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(lift)

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(lift 12 - Feb 24 2012 - Geneva)

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(Rufus Pollock - Stories)

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[Rufus Pollock] Just to say for those of you who don't know:

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the Open Knowledge Foundation is a not-profit -- not for profit

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founded in 2004

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and which builds tools and communities

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to create, use and share open information

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and that's information that anyone can use, reuse and redistribute.

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And as such, we've been working on open data for quite a long time

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since we started in 2004.

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And today, I want to start the story by going back in time 5000 years,

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to ancient Mesopotamia.

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There, between the Tigris and the Euphrates rivers,

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flourished the Sumerian civilization.

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And they were confronted by a problem.

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They were confronted by the limitations of human memory

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in the recording of taxes, food and other goods.

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And those ancient civil servants and businessmen hit on a novel solution:

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What they decided to do was they would start counting things with small clay chits,

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which they would bake inside of a clay -- a little clay box

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and then mark, on the outside of that box, what they were counting.

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You know, was it grain, was it tax payments, whatever.

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And so, born out of necessity for a state and a society,

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came one of the great information technology revolutions of all time: writing.

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The Sumerians invented writing via cuneiform.

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And if we fast-forward from that a few thousand years, we come to the UK census.

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Again, it's always interesting that states, governments are often at the forefront

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of at least driving information technology and information systems innovations.

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The UK census: again, the state,

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this is during the Napoleon Wars,

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desired to count the population more accurately:

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and we have the first UK census in 1801.

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And in the US, they also had censuses, in fact starting in 1790.

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And one of the problems encountered in the 1880 census

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was they tabulated the census by hand.

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And by the 1880 census, it was taking seven years

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to tabulate the census.

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So after it got taken in 1880, it wasn't until 1887

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they actually had any data they could use.

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And they calculated that for the next census in 1890,

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they wouldn't be finished by 1900.

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They still wouldn't have the results of the census by the time they started the next one.

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They had a crisis of information technology.

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And what they went and did is they commissioned Herman Hollerith

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to build the first automatic tabulator.

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And for those of you who know your company history, of course,

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Herman Hollerith's company went on

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to be one of the founders, if you like,

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one of the companies that came and created IBM.

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And IBM, by the sixties, were building this

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-- they replaced those hand --

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those kind of wooden, mechanical tabulators

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with this stuff: digital tabulators,

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the modern computer of this age.

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And again, much of this -- I don't know if you guys know --

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IBM would have gone bankrupt

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if it hadn't been for Franklin Roosevelt passing the Social Security Act in the States,

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which necessitated a huge amount of new tabulation.

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So, again, a lot of innovation in this space came out of government need

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and also, of course, the nuclear program,

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the other great needer of computational power.

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And today, today,

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we find ourselves again in the midst of a revolution.

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It's a revolution driven by two needs:

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ones that have been the same throughout history as I've just shown,

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information complexity, which is the necessity,

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and information technology, which is the opportunity.

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And what we're doing in this case is a policy innovation, if you like.

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We are innovating by opening up information.

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So just take the obvious example, government,

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as I said, often the innovator.

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In the last -- 3 years ago, you go back 3 years,

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there's almost no open government data initiatives

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in the world.

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Today there are dozens.

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The UK, the US, Finland, Kenya, The Netherlands,

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and there's new ones almost every week.

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There's been a launch of an official kind of movement as a part of the UN

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called the Open Government Partnership in which countries sign up,

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and among other things, they open up their data.

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And of course, it's been, in the UK and other countries,

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Tim Berners-Lee has been involved.

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I've helped advise the government around this in the UK.

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But it's not just government, it's also companies.

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Companies are opening up data.

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Very interestingly, last year,

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Nike started an open data initiative there

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to open up supply chain and sustainability data,

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for themselves and also for their suppliers,

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which I think is a very interesting change.

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And it's also communities.

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Often, in fact, back there in the beginning,

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this incredible map that you saw in an earlier slide,

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is a OpenStreetMap activity, around the world.

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People adding to this crowd-built map of the world.

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And in the last 6 years, OpenStreetMap,

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from a bottom-up community,

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have built a complete, comprehensive,

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map of the world, of fully open data.

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So I've just gone on about Open Data,

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and one thing I'm aware of, of this audience,

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is you might not all know what it is.

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So I'm going to take a brief moment, a brief moment, to say what it is.

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What does it mean when I say 'open'?

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And was it, you know, what's different from anything else? What's different from simply public data?

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So there's actually a definition,

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a definition we the Open Knowledge Foundation helped write, it's very simple.

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In a nutshell, a piece of information, a piece of data,

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is open if anyone is free to use, reuse,

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and redistribute it, subject only at most

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to a requirement to attribute and share alike.

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And anyone means anyone!

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It doesn't mean -- there can't be any commercial restrictions.

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You can't say: hey, here's this data, but only people using it for non-commercial purposes.

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Or only people working in education.

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Or only people living in the developing world, or the developed world.

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There can't be any restrictions like that.

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And there's a reason for this, by the way,

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and it isn't just because one's obsessed about if you like, trademarking an attractive term.

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It's because it's about interoperability.

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One of my experiences at this conference, which I remember from previous trips to Geneva,

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is I've been unable to plug in my laptop!

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Even though I have a French adaptor, in fact, these wonderful Swiss plugs here, are, you know,

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these wonderful, small octagonal shape.

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And even with my adaptor I can't plug in.

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Right? And it's called interoperability.

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When we travel around to different countries, our power adaptors don't actually fit in.

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We have to buy something.

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And the point about this definition, and the point about caring about Open Data,

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is, it's about interoperability.

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The dream of Open Data is interoperability.

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Of seamlessly being able to share and interweave information.

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And if every time I get information from two different people I have to consult a lawyer,

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I have to work out whether I'm allowed to do it, whether I'm allowed to put these things together,

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we lose that dream, that dream is shattered.

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And the key point is, this definition, and those conditions, ensure interoperability.

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If you comply with them, we know that any piece of info, of Open Data,

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will work with any other piece of Open Data.

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And also, it's worth saying for a quick moment, what kind of data, and to emphasize a point.

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Just to foreclose those kinds of questions, otherwise I always get asked.

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When we talk about opening up data, in general,

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we're not talking about personal data.

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We're not talking about opening up your private health records

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or opening up your personal tax information.

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We're talking about information that is non-personal in nature.

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And for the government for example: transport, geodata, statistics, electoral, legal.

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Stuff that the UK has, in fact, for example been opening up over the last few years.

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This financial information, on government spending, this information on health outcomes,

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on prescriptions, this information on educational outcomes, this information on the law.

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This information -- statistical information.

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That's the kind of thing that we're talking about.

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Now, I want to say, it's in this story, we have this story of over time.

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But why governments are doing it now?

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And why Open Data?

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So, okay, for thousands of years, governments innovate,

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but why do they innovate at this particular moment and in this way?

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So I want to start here with a quick story, a story of medicine gone wrong.

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It is from a great book by a guy called Stephen Klaidman.

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It's in fact one of the things

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that made me think quite deeply about this:

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why I was interested in Open Data.

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In that picture there, you can see

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what was the Redding Medical Centre in Northern California.

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There, in 2002, in the Summer of 2002, John Corapi,

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in typical American style,

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an ex-accountant from Vegas turned Catholic priest,

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[scattered laughter]

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...arrived at the Redding Medical Centre having been referred by his doctor for having chest pains.

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He had a cardiogram by the local cardiologist and was told that he needed an immediate heart bypass,

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that he was at serious risk, and that he should come back later that day,

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or at the latest, tomorrow, to have open heart surgery.

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Rather shocked, and dazed by this news,

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he returned home to pack his bags in order to return to hospital.

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He called up his best friend, who was still an accountant in Vegas,

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whose partner was a hospital nurse, and who advised him that he should get a second opinion,

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that, according to his partner, it was not, you know,

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it was very unusual that you would need to have immediate open heart surgery,

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and that he should get a second opinion.

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Rather doubtful about this, because he was extremely worried, he did get on a plane.

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He went to Vegas, he got seen by another specialist...

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who, to his complete surprise, told him there was nothing wrong with his heart.

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He saw another specialist, just to make sure.

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They told him also, there was nothing wrong with his heart.

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Relieved, and rather, you know, happy, he returned home and just wanted to really forget about it.

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But his friend said: "No, what's going on here? Something's wrong".

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And they went in to see the CEO of the Tenet Healthcare, the people running this hospital

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(which, by the way, was a private hospital), and said:

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"Look, something's wrong, what's going on, what are you going to do about this?"

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And basically they were told: not very much.

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You know, mistakes get made, it's bad luck, don't worry about it,

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we'll look into it, but thank you very much.

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They weren't convinced by this, and eventually they decided to contact the FBI.

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The reason they contacted the FBI, by the way,

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is it's a private healthcare provider in the United States,

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they provide Medicare provision of healthcare to the Federal Government.

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So, if the Federal Government is getting defrauded, the FBI can get involved.

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The FBI started investigating.

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Eventually it turned out, that hundreds, probably thousands of people

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over a ten or longer year period, had been operated on unnecessarily.

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Most of them had had serious procedures performed on them,

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open heart surgery, some had died as a result.

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Obviously it's quite a serious operation.

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Some people had basically been condemned to a lifetime of pain.

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One of the most traumatic examples was a 36-year-old, he had been cut open,

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which is obviously what happens in open heart surgery,

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and his chest had never knitted back together correctly.

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Basically, he would be in pain for the rest of his life.

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So, hundreds, thousands of people had been harmed.

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One of the interesting things was that in this community

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there was already some suspicion, there were anecdotes.

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I mean, one of the ones I really liked from this book was the story that went:

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'Don't get a flat tyre outside of Redding Medical Centre because you'll end up with a heart bypass.'

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[scattered laughter]

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You know, but the thing was, there was no data.

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People were you know, a bit suspicious, but it was among doctors who knew,

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you know, in the community, and who wants to doubt it.

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And guess what? Also, Redding Medical Centre had one of the best mortality rates,

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for cardiac procedures in the United States,

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because if you operate on healthy people, you have a good mortality rate!

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[scattered laughter]

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So, the other thing, though,

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and this is the point that comes to Open Data for me

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the other red flag if you had been looking at the data,

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was these two things: one is incredibly low mortality rate,

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and (B) that it had almost the highest number of procedures

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for the population that it covered in the United States,

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which should be a red flag, right?

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Because, one, it's just a massive outlier on that basis, and also,

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the more people you should be operating on, the more you're doing marginal cases,

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the higher should be your mortality rate unless something very odd is going on.

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The thought was: what if people had been looking at this data?

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What if we'd - if this data had been open and public,

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and not maybe just for particular researchers to look at or the government?

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And it kind of reminded me of a phrase that's very famous in Open Source software, which is:

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"To many eyes, all bugs are shallow".

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What's great about Open Source software is lots of people can look at it, lots of people can fix it.

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And for me, what this was saying was: to many eyes, all anomalies are noticeable.

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It's somewhat of an exaggeration,

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but what happens if rather than ten or twenty people

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who worked in monitoring Medicare provision in the US government,

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we'd had thousands or millions of people?

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If the local journalists or citizens, who had suspicions,

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had been able to go and look at that data and say:

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"Whoa! What's going on here? This isn't just anecdotes, there's some data".

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And so, and it's not just then, about kind of spotting healthcare errors, or issues, or risks,

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it's also about things like apps and services

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that you can build with Open Data.

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This is a great app built by mySociety in the UK,

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called Mapumental.

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And the question is, I don't know if people know,

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London house prices are very expensive,

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I don't know whether they rival Geneva's,

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but they're, it's a pretty difficult thing.

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And one of the questions was, if I have to work somewhere,

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and I want to know where I can live, and afford,

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and I can commute to work in a certain time, and it's not too ugly,

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this is what this app does.

0:13:07.583,0:13:11.200
You can choose the price, you can say where you're going to work,

0:13:11.200,0:13:14.195
you can choose the commute time, and you can choose the scenicness.

0:13:14.195,0:13:17.167
And it will show you, on this map, where you can live.

0:13:18.427,0:13:20.796
Another example, more about transparency,

0:13:20.796,0:13:22.746
is a project we did called "Where Does My Money Go?".

0:13:23.976,0:13:25.406
It's an interactive version,

0:13:25.406,0:13:26.211
you can kind of draw it out,

0:13:26.211,0:13:29.114
so what it starts with, is one, is it tells you what your tax is,

0:13:29.114,0:13:30.821
something that most people often don't know,

0:13:30.821,0:13:33.668
and it will tell you how much you're paying each day

0:13:33.668,0:13:36.254
to a particular area of society.

0:13:36.254,0:13:37.328
And the dream for me,

0:13:37.328,0:13:39.127
a dream that we're on the way to realising,

0:13:39.127,0:13:42.817
is in this visualisation, you can drill down into areas.

0:13:42.817,0:13:45.092
And my dream is to keep drilling down.

0:13:45.472,0:13:47.633
So depending on what day we have, I want to go down,

0:13:47.633,0:13:49.628
right down through those bubbles, step by step,

0:13:49.628,0:13:52.403
until I see the money spent on street lights on my street,

0:13:52.403,0:13:55.270
on filling in potholes, on collecting my rubbish.

0:13:56.190,0:13:57.138
And for two reasons:

0:13:57.138,0:13:59.704
One, obviously there's a question, particularly in some countries,

0:13:59.704,0:14:01.016
of inefficiency or corruption,

0:14:01.436,0:14:05.176
but also, just because most of us don't feel very happy about paying tax.

0:14:06.066,0:14:08.157
It's not one of those things people welcome!

0:14:08.157,0:14:09.817
But it's something that we should.

0:14:09.817,0:14:11.960
Government does an awful lot for us,

0:14:11.960,0:14:14.287
and having a better sense of where it's going

0:14:14.287,0:14:17.120
could make us feel an awful lot better about paying that tax.

0:14:17.120,0:14:18.657
In the way that when we go to a restaurant,

0:14:18.657,0:14:21.397
we don't, when we get the bill, we don't necessarily feel bad.

0:14:21.397,0:14:24.334
We feel "Wow, I had a great meal. That was worth it."

0:14:25.274,0:14:26.366
But why Open?

0:14:26.366,0:14:29.079
I've given you examples, and you know, we see a lot of apps and services.

0:14:29.079,0:14:30.948
Why is Open relevant here?

0:14:31.598,0:14:36.350
This goes back to what I said about the information technology, the revolution.

0:14:36.350,0:14:37.813
So it's the challenge and the opportunity.

0:14:37.813,0:14:42.387
It's the challenge that we see today, is exploding informational complexity.

0:14:42.797,0:14:43.924
I mean, another great story:

0:14:43.924,0:14:47.728
in the 1820s, all bank clearing in the largest financial centre in the world

0:14:47.728,0:14:51.848
was done in a single room, where people -- one person from each bank gathered

0:14:51.848,0:14:56.402
and they'd go round the room pulling out gold, and swapping it around, between different banks.

0:14:56.402,0:14:58.434
And that's how they did bank clearing.

0:14:59.074,0:15:01.991
Today we have billions of transactions a minute.

0:15:01.991,0:15:07.945
And the way we as humans deal with complexity is by dividing and conquering it.

0:15:07.945,0:15:10.683
We split it up into manageable chunks that we deal with.

0:15:11.013,0:15:12.381
The other answer,

0:15:12.381,0:15:14.883
and this answer's particularly relevant about Open Data,

0:15:14.883,0:15:16.219
is information technology.

0:15:16.219,0:15:18.951
Today, a smartphone has as much computing power

0:15:18.951,0:15:22.260
as the system that ran the Apollo moon landings.

0:15:22.260,0:15:24.027
And an even better example is storage:

0:15:24.027,0:15:26.930
one terabyte of storage today is a hundred dollars.

0:15:26.930,0:15:30.297
In 1994, this would have cost 400,000 dollars.

0:15:30.297,0:15:33.977
I can have every financial transaction

0:15:33.977,0:15:38.376
the UK government, or the US government made last year, or even for the last decade,

0:15:38.376,0:15:39.665
on my laptop.

0:15:39.665,0:15:43.543
That was not possible for an average citizen a decade ago.

0:15:44.283,0:15:48.187
So it's mass participation, information access, processing, and production.

0:15:48.187,0:15:49.557
It's decentralisation.

0:15:49.557,0:15:51.728
And the claim here is that openness is key.

0:15:51.728,0:15:53.957
It's because it's about scaling.

0:15:54.547,0:15:57.399
What we are doing is weaving data together.

0:15:57.399,0:15:59.615
As I said, we deal with complexity by splitting it up.

0:15:59.615,0:16:02.928
We componentise, we split data up into blocks

0:16:02.928,0:16:04.670
that we recombine.

0:16:04.670,0:16:07.201
But if we are going to recombine information,

0:16:07.961,0:16:10.076
we need to put Humpty Dumpty back together again,

0:16:10.076,0:16:12.909
it won't work most of the time if it is closed.

0:16:13.449,0:16:17.039
We need Open Data to scale and to componentise.

0:16:17.679,0:16:21.518
And it's a point just to make here in this respect, that you might think:

0:16:21.518,0:16:23.351
"Well you know, you're talking about Open Data,

0:16:23.351,0:16:24.721
you know, this could be true of anything!

0:16:24.721,0:16:25.789
Why don't we have like,

0:16:25.789,0:16:28.232
Open Cars, and Open Shoes, and you know,

0:16:28.232,0:16:29.578
why don't we just share everything, man!

0:16:29.578,0:16:31.026
It would be so beautiful!".

0:16:31.706,0:16:33.393
Right? And the sad thing is,

0:16:33.393,0:16:39.070
is that that hasn't generally worked as a way of organising most production in our society.

0:16:39.070,0:16:44.074
Instead, we have private property, and so we don't do that much openness relatively.

0:16:44.074,0:16:45.848
But there's something different about digital information.

0:16:45.848,0:16:48.944
We all know it, but it's worth emphasising, which is, it's very cheaply copied.

0:16:49.344,0:16:52.782
I mean, give me a copy of your data isn't a problem if you're the government.

0:16:52.782,0:16:56.393
Give me a copy of your car, or your house, or whatever, is.

0:16:56.803,0:16:58.584
And it's also about innovation here.

0:16:58.584,0:17:01.219
I mean, in a way it's almost the purest aspect of markets.

0:17:01.219,0:17:05.619
Markets are about moving things to the person who could use them most best.

0:17:06.389,0:17:07.200
And that's true of data.

0:17:07.200,0:17:10.660
The best thing to do with your data will likely be thought of by someone else.

0:17:11.340,0:17:14.973
And vice versa! You will think of the best thing to do with someone else's data.

0:17:15.783,0:17:20.103
And Open Data allows us, in the most frictionless, easiest way,

0:17:20.103,0:17:22.708
to move data to where it can be most optimally used,

0:17:22.728,0:17:23.905
particularly if you're government.

0:17:24.275,0:17:26.843
So in short, it's about better understanding, it's about better government,

0:17:26.843,0:17:29.115
it's about better research, it's about better economy.

0:17:29.115,0:17:31.124
And something also for companies and governments:

0:17:31.124,0:17:32.707
I think it's about better engagement.

0:17:33.137,0:17:34.986
It's about a closer relationship, sometimes,

0:17:34.986,0:17:37.331
between your citizens and you as the government.

0:17:37.331,0:17:40.960
Between you, even possibly, as a company, and your users.

0:17:41.690,0:17:43.972
So I wanted to kind of finish here by saying where we're going.

0:17:43.972,0:17:46.429
The story was, of this talk, was, you know, where are we?

0:17:46.869,0:17:49.654
Why have we got here? And where are we going?

0:17:50.734,0:17:52.248
So one answer is just more use.

0:17:52.248,0:17:55.313
So right now, I just said at the beginning, Open Data is relatively young.

0:17:55.313,0:17:57.844
This vast outpouring, for example, of government data,

0:17:57.844,0:18:02.058
that anyone can freely use, reuse, and redistribute, is really new,

0:18:02.058,0:18:03.382
even if it's done three years ago.

0:18:03.382,0:18:06.479
For example, in the UK, much of the most useful data that could be released

0:18:06.479,0:18:09.091
has only been released in the last six months or a year.

0:18:09.091,0:18:10.478
You want prescription data?

0:18:10.478,0:18:11.894
Are you a pharmaceutical company,

0:18:11.894,0:18:15.261
and you want to know what kind of prescription habits are going on in the UK?

0:18:15.261,0:18:18.837
I would emphasise: at an anonymised or somewhat aggregate level.

0:18:18.837,0:18:20.707
Do you want to know about what crime is going on?

0:18:20.707,0:18:24.241
Are you building a real estate website and you want data on environment,

0:18:24.241,0:18:25.742
or you want data on unemployment,

0:18:25.742,0:18:28.742
or other information about where properties are situated?

0:18:28.742,0:18:30.077
You can now get that.

0:18:30.687,0:18:32.783
So I think there's going to be a lot more use from business.

0:18:33.743,0:18:35.398
There'll be a lot more use from everyone.

0:18:35.398,0:18:38.686
But I think particularly business is going to wake up to the opportunities here.

0:18:38.686,0:18:40.192
I think it's also going to lead to more data.

0:18:40.192,0:18:41.789
One is, government is going to be more data.

0:18:41.789,0:18:45.597
I think also businesses are going to realise, and communities,

0:18:45.597,0:18:47.719
that they want to share back some of that data,

0:18:47.719,0:18:48.764
some of the data they have.

0:18:48.764,0:18:50.863
It's not going to be their kind of crown jewels,

0:18:50.863,0:18:53.707
and it's not going -- often start out with data that's not core to their business.

0:18:53.707,0:18:58.108
It's like. kind of Nike, they realised that by opening and sharing data,

0:18:58.108,0:19:00.569
they can scale in a way they can't on their own.

0:19:01.029,0:19:03.069
And does it mean that richer data, going back

0:19:03.069,0:19:05.809
-- how could I leave out Hegel and Marx in a talk like this --

0:19:05.809,0:19:08.869
"Quantity changes quality" as Hegel told us.

0:19:09.319,0:19:14.156
And more data, going back to that woven ball, more data actually means better data.

0:19:14.156,0:19:17.634
It means richer data, it's a qualitative difference in what we can do.

0:19:17.634,0:19:19.781
Geodata on it's own isn't that useful.

0:19:19.781,0:19:21.593
Transport data on it's own isn't useful.

0:19:21.593,0:19:24.023
Geodata plus transport data is useful!

0:19:24.703,0:19:26.187
And we're going to be seeing data refining.

0:19:26.187,0:19:27.441
Data is the new oil, right?

0:19:27.441,0:19:28.928
So, we're going to refine it.

0:19:28.928,0:19:32.037
And that's going to be a big business: higher quality data.

0:19:32.487,0:19:34.149
I want to leave you with a couple of thoughts.

0:19:34.149,0:19:35.646
So, one is, some people say:

0:19:35.646,0:19:37.527
"Well, okay, but, you know, selling data is big business".

0:19:37.527,0:19:40.987
And it is, but going forward in some of these things like software,

0:19:40.987,0:19:42.485
data is going to be a platform.

0:19:42.485,0:19:43.720
It's not a commodity.

0:19:43.720,0:19:46.084
Businesses built purely on selling data,

0:19:46.084,0:19:47.558
I just don't think are going to make it.

0:19:47.558,0:19:51.668
You need to be building on your data, not attempting to purely sell it.

0:19:52.588,0:19:54.638
And the other answer is to be modest.

0:19:55.278,0:19:56.797
So I said: where are we going?

0:19:56.797,0:19:57.917
I don't know if people know

0:19:57.917,0:20:02.124
-- and this takes us back to an earlier age, an age of electricity and steam -- of Faraday.

0:20:02.124,0:20:04.678
So he's demonstrating electricity at the Royal Society,

0:20:04.678,0:20:08.316
and Gladstone, the future Prime Minister of England, sees him do this stuff, you know,

0:20:08.316,0:20:10.445
the frog legs move, and Gladstone's like:

0:20:10.445,0:20:12.392
"Well, I mean, this is party trick, Faraday.

0:20:12.392,0:20:15.971
It's great, but, what's really, you know, what's electricity going to amount to?" (20:16)

0:20:15.971,0:20:20.832
And Faraday says to him: "Well, what's the use of a baby?"

0:20:20.832,0:20:24.299
You know, a baby when it's young is not very useful.

0:20:24.299,0:20:25.718
[scattered laughter]

0:20:25.718,0:20:27.426
But it grows up into something!

0:20:27.916,0:20:29.725
And that is where we are going today.

0:20:29.725,0:20:32.488
We are the beginning of the Open Data journey.

0:20:33.088,0:20:36.157
And partly is, we don't know what it's going to grow up into.

0:20:36.157,0:20:37.004
Thank you very much!

0:20:37.004,0:20:40.558
[Applause]

0:20:40.558,0:20:44.708
[Questioner] Um, citizens and I guess patients at hospitals,

0:20:44.708,0:20:48.683
assume that the institutions have all this data and it's very well organised,

0:20:48.683,0:20:49.925
and it's a question of will.

0:20:50.555,0:20:53.889
Have you encountered cases in which they simply don't have it,

0:20:53.889,0:20:57.268
or they have it, and it's just such a mess that they're too embarrassed to give it out?

0:20:57.268,0:20:58.661
[Rufus Pollock] Absolutely.

0:20:58.661,0:21:00.959
I mean, one story that kind of intrigues me,

0:21:00.959,0:21:03.914
is we've been building this "Where Does My Money Go?" open spending project.

0:21:04.094,0:21:06.948
And one of the things the government mandated was giving out,

0:21:06.948,0:21:09.065
rather than just high-level financial information,

0:21:09.065,0:21:11.445
giving out information at a detailed level, you know,

0:21:11.445,0:21:12.838
so they now publish, for example,

0:21:12.838,0:21:15.601
spending data from each government department monthly,

0:21:15.601,0:21:17.713
every transaction within 5,000 pounds (check).

0:21:17.713,0:21:22.119
Every purchase they make, every mobile phone provider they contract with, we get that data.

0:21:22.119,0:21:25.590
And one of the intriguing things, of their mandating this, was it turned out,

0:21:25.590,0:21:30.022
before, they had no way, before they did this, of actually seeing, on any regular basis,

0:21:30.022,0:21:31.232
what their department spent money on.

0:21:31.232,0:21:35.029
Because in fact, the only thing they reported up on to, in central government to Treasury,

0:21:35.029,0:21:38.871
was kind of like, how much did you spend against Project X that you were allocated budget for?

0:21:38.871,0:21:41.156
You know, departments, were actually really intrigued, they [say]:

0:21:41.156,0:21:43.435
"Oh, that other department's going with Vodafone,

0:21:43.435,0:21:46.349
and we're with Orange, and look how much they're paying per month!"

0:21:46.349,0:21:49.310
So I think in essence, it is really driving changes in government,

0:21:49.310,0:21:52.700
and yeah, there are people, I think you'd been worried about giving out data quality.

0:21:52.700,0:21:54.841
I was just talking to the Department of Education last week and they said

0:21:54.841,0:21:57.650
-- you know, one of the things -- they had financial information from schools,

0:21:57.650,0:21:59.567
and which they were slowly being mandated to publish.

0:21:59.567,0:22:01.220
And schools are suddenly all ringing up, saying:

0:22:01.220,0:22:04.290
"Well we never really bothered to really update that information to be accurate!

0:22:04.290,0:22:05.900
Uh, we really want to do it right now".

0:22:06.210,0:22:08.164
So I think that definitely does happen, yep.

0:22:08.164,0:22:12.460
[Questioner] Are you seeing now new roles in government, to help facilitate this?

0:22:13.099,0:22:16.009
[Rufus Pollock] Yeah. I mean, to take another example, I, sorry.

0:22:16.009,0:22:19.492
Both in government, so the UK government has a transparency kind of 'czar' if you like.

0:22:19.492,0:22:22.812
Also I learnt, is Nike hired an Open Data evangelist.

0:22:22.812,0:22:24.611
One of the things they, while they were implementing this programme,

0:22:24.611,0:22:27.990
they actually hired explicitly, an Open Data evangelist.

0:22:27.990,0:22:30.023
So yeah, I think we are, we're definitely seeing this in government.

0:22:30.023,0:22:32.357
Both in the tech level, but also at the policy level.

0:22:32.357,0:22:34.729
And I think it's not just government,

0:22:34.729,0:22:37.957
it will also be companies doing this, and so on, who will be saying:

0:22:37.957,0:22:39.192
"We need an Open Data expert.

0:22:39.192,0:22:43.662
We need to be aware of what's going on here and be able to plan it as part of our strategy."

0:22:44.472,0:22:45.489
[Questioner] A final question.

0:22:45.489,0:22:48.972
You mentioned that, kind of outsourcing, almost, some of this data refining,

0:22:48.972,0:22:51.306
outside government or the big institutions, has helped them.

0:22:51.306,0:22:54.446
Can you tell us any stories of kind of gratitude being expressed by the government? I mean...

0:22:55.146,0:22:57.247
[Rufus Pollock] Well, I mean, to kind of, yeah.

0:22:57.247,0:22:58.773
I mean there was an interesting example actually

0:22:58.773,0:23:01.792
where we had some complaint because the open spending data I told you about

0:23:01.792,0:23:05.323
where we're aggregating the government spending and financial data

0:23:05.323,0:23:10.651
-- you know, the site had a few performance issues, occasionally, as we loaded more data in.

0:23:10.651,0:23:12.755
I remember kind of getting this call kind of going :

0:23:12.755,0:23:16.266
"Well, you know, we're a little bit upset, you know, data.gov.uk,"

0:23:16.266,0:23:19.133
and it turned out the reason was, the Treasury kept looking at this data,

0:23:19.133,0:23:21.002
and they were annoyed when the site was going down.

0:23:21.002,0:23:22.563
So that was really intriguing to me

0:23:22.563,0:23:26.139
that we were kind of one of the best, at least, up-to-date aggregators out there.

0:23:26.139,0:23:29.484
Um, I think you are already seeing people doing stuff with the data

0:23:29.484,0:23:30.979
and kind of doing stuff, sometimes for free, you know.

0:23:31.289,0:23:33.131
You don't have to have the shiny front-end.

0:23:33.131,0:23:35.019
I mean, one of the things we went about, on about,

0:23:35.019,0:23:36.493
I know Tim Berners-Lee went on about --

0:23:36.493,0:23:41.393
raw data now, you know, you can build fewer shiny front-ends, and just release raw data.

0:23:41.393,0:23:46.455
And you know, someone else will help you build the app, the front-end, the interface,

0:23:46.455,0:23:47.762
and help you innovate about it.

0:23:47.762,0:23:50.917
What is the best way to provide healthcare data to citizens,

0:23:50.917,0:23:52.254
or education data to citizens,

0:23:52.254,0:23:54.367
so they make better and more informed choices?

0:23:54.367,0:23:56.608
I don't know, and the government probably doesn't know.

0:23:56.608,0:23:59.155
But somewhere out there, someone is going to innovate

0:23:59.155,0:24:02.672
and really provide the best way for us to deliver that kind of information to citizens.

0:24:02.672,0:24:04.250
QUESTIONER: Thank you very much.

0:24:04.250,0:24:05.296
[Rufus Pollock] Thank you.

0:24:05.296,0:24:06.901
[Applause]

0:24:06.901,0:24:09.423
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0:24:09.423,0:24:11.311
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