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Open Data: How We Got Here, and Where We’re Going

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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.
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    You can choose the price, you can say where you're going to work,
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    you can choose the commute time, and you can choose the scenicness.
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    And it will show you, on this map, where you can live.
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    Another example, more about transparency,
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    is a project we did called "Where Does My Money Go?".
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    It's an interactive version,
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    you can kind of draw it out,
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    so what it starts with, is one, is it tells you what your tax is,
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    something that most people often don't know,
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    and it will tell you how much you're paying each day
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    to a particular area of society.
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    And the dream for me,
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    a dream that we're on the way to realising,
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    is in this visualisation, you can drill down into areas.
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    And my dream is to keep drilling down.
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    So depending on what day we have, I want to go down,
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    right down through those bubbles, step by step,
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    until I see the money spent on street lights on my street,
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    on filling in potholes, on collecting my rubbish.
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    And for two reasons:
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    One, obviously there's a question, particularly in some countries,
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    of inefficiency or corruption,
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    but also, just because most of us don't feel very happy about paying tax.
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    It's not one of those things people welcome!
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    But it's something that we should.
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    Government does an awful lot for us,
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    and having a better sense of where it's going
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    could make us feel an awful lot better about paying that tax.
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    In the way that when we go to a restaurant,
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    we don't, when we get the bill, we don't necessarily feel bad.
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    We feel "Wow, I had a great meal. That was worth it."
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    But why Open?
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    I've given you examples, and you know, we see a lot of apps and services.
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    Why is Open relevant here?
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    This goes back to what I said about the information technology, the revolution.
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    So it's the challenge and the opportunity.
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    It's the challenge that we see today, is exploding informational complexity.
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    I mean, another great story:
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    in the 1820s, all bank clearing in the largest financial centre in the world
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    was done in a single room, where people -- one person from each bank gathered
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    and they'd go round the room pulling out gold, and swapping it around, between different banks.
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    And that's how they did bank clearing.
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    Today we have billions of transactions a minute.
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    And the way we as humans deal with complexity is by dividing and conquering it.
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    We split it up into manageable chunks that we deal with.
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    The other answer,
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    and this answer's particularly relevant about Open Data,
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    is information technology.
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    Today, a smartphone has as much computing power
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    as the system that ran the Apollo moon landings.
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    And an even better example is storage:
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    one terabyte of storage today is a hundred dollars.
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    In 1994, this would have cost 400,000 dollars.
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    I can have every financial transaction
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    the UK government, or the US government made last year, or even for the last decade,
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    on my laptop.
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    That was not possible for an average citizen a decade ago.
  • 15:44 - 15:48
    So it's mass participation, information access, processing, and production.
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    It's decentralisation.
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    And the claim here is that openness is key.
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    It's because it's about scaling.
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    What we are doing is weaving data together.
  • 15:57 - 16:00
    As I said, we deal with complexity by splitting it up.
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    We componentise, we split data up into blocks
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    that we recombine.
  • 16:05 - 16:07
    But if we are going to recombine information,
  • 16:08 - 16:10
    we need to put Humpty Dumpty back together again,
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    it won't work most of the time if it is closed.
  • 16:13 - 16:17
    We need Open Data to scale and to componentise.
  • 16:18 - 16:22
    And it's a point just to make here in this respect, that you might think:
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    "Well you know, you're talking about Open Data,
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    you know, this could be true of anything!
  • 16:25 - 16:26
    Why don't we have like,
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    Open Cars, and Open Shoes, and you know,
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    why don't we just share everything, man!
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    It would be so beautiful!".
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    Right? And the sad thing is,
  • 16:33 - 16:39
    is that that hasn't generally worked as a way of organising most production in our society.
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    Instead, we have private property, and so we don't do that much openness relatively.
  • 16:44 - 16:46
    But there's something different about digital information.
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    We all know it, but it's worth emphasising, which is, it's very cheaply copied.
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    I mean, give me a copy of your data isn't a problem if you're the government.
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    Give me a copy of your car, or your house, or whatever, is.
  • 16:57 - 16:59
    And it's also about innovation here.
  • 16:59 - 17:01
    I mean, in a way it's almost the purest aspect of markets.
  • 17:01 - 17:06
    Markets are about moving things to the person who could use them most best.
  • 17:06 - 17:07
    And that's true of data.
  • 17:07 - 17:11
    The best thing to do with your data will likely be thought of by someone else.
  • 17:11 - 17:15
    And vice versa! You will think of the best thing to do with someone else's data.
  • 17:16 - 17:20
    And Open Data allows us, in the most frictionless, easiest way,
  • 17:20 - 17:23
    to move data to where it can be most optimally used,
  • 17:23 - 17:24
    particularly if you're government.
  • 17:24 - 17:27
    So in short, it's about better understanding, it's about better government,
  • 17:27 - 17:29
    it's about better research, it's about better economy.
  • 17:29 - 17:31
    And something also for companies and governments:
  • 17:31 - 17:33
    I think it's about better engagement.
  • 17:33 - 17:35
    It's about a closer relationship, sometimes,
  • 17:35 - 17:37
    between your citizens and you as the government.
  • 17:37 - 17:41
    Between you, even possibly, as a company, and your users.
  • 17:42 - 17:44
    So I wanted to kind of finish here by saying where we're going.
  • 17:44 - 17:46
    The story was, of this talk, was, you know, where are we?
  • 17:47 - 17:50
    Why have we got here? And where are we going?
  • 17:51 - 17:52
    So one answer is just more use.
  • 17:52 - 17:55
    So right now, I just said at the beginning, Open Data is relatively young.
  • 17:55 - 17:58
    This vast outpouring, for example, of government data,
  • 17:58 - 18:02
    that anyone can freely use, reuse, and redistribute, is really new,
  • 18:02 - 18:03
    even if it's done three years ago.
  • 18:03 - 18:06
    For example, in the UK, much of the most useful data that could be released
  • 18:06 - 18:09
    has only been released in the last six months or a year.
  • 18:09 - 18:10
    You want prescription data?
  • 18:10 - 18:12
    Are you a pharmaceutical company,
  • 18:12 - 18:15
    and you want to know what kind of prescription habits are going on in the UK?
  • 18:15 - 18:19
    I would emphasise: at an anonymised or somewhat aggregate level.
  • 18:19 - 18:21
    Do you want to know about what crime is going on?
  • 18:21 - 18:24
    Are you building a real estate website and you want data on environment,
  • 18:24 - 18:26
    or you want data on unemployment,
  • 18:26 - 18:29
    or other information about where properties are situated?
  • 18:29 - 18:30
    You can now get that.
  • 18:31 - 18:33
    So I think there's going to be a lot more use from business.
  • 18:34 - 18:35
    There'll be a lot more use from everyone.
  • 18:35 - 18:39
    But I think particularly business is going to wake up to the opportunities here.
  • 18:39 - 18:40
    I think it's also going to lead to more data.
  • 18:40 - 18:42
    One is, government is going to be more data.
  • 18:42 - 18:46
    I think also businesses are going to realise, and communities,
  • 18:46 - 18:48
    that they want to share back some of that data,
  • 18:48 - 18:49
    some of the data they have.
  • 18:49 - 18:51
    It's not going to be their kind of crown jewels,
  • 18:51 - 18:54
    and it's not going -- often start out with data that's not core to their business.
  • 18:54 - 18:58
    It's like. kind of Nike, they realised that by opening and sharing data,
  • 18:58 - 19:01
    they can scale in a way they can't on their own.
  • 19:01 - 19:03
    And does it mean that richer data, going back
  • 19:03 - 19:06
    -- how could I leave out Hegel and Marx in a talk like this --
  • 19:06 - 19:09
    "Quantity changes quality" as Hegel told us.
  • 19:09 - 19:14
    And more data, going back to that woven ball, more data actually means better data.
  • 19:14 - 19:18
    It means richer data, it's a qualitative difference in what we can do.
  • 19:18 - 19:20
    Geodata on it's own isn't that useful.
  • 19:20 - 19:22
    Transport data on it's own isn't useful.
  • 19:22 - 19:24
    Geodata plus transport data is useful!
  • 19:25 - 19:26
    And we're going to be seeing data refining.
  • 19:26 - 19:27
    Data is the new oil, right?
  • 19:27 - 19:29
    So, we're going to refine it.
  • 19:29 - 19:32
    And that's going to be a big business: higher quality data.
  • 19:32 - 19:34
    I want to leave you with a couple of thoughts.
  • 19:34 - 19:36
    So, one is, some people say:
  • 19:36 - 19:38
    "Well, okay, but, you know, selling data is big business".
  • 19:38 - 19:41
    And it is, but going forward in some of these things like software,
  • 19:41 - 19:42
    data is going to be a platform.
  • 19:42 - 19:44
    It's not a commodity.
  • 19:44 - 19:46
    Businesses built purely on selling data,
  • 19:46 - 19:48
    I just don't think are going to make it.
  • 19:48 - 19:52
    You need to be building on your data, not attempting to purely sell it.
  • 19:53 - 19:55
    And the other answer is to be modest.
  • 19:55 - 19:57
    So I said: where are we going?
  • 19:57 - 19:58
    I don't know if people know
  • 19:58 - 20:02
    -- and this takes us back to an earlier age, an age of electricity and steam -- of Faraday.
  • 20:02 - 20:05
    So he's demonstrating electricity at the Royal Society,
  • 20:05 - 20:08
    and Gladstone, the future Prime Minister of England, sees him do this stuff, you know,
  • 20:08 - 20:10
    the frog legs move, and Gladstone's like:
  • 20:10 - 20:12
    "Well, I mean, this is party trick, Faraday.
  • 20:12 - 20:16
    It's great, but, what's really, you know, what's electricity going to amount to?" (20:16)
  • 20:16 - 20:21
    And Faraday says to him: "Well, what's the use of a baby?"
  • 20:21 - 20:24
    You know, a baby when it's young is not very useful.
  • 20:24 - 20:26
    [scattered laughter]
  • 20:26 - 20:27
    But it grows up into something!
  • 20:28 - 20:30
    And that is where we are going today.
  • 20:30 - 20:32
    We are the beginning of the Open Data journey.
  • 20:33 - 20:36
    And partly is, we don't know what it's going to grow up into.
  • 20:36 - 20:37
    Thank you very much!
  • 20:37 - 20:41
    [Applause]
  • 20:41 - 20:45
    [Questioner] Um, citizens and I guess patients at hospitals,
  • 20:45 - 20:49
    assume that the institutions have all this data and it's very well organised,
  • 20:49 - 20:50
    and it's a question of will.
  • 20:51 - 20:54
    Have you encountered cases in which they simply don't have it,
  • 20:54 - 20:57
    or they have it, and it's just such a mess that they're too embarrassed to give it out?
  • 20:57 - 20:59
    [Rufus Pollock] Absolutely.
  • 20:59 - 21:01
    I mean, one story that kind of intrigues me,
  • 21:01 - 21:04
    is we've been building this "Where Does My Money Go?" open spending project.
  • 21:04 - 21:07
    And one of the things the government mandated was giving out,
  • 21:07 - 21:09
    rather than just high-level financial information,
  • 21:09 - 21:11
    giving out information at a detailed level, you know,
  • 21:11 - 21:13
    so they now publish, for example,
  • 21:13 - 21:16
    spending data from each government department monthly,
  • 21:16 - 21:18
    every transaction within 5,000 pounds (check).
  • 21:18 - 21:22
    Every purchase they make, every mobile phone provider they contract with, we get that data.
  • 21:22 - 21:26
    And one of the intriguing things, of their mandating this, was it turned out,
  • 21:26 - 21:30
    before, they had no way, before they did this, of actually seeing, on any regular basis,
  • 21:30 - 21:31
    what their department spent money on.
  • 21:31 - 21:35
    Because in fact, the only thing they reported up on to, in central government to Treasury,
  • 21:35 - 21:39
    was kind of like, how much did you spend against Project X that you were allocated budget for?
  • 21:39 - 21:41
    You know, departments, were actually really intrigued, they [say]:
  • 21:41 - 21:43
    "Oh, that other department's going with Vodafone,
  • 21:43 - 21:46
    and we're with Orange, and look how much they're paying per month!"
  • 21:46 - 21:49
    So I think in essence, it is really driving changes in government,
  • 21:49 - 21:53
    and yeah, there are people, I think you'd been worried about giving out data quality.
  • 21:53 - 21:55
    I was just talking to the Department of Education last week and they said
  • 21:55 - 21:58
    -- you know, one of the things -- they had financial information from schools,
  • 21:58 - 22:00
    and which they were slowly being mandated to publish.
  • 22:00 - 22:01
    And schools are suddenly all ringing up, saying:
  • 22:01 - 22:04
    "Well we never really bothered to really update that information to be accurate!
  • 22:04 - 22:06
    Uh, we really want to do it right now".
  • 22:06 - 22:08
    So I think that definitely does happen, yep.
  • 22:08 - 22:12
    [Questioner] Are you seeing now new roles in government, to help facilitate this?
  • 22:13 - 22:16
    [Rufus Pollock] Yeah. I mean, to take another example, I, sorry.
  • 22:16 - 22:19
    Both in government, so the UK government has a transparency kind of 'czar' if you like.
  • 22:19 - 22:23
    Also I learnt, is Nike hired an Open Data evangelist.
  • 22:23 - 22:25
    One of the things they, while they were implementing this programme,
  • 22:25 - 22:28
    they actually hired explicitly, an Open Data evangelist.
  • 22:28 - 22:30
    So yeah, I think we are, we're definitely seeing this in government.
  • 22:30 - 22:32
    Both in the tech level, but also at the policy level.
  • 22:32 - 22:35
    And I think it's not just government,
  • 22:35 - 22:38
    it will also be companies doing this, and so on, who will be saying:
  • 22:38 - 22:39
    "We need an Open Data expert.
  • 22:39 - 22:44
    We need to be aware of what's going on here and be able to plan it as part of our strategy."
  • 22:44 - 22:45
    [Questioner] A final question.
  • 22:45 - 22:49
    You mentioned that, kind of outsourcing, almost, some of this data refining,
  • 22:49 - 22:51
    outside government or the big institutions, has helped them.
  • 22:51 - 22:54
    Can you tell us any stories of kind of gratitude being expressed by the government? I mean...
  • 22:55 - 22:57
    [Rufus Pollock] Well, I mean, to kind of, yeah.
  • 22:57 - 22:59
    I mean there was an interesting example actually
  • 22:59 - 23:02
    where we had some complaint because the open spending data I told you about
  • 23:02 - 23:05
    where we're aggregating the government spending and financial data
  • 23:05 - 23:11
    -- you know, the site had a few performance issues, occasionally, as we loaded more data in.
  • 23:11 - 23:13
    I remember kind of getting this call kind of going :
  • 23:13 - 23:16
    "Well, you know, we're a little bit upset, you know, data.gov.uk,"
  • 23:16 - 23:19
    and it turned out the reason was, the Treasury kept looking at this data,
  • 23:19 - 23:21
    and they were annoyed when the site was going down.
  • 23:21 - 23:23
    So that was really intriguing to me
  • 23:23 - 23:26
    that we were kind of one of the best, at least, up-to-date aggregators out there.
  • 23:26 - 23:29
    Um, I think you are already seeing people doing stuff with the data
  • 23:29 - 23:31
    and kind of doing stuff, sometimes for free, you know.
  • 23:31 - 23:33
    You don't have to have the shiny front-end.
  • 23:33 - 23:35
    I mean, one of the things we went about, on about,
  • 23:35 - 23:36
    I know Tim Berners-Lee went on about --
  • 23:36 - 23:41
    raw data now, you know, you can build fewer shiny front-ends, and just release raw data.
  • 23:41 - 23:46
    And you know, someone else will help you build the app, the front-end, the interface,
  • 23:46 - 23:48
    and help you innovate about it.
  • 23:48 - 23:51
    What is the best way to provide healthcare data to citizens,
  • 23:51 - 23:52
    or education data to citizens,
  • 23:52 - 23:54
    so they make better and more informed choices?
  • 23:54 - 23:57
    I don't know, and the government probably doesn't know.
  • 23:57 - 23:59
    But somewhere out there, someone is going to innovate
  • 23:59 - 24:03
    and really provide the best way for us to deliver that kind of information to citizens.
  • 24:03 - 24:04
    [Questioner] Thank you very much.
  • 24:04 - 24:05
    [Rufus Pollock] Thank you.
  • 24:05 - 24:07
    [Applause]
  • 24:07 - 24:09
    lift _ Video Production ACTUA
  • 24:09 - 24:11
    Copyright (c) 2012 Lift conference
Title:
Open Data: How We Got Here, and Where We’re Going
Description:

Rufus Pollock at the Lift 12 conference. More info in http://okfn.org/opendata/ , where the video is embedded.

See also http://blog.okfn.org/2012/04/02/talk-at-lift-2012-open-data-how-we-got-here-and-where-were-going/ , where Rufus Pollock's slides can be downloaded.

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Video Language:
English
Team:
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  • These are Argus' "English" subtitles, reuploaded in revision 1. I'm using this "English, British" track to try and split some of his longer ones when they hide a slide shown on the video. Not finished yet.

English, British subtitles

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