at health and social care integration in the US and the UK finds that it’s a very, very problematic set of issues. But the classic version of this the bed blocking crisis. So in hospitals we have, in particular, a lot of people who are medically fit to do the hospital but there is no social care environment and no adequate social care for them to be discharged into. So the doctors are unwilling to discharge them because there’s nowhere for them to go and there’s no care or support package for them. So they stay in hospital, they block the beds and we have a regular bed blocking crisis. We’ve tried to overcome that with things like the Health and Social Care Act which has attempted to at least pull some budgets between health and social care and have created these current strategic plans that we have, which we’re attempting to get around this because part of the problem of running health and care in separate silos is that people get traded between them. So, “Here you go, you take this person”. “No, I can’t have them, you need to have them back” and the costs and benefits of delivering services don’t fall appropriately on the two sides of the border. So, on top of that, we’ve had a crisis in adult social care from about 2012, I’ll say a bit more about that in the next slide, and even more recently we’ve had the joy of Brexit, which has and hasn’t disrupted everything. But I think it’s caused a tension to move away from some of these issues. So the programs and things to move forward but everybody is a bit concerned about what the implications of Brexit are going to be for this.

So in health and social care, for example, we are suddenly raising questions about whether we are going to treat Europeans in our health and social care system, how we are going to treat them. An awful lot of attention has been given to gathering data about Europeans who are working in the UK and that’s become a dominant focus, so it’s taken a lot of attention away. The crisis of social care has some common features but it has some very specific UK ones. So we have an aging population as most other people do and we have a lot of long-term conditions associated with aging and so forth. It’s interesting, these are the latest figures I could find from the King’s Fund.

So central government has reduced its funding to local government by 37% in real terms over the five years from 2010 to 2015. That’s a third of local government funding has gone. As a result of that, local government has had to become much more selective in how it uses that funding. We have personalised budgets and personalisation of care but that’s a much smaller group, it’s a residualisation of a group of people it serves, 26% fewer people are being served and that number seems to be getting more and more focussed on the acutely needed parts.

Funding cuts for care get passed on to care providers, in inflation increases. So the care provider market, for example, care homes, people providing care at home services, complain that their income is being restricted and constrained very tightly. They’re fighting against shortages


of skilled and capable workers and increases in standards, the introduction of the national living wage, so their costs are being pushed up partly by shortages and partly by money at the other end. So we’re now in a situation where we have this chronic crisis of care so understanding data is in the context of this really acute crisis. Everybody is now saying, “It’s only a matter of time before there is a large scale failure where one of the large scale providers goes out of business”. It’s a real problem. So people are looking at the use of data in social care, not in the context of efficiency gains and small scale moves, but the attempt to create a new settlement. So there is a sense of the need for some real radical innovation in the way in which things [1:02:46].

So you also have this metaphor of a burning platform. We definitely have a burning platform and everybody is looking around for some halfway decent live plates, and data has become one of those. But when you look at the kind of things that have been going, this is the UK, supposedly one of the leading countries in terms of doing this. This is the latest piece of research, Ian talked a little bit about it by Nesta, the innovation foundation in the UK, social innovation foundation. And they made this point that a lot of this data innovation in reporting to authorities, in the care sector in particular which is a large part of what local authorities do, adult social care, a lot of it has been around making services more effective and around ideas about supporting the local economy. The dominant force has been this problem of managing budget cuts. So it’s attempting to do this in this particularly harsh, austere kind of environment. It’s interesting, I looked at a report and thought, “This is fantastic!” This came out two days ago, three days ago, and thought, “This is just what I need given Ian’s just told me I have to do a presentation. This is great, I can raid this, this is great, I’ll get some fantastic material out of this, this is really useful”. And they’ve done it systematically and they’ve looked at a number, looked at about eight or nine case studies. But when I looked at it I was kind of disappointed in what they found and in the kind of recommendations they had. So I don’t think there were many surprises in terms of the barriers and things that they’ve uncovered, they did similar sorts of things, Ian’s talked about most of this already, poor data quality, lack of standards, lack of familiarity and skills amongst staff, some kind of resistance, governance problems, information and data governance concerns and so on, dynamic and all of those sorts of problems and so on.

And they had a fairly familiar set of recommendations, I think: “Make sure you’re clear about the problem you’re trying to solve. Make sure you’ve got clear objectives. Get some buy-in. Find some quick wins”. This is not difficult kind of stuff in a way.

What I thought was interesting here, I was looking, given this kind of rhetoric we have about the burning platform and need for radical innovation, was that these local authorities had limited space for really disruptive innovation. There’s not as much space there so I think this message about leading countries beginning to stall, the difficult teenage years of data driven innovation and an awful lot more of it was about getting our house in order, making sure our data sets were consistent, available, shared within the Council, within the local authority between, for example, I’ve done a lot of work in the past on this troubled families program. And of course one of the interesting issues with families is that


it requires adult social work and children social work to communicate with each other, which surprisingly is making the social workers they exist often being next door to each other is a surprisingly difficult set of issues. So a lot of it is about just doing the things that we do, nothing very radical, just doing that stuff better, properly. So I think the message from the UK is that we are now hitting the difficult moment when experiments are beginning to get evaluated, we’re beginning to learn and realise that this stuff is an awful lot harder than we might think.

So I’m going to give you just one case study because Clickability is a really interesting example here and as somebody said it’s not the first Trip Advisor for care homes. So we’ve had quite an interesting experience in the UK in terms of the development of TripAdvisor for care homes from a big government perspective. The government set up a thing called NHS Choices and NHS Choices is an attempt to empower consumers by giving them more information about originally healthcare providers. But they extended NHS Choices into care providers and in particular care homes. So care homes are a very important part of the system, under a lot of pressure; so big choices. It’s a significant choice where you’ll be going for potentially for quite a long period of time, four, five, six, seven years at the end of your life or for people with other kinds of disabilities maybe for shorter periods of time or for respite care. But it’s significant, it’s a big and significant choice. So NHS Choices is built on the classic, no-door government review. The CQC is the Care Quality Commission, it’s the regulator, the regulating body for social care in the UK and the CQC data is the basis of the way in which NHS Choices presents care homes.

But they were interested in this concept and so about four years ago they began to offer the opportunity for people to produce some peer-to-peer data into the system, possibility for user generated content, not very much taken up. If you have a look at NHS Choices look up some care homes, you’ll find that… I did a very rough rule of thumb piece and found, I think out of the 100 care homes I looked at there are about five homes that have at least one user comment. So some real problems there, not much evidence of use. Of course NHS Choices is primarily something that people think about in terms of NHS and medicine and medical care rather than social care perhaps.

So I also looked at a range of not-for-profit and commercial providers who took the CQC data, open data, survey [1:09:09] data, and created their own TripAdvisor for care home type products, yourcarehome.com is one of them, there are five or six and they have very similar outcomes. The actual volume of traffic isn’t that high. There have then been a set of concerns about the use of review sites generally in the UK and the care sector is one of a number of sectors that the CMA, the Competition Markets Authority which is the economic, the general economic regulator looked at and a number of these sites, they had an admonishing letter from the CMA suggesting that they might like to look at how they moderate these sites and might like to consider to what extent these reviews were genuine or were sock puppets, either of the owners of their care homes or of rival care homes attempting to make derogatory comments about other care homes. So there’s been a bit of concern about that and there’s been some writing around that. And a number of the people who have been involved, in particular the non-profit people


who have been involved in trying to develop these, have written up their experience and talked about how difficult it’s been to get this apparently simple idea of TripAdvisor. So they talked about a lack of volume, they’ve talked about the problems of personal identification and moderation of that kind of data. This is not like Amazon where you just let people write their own reviews of this stuff, you do need to moderate it, there’s legal implications for this sort of stuff. These are long-term decisions, this isn’t like buying a book or staying in a hotel for a couple of nights, this is major life decisions and therefore people [1:11:00] look at it and it’s not clear who the users are.

So I think some of the lessons of this involve being a bit sceptical about the quick win version. So the first question is, and certainly in talking to people about this you get this phrase that comes up over again, build it and they don’t necessarily come. So NHS Choices already existed as a service but even NHS Choices more generally, the research on that suggests that only 4% of users actually use it when deciding where to go. What they do is what I would do which is, where should I go? I’ll ask my physician, my doctor, the person I trust or I would ask word of mouth, I would use information from people who had had the same kind of treatment or surgery and so on. So there’s no that much use of that stuff. Pump up the volume, try and get more people coming through, well part of the problem with care homes is they’re not like hotels, you don’t check into lots of different hotels in lots of different places, you don’t have very much comparative experience for care homes, you tend to have one or two care homes. You’re restricted in your geographical location so you’re not going to generate the same kind of comparative volume of reviews. It’s not going to work like that, the care home market isn’t like the hotel market, so the TripAdvisor thing starts to break down.

Also, people have a pretty good idea of how to rate hotels. But people don’t have a very good idea about how to rate care homes, we don’t quite know how to do that. I think one of the great things about Clickability is that they understand that as a problem. We have to train users in how to actually use these kinds of technologies. This isn’t an out of the box automatic activity. We’ve then got a problem of who the customer is, so who’s actually filling in these customer reviews of care homes?

Is it the people in the care home themselves? Possibly, if we believe in the Silver [Internet] Surfer, this character here, we believe in this model of the Silver Surfer and possibly those people are filing in their accounts of what service is like in a care home. But predominantly when you look at the reviews that are there, they are filled in by relatives, they are filled in by people who aren’t experiencing it themselves. What we’re getting is a review of how good is a care home from the point of view of the guilty children who are [1:13:42]. And then I think there’s a set of problems about “says who?” So what is the value, what’s the status and standing of peer-to-peer reviews in these kind of contexts? There seems to be concerns, increasing set of concerns, and I’ll say a bit more about these in a moment, about trusting social media, not helped by Facebook experimenting on people without their consent, maybe not helped by the recent stories about Americans getting their information from Facebook and some of the other events that are going on in the world. So there’s a kind of tension between the capacity of the social media to provide access


to a wide range of point of view and a lack of trust in some of those things, which means people look back to… all the studies show people look for word of mouth, direct evidence from going to visit them.

Interesting, I was looking at university students in my business, exactly the same thing. So we produce endless amounts of information, public information review sites and so on but all of the evidence is students choose their university and their courses on the basis of word of mouth coupled with student engagement visits, actually going and having a look at universities. So those are by far the most important sources of information. So the TripAdvisor thing in the UK, it’s ground into the sand a bit and I think there’s some useful and interesting lessons there. Clickability seemed to have a slightly different proposition; I think a good question is whether that’s really going to work.

So I’m going to end by trying to raise a bit the analytical level and one of the phrases that’s really been associated less with open data and more in the big data movement, is the idea that we can let the data speak, that the data will tell us something automatically. And I think the problem is, or actually more likely we let the data draw us a picture visualising. Actually we don’t let the data speak, the data can say something but somebody’s got to be there to translate that into what that means for a particular set of actors at a particular time and that’s this concept that I’m calling interpretative communities. Because being able to understand what the data says needs input from a range of different actors and different players and a lot of the problem is in how we bring those people together, what are the platforms and spaces and occasions are that enable us to bring the various bodies and knowledge that are required in order to turn that data into something that conforms to people’s individual decisions. With more data comes more potential and possible interpretations, there are more patterns for us to find, some of those patterns will be spurious and misleading; many of them perhaps. So interpretation becomes more important and potentially more contested, especially as data becomes available to other kinds of factors.

So in a way is that a problem, lots of people interpret the data in lots of different ways? Great, that’s what an open and plural society is about. Well to some extent that’s fine, that works okay but when you talk to people engaged in that kind of process there are requirements for certain shared understandings that have to go beyond my local interpretation of what this data means in order for us to link together perspectives and ideas from different parts of the social environment, of the ecosystem as it appears to be called. So we need shared stabilised core data sets where meaning is established and shared between different groups, we need to establish a certain common ground in order for us to then have our disputes and debates about other aspects. So some areas are very important, for example geocoding because that links together hugely varied datasets around common geography. So who needs to be involved in these communities? Well we need experts, we need technical experts, we need people who understand data, data analytics and so on. These people are in very short supply; these people command a big premium. We also need domain experts and this is where I think some of the things that Yolande and Ian have been picking up. There’s a suspicion between domain experts who know a lot about social care or policing or about


those kind of areas, they are imbued with it. And there is a certain suspicion about the, perhaps more cool-like-Kanye characters who are turning up using these very generic algorithms and tools to try and come along and tell them how to do their job. So there’s a certain tension there.

And where do we get these experts from? Do we get them from government? Well government in the UK has lost quite a lot of its expertise, we’ve outsourced an awful lot of things. We don’t have a lot of these kinds of people. We’ve got policy experts, we don’t really have practice experts so much anymore in government, we’ve outsourced a lot of that. Do we find it in academia? Are there experts on these issues in academia? Well there are a few but a lot of the pressures in academia point away from getting involved in things that are too policy relevant that are seen as too hands on and so on, which I think was interesting in the comments in the beginning that this kind of project, kind of, leans against that sort of thing.

What about the press and media? Well they have their own problems, they are being disintermediated, it’s very difficult to make a living as a journalist now, there’s a bunch of issues there that I think are quite difficult. What about business people? Well I think they’ve got plenty of work. If you’re good at this kind of work there are plenty of other things that people are looking at so it’s difficult to find the experts. But it’s not just about experts, it’s not just about policy makers and practice experts, it’s also about the people who are on the receiving end of these algorithms and so. Lay people, data subjects, now they’re an important part of the way in which interpretative communities develop and we’ve got the problem of local versus global interpretation. So to what extent do we want a [1:20:21] interpretation of what it’s going to mean and to what extent are we happy that what this means in Newcastle might be different to what it means in Norwich, which might be different to what it means in Nantwich, for example.

And I think you get different kinds of answers about how interpretative communities get formed depending on the models you use, the big data model, sophisticated, predictive analytics, complex analytics, difficult to understand. No, I don’t really want to have a lecture on Bayesian statistics if I can possibly avoid it, often seems bizarre, difficult to get your head around. That kind of stuff tends to remain a bit of a black box in the realm of the expert. Open data generates rival interpretive camps who may or may not agree and may or may not conflict, who fight it out, “Make of this data what you will, here you go”. The peer-to-peer data I think is some of the most interesting because it has the possibility for everyone to interpret this stuff in their own way.

You read the Amazon reviews? Sometimes I read the Amazon reviews and I look for a real stinker and think, “That could be really good. I could really enjoy that. The right kind of person thinks it’s dreadful.” I interpret that in a different kind of way, I have my own personal interpretation, localised [1:21:39] and personal meanings but the possibility of emerging meanings from that data over time, so emerging practices come out. Shared, emerging, even subjective outcomes, what some people call a folks-onomy, a bottom up ontology, a bottom up kind of understanding. But there’s also danger in these filter bubbles, so the whole filter bubble


notion that we’ve seen can be really, really toxic in contexts like potentially the US election. So in thinking about these kinds of approaches in the way in which we work with data, I’ve been doing some work with an organisation called the Centre of Excellence and Information Sharing in the UK and the Centre of Excellence has been trying to tackle some of the problems of getting information and data moving around the public sector and its partners. And I think through working with them we’ve started to develop a picture of how we thought about information sharing. We’re going to talk about this, not as a strict chronology, it’s not one, two, three, it’s three kinds of logics that fight it out and they’re complimentary in certain sorts of ways. But I think you can see them as residual, dominant and emergent logics in terms of the way in which people approach data and data in particular in the health and social care, data is a bit of a social sector.

And I think if you go back to the beginning of the century we had, certainly in central government, and to a certain extent in local government, we had this design logic. We had this idea that the problem was to design a product, a system, design an information system, create some process maps and use those to implement a design system that we move data and information appropriately around the actors in the ecosystem. Think of a classic e-government kind of era, we had variously big and small e-government projects in the UK, we had this huge £12 billion project to wire up the health service, program for IT and health service and that is the kind of model, it fought against it but in the end it was basically a technologically determinist [1:24:06] model, it was about how the technology was going to set us free. The phrase that Ian and I remember with fear when working on, some of those projects that you would get from Whitehall was ‘we want to a shrink wrapped product’, that was what we produced, a shrink-wrapped product that we could roll out, that was the vision.

And I think in that kind of context the data scientist is a kind of technologist, we’re talking about this landscape, this landscape element, we’re talking about Hadoop and other technologies, and so on, involved. But I think we’ve stopped doing that, I think it’s interesting that the idea really the big computer programs, big computerisation initiatives are a bit out of fashion now.

So we moved to another centre of activity, of course the technology still evolves on and it’s still important, but it’s not the dominant force. The dominant force, I think, in the period for about 2005 until now has been a control logic. So the concern a lot of the time has been about controlling the flow of information and data in a legalistic kind of way. So the classic, bureaucratic information governance. We have organisations now called NHS Digital but it was for a while known as the Health and Social Care Information Centre, which sought to manage and control the flow of information around health and social care. We’ve got a thing call the information governance alliance which brings together a lot of the actors who are concerned with information governance. The classic product wasn’t a shrink-wrapped solution; it was these things, information sharing agreements, regulation of information sharing. So in these kind of contexts, the data scientist was an information manager, keep calm and ask the information scientists, they were a source of advice about what we


were allowed and permitted to do, maybe even encouraged to do. And then I think what we’ve realised is that a lot of the information control activity was based around the idea that if we made what people could do with information and data clear to them, they would do something with it. And once again this isn’t quite a ‘build it and they will come’ this was more a create a good governance structure and some guidelines and guidance and they will use the data in order to innovate, and they don’t. In fact quite a lot of the time the governance structures are things that we see developed out of this control logic, have been used to support that kind of conservative stability orientation so people have used information governance as an excuse for not sharing information. They’ve used it to defend their traditional silos and activities.

So we’ve moved onto this third way of thinking about how data operates, which I’m calling an enculturation logic. So it’s focus on behaviour, culture and a practice logic and it’s interesting, its products and tools aren’t guidelines or telling people what to do and they aren’t shrink-wrapped products and software and systems. They’re stories by and large. What the Centre of Excellence and Information Sharing say they’ve produce is storytelling. What they engage in is cultural change programs, using tools like appreciative enquiry, that’s the kind of work that they’re doing, getting people to share why they’ve managed to make things work, trying to make people feel empowered. It’s all at the level of skilled practice by people on the ground. So the data scientist in this kind of context is a cultural change agent. What they’re bringing isn’t specific technological skill, nor a legalistic governance knowledge, but they do have to have both of those things of course. What they’re really bringing is this ability to reconceive what the problem is, to re-see how data can help and support particular kinds of activities.

So this notion that there’s a shift in landscape here in terms of the way in which the UK Government is engaging is quite helpful I think. And I’m going to end up just by saying something about the concerns and limits and I’ve just been reading this book which is quite a nice book but it’s got a great title, it’s called Weapons of Math Destruction. But to push back a little bit, part of our understanding, part of this more cultural understanding, more practice-based understanding, is seeing information and data not as Ian said, as something that’s given pre-theoretical, pre-moral, something that exists outside of those kinds of issues, moral and ethical issues, but rather as something that at least the moment one has some engagement with it, becomes of some ethical and moral significance and input. So interpretation and interpretative communities aren’t just a technical problem, they’re also a moral problem. In order to interpret something we have to have some kind of understanding or standing in terms of the value, not just the economic value, but also the values of information and data. So fairness and justice as well as efficiency and effectiveness. So part of the concern, I think, is that given austerity, given those concerns, fairness and justice tend to come in a bit late in the process where we’re so concerned with efficiency and effectiveness. And these questions about models of what the good life is, models of how we might then interpret data about individuals, what these things actually mean relative to some model of what it is we’re trying to achieve. What is care? What is the nature of a good life? What is the nature of end of life care? What is the status of these individuals?


So in terms of the wellbeing literature, for example, the well-known discussion between hedonic and eudemonic motions of wellbeing, what are we trying to achieve? Are we trying to stretch out somebody’s life for as long as possible or are we trying to give them a good death in a care home kind of context? So the moral economy of data is quite important in terms of interpretation and understanding that is quite elaborate. The idea of moral economy comes from some work that Edward Thompson did way back in the 1960s about bread riots in the 18th Century. And bread riots, what he was interested in was the moment when the crowd decided that the price that people were charging for bread had become unacceptable. People are willing to accept fluctuation in prices, they’re willing to accept that up to a certain point. But at a certain point, we decide that this isn’t an acceptable behaviour by the baker, now the baker is gouging us, they’re doing something unacceptable. They are no longer prepared to accept this variation. And I think you can think the same kind of thing about people’s relationships with their personal data.

So there’s a shifting culturally defined limit to what people are prepared to accept in terms of the exchange of their data for good services, quality, efficiency and so on. There are barriers that people put around it; the best case in the UK was our version of your [My Health Record] was care.data. So this was Tim Kelsey’s great move in the Department of Health to try and bring together all of the records or at least aspects of all of people’s health records into one central place. And it was handled very, very badly and it wasn’t just that a small group of activists rejected it, large numbers of people actively decided, “I’m not taking part in this. I’m not engaging in this”.

So there was a set of assumptions about what the moral economy of data was that were bound into that strategy and they misread it badly. So I think understanding that’s quite important and underlying that, the idea that data is a relationship rather than a thing. There was a very nice book which makes a very good point about data. Data literally means ‘that which is given’ but often when we’re talking about data here, data is that which is taken. So maybe we should think about it a slightly different way. And then finally this lovely phrase Weapons of Math Destruction, I don’t think we can see yet a lot of examples of weapons of math destruction in the social care sector, partly because these things haven’t developed yet. But there is some concern, and there are examples in other parts of public services and the first problem with weapons of math destruction is where the algorithms and analyses start to define reality rather than map reality. So the classic example Ian referred to the Sus Laws in the UK, there’s been some evidence in America where, for example, crime mapping means that we identify high crime areas. The high crime areas are then flooded with policing in order to address the high crime. Because there are more policemen there any crime that does take place is more likely to be identified and registered, therefore it gets fed back into the model which identifies the area as a high crime area. If, you put all of those policemen in another area they would identify possibly other groups of people or other kinds of activities there as crime.

I remember very clearly, this is the last anecdote, being in a discussion way back around about 2000 in Durham about a concept we had in the UK which was known as the Shifty Fifty. And an argument was that in every


local authority area there were 50 – usually young, usually male – individuals who were causing 80% of costs, trouble, vandalism, crime issues. If only we could identify those 50 people and the second thing we knew about them was when we actually did catch up with these people we would find that at some point public services had failed them. They truanted from school and nobody had followed up. They had been in the care system and they had been abused. At some point our public services had failed them, the Shifty Fifty. And this debate was going on and then suddenly someone at the back piped up and said, “What about the fraudsters? What about white collar crime? What about that group of people, what about the impact that they have? Actually maybe we shouldn’t be standing outside the…” obviously because they’re Trinity we are standing outside the school gates in the poorer areas of county Durham. “Maybe we should be outside the private schools where our future fraudsters and so on are being educated.” So there’s this danger of letting the data drive the way in which we think about the world and we end up with these self-fulfilling logics, if we look for crime there we’ll find crime there.

So there’s some concerns about how that stuff works and tightly coupled, tightly linked systems with opaque algorithms that nobody really understands what they’re doing is a recipe for what Charles Perrow calls ‘normal accidents’, the idea that this kind of tight feedback coupled with opaque systems that are hard to understand generate normal accidents. So you can have some really potentially dangerous things coming up. I don’t think we’re there yet but we’re getting close to it.

So to summarise what I have to say, and I’ve gone on way too long, I think what you’ll find in the UK is a strong institution innovation, quite a strong set of bodies, Open Data Institute, Nesta who might pick up data.gov.uk, Centre of Excellence and Information Sharing, strong institutional innovation set of bodies. And there’s a number of these iconic case studies that people point to and talk to but few really sustained examples. There aren’t a lot of things that we can find that seem to have legs, that seem to have the capacity to scale up or replicate or duplicate, grow. So we’re in this kind of moment where we’re not quite sure whether it’s let a thousand flowers bloom let a thousand schools of thought contend, as Chairman Mao said. Or whether we’re actually in the situation of having lots of these experiments, none of which seem to really fulfil their promise, let a thousand flowers bloom.

The other issues we are now grappling with is how to interpret this data and how we bring the right kinds of people into the room to make sense of this stuff, and that’s actually quite a difficult problem and how that relates to this problem of a moral economy of information and data in health and social care. And finally, I said I’d come back to this oil metaphor. Metaphors are very dangerous things aren’t they? I can’t remember who said, “Words are like weapons”, so check whether the word is loaded before you put it to the head and pull the button. If data is the new oil for government and the social care sector, yeah oil is amazing stuff, you can do amazing things with it, it’s incredibly compact way of moving energy around the system. But it’s also a major pollutant and a major cause of greenhouse gas. So thinking about data as the new oil, yeah, that’s a really good metaphor but we need to follow it through into some of its


negative issues and concerns too, otherwise we may find ourselves in the situation that we do with regard to climate change and real oil. Okay, I’m going to stop there.

Ian McLoughlinYou’ve probably been trying to work out whether we’ve been trying to keep you alive as long as possible or give you a good death. So the best way to ponder on that is to take a break and have a cup of tea and then we’ll come back and start with some general…

<end of transcript>


Documents

[playing video - Web viewSo we kept the navigational method for going ... the Federal Data Policy Statement, ... I think it was the Chinese were blamed on the night for hacking into