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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.

238
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And for me, what this was saying was: to many eyes, all anomalies are noticeable.

239
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It's somewhat of an exaggeration,

240
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but what happens if rather than ten or twenty people

241
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who worked in monitoring Medicare provision in the US government,

242
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we'd had thousands or millions of people?

243
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If the local journalists or citizens, who had suspicions,

244
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had been able to go and look at that data and say:

245
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"Whoa! What's going on here? This isn't just anecdotes, there's some data".

246
00:12:34,205 --> 00:12:40,225
And so, and it's not just then, about kind of spotting healthcare errors, or issues, or risks,

247
00:12:40,225 --> 00:12:42,415
it's also about things like apps and services

248
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that you can build with Open Data.

249
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This is a great app built by mySociety in the UK,

250
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called Mapumental.

251
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And the question is, I don't know if people know,

252
00:12:48,974 --> 00:12:50,646
London house prices are very expensive,

253
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I don't know whether they rival Geneva's,

254
00:12:52,510 --> 00:12:55,238
but they're, it's a pretty difficult thing.

255
00:12:55,238 --> 00:12:57,978
And one of the questions was, if I have to work somewhere,

256
00:12:57,978 --> 00:13:01,752
and I want to know where I can live, and afford,

257
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and I can commute to work in a certain time, and it's not too ugly,

258
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this is what this app does.

259
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You can choose the price, you can say where you're going to work,

260
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you can choose the commute time, and you can choose the scenicness.

261
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And it will show you, on this map, where you can live.

262
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Another example, more about transparency,

263
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is a project we did called "Where Does My Money Go?".

264
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It's an interactive version,

265
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you can kind of draw it out,

266
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so what it starts with, is one, is it tells you what your tax is,

267
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something that most people often don't know,

268
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and it will tell you how much you're paying each day

269
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to a particular area of society.

270
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And the dream for me,

271
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a dream that we're on the way to realising,

272
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is in this visualisation, you can drill down into areas.

273
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And my dream is to keep drilling down.

274
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So depending on what day we have, I want to go down,

275
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right down through those bubbles, step by step,

276
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until I see the money spent on street lights on my street,

277
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on filling in potholes, on collecting my rubbish.

278
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And for two reasons:

279
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One, obviously there's a question, particularly in some countries,

280
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of inefficiency or corruption,

281
00:14:01,436 --> 00:14:05,176
but also, just because most of us don't feel very happy about paying tax.

282
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It's not one of those things people welcome!

283
00:14:08,157 --> 00:14:09,817
But it's something that we should.

284
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Government does an awful lot for us,

285
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and having a better sense of where it's going

286
00:14:14,287 --> 00:14:17,120
could make us feel an awful lot better about paying that tax.

287
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In the way that when we go to a restaurant,

288
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we don't, when we get the bill, we don't necessarily feel bad.

289
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We feel "Wow, I had a great meal. That was worth it."

290
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But why Open?

291
00:14:26,366 --> 00:14:29,079
I've given you examples, and you know, we see a lot of apps and services.

292
00:14:29,079 --> 00:14:30,948
Why is Open relevant here?

293
00:14:31,598 --> 00:14:36,350
This goes back to what I said about the information technology, the revolution.

294
00:14:36,350 --> 00:14:37,813
So it's the challenge and the opportunity.

295
00:14:37,813 --> 00:14:42,387
It's the challenge that we see today, is exploding informational complexity.

296
00:14:42,797 --> 00:14:43,924
I mean, another great story:

297
00:14:43,924 --> 00:14:47,728
in the 1820s, all bank clearing in the largest financial centre in the world

298
00:14:47,728 --> 00:14:51,848
was done in a single room, where people -- one person from each bank gathered

299
00:14:51,848 --> 00:14:56,402
and they'd go round the room pulling out gold, and swapping it around, between different banks.

300
00:14:56,402 --> 00:14:58,434
And that's how they did bank clearing.

301
00:14:59,074 --> 00:15:01,991
Today we have billions of transactions a minute.

302
00:15:01,991 --> 00:15:07,945
And the way we as humans deal with complexity is by dividing and conquering it.

303
00:15:07,945 --> 00:15:10,683
We split it up into manageable chunks that we deal with.

304
00:15:11,013 --> 00:15:12,381
The other answer,

305
00:15:12,381 --> 00:15:14,883
and this answer's particularly relevant about Open Data,

306
00:15:14,883 --> 00:15:16,219
is information technology.

307
00:15:16,219 --> 00:15:18,951
Today, a smartphone has as much computing power

308
00:15:18,951 --> 00:15:22,260
as the system that ran the Apollo moon landings.

309
00:15:22,260 --> 00:15:24,027
And an even better example is storage:

310
00:15:24,027 --> 00:15:26,930
one terabyte of storage today is a hundred dollars.

311
00:15:26,930 --> 00:15:30,297
In 1994, this would have cost 400,000 dollars.

312
00:15:30,297 --> 00:15:33,977
I can have every financial transaction

313
00:15:33,977 --> 00:15:38,376
the UK government, or the US government made last year, or even for the last decade,

314
00:15:38,376 --> 00:15:39,665
on my laptop.

315
00:15:39,665 --> 00:15:43,543
That was not possible for an average citizen a decade ago.

316
00:15:44,283 --> 00:15:48,187
So it's mass participation, information access, processing, and production.

317
00:15:48,187 --> 00:15:49,557
It's decentralisation.

318
00:15:49,557 --> 00:15:51,728
And the claim here is that openness is key.

319
00:15:51,728 --> 00:15:53,957
It's because it's about scaling.

320
00:15:54,547 --> 00:15:57,399
What we are doing is weaving data together.

321
00:15:57,399 --> 00:15:59,615
As I said, we deal with complexity by splitting it up.

322
00:15:59,615 --> 00:16:02,928
We componentise, we split data up into blocks

323
00:16:02,928 --> 00:16:04,670
that we recombine.

324
00:16:04,670 --> 00:16:07,201
But if we are going to recombine information,

325
00:16:07,961 --> 00:16:10,076
we need to put Humpty Dumpty back together again,

326
00:16:10,076 --> 00:16:12,909
it won't work most of the time if it is closed.

327
00:16:13,449 --> 00:16:17,039
We need Open Data to scale and to componentise.

328
00:16:17,679 --> 00:16:21,518
And it's a point just to make here in this respect, that you might think:

329
00:16:21,518 --> 00:16:23,351
"Well you know, you're talking about Open Data,

330
00:16:23,351 --> 00:16:24,721
you know, this could be true of anything!

331
00:16:24,721 --> 00:16:25,789
Why don't we have like,

332
00:16:25,789 --> 00:16:28,232
Open Cars, and Open Shoes, and you know,

333
00:16:28,232 --> 00:16:29,578
why don't we just share everything, man!

334
00:16:29,578 --> 00:16:31,026
It would be so beautiful!".

335
00:16:31,706 --> 00:16:33,393
Right? And the sad thing is,

336
00:16:33,393 --> 00:16:39,070
is that that hasn't generally worked as a way of organising most production in our society.

337
00:16:39,070 --> 00:16:44,074
Instead, we have private property, and so we don't do that much openness relatively.

338
00:16:44,074 --> 00:16:45,848
But there's something different about digital information.

339
00:16:45,848 --> 00:16:48,944
We all know it, but it's worth emphasising, which is, it's very cheaply copied.

340
00:16:49,344 --> 00:16:52,782
I mean, give me a copy of your data isn't a problem if you're the government.

341
00:16:52,782 --> 00:16:56,393
Give me a copy of your car, or your house, or whatever, is.

342
00:16:56,803 --> 00:16:58,584
And it's also about innovation here.

343
00:16:58,584 --> 00:17:01,219
I mean, in a way it's almost the purest aspect of markets.

344
00:17:01,219 --> 00:17:05,619
Markets are about moving things to the person who could use them most best.

345
00:17:06,389 --> 00:17:07,200
And that's true of data.

346
00:17:07,200 --> 00:17:10,660
The best thing to do with your data will likely be thought of by someone else.

347
00:17:11,340 --> 00:17:14,973
And vice versa! You will think of the best thing to do with someone else's data.

348
00:17:15,783 --> 00:17:20,103
And Open Data allows us, in the most frictionless, easiest way,

349
00:17:20,103 --> 00:17:22,708
to move data to where it can be most optimally used,

350
00:17:22,728 --> 00:17:23,905
particularly if you're government.

351
00:17:24,275 --> 00:17:26,843
So in short, it's about better understanding, it's about better government,

352
00:17:26,843 --> 00:17:29,115
it's about better research, it's about better economy.

353
00:17:29,115 --> 00:17:31,124
And something also for companies and governments:

354
00:17:31,124 --> 00:17:32,707
I think it's about better engagement.

355
00:17:33,137 --> 00:17:34,986
It's about a closer relationship, sometimes,

356
00:17:34,986 --> 00:17:37,331
between your citizens and you as the government.

357
00:17:37,331 --> 00:17:40,960
Between you, even possibly, as a company, and your users.

358
00:17:41,690 --> 00:17:43,972
So I wanted to kind of finish here by saying where we're going.

359
00:17:43,972 --> 00:17:46,429
The story was, of this talk, was, you know, where are we?

360
00:17:46,869 --> 00:17:49,654
Why have we got here? And where are we going?

361
00:17:50,734 --> 00:17:52,248
So one answer is just more use.

362
00:17:52,248 --> 00:17:55,313
So right now, I just said at the beginning, Open Data is relatively young.

363
00:17:55,313 --> 00:17:57,844
This vast outpouring, for example, of government data,

364
00:17:57,844 --> 00:18:02,058
that anyone can freely use, reuse, and redistribute, is really new,

365
00:18:02,058 --> 00:18:03,382
even if it's done three years ago.

366
00:18:03,382 --> 00:18:06,479
For example, in the UK, much of the most useful data that could be released

367
00:18:06,479 --> 00:18:09,091
has only been released in the last six months or a year.

368
00:18:09,091 --> 00:18:10,478
You want prescription data?

369
00:18:10,478 --> 00:18:11,894
Are you a pharmaceutical company,

370
00:18:11,894 --> 00:18:15,261
and you want to know what kind of prescription habits are going on in the UK?

371
00:18:15,261 --> 00:18:18,837
I would emphasise: at an anonymised or somewhat aggregate level.

372
00:18:18,837 --> 00:18:20,707
Do you want to know about what crime is going on?

373
00:18:20,707 --> 00:18:24,241
Are you building a real estate website and you want data on environment,

374
00:18:24,241 --> 00:18:25,742
or you want data on unemployment,

375
00:18:25,742 --> 00:18:28,742
or other information about where properties are situated?

376
00:18:28,742 --> 00:18:30,077
You can now get that.

377
00:18:30,687 --> 00:18:32,783
So I think there's going to be a lot more use from business.

378
00:18:33,743 --> 00:18:35,398
There'll be a lot more use from everyone.

379
00:18:35,398 --> 00:18:38,686
But I think particularly business is going to wake up to the opportunities here.

380
00:18:38,686 --> 00:18:40,192
I think it's also going to lead to more data.

381
00:18:40,192 --> 00:18:41,789
One is, government is going to be more data.

382
00:18:41,789 --> 00:18:45,597
I think also businesses are going to realise, and communities,

383
00:18:45,597 --> 00:18:47,719
that they want to share back some of that data,

384
00:18:47,719 --> 00:18:48,764
some of the data they have.

385
00:18:48,764 --> 00:18:50,863
It's not going to be their kind of crown jewels,

386
00:18:50,863 --> 00:18:53,707
and it's not going -- often start out with data that's not core to their business.

387
00:18:53,707 --> 00:18:58,108
It's like. kind of Nike, they realised that by opening and sharing data,

388
00:18:58,108 --> 00:19:00,569
they can scale in a way they can't on their own.

389
00:19:01,029 --> 00:19:03,069
And does it mean that richer data, going back

390
00:19:03,069 --> 00:19:05,809
-- how could I leave out Hegel and Marx in a talk like this --

391
00:19:05,809 --> 00:19:08,869
"Quantity changes quality" as Hegel told us.

392
00:19:09,319 --> 00:19:14,156
And more data, going back to that woven ball, more data actually means better data.

393
00:19:14,156 --> 00:19:17,634
It means richer data, it's a qualitative difference in what we can do.

394
00:19:17,634 --> 00:19:19,781
Geodata on it's own isn't that useful.

395
00:19:19,781 --> 00:19:21,593
Transport data on it's own isn't useful.

396
00:19:21,593 --> 00:19:24,023
Geodata plus transport data is useful!

397
00:19:24,703 --> 00:19:26,187
And we're going to be seeing data refining.

398
00:19:26,187 --> 00:19:27,441
Data is the new oil, right?

399
00:19:27,441 --> 00:19:28,928
So, we're going to refine it.

400
00:19:28,928 --> 00:19:32,037
And that's going to be a big business: higher quality data.

401
00:19:32,487 --> 00:19:34,149
I want to leave you with a couple of thoughts.

402
00:19:34,149 --> 00:19:35,646
So, one is, some people say:

403
00:19:35,646 --> 00:19:37,527
"Well, okay, but, you know, selling data is big business".

404
00:19:37,527 --> 00:19:40,987
And it is, but going forward in some of these things like software,

405
00:19:40,987 --> 00:19:42,485
data is going to be a platform.

406
00:19:42,485 --> 00:19:43,720
It's not a commodity.

407
00:19:43,720 --> 00:19:46,084
Businesses built purely on selling data,

408
00:19:46,084 --> 00:19:47,558
I just don't think are going to make it.

409
00:19:47,558 --> 00:19:51,668
You need to be building on your data, not attempting to purely sell it.

410
00:19:52,588 --> 00:19:54,638
And the other answer is to be modest.

411
00:19:55,278 --> 00:19:56,797
So I said: where are we going?

412
00:19:56,797 --> 00:19:57,917
I don't know if people know

413
00:19:57,917 --> 00:20:02,124
-- and this takes us back to an earlier age, an age of electricity and steam -- of Faraday.

414
00:20:02,124 --> 00:20:04,678
So he's demonstrating electricity at the Royal Society,

415
00:20:04,678 --> 00:20:08,316
and Gladstone, the future Prime Minister of England, sees him do this stuff, you know,

416
00:20:08,316 --> 00:20:10,445
the frog legs move, and Gladstone's like:

417
00:20:10,445 --> 00:20:12,392
"Well, I mean, this is party trick, Faraday.

418
00:20:12,392 --> 00:20:15,971
It's great, but, what's really, you know, what's electricity going to amount to?" (20:16)

419
00:20:15,971 --> 00:20:20,832
And Faraday says to him: "Well, what's the use of a baby?"

420
00:20:20,832 --> 00:20:24,299
You know, a baby when it's young is not very useful.

421
00:20:24,299 --> 00:20:25,718
[scattered laughter]

422
00:20:25,718 --> 00:20:27,426
But it grows up into something!

423
00:20:27,916 --> 00:20:29,725
And that is where we are going today.

424
00:20:29,725 --> 00:20:32,488
We are the beginning of the Open Data journey.

425
00:20:33,088 --> 00:20:36,157
And partly is, we don't know what it's going to grow up into.

426
00:20:36,157 --> 00:20:37,004
Thank you very much!

427
00:20:37,004 --> 00:20:40,558
[Applause]

428
00:20:40,558 --> 00:20:44,708
[Questioner] Um, citizens and I guess patients at hospitals,

429
00:20:44,708 --> 00:20:48,683
assume that the institutions have all this data and it's very well organised,

430
00:20:48,683 --> 00:20:49,925
and it's a question of will.

431
00:20:50,555 --> 00:20:53,889
Have you encountered cases in which they simply don't have it,

432
00:20:53,889 --> 00:20:57,268
or they have it, and it's just such a mess that they're too embarrassed to give it out?

433
00:20:57,268 --> 00:20:58,661
[Rufus Pollock] Absolutely.

434
00:20:58,661 --> 00:21:00,959
I mean, one story that kind of intrigues me,

435
00:21:00,959 --> 00:21:03,914
is we've been building this "Where Does My Money Go?" open spending project.

436
00:21:04,094 --> 00:21:06,948
And one of the things the government mandated was giving out,

437
00:21:06,948 --> 00:21:09,065
rather than just high-level financial information,

438
00:21:09,065 --> 00:21:11,445
giving out information at a detailed level, you know,

439
00:21:11,445 --> 00:21:12,838
so they now publish, for example,

440
00:21:12,838 --> 00:21:15,601
spending data from each government department monthly,

441
00:21:15,601 --> 00:21:17,713
every transaction within 5,000 pounds (check).

442
00:21:17,713 --> 00:21:22,119
Every purchase they make, every mobile phone provider they contract with, we get that data.

443
00:21:22,119 --> 00:21:25,590
And one of the intriguing things, of their mandating this, was it turned out,

444
00:21:25,590 --> 00:21:30,022
before, they had no way, before they did this, of actually seeing, on any regular basis,

445
00:21:30,022 --> 00:21:31,232
what their department spent money on.

446
00:21:31,232 --> 00:21:35,029
Because in fact, the only thing they reported up on to, in central government to Treasury,

447
00:21:35,029 --> 00:21:38,871
was kind of like, how much did you spend against Project X that you were allocated budget for?

448
00:21:38,871 --> 00:21:41,156
You know, departments, were actually really intrigued, they [say]:

449
00:21:41,156 --> 00:21:43,435
"Oh, that other department's going with Vodafone,

450
00:21:43,435 --> 00:21:46,349
and we're with Orange, and look how much they're paying per month!"

451
00:21:46,349 --> 00:21:49,310
So I think in essence, it is really driving changes in government,

452
00:21:49,310 --> 00:21:52,700
and yeah, there are people, I think you'd been worried about giving out data quality.

453
00:21:52,700 --> 00:21:54,841
I was just talking to the Department of Education last week and they said

454
00:21:54,841 --> 00:21:57,650
-- you know, one of the things -- they had financial information from schools,

455
00:21:57,650 --> 00:21:59,567
and which they were slowly being mandated to publish.

456
00:21:59,567 --> 00:22:01,220
And schools are suddenly all ringing up, saying:

457
00:22:01,220 --> 00:22:04,290
"Well we never really bothered to really update that information to be accurate!

458
00:22:04,290 --> 00:22:05,900
Uh, we really want to do it right now".

459
00:22:06,210 --> 00:22:08,164
So I think that definitely does happen, yep.

460
00:22:08,164 --> 00:22:12,460
[Questioner] Are you seeing now new roles in government, to help facilitate this?

461
00:22:13,099 --> 00:22:16,009
[Rufus Pollock] Yeah. I mean, to take another example, I, sorry.

462
00:22:16,009 --> 00:22:19,492
Both in government, so the UK government has a transparency kind of 'czar' if you like.

463
00:22:19,492 --> 00:22:22,812
Also I learnt, is Nike hired an Open Data evangelist.

464
00:22:22,812 --> 00:22:24,611
One of the things they, while they were implementing this programme,

465
00:22:24,611 --> 00:22:27,990
they actually hired explicitly, an Open Data evangelist.

466
00:22:27,990 --> 00:22:30,023
So yeah, I think we are, we're definitely seeing this in government.

467
00:22:30,023 --> 00:22:32,357
Both in the tech level, but also at the policy level.

468
00:22:32,357 --> 00:22:34,729
And I think it's not just government,

469
00:22:34,729 --> 00:22:37,957
it will also be companies doing this, and so on, who will be saying:

470
00:22:37,957 --> 00:22:39,192
"We need an Open Data expert.

471
00:22:39,192 --> 00:22:43,662
We need to be aware of what's going on here and be able to plan it as part of our strategy."

472
00:22:44,472 --> 00:22:45,489
[Questioner] A final question.

473
00:22:45,489 --> 00:22:48,972
You mentioned that, kind of outsourcing, almost, some of this data refining,

474
00:22:48,972 --> 00:22:51,306
outside government or the big institutions, has helped them.

475
00:22:51,306 --> 00:22:54,446
Can you tell us any stories of kind of gratitude being expressed by the government? I mean...

476
00:22:55,146 --> 00:22:57,247
[Rufus Pollock] Well, I mean, to kind of, yeah.

477
00:22:57,247 --> 00:22:58,773
I mean there was an interesting example actually

478
00:22:58,773 --> 00:23:01,792
where we had some complaint because the open spending data I told you about

479
00:23:01,792 --> 00:23:05,323
where we're aggregating the government spending and financial data

480
00:23:05,323 --> 00:23:10,651
-- you know, the site had a few performance issues, occasionally, as we loaded more data in.

481
00:23:10,651 --> 00:23:12,755
I remember kind of getting this call kind of going :

482
00:23:12,755 --> 00:23:16,266
"Well, you know, we're a little bit upset, you know, data.gov.uk,"

483
00:23:16,266 --> 00:23:19,133
and it turned out the reason was, the Treasury kept looking at this data,

484
00:23:19,133 --> 00:23:21,002
and they were annoyed when the site was going down.

485
00:23:21,002 --> 00:23:22,563
So that was really intriguing to me

486
00:23:22,563 --> 00:23:26,139
that we were kind of one of the best, at least, up-to-date aggregators out there.

487
00:23:26,139 --> 00:23:29,484
Um, I think you are already seeing people doing stuff with the data

488
00:23:29,484 --> 00:23:30,979
and kind of doing stuff, sometimes for free, you know.

489
00:23:31,289 --> 00:23:33,131
You don't have to have the shiny front-end.

490
00:23:33,131 --> 00:23:35,019
I mean, one of the things we went about, on about,

491
00:23:35,019 --> 00:23:36,493
I know Tim Berners-Lee went on about --

492
00:23:36,493 --> 00:23:41,393
raw data now, you know, you can build fewer shiny front-ends, and just release raw data.

493
00:23:41,393 --> 00:23:46,455
And you know, someone else will help you build the app, the front-end, the interface,

494
00:23:46,455 --> 00:23:47,762
and help you innovate about it.

495
00:23:47,762 --> 00:23:50,917
What is the best way to provide healthcare data to citizens,

496
00:23:50,917 --> 00:23:52,254
or education data to citizens,

497
00:23:52,254 --> 00:23:54,367
so they make better and more informed choices?

498
00:23:54,367 --> 00:23:56,608
I don't know, and the government probably doesn't know.

499
00:23:56,608 --> 00:23:59,155
But somewhere out there, someone is going to innovate

500
00:23:59,155 --> 00:24:02,672
and really provide the best way for us to deliver that kind of information to citizens.

501
00:24:02,672 --> 00:24:04,250
QUESTIONER: Thank you very much.

502
00:24:04,250 --> 00:24:05,296
[Rufus Pollock] Thank you.

503
00:24:05,296 --> 00:24:06,901
[Applause]

504
00:24:06,901 --> 00:24:09,423
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505
00:24:09,423 --> 00:24:11,311
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