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  • 00:00

    Big data. What is it and why should you care?
    Big data. What is it and why should you care?

  • 00:05

    Just by starting to watch this video,
    Just by starting to watch this video,

  • 00:07

    you've added new data to your profile in somebody's database.
    you've added new data to your profile in somebody's database.

  • 00:11

    That profile will be used to target you for adverts or political campaigns
    That profile will be used to target you for adverts or political campaigns

  • 00:15

    or to predict what people like you will do in the future.
    or to predict what people like you will do in the future.

  • 00:19

    Whether you get a job interview or go to jail could depend on the data
    Whether you get a job interview or go to jail could depend on the data

  • 00:22

    other people have collected about you.
    other people have collected about you.

  • 00:24

    So you might want to spend five minutes learning a bit more
    So you might want to spend five minutes learning a bit more

  • 00:27

    about how it works and what kind of world it's building around us.
    about how it works and what kind of world it's building around us.

  • 00:32

    If you stop watching now, by the way,
    If you stop watching now, by the way,

  • 00:34

    that will also produce data.
    that will also produce data.

  • 00:36

    Probably pushing your profile's impulsivity score up
    Probably pushing your profile's impulsivity score up

  • 00:39

    by 0.7 points, meaning that in five years time
    by 0.7 points, meaning that in five years time

  • 00:42

    some recruiting algorithm will reject you for that job as an astronaut.
    some recruiting algorithm will reject you for that job as an astronaut.

  • 00:47

    So stick around while I tell you about big data.
    So stick around while I tell you about big data.

  • 00:52

    Data is nothing new; it's just information in a transferable form.
    Data is nothing new; it's just information in a transferable form.

  • 00:56

    About 30,000 years ago, in a cave in central Europe,
    About 30,000 years ago, in a cave in central Europe,

  • 01:00

    somebody carved 57 notches into a wolf shin bone in groups of five
    somebody carved 57 notches into a wolf shin bone in groups of five

  • 01:04

    like you tally something today.
    like you tally something today.

  • 01:07

    That's data.
    That's data.

  • 01:08

    We don't know what they were counting or why but we know there were 57 of whatever it was.
    We don't know what they were counting or why but we know there were 57 of whatever it was.

  • 01:14

    That's the oldest example I know of digital data.
    That's the oldest example I know of digital data.

  • 01:17

    Digital because you could count it on your digits
    Digital because you could count it on your digits

  • 01:20

    and it must have been quite a revolution in information technology back in the ice age.
    and it must have been quite a revolution in information technology back in the ice age.

  • 01:26

    How big is big data?
    How big is big data?

  • 01:28

    It's getting bigger so fast that if I gave you a figure,
    It's getting bigger so fast that if I gave you a figure,

  • 01:31

    it would be out of date before the end of this video.
    it would be out of date before the end of this video.

  • 01:34

    Ten years ago, Google was processing 20,000 terabytes of data every day.
    Ten years ago, Google was processing 20,000 terabytes of data every day.

  • 01:40

    Last year, American retailer Walmart collected 2,500 terabytes of data on its customers every hour
    Last year, American retailer Walmart collected 2,500 terabytes of data on its customers every hour

  • 01:49

    but though size does matter it's not the whole story.
    but though size does matter it's not the whole story.

  • 01:52

    There are four other things that make big data special
    There are four other things that make big data special

  • 01:56

    and I've made them into a backronym for you,
    and I've made them into a backronym for you,

  • 01:58

    using D A T A for data.
    using D A T A for data.

  • 02:01

    D is for dimensions, if you want the technical word
    D is for dimensions, if you want the technical word

  • 02:04

    or diverse or different if you prefer.
    or diverse or different if you prefer.

  • 02:07

    By combining data of different types from diverse sources,
    By combining data of different types from diverse sources,

  • 02:11

    you can get a multi-dimensional picture.
    you can get a multi-dimensional picture.

  • 02:13

    For example, I asked neuroscientist Prof Paul Matthews
    For example, I asked neuroscientist Prof Paul Matthews

  • 02:18

    if he was excited about using all the data from brain scans but he said that's just large data.
    if he was excited about using all the data from brain scans but he said that's just large data.

  • 02:25

    Big data is when I put those brain scans together with the patients' medical records,
    Big data is when I put those brain scans together with the patients' medical records,

  • 02:29

    the postcodes where they've lived and the weather records
    the postcodes where they've lived and the weather records

  • 02:32

    for those postcodes. And then I asked a completely new question.
    for those postcodes. And then I asked a completely new question.

  • 02:36

    For example, what's the relationship between the hours of sunshine they got
    For example, what's the relationship between the hours of sunshine they got

  • 02:41

    and the way that their multiple sclerosis has progressed.
    and the way that their multiple sclerosis has progressed.

  • 02:46

    A is for automatic.
    A is for automatic.

  • 02:48

    The way data is collected by default, every time we do anything on our computers
    The way data is collected by default, every time we do anything on our computers

  • 02:53

    or with our bank cards or just move around carrying our smartphones.
    or with our bank cards or just move around carrying our smartphones.

  • 02:57

    In fact, pretty much anything we do these days generates data which somebody can use.
    In fact, pretty much anything we do these days generates data which somebody can use.

  • 03:02

    Your smart meter, your car, your fitbit, we don't even notice it being collected most of the time.
    Your smart meter, your car, your fitbit, we don't even notice it being collected most of the time.

  • 03:08

    And T is for time.
    And T is for time.

  • 03:11

    because the data is being collected pretty much in real time,
    because the data is being collected pretty much in real time,

  • 03:15

    those patterns can be projected forward to help make predictions about the future.
    those patterns can be projected forward to help make predictions about the future.

  • 03:21

    Things like when the trains will be busiest or how much electricity we'll need
    Things like when the trains will be busiest or how much electricity we'll need

  • 03:27

    or how fast malaria will spread.
    or how fast malaria will spread.

  • 03:30

    And the last day is for A.I.
    And the last day is for A.I.

  • 03:32

    Artificial intelligence.
    Artificial intelligence.

  • 03:34

    Which is not really intelligence like a human is intelligent
    Which is not really intelligence like a human is intelligent

  • 03:37

    but an AI computer program that can find patterns in the data
    but an AI computer program that can find patterns in the data

  • 03:42

    often using techniques like machine learning where you don't give it every step of the instructions,
    often using techniques like machine learning where you don't give it every step of the instructions,

  • 03:47

    you just tell it to sort a from b, cat pictures from dog pictures maybe,
    you just tell it to sort a from b, cat pictures from dog pictures maybe,

  • 03:52

    or good job applicants from bad job applicants.
    or good job applicants from bad job applicants.

  • 03:56

    People are doing lots of exciting things with big data.
    People are doing lots of exciting things with big data.

  • 04:00

    Tracking insects to fight diseases like malaria and zika,
    Tracking insects to fight diseases like malaria and zika,

  • 04:04

    predicting aircraft engine failures before they happen,
    predicting aircraft engine failures before they happen,

  • 04:07

    finding new particles or new antibiotics.
    finding new particles or new antibiotics.

  • 04:11

    When the same techniques are applied to humans,
    When the same techniques are applied to humans,

  • 04:13

    things get more tricky.
    things get more tricky.

  • 04:15

    Should we use big data to predict the chance someone will reoffend, and sentence them accordingly?
    Should we use big data to predict the chance someone will reoffend, and sentence them accordingly?

  • 04:22

    Because that's already happening.
    Because that's already happening.

  • 04:24

    Did big data change the results of recent votes
    Did big data change the results of recent votes

  • 04:26

    by targeting voters with personalised adverts?
    by targeting voters with personalised adverts?

  • 04:30

    Certainly it helped campaigners know which voters to target
    Certainly it helped campaigners know which voters to target

  • 04:34

    and what kind of people they were by combining
    and what kind of people they were by combining

  • 04:36

    different sources of data to build multi-dimensional profiles
    different sources of data to build multi-dimensional profiles

  • 04:40

    and target them accordingly.
    and target them accordingly.

  • 04:42

    That approach started with US President Obama's successful election campaign
    That approach started with US President Obama's successful election campaign

  • 04:47

    but would it be fair to say Obama only won because of big data?
    but would it be fair to say Obama only won because of big data?

  • 04:52

    I don't think so, I think he offered something that voters wanted
    I don't think so, I think he offered something that voters wanted

  • 04:56

    and big data just helped him reach the voters whose votes were most crucial.
    and big data just helped him reach the voters whose votes were most crucial.

  • 05:01

    Politics is about the content of the messages not the platform that delivers them.
    Politics is about the content of the messages not the platform that delivers them.

  • 05:06

    Should we worry about politics being reduced to a marketing campaign?
    Should we worry about politics being reduced to a marketing campaign?

  • 05:10

    Yes but that's a much broader problem than the technology.
    Yes but that's a much broader problem than the technology.

  • 05:15

    Big data has its limitations, especially when it comes to human beings,
    Big data has its limitations, especially when it comes to human beings,

  • 05:19

    but it also has immense potential to improve human lives.
    but it also has immense potential to improve human lives.

  • 05:23

    Let's just make sure we use it the right way and keep the humans in control.
    Let's just make sure we use it the right way and keep the humans in control.

All idiom
is it
//

idiom

Is that so? You don't say? Really? (Sometimes colloquially joined as one word, alternately spelled "isit" or "izit," and pronounced as the latter.) Primarily heard in South Africa.

Big data: why should you care?

38,444 views

Intro:

Big data. What is it and why should you care?. Just by starting to watch this video,. you've added new data to your profile in somebody's database.
That profile will be used to target you for adverts or political campaigns
or to predict what people like you will do in the future.
Whether you get a job interview or go to jail could depend on the data
other people have collected about you.. So you might want to spend five minutes learning a bit more
about how it works and what kind of world it's building around us.
If you stop watching now, by the way,. that will also produce data.. Probably pushing your profile's impulsivity score up
by 0.7 points, meaning that in five years time. some recruiting algorithm will reject you for that job as an astronaut.
So stick around while I tell you about big data.. Data is nothing new; it's just information in a transferable form.
About 30,000 years ago, in a cave in central Europe,
somebody carved 57 notches into a wolf shin bone in groups of five
like you tally something today.. That's data..

Video Vocabulary

/ˈalɡəˌriT͟Həm/

noun

process or set of rules to be followed in calculations or other problem-solving operations.

/ˌinfərˈmāSH(ə)n/

noun

Collection of facts and details about something.

/ˈnəTHiNG/

adjective adverb noun pronoun

of no value. not at all. Number or value of zero. not anything.

/wäCH/

verb

To guard a place or people; protect child, etc..

/tekˈnäləjē/

noun

Use or knowledge of science in industry etc..

/ˈsentrəl/

adjective noun

Most significant or important. place with high concentration of specified type of person or thing.

/ˈin(t)ərˌvyo͞o/

noun verb

Meeting to ask questions to get information. To formally ask questions about a given topic.

/prəˈdikt/

verb

say or estimate that specified thing will happen in future or will be consequence of something.

/stärt/

verb

begin or be reckoned from particular point in time or space.

/ˌrevəˈlo͞oSH(ə)n/

noun

Full circular movement around something.

/ˈadˌvərt/

noun other

advertisement. Shortened form of 'advertisements'.

noun verb

agricultural products. To make something appear.

/pəˈlidək(ə)l/

adjective

Concerning government or public affairs.

/ˈdijidl/

adjective

referring to signals/information represented by discrete values of quantity.

/transˈfərəb(ə)l/

adjective

able to be transferred or made over to possession of another person.