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Speaker 1: Welcome to tech Stuff, a production from I Heart Radio.

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Speaker 1: Hey there, and welcome to tech Stuff. I'm here host

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Speaker 1: Jonathan Strickland. I'm an executive producer with I Heart Radio.

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Speaker 1: And how the tech are you. It's Friday. It's time

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Speaker 1: for a classic episode. This episode is called the Evils

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Speaker 1: of Data Tracking and it originally published on December six,

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Speaker 1: two thousand fifteen, and Ben Johnson, another podcaster and journalist,

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Speaker 1: joined the show for this discussion. Keep in mind, you

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Speaker 1: know this was done back in the issues with data

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Speaker 1: tracking have only gotten worse since then. It has not

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Speaker 1: improved in any stretch except for the various agencies that

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Speaker 1: are tracking data, whether those are corporations or governments. So um,

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Speaker 1: fun times, but enjoy this classic episode. I got to

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Speaker 1: chance to be a guest host on a show you

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Speaker 1: host called code Breaker. Yes, And on code Breaker when

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Speaker 1: we talked, I made a reference to Shakespeare. Yes. Now

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Speaker 1: my question for you is are you ever confused with

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Speaker 1: the seventeenth century British playwright Ben Johnson? Oh man, all

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Speaker 1: the time opens, all the time embarrassing. Yeah, I know

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Speaker 1: I've been. You know, I have been to Stratford upon Avon.

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Speaker 1: I have done that whole thing and and and I

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Speaker 1: used to you know, I never get confused for that

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Speaker 1: Ben Johnson. But in the past, at least people made

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Speaker 1: jokes about steroids because of the other Ben Johnson, Nadian Runner,

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Speaker 1: and I used to have a T shirt that said

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Speaker 1: I'm not on steroids, but thanks for asking, which was

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Speaker 1: a kind of inside joke among my friends, but it

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Speaker 1: was you know, I've never been no, I've never been

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Speaker 1: that poetic, I've never been that good at sonnets. So

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Speaker 1: usually I'm not not confused. It's really funny because I'm

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Speaker 1: in in an upcoming episode of tech Stuff. I'm going

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Speaker 1: to have my friend i As actar from c net

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Speaker 1: on and his his Twitter handle is at i as,

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Speaker 1: so he gets a lot of hip hop and rap questions.

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Speaker 1: So yeah, it's like me not so much. I'm okay

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Speaker 1: with that. But the reason I have been on today

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Speaker 1: is because we're doing something kind of a complimentary episode

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Speaker 1: to the Codebreaker episode I was on, which was all

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Speaker 1: about data tracking and data mining, and to kind of

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Speaker 1: talk about what that is, what it's used for, what

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Speaker 1: is the best case scenario, like what why do we

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Speaker 1: want it? And what are some really really good reasons

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Speaker 1: why we may not want it. Uh, despite the fact

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Speaker 1: that at this point it's here to stay. I think

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Speaker 1: a spoiler alert, data tracking is not going away, right

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Speaker 1: until the sort of posted a post apocalyptic world descents,

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Speaker 1: until we're all in um fallout for world or whatever

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Speaker 1: it is, or or we reach singularity and we're sharing

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Speaker 1: the same consciousness anyway, right, yeah, and then it doesn't

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Speaker 1: matter who buys what. There's no such thing as an

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Speaker 1: individual at that point. So until then we're stuck with it.

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Speaker 1: So data tracking and data mining these are terms that

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Speaker 1: kind of relate to another buzz term that you've probably

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Speaker 1: heard over and over again, which is big data. Um.

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Speaker 1: And the reason why we're even talking about this at

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Speaker 1: all is because information has value, but it's hard to

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Speaker 1: pin down exactly what the value is because it may

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Speaker 1: be very different from one person to another, or between

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Speaker 1: the individual whose data it is and some other organization

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Speaker 1: or a chain of organizations, and the whole thing gets

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Speaker 1: really messy. And uh, yeah, we'll be talking about information

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Speaker 1: and information about information like metadata. I mean this it

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Speaker 1: becomes a rabbit hole that you can easily get lost

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Speaker 1: in as you start to try and unravel everything and

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Speaker 1: figure out, Okay, well, what's the heart of this story.

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Speaker 1: And to be fair, data treking is something that's been

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Speaker 1: going on for ages. It's not new. It's just that

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Speaker 1: the Internet and the tools we use today allow us

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Speaker 1: to generate, gather, and sift through more information than we've

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Speaker 1: ever been able to do before. It's kind of a

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Speaker 1: catch all phrase, right, Jonathan. It's it's this kind of

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Speaker 1: catch all phrase for for computers in software to build

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Speaker 1: a really comprehensive um data profile about you in some ways,

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Speaker 1: but it's something that credit card companies and banks and

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Speaker 1: advertisers have endeavored to do for decades exactly. I mean,

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Speaker 1: and this can be done for very simple, quote unquote

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Speaker 1: innocent reasons. Like one of the examples I like to

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Speaker 1: say is you might have a shop and you in

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Speaker 1: your shop, you sell ice cream, and you pay attention

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Speaker 1: to see which flavors of ice cream sell the best,

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Speaker 1: and that informs you of which flavors of ice cream

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Speaker 1: you need to stock up on. You are tracking the

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Speaker 1: data of sales. In this case, you're not necessarily looking

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Speaker 1: at individuals. You don't care who it is that buys

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Speaker 1: the vanilla, but if everyone's buying vanilla, you want to

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Speaker 1: make sure you have more vanilla than the other flavors

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Speaker 1: that aren't selling as well. So that's one that's like

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Speaker 1: the very simplest version of data tracking there where you

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Speaker 1: know you've scrubbed the identity of the individuals. That's not

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Speaker 1: even that doesn't even come up. It's not important. But

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Speaker 1: it can go all the way to the other extreme,

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Speaker 1: where you want to create targeted advertising that is going

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Speaker 1: to appeal to each specific person that visits a particular

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Speaker 1: site or uses a particular service, and the whole point

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Speaker 1: of that is to attempt to get them to buy

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Speaker 1: something or to enroll in something. It's it's the goal

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Speaker 1: of all advertising. It's to get you to act in

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Speaker 1: a certain way. And the idea is that, well, if

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Speaker 1: we can narrow the target down to the most likely candidates,

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Speaker 1: will get a better return on our efforts. And this

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Speaker 1: is theoretically good for the user, right Jonathan. Theoretically it's

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Speaker 1: it's sort of like, we want to give you the

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Speaker 1: thing that you really want, and we want to give

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Speaker 1: it to you in the most efficient way and in

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Speaker 1: the most sort of sympatico way with with what you

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Speaker 1: are interested in doing already. Um, but it can sort

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Speaker 1: of like data tracting to me means so many things, Jonathan, right.

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Speaker 1: It means your online browsing behavior, or the things you

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Speaker 1: choose to buy, the platforms, the software you use, the

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Speaker 1: things you download, your name, age, race, sexual orientation, when

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Speaker 1: you're sick, what what you're sick with, your eating habits,

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Speaker 1: your television watching habits, your fears, hope streams right when

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Speaker 1: you're awake, when you're asleep. I mean, like, we we're

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Speaker 1: talking about a world now where where we we want

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Speaker 1: these wearables. They can give us real time data feedback

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Speaker 1: on how we are doing throughout the day. Maybe you

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Speaker 1: are an extreme type a personality and you are trying

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Speaker 1: to schedule your day so that when you're hitting those

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Speaker 1: peak moments of productivity, that's when you're tackling the most

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Speaker 1: important tasks to you, and you're wearing wearables, you're getting

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Speaker 1: that feedback and you're understanding all this stuff. That data

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Speaker 1: may also be going to other places and there may

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Speaker 1: not be any use for it right now. Like right now,

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Speaker 1: as you're generating that data, that's fine, no one's looking

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Speaker 1: at it. But down the road that might not be

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Speaker 1: the case for various reasons, some of which are terrifying.

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Speaker 1: But but you know you were talking about the different

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Speaker 1: types of data. There are actually three kind of broad

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Speaker 1: categories we can classify the data that we are creating

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Speaker 1: that that various entities are really interested in. There's there's

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Speaker 1: volunteered data, and that's the stuff we share, right that's

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Speaker 1: when we go on facebooks exactly, that's my tweets, your tweets,

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Speaker 1: your your YouTube's, your facebooks, your Pinterests, it's it's the

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Speaker 1: stuff where we are participating in the conversation and generating

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Speaker 1: that information. We're not just doing that for the people

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Speaker 1: that we are trying to impress, or our friends or

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Speaker 1: sometimes our enemies if we want to rub their noses

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Speaker 1: and stuff because they think they're so big. But that's

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Speaker 1: that's the stuff that we are actively sharing. Then there's

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Speaker 1: the observed data. That's the stuff that companies and other

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Speaker 1: entities can can figure out about us just by watching

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Speaker 1: our behavior. So this would be kind of the stuff

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Speaker 1: we buy. The browsing habits we have where we log

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Speaker 1: in from. Like, if you are doing most of your

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Speaker 1: browsing on a mobile device, you better bet their their

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Speaker 1: companies out there that want to know that. They want

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Speaker 1: to know that you access stuff on your phone. More

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Speaker 1: than you do on a laptop or desktop. That's important information.

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Speaker 1: And then there's the inferred data, and that's the stuff

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Speaker 1: that companies guests is relevant to you based upon the

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Speaker 1: information they gather from the other two types of sources.

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Speaker 1: And it's it's this collection that's important. And you know

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Speaker 1: I mentioned about value and how value is different for

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Speaker 1: different people. So the Telegraph did a survey UH in

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Speaker 1: which they asked people what they felt their personal information

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Speaker 1: was worth to them personally, like, what is your identity

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Speaker 1: worth to you? Uh? Based upon the kind of information

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Speaker 1: to get shared around these entities? Can you make a

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Speaker 1: ballpark guess? I'll tell you this. It's it's it's less

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Speaker 1: than ten thousand dollars, but it's more than five dollars. Man, man,

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Speaker 1: I'm gonna go with I'm gonna go with twenty bucks

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Speaker 1: or no, no, no no, wait, wait, hundred bucks. You know

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Speaker 1: it's probably because we share so much ben that we

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Speaker 1: value it so little. But the average person five thousand dollars,

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Speaker 1: Oh well, that's good that that makes and that makes

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Speaker 1: the average person a lot smarter than me. Well, to

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Speaker 1: be fair, it was three thousand, two pounds sterling. But

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Speaker 1: I did the conversion fair enough, well done, Yeah and uh.

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Speaker 1: And they also mentioned the fact that, um that while

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Speaker 1: they were doing this, they they found that there was

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Speaker 1: a disparity that that women in the survey tended to

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Speaker 1: answer more frequently that their personal information was priceless, that

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Speaker 1: there was no price you could put on it, that

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Speaker 1: they would feel comfortable selling it okay, and men were

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Speaker 1: less likely to do that. They also said an interesting

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Speaker 1: data point, if you will, Yeah, I also mentioned that

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Speaker 1: older people were less likely to want to sell their

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Speaker 1: information than younger people, which kind of young younger people

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Speaker 1: are just giving it away. Yeah they are, I mean

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Speaker 1: they you know, we're seeing that more and more with

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Speaker 1: the the the very cavalier behavior of certain executives like Zuckerberg,

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Speaker 1: who have said that privacy is dead. No, privacy is

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Speaker 1: no longer a thing. That seems to be kind of

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Speaker 1: the message that younger generations have not only absorbed, but

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Speaker 1: adopted at least to some extent. Say you and I

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Speaker 1: s old kajers. So we claim, yeah, no, if your

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Speaker 1: generation X or older, you're like, get your dirty, gruvy

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Speaker 1: hands off my personal information, you damn millennial right so

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Speaker 1: now here. So that's how much the person values their

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Speaker 1: personal information in the thousands of dollars range. Okay, I

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Speaker 1: know what you're gonna do next, Right, You're gonna give

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Speaker 1: me the average company's value on our data. What's going

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Speaker 1: to happen? That is exactly what's gonna happen. So here's

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Speaker 1: the deal. Your personal information to a company at best,

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Speaker 1: like at its peak for average amount of information. I'm

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Speaker 1: not talking about very specialized stuff like if you were

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Speaker 1: talking about that wearable example. I was saying earlier that

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Speaker 1: would that would be worth more because it's more comprehensive data.

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Speaker 1: But you're average stuff like you're the stuff you might

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Speaker 1: find on a Facebook profile page. It is worth point

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Speaker 1: zero zero five dollars. So half a cent is how

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Speaker 1: much your data is work at the peak average is

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Speaker 1: closer to point zero zero zero five dollars. To know that, I'm,

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Speaker 1: you know, a boring, middle aged white guy who's probably

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Speaker 1: going to see the Star Wars movie. Probably, I mean

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Speaker 1: I would have paid you for definitely, Okay, I mean

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Speaker 1: it's seven days away, dude, It is seven days away

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Speaker 1: as we record. That can so so at any rate? Yeah, exactly,

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Speaker 1: Like the companies like, that's how much your information is

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Speaker 1: worth to them. So to you, it's in the thousands

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Speaker 1: of dollars. To them, it doesn't even equal a penny

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Speaker 1: for the but in the aggregate, right, it's gonna be

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Speaker 1: worth so much more. Well, exactly because because when a

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Speaker 1: company is buying information, they're not buying one person's information.

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Speaker 1: They're buying bundles of thousands upon thousands of people's information.

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Speaker 1: Then it's that company's job to either sell that information

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Speaker 1: off to someone else. These are data brokers. This is

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Speaker 1: all they do. That's that's their job. That's their business.

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Speaker 1: They buy and sell info. They don't make stuff, they

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Speaker 1: don't provide any services. They aggregate data and sell it

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Speaker 1: off to other people. This is a fascinating part of

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Speaker 1: this whole thing to me, Jonathan, because it's like they're

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Speaker 1: these companies are really I mean, they're they're kind of

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Speaker 1: like in the shadows, you know, they're they're they're like

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Speaker 1: companies with like weird names that that that have you know,

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Speaker 1: data centers in in in the middle of nowhere, and

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Speaker 1: they're really hard to find. And you you know, I

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Speaker 1: mean you and I probably both remember the days of

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Speaker 1: like do not call me at home, you know, getting

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Speaker 1: getting that phone call from marketers the pre do not

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Speaker 1: call list days, right, And we don't really have a

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Speaker 1: do not call list for data yet. Right. We have

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Speaker 1: some tools, but we don't really have that. And I

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Speaker 1: feel like we're in this kind of um, I don't

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Speaker 1: know gold rush on data exactly. We don't have We

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Speaker 1: don't have the power to say here is what you

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Speaker 1: can use my data to do, and here is what

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Speaker 1: you cannot use my data to do. Uh. You know,

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Speaker 1: if you're lucky you will be or if you're very

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Speaker 1: um you know, careful and you're and you're paying really

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Speaker 1: close attention, then you might use services where you're looking

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Speaker 1: at the user agreement and looking to see do they

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Speaker 1: have a policy? Do they do they specifically state they

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Speaker 1: will not share your information with third parties. Um, you

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Speaker 1: know you can find some of those, but honestly, that's

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Speaker 1: not the way bus this works. For most of these companies.

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Speaker 1: You are the product, right, like like Google when the

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Speaker 1: service is free. Yep, you're the one product. That's that's

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Speaker 1: what Google, your Google's product, your Facebook product, you are

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Speaker 1: what they are selling to other other interested parties. And uh,

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Speaker 1: even if you look at ones that say that, oh,

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Speaker 1: don't worry, we scrub all the identifiable stuff from your day.

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Speaker 1: Animize your data if you will. Yeah, that, Um, that

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Speaker 1: don't work so good, Ben, it doesn't work very good.

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Speaker 1: There have been studies about this in the last few years, right,

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Speaker 1: I think there was a study that came out of

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Speaker 1: the UK that suggested, um that really in order to

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Speaker 1: identify a person, um, you really only needed like one

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Speaker 1: or two pieces of information about them. Right. Do you

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Speaker 1: remember that study that came out in the last I

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Speaker 1: don't know year or so. It was like, I remember

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Speaker 1: that one. And there's one that Latania Sweeney Harvard she

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Speaker 1: did one or she proved that with three points of data,

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Speaker 1: which was gender, birth date, and ZIP code, she could

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Speaker 1: identify of US residents. So so nearly of all people

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Speaker 1: living in the United States she could identify just with

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Speaker 1: those three pieces of information. That One of the things

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Speaker 1: I like to stress to people is your name, which

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Speaker 1: you try to protect when you are when you think

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Speaker 1: you're being anonymous on the Internet and you use a handle,

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Speaker 1: so you're not using your name like I use my

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Speaker 1: name everywhere as my handle. That's just but most of

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Speaker 1: the time, but a lot of people like, no, I'm

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Speaker 1: going to use an anonymous handle. Your name is the

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Speaker 1: least important thing about you. I hate to break it

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Speaker 1: to you that because honestly, most of these companies don't care.

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Speaker 1: Just like I was saying, the shop owner with the

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Speaker 1: ice cream doesn't care who you are. Most of these

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Speaker 1: companies don't really care who you are. They care about

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Speaker 1: what you do. Yeah, and it's really interesting too, Like Google,

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Speaker 1: for instance, leverages large amounts of money and even some

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Speaker 1: of their best algorithmic computing power to make sure that

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Speaker 1: advertisers and marketers, for instance, don't engage in dishonest practices

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Speaker 1: or break Google's own rules about tracking data. And even

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Speaker 1: for Google, this is becoming like a herculean task. We

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Speaker 1: I talked to in for Cobreaker, I talked to some

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Speaker 1: people at Columbia's, some Columbia grad students who are studying this,

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Speaker 1: and it's really interesting to see what gets pulled from

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Speaker 1: even like emails that you might send in Gmail, for instance. Yeah, yeah,

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Speaker 1: this is this is kind of um, kind of terrifying.

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Speaker 1: Also if you if you think about because you think

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Speaker 1: of email as being this this uh, private means of

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Speaker 1: communication that you know, just as you wouldn't expect someone

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Speaker 1: else to read your snail mail. That would be a

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Speaker 1: violation of trust to find out that stuff is popping

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Speaker 1: up based on things that you've typed. Like if you've

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Speaker 1: written an email recently and then something that is related

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Speaker 1: to that email starts seemingly to pop up in places

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Speaker 1: you weren't expecting, you might think at first that that's

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Speaker 1: a weird coincidence. But if this happens a few times, like, Okay,

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Speaker 1: this is not a coincidence. What is going on? Yeah,

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Speaker 1: it's really interesting. And I think that's something, Jonathan, that

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Speaker 1: so many people can relate to, right, this idea of like,

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Speaker 1: oh I looked at those shoes once, or I looked

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Speaker 1: at uh, you know, whatever, the whatever item that you

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Speaker 1: might buy online once and that advertisement follows you around forever. Right, Yeah,

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Speaker 1: you had that great moment and codebreaker in fact, about

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Speaker 1: the sandals. Yes, the sandals that followed Molly around. Every

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Speaker 1: time I logged into Facebook for a straight week, they

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Speaker 1: would pop up. And I was so scared because I

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Speaker 1: was like, oh my god, I'm gonna be sitting next

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Speaker 1: to my boyfriend and I'm gonna log onto Facebook and

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Speaker 1: these sandals are gonna come up, and it's gonna reopen

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Speaker 1: the wound. And he's gonna know that I was sending

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Speaker 1: them to people asking for an opinion. Did you change

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Speaker 1: your behavior when you were hanging out with him? Yeah,

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Speaker 1: Like if we were loafing around, I would open so

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Speaker 1: many tabs and none of them would be Facebook. This

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Speaker 1: is a really funny story. What does it make you

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Speaker 1: think about? It made me wish that I could go

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Speaker 1: back to the days where Facebook was just showing me

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Speaker 1: things that I wanted to buy and not things that

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Speaker 1: I wanted to run away from. That's that's such a

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Speaker 1: I mean, I've seen the same sort of stuff, and

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Speaker 1: I've seen advertisements that it's odd, like things that do

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Speaker 1: not interest me at all. They're just completely off base

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Speaker 1: and I can't It's one of those things where I think, well,

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Speaker 1: maybe now, it's just that the system hasn't gathered enough

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Speaker 1: information about me to get a good feel, right, so

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Speaker 1: I'm seeing stuff. It's like, this is the shotgun approach.

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Speaker 1: I'm throwing everything I can and hopefully stuff will work.

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Speaker 1: We'll be back to talk more about the evils of

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Speaker 1: data tracking after these messages. Systems like Facebook, for example,

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Speaker 1: where you can actually go in to an ad and

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Speaker 1: say I am not interested in this, you know, then

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Speaker 1: maybe you'll end up getting a different selection of stuff,

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Speaker 1: but sometimes you'll just get different versions of the same thing.

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Speaker 1: You already said, that's not that's not me man. I

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Speaker 1: appreciate the muscles on this dude, but I Am not

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Speaker 1: going to be that guy. So I'm into I'm into

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Speaker 1: my little pony. But I I think I've outgrown it.

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Speaker 1: I look my collection, My collection can only be so complete.

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Speaker 1: That's you know, there's you gotta draw the line after

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Speaker 1: like series five, I gotta say I'm done. I have

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Speaker 1: no more shelf space. Um yeah, this is this is

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Speaker 1: one of those those amazing stories, like the story about

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Speaker 1: the sandals. Just just one of those things that has

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Speaker 1: happened to pretty much anyone who has been active online

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Speaker 1: over the last couple of years. And it's probably one

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Speaker 1: of those stories I would imagine that people start to share,

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Speaker 1: not realizing that this is something that other people have experienced. Yeah, yeah, right,

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Speaker 1: it is. It feels like a very personal experience, but

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Speaker 1: it's something that seems to happen to everyone, and it

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Speaker 1: was interesting to see. Relatively recently, Facebook, I believe um

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Speaker 1: made it easier for people to control how much of

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Speaker 1: their ex girlfriend or boyfriend or significant others information surfaced

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Speaker 1: on their own timeline. Did you see that story that

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Speaker 1: came out relatively recently. He was interesting this idea that like,

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Speaker 1: you know, you know, even the tech companies they're really

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Speaker 1: really good at data tracking at this point, are still

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Speaker 1: trying to figure out when it goes wrong and how

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Speaker 1: to help the user in some cases, um make it

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Speaker 1: a more kind of custom experience, right. And and we

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Speaker 1: should also stress that that the overwhelming majority of companies

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Speaker 1: out there do not want it to go wrong, because

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Speaker 1: that's that's counterproductive to what the end end goal is,

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Speaker 1: at least for the person that actually bought the advertisement

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Speaker 1: and the person who owns the place where that ad

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Speaker 1: is being shown in the case of targeted advertising, at

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Speaker 1: least like they don't want that to go badly. But

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Speaker 1: in other cases, you know, they don't want to go

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Speaker 1: badly either, that that's counterproductive. But it is possible for

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Speaker 1: this to go wrong even if everyone is trying to

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Speaker 1: use it to to people's benefit. And on Codebreaker, you

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Speaker 1: had a heartbreaking story about about a father who shared

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Speaker 1: some information not not knowing, not even realizing that that

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Speaker 1: information was somehow going to make its way into a

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Speaker 1: database and then came back to haunt him. Yeah, this

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Speaker 1: guy Mike Say, and and this is happened a few

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Speaker 1: years back. He you know, unfortunately, um, his his one

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Speaker 1: of his children, his daughter died in a car accident.

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Speaker 1: And he went to buy some uh you know, he

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Speaker 1: went to buy some some actually some some um some

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Speaker 1: stuff for photos, like he was putting together some photo albums,

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Speaker 1: and he went to buy some frames for those photos.

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Speaker 1: And the place that he called to do that, um,

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Speaker 1: he just in passing mentioned uh that his daughter had

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Speaker 1: died in this car accident. And the person that he

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Speaker 1: was talking to actually took down that information. And it's

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Speaker 1: still not clear how it how all of this happened,

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Speaker 1: but apparently they took down the information and that information

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Speaker 1: ended up getting sold to a data broker that's sold

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Speaker 1: the information from the company he originally called to Office Max,

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Speaker 1: and then Office Max sent a letter to him. Um

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Speaker 1: there was addressed to owner of the house or Mike

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Speaker 1: Say daughter died in a car accident. Um. And that's

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Speaker 1: when it goes really really wrong, right. The picture frames

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Speaker 1: that was was buying with through my children. In the

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Speaker 1: bottom it had Ashley's name and her birth and death

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Speaker 1: date and that she loved them, and you know what

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Speaker 1: I mean, that's how she the conversation came about that

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Speaker 1: what is this for? And she took you know, the

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Speaker 1: information that would go on the bottom and put that,

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Speaker 1: you know, on whatever to get it stamp. But they

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Speaker 1: had nothing to it. Didn't say killed in car crashing

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Speaker 1: out of that. That was all in her own. And

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Speaker 1: this is a totally separate company from Office Max. And

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Speaker 1: somehow Office Max got the information. They bought the information

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Speaker 1: from a data group, so that information was sold from

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Speaker 1: things remembered to a data group that collects data and

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Speaker 1: then taken and sold to Office Max in an Office

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Speaker 1: Max used without looking at it. What an an emotional

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Speaker 1: uh punch to the gut to see something like that.

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Speaker 1: And and honestly, you know, I mean, you know, Office

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Speaker 1: Max did not intend that. There's no sane reason anyone

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Speaker 1: would do that. So it's clearly an epic mistake that

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Speaker 1: that could result in true emotional trauma, even if it's

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Speaker 1: just a momentary thing. You don't want any person to

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Speaker 1: experience that kind of thing. And uh, it was a

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Speaker 1: pure accident, I imagine based upon way back in the day,

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Speaker 1: I used to work for consultants and when when I

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Speaker 1: worked for consultants, one of the things I had to

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Speaker 1: do was arranged giant mailing lists and print out tons

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Speaker 1: and tons and tons of mailing labels. And so you're

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Speaker 1: you're working with these big spreadsheets there possibly thousands of

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Speaker 1: names long. So I'm guessing what happened was you had

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Speaker 1: the data entered into some sort of spreadsheet, and probably

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Speaker 1: the spreadsheet was converted at least once, and in that conversion,

429
00:26:29,880 --> 00:26:33,040
Speaker 1: a tab that had been or you know, a cell

430
00:26:33,080 --> 00:26:37,400
Speaker 1: that had just been labeled miscellaneous got put into address

431
00:26:37,440 --> 00:26:41,280
Speaker 1: instead of miscellanys or title or something along those lines.

432
00:26:41,960 --> 00:26:45,919
Speaker 1: And that's when it went from one point where it

433
00:26:46,000 --> 00:26:49,800
Speaker 1: was a piece of data, which, as as Mike said,

434
00:26:49,960 --> 00:26:52,520
Speaker 1: like why do people even need to know this? No

435
00:26:52,560 --> 00:26:54,600
Speaker 1: one needs to know this. This is this is not

436
00:26:54,720 --> 00:26:57,400
Speaker 1: a relevant piece of information. It should never have gone

437
00:26:57,440 --> 00:27:00,960
Speaker 1: into a database. But for that to then accidentally get

438
00:27:00,960 --> 00:27:04,400
Speaker 1: transposed into a cell that is going to show up

439
00:27:04,440 --> 00:27:11,400
Speaker 1: on a mailing label is incredibly it feels incredibly callous, yeah,

440
00:27:11,400 --> 00:27:14,879
Speaker 1: and invasive. And it also, you know, it reminds you that,

441
00:27:16,840 --> 00:27:18,679
Speaker 1: I mean, it reminds us all that the way we

442
00:27:18,760 --> 00:27:23,160
Speaker 1: view ourselves isn't always the way we're viewed by the world, right,

443
00:27:23,880 --> 00:27:27,280
Speaker 1: and and and and that's an interesting aspect of this too,

444
00:27:27,440 --> 00:27:34,000
Speaker 1: This idea that basically a data profile of most users

445
00:27:34,040 --> 00:27:37,080
Speaker 1: on the Internet is being slowly put together over time,

446
00:27:37,440 --> 00:27:41,679
Speaker 1: and and you might be we all might be really

447
00:27:41,760 --> 00:27:47,400
Speaker 1: curious to know, um or disappointed to know um, what

448
00:27:47,480 --> 00:27:50,639
Speaker 1: that data profile looks like from the other side. Now,

449
00:27:51,160 --> 00:27:53,399
Speaker 1: then let me ask you this. I'm going to paint

450
00:27:53,440 --> 00:27:58,359
Speaker 1: a picture for you of a dystopia. Okay, that's that's

451
00:27:58,400 --> 00:28:03,280
Speaker 1: sadly not difficult to imagine based on our current events.

452
00:28:03,280 --> 00:28:06,199
Speaker 1: All Right, so I'm sure you heard. I mean, I'm

453
00:28:06,240 --> 00:28:09,800
Speaker 1: absolutely certain you heard about the app people p E

454
00:28:09,800 --> 00:28:13,280
Speaker 1: E p l E. Right, Yes, this was the one

455
00:28:13,320 --> 00:28:16,200
Speaker 1: that was supposed to be the yelp for people where

456
00:28:16,240 --> 00:28:21,080
Speaker 1: you would be able to rate other human beings and

457
00:28:21,160 --> 00:28:23,520
Speaker 1: so then other human beings could see your rating and

458
00:28:23,560 --> 00:28:26,240
Speaker 1: could see ratings of other people. And it just seemed

459
00:28:26,280 --> 00:28:30,200
Speaker 1: like it was going to lead to fallout. For that's

460
00:28:30,200 --> 00:28:33,439
Speaker 1: what literally an idea, an idea whose time should have

461
00:28:33,480 --> 00:28:37,320
Speaker 1: never come exactly. Now, let's take that same basic concept,

462
00:28:37,400 --> 00:28:40,960
Speaker 1: but instead of you rating people, imagine that you get

463
00:28:41,000 --> 00:28:45,000
Speaker 1: to look up what that person's data profile is worth

464
00:28:45,400 --> 00:28:50,520
Speaker 1: compared to your own because guess what you could do

465
00:28:50,600 --> 00:28:54,360
Speaker 1: that you could build something where you start to look

466
00:28:54,400 --> 00:28:58,120
Speaker 1: at the the robustness of a data profile, at least

467
00:28:58,160 --> 00:29:02,280
Speaker 1: for a certain give and set of transactions. Because keep

468
00:29:02,280 --> 00:29:04,400
Speaker 1: in mind, there are a lot of data brokers out there,

469
00:29:04,720 --> 00:29:07,360
Speaker 1: and your profile in one data broker's database may be

470
00:29:07,520 --> 00:29:10,840
Speaker 1: very different from another because it may be less or

471
00:29:10,880 --> 00:29:15,920
Speaker 1: more complete or from totally different sources than the other one.

472
00:29:16,160 --> 00:29:19,800
Speaker 1: But that would be a weird world where you say, wow,

473
00:29:20,760 --> 00:29:25,720
Speaker 1: my life is worth this much to a potential marketer,

474
00:29:26,200 --> 00:29:29,600
Speaker 1: and my friends life is worth twice as much. What

475
00:29:29,720 --> 00:29:32,440
Speaker 1: am I doing wrong in my life where I'm worth less?

476
00:29:32,520 --> 00:29:37,960
Speaker 1: I mean, it's this isies are looking at us. Yeah,

477
00:29:38,040 --> 00:29:40,000
Speaker 1: and it is scary. I mean, I think it's also

478
00:29:40,040 --> 00:29:42,520
Speaker 1: fair to say that this is as scary as it

479
00:29:42,600 --> 00:29:45,440
Speaker 1: is to imagine that it is. It does also have

480
00:29:45,520 --> 00:29:47,760
Speaker 1: some connection to reality. I mean, when you go into

481
00:29:47,840 --> 00:29:51,640
Speaker 1: a bank and apply for a loan um, you're also

482
00:29:51,760 --> 00:29:57,680
Speaker 1: being sort of sized up right and over time, Um,

483
00:29:57,720 --> 00:29:59,960
Speaker 1: you know, we've we've we've seen some actually some really

484
00:30:00,000 --> 00:30:04,640
Speaker 1: of big problems arise from how banks look at people

485
00:30:04,920 --> 00:30:07,680
Speaker 1: and look at people's ability to pay things back. And

486
00:30:07,680 --> 00:30:10,200
Speaker 1: how credit card companies do this, how your net worth

487
00:30:10,280 --> 00:30:13,960
Speaker 1: is calculated, how you're um, you know, your your likelihood

488
00:30:13,960 --> 00:30:17,280
Speaker 1: of paying a loan back is calculated. And but at

489
00:30:17,280 --> 00:30:20,920
Speaker 1: the same time, over time, there have been some safeguards

490
00:30:20,960 --> 00:30:25,000
Speaker 1: put in against discrimination, right, and a lot of people

491
00:30:25,040 --> 00:30:28,000
Speaker 1: have actually talked about this idea of data discrimination over

492
00:30:28,040 --> 00:30:32,240
Speaker 1: time and how um we really need to think about

493
00:30:32,360 --> 00:30:34,760
Speaker 1: as we go forward and as more and more data

494
00:30:34,760 --> 00:30:37,760
Speaker 1: gets collected on on on users over time. I mean,

495
00:30:37,840 --> 00:30:40,600
Speaker 1: people that were born you know, in the last ten

496
00:30:40,680 --> 00:30:43,440
Speaker 1: years are going to have a very different life on

497
00:30:43,480 --> 00:30:46,080
Speaker 1: the internet than even you or I have had, right,

498
00:30:46,120 --> 00:30:48,320
Speaker 1: I mean I wasn't on the internet until I was

499
00:30:48,760 --> 00:30:51,959
Speaker 1: a teenager. So it's interesting to think about it. And

500
00:30:51,960 --> 00:30:54,800
Speaker 1: it's also you know, you would you would hope some

501
00:30:54,840 --> 00:30:58,880
Speaker 1: people are talking about this idea of like, you know,

502
00:30:59,120 --> 00:31:04,800
Speaker 1: if you all are perceived to be a a person

503
00:31:04,840 --> 00:31:09,960
Speaker 1: of a certain race, a certain education level, tax bracket

504
00:31:10,080 --> 00:31:14,680
Speaker 1: like that the tax bracket, then you might actually see

505
00:31:15,560 --> 00:31:19,800
Speaker 1: a web that is different from the web that someone

506
00:31:19,880 --> 00:31:23,480
Speaker 1: else with a different perceived data profile would see. Yeah,

507
00:31:23,480 --> 00:31:26,360
Speaker 1: this is this is like a dark twisted version of

508
00:31:26,400 --> 00:31:30,640
Speaker 1: what the semantic web is supposed to be. The semantic

509
00:31:30,640 --> 00:31:34,600
Speaker 1: web is supposed to learn about you and then respond

510
00:31:34,680 --> 00:31:37,040
Speaker 1: to what you want, so that you don't have to

511
00:31:37,680 --> 00:31:41,040
Speaker 1: play the game of the web search or the web browser.

512
00:31:41,400 --> 00:31:43,840
Speaker 1: You just say what you want and it returns exactly

513
00:31:43,840 --> 00:31:47,200
Speaker 1: what you want, possibly from multiple sources, so you're not

514
00:31:47,240 --> 00:31:50,360
Speaker 1: even going to a web page in the semantic web necessarily.

515
00:31:50,400 --> 00:31:53,600
Speaker 1: It may be that it's pulling things from various sources

516
00:31:53,680 --> 00:31:57,160
Speaker 1: and presenting you kind of a united view. That's one

517
00:31:57,280 --> 00:32:00,760
Speaker 1: realization of what those semantic web would look like. This

518
00:32:00,760 --> 00:32:03,760
Speaker 1: this is similar to that, but a very dark version

519
00:32:03,840 --> 00:32:07,800
Speaker 1: where it's not responding to what you want, it's responding

520
00:32:07,840 --> 00:32:11,360
Speaker 1: to what the other side wants based upon what they

521
00:32:11,400 --> 00:32:14,400
Speaker 1: think they can get out of you. Yeah, and that's

522
00:32:14,400 --> 00:32:18,400
Speaker 1: a really scary thing. I mean, there's also some potential

523
00:32:18,480 --> 00:32:21,920
Speaker 1: good from that can come from this, right, Um, And

524
00:32:21,920 --> 00:32:25,320
Speaker 1: and you know, other people are talking about this idea

525
00:32:25,440 --> 00:32:30,560
Speaker 1: of you know, a time at which maybe people actually

526
00:32:30,600 --> 00:32:34,080
Speaker 1: start to you know, big data is more valuable. Big data,

527
00:32:34,560 --> 00:32:37,080
Speaker 1: you know, it's all perceived to be more valuable to

528
00:32:37,280 --> 00:32:41,240
Speaker 1: companies and marketers and and and and certain organizations in

529
00:32:41,280 --> 00:32:45,200
Speaker 1: the aggregate. Right, So what if we actually reached a

530
00:32:45,320 --> 00:32:49,800
Speaker 1: time where, um, not. I won't say like data gets

531
00:32:49,920 --> 00:32:53,680
Speaker 1: unionized necessarily, but like, what if you reach the time

532
00:32:53,760 --> 00:33:00,080
Speaker 1: where users actually banded together and did collective bargating on

533
00:33:00,160 --> 00:33:04,200
Speaker 1: behalf of their data in order to say, hey, Okay,

534
00:33:04,240 --> 00:33:09,320
Speaker 1: my data maybe only half of a penny value, but

535
00:33:09,880 --> 00:33:12,320
Speaker 1: when I get together with this large group of other

536
00:33:12,480 --> 00:33:16,320
Speaker 1: users that i'm that I'm aligned with, it's actually worth

537
00:33:16,360 --> 00:33:19,640
Speaker 1: a lot more. And I want to align myself with

538
00:33:19,680 --> 00:33:23,920
Speaker 1: this large group and then sell that um and actually

539
00:33:24,160 --> 00:33:27,520
Speaker 1: gain some agency in that way and gain maybe some

540
00:33:27,520 --> 00:33:31,040
Speaker 1: some money. You're you're like creating a brand new career

541
00:33:31,400 --> 00:33:37,800
Speaker 1: a data agent, like agent. Yeah, agent, they're like Dave

542
00:33:37,880 --> 00:33:42,520
Speaker 1: Walkers really wouldn't if you want to go the vampire route, Yeah,

543
00:33:42,560 --> 00:33:46,960
Speaker 1: that's that. That's I I honestly had never considered that,

544
00:33:47,000 --> 00:33:50,120
Speaker 1: but that is a really compelling idea, especially when you

545
00:33:50,160 --> 00:33:52,520
Speaker 1: think even if you go on a route where you're like,

546
00:33:52,560 --> 00:33:54,520
Speaker 1: all right, we're not doing this to make money, We're

547
00:33:54,520 --> 00:33:57,440
Speaker 1: doing this so that we're only opting into the things

548
00:33:57,760 --> 00:34:01,680
Speaker 1: that we agree with that we find acceptable. One of

549
00:34:01,680 --> 00:34:04,080
Speaker 1: the other things about data tracking that I think is

550
00:34:05,280 --> 00:34:11,240
Speaker 1: problematic is that sure, uh, you might be getting stuff

551
00:34:11,320 --> 00:34:14,560
Speaker 1: that's reacting to your behavior but a lot of that

552
00:34:14,640 --> 00:34:17,440
Speaker 1: is going to be based on guesswork. So for example,

553
00:34:17,520 --> 00:34:21,120
Speaker 1: if I have posted a lot about hiking, it maybe

554
00:34:21,400 --> 00:34:25,400
Speaker 1: that I get a lot of advertisements for outdoor supply

555
00:34:25,520 --> 00:34:28,040
Speaker 1: stores like camping gear, that kind of stuff. And it

556
00:34:28,120 --> 00:34:30,960
Speaker 1: may be that that doesn't fully overlap with my interests.

557
00:34:31,000 --> 00:34:34,439
Speaker 1: That's not terrible or whatever, but it also could mean

558
00:34:34,600 --> 00:34:39,040
Speaker 1: that I start to have a narrower selection of things

559
00:34:39,080 --> 00:34:41,440
Speaker 1: presented to me, which in one way is good, but

560
00:34:41,480 --> 00:34:48,720
Speaker 1: it also means it takes away the possibility of discovery. Yeah,

561
00:34:48,760 --> 00:34:52,360
Speaker 1: this is a huge I mean, this is a really

562
00:34:52,560 --> 00:34:55,319
Speaker 1: interesting idea and a really interesting topic, right. I mean,

563
00:34:55,800 --> 00:34:59,320
Speaker 1: Amazon is is kind of thinking about, I think trying

564
00:34:59,320 --> 00:35:01,560
Speaker 1: to do this. I mean, there there are companies that

565
00:35:01,600 --> 00:35:03,840
Speaker 1: would would love to be able to basically when you

566
00:35:03,920 --> 00:35:07,040
Speaker 1: go to their homepage they basically say, or not that

567
00:35:07,080 --> 00:35:09,400
Speaker 1: we would ever necessarily do that in the future, but

568
00:35:09,920 --> 00:35:13,359
Speaker 1: you you, whatever, you you engage with their platform, they

569
00:35:13,400 --> 00:35:20,160
Speaker 1: would say, here's the three products you can choose from today. Yeah, right, right,

570
00:35:20,680 --> 00:35:25,400
Speaker 1: And that does, as you say, really reduce this idea

571
00:35:25,400 --> 00:35:27,759
Speaker 1: of discovery. And this is you know, this happens in

572
00:35:27,800 --> 00:35:31,200
Speaker 1: the media world to write this idea of like UM

573
00:35:31,280 --> 00:35:35,279
Speaker 1: and in the social media world, this idea of UM

574
00:35:35,320 --> 00:35:39,880
Speaker 1: being able to curate so in such a you know,

575
00:35:39,960 --> 00:35:44,200
Speaker 1: a focused way to the individual user also makes it

576
00:35:44,320 --> 00:35:48,120
Speaker 1: so that user doesn't I don't know, read the New

577
00:35:48,200 --> 00:35:52,800
Speaker 1: York Times and learn anything about what goes on outside

578
00:35:52,840 --> 00:35:55,279
Speaker 1: of their own interest. Here it becomes it becomes like

579
00:35:55,320 --> 00:36:00,000
Speaker 1: the ultimate echo chamber, where where you know you're you're

580
00:36:00,440 --> 00:36:03,680
Speaker 1: you're stuck there seeing the same stuff and maybe you

581
00:36:03,719 --> 00:36:06,879
Speaker 1: don't even realize that we're we're heading to like this

582
00:36:06,920 --> 00:36:09,440
Speaker 1: is almost a Terry Gilliam film what we're describing at

583
00:36:09,440 --> 00:36:13,960
Speaker 1: this right Like we're heading to Brazil right now, let's

584
00:36:14,080 --> 00:36:16,359
Speaker 1: head let's say I'm ready for that movie. I mean,

585
00:36:16,400 --> 00:36:19,480
Speaker 1: I don't want to be in the reality that it depicts.

586
00:36:19,520 --> 00:36:21,799
Speaker 1: I don't want to be I don't want it to

587
00:36:21,800 --> 00:36:24,719
Speaker 1: be reality. But I would totally watch that movie. Yeah,

588
00:36:24,800 --> 00:36:27,800
Speaker 1: me too. When we're talking big data, when we're talking

589
00:36:27,840 --> 00:36:30,120
Speaker 1: about all the information that's going out there, and we

590
00:36:30,200 --> 00:36:33,960
Speaker 1: mentioned the fact that companies are buying profiles in the

591
00:36:34,080 --> 00:36:38,279
Speaker 1: tens or hundreds of thousands or more, here's why. Let

592
00:36:38,280 --> 00:36:41,080
Speaker 1: me give you a few statistics on how much information

593
00:36:41,760 --> 00:36:45,799
Speaker 1: is hitting the Internet on a per minute basis. This

594
00:36:45,920 --> 00:36:48,920
Speaker 1: is based off of research that was conducted by a

595
00:36:49,040 --> 00:36:52,520
Speaker 1: data company called Domo, and this was published in August

596
00:36:52,640 --> 00:36:55,320
Speaker 1: of two thousand fifteen. So keep in mind these numbers

597
00:36:55,360 --> 00:37:01,239
Speaker 1: are already old. But every single minute Amazon receives four

598
00:37:01,239 --> 00:37:06,480
Speaker 1: thousand three unique visitors. Well, we're going to pause the

599
00:37:06,640 --> 00:37:20,759
Speaker 1: data tracking conversation for another quick break. So Amazon is

600
00:37:20,800 --> 00:37:23,960
Speaker 1: one of those companies that's really good at doing this

601
00:37:24,080 --> 00:37:27,000
Speaker 1: focused data tracking that you know, this is where you

602
00:37:27,120 --> 00:37:30,040
Speaker 1: buy a product and it says, hey, people who bought

603
00:37:30,040 --> 00:37:35,040
Speaker 1: this product also bought these other things, and then you think, oh, right, right,

604
00:37:35,160 --> 00:37:37,440
Speaker 1: I should get that. It's like it's like the perfect

605
00:37:37,800 --> 00:37:44,799
Speaker 1: impulse by shelf and it's customized for every single purchase. Right,

606
00:37:44,880 --> 00:37:49,560
Speaker 1: I do need batteries and dog biscuits. How did you know? Well,

607
00:37:49,600 --> 00:37:53,160
Speaker 1: there's there's there's there's this other level two, uh that

608
00:37:53,680 --> 00:37:56,600
Speaker 1: that people are starting to imagine when you think about

609
00:37:56,800 --> 00:38:01,520
Speaker 1: companies like nest Um, and you think about smart televisions

610
00:38:01,560 --> 00:38:04,200
Speaker 1: that actually interact with your refrigerator. And one of the

611
00:38:04,239 --> 00:38:09,200
Speaker 1: most convincing sort of imagined scenarios that was ever presented

612
00:38:09,239 --> 00:38:12,480
Speaker 1: to me in recent years was this idea that essentially,

613
00:38:12,520 --> 00:38:17,880
Speaker 1: over time, your refrigerator learns that you go for ice cream.

614
00:38:18,080 --> 00:38:21,319
Speaker 1: You go in for that vanilla ice cream, Um, right

615
00:38:21,360 --> 00:38:27,120
Speaker 1: around eight pm, and um, it tells your television that.

616
00:38:27,640 --> 00:38:31,080
Speaker 1: And then in the world of streaming that you know,

617
00:38:31,160 --> 00:38:33,680
Speaker 1: more and more people are doing on their actual television

618
00:38:33,800 --> 00:38:36,000
Speaker 1: instead of having it connected to a you know, a

619
00:38:36,040 --> 00:38:40,880
Speaker 1: cable channel. Um, you actually get at eight forty five,

620
00:38:41,120 --> 00:38:45,839
Speaker 1: you get an advertisement for ice cream, which reminds you

621
00:38:46,160 --> 00:38:48,279
Speaker 1: to go and eat some ice cream, and then you

622
00:38:48,400 --> 00:38:50,560
Speaker 1: end up eating more ice cream, and then you end

623
00:38:50,640 --> 00:38:53,560
Speaker 1: up buying This is the world where the appliances are

624
00:38:53,560 --> 00:38:57,839
Speaker 1: working against you. Yes, they're not telling you they eat

625
00:38:57,880 --> 00:39:00,719
Speaker 1: a kale set, or maybe they are. We Yeah, that's

626
00:39:00,760 --> 00:39:02,960
Speaker 1: also possible, Like if you have if you have this

627
00:39:03,040 --> 00:39:05,480
Speaker 1: thing where you're going out and buying kale salads and

628
00:39:05,480 --> 00:39:08,200
Speaker 1: then you're watching a lot of you know, food type stuff,

629
00:39:08,280 --> 00:39:10,759
Speaker 1: and your you might end up getting, Hey, here's a

630
00:39:10,840 --> 00:39:13,680
Speaker 1: quick recipe, and here are the fourteen ingredients you don't

631
00:39:13,719 --> 00:39:17,640
Speaker 1: have that you need to go to Whole Foods and buy. Um. Yeah,

632
00:39:18,120 --> 00:39:20,800
Speaker 1: it's a world. I host a show. I host another

633
00:39:20,800 --> 00:39:24,439
Speaker 1: show called forward Thinking, and in that show, it's it's

634
00:39:24,440 --> 00:39:26,880
Speaker 1: an optimistic view of the future and I have often

635
00:39:26,920 --> 00:39:29,839
Speaker 1: looked at this side from the optimist side, and it

636
00:39:29,920 --> 00:39:32,880
Speaker 1: is similar to what you were saying about every single

637
00:39:32,960 --> 00:39:37,640
Speaker 1: person's Web experience could be unique to them based upon

638
00:39:37,719 --> 00:39:40,720
Speaker 1: these factors. Uh. And and there's a way of doing

639
00:39:40,760 --> 00:39:44,600
Speaker 1: that where it is not kind of the scary, oppressive

640
00:39:44,840 --> 00:39:47,400
Speaker 1: type of way. But I I talk about how with

641
00:39:47,520 --> 00:39:51,239
Speaker 1: the Internet of things and with this connectivity of different appliances,

642
00:39:52,000 --> 00:39:54,359
Speaker 1: it's not that far of a stretch to imagine not

643
00:39:54,520 --> 00:39:59,640
Speaker 1: just the Web, your experience of reality could get to

644
00:39:59,640 --> 00:40:02,760
Speaker 1: a point where a lot of it is catered specifically

645
00:40:02,840 --> 00:40:06,120
Speaker 1: to you. Yeah, and there could be a lot of

646
00:40:06,120 --> 00:40:09,200
Speaker 1: good things that come out of that, right, um and

647
00:40:09,200 --> 00:40:13,440
Speaker 1: and and and I totally believe in that possibility. I mean,

648
00:40:13,480 --> 00:40:18,000
Speaker 1: like like we like you said, I think so, um

649
00:40:18,200 --> 00:40:21,759
Speaker 1: so thoughtfully when you came in and and and uh

650
00:40:22,120 --> 00:40:25,000
Speaker 1: talked about this stuff with me on Codebreaker, was was

651
00:40:25,040 --> 00:40:29,400
Speaker 1: basically this idea that look, it's um, I'm not going

652
00:40:29,440 --> 00:40:32,359
Speaker 1: to pull out the quote so eloquently as you did,

653
00:40:32,400 --> 00:40:36,680
Speaker 1: but look, this technology can it's us that makes it

654
00:40:36,760 --> 00:40:39,920
Speaker 1: good or bad. Right we we we have the power

655
00:40:40,080 --> 00:40:43,239
Speaker 1: theoretically to to make all of this stuff and make

656
00:40:43,320 --> 00:40:46,120
Speaker 1: data tracking work for us. As users and work for

657
00:40:46,200 --> 00:40:49,400
Speaker 1: us as organizations and companies. I mean, I think a

658
00:40:49,440 --> 00:40:52,840
Speaker 1: lot about the possibility of data tracking being really good

659
00:40:53,320 --> 00:40:57,440
Speaker 1: in the future where our climate is changing, um and

660
00:40:57,440 --> 00:41:00,400
Speaker 1: and the fact that we really understand and on a

661
00:41:00,440 --> 00:41:04,960
Speaker 1: global level, a lot more about our climate than we

662
00:41:05,040 --> 00:41:09,239
Speaker 1: did even thirty years ago thanks to an increased level

663
00:41:09,239 --> 00:41:12,160
Speaker 1: of data tracking, right, And that is an important point

664
00:41:12,160 --> 00:41:15,440
Speaker 1: to make, Ben, is that data tracking does not necessarily

665
00:41:15,520 --> 00:41:19,040
Speaker 1: mean it's about just people. Although people are an important

666
00:41:19,080 --> 00:41:21,239
Speaker 1: factor when you're looking even at climate change, because you

667
00:41:21,280 --> 00:41:24,240
Speaker 1: want to see, well, how is this affecting actual human

668
00:41:24,280 --> 00:41:27,359
Speaker 1: beings as well as the environment in general. You need

669
00:41:27,400 --> 00:41:30,040
Speaker 1: to know the whole picture. But data tracking is really

670
00:41:30,640 --> 00:41:36,839
Speaker 1: it's it's agnostic as far as what the data actually represents.

671
00:41:36,920 --> 00:41:40,840
Speaker 1: Like we're talking about data about everything, you know, anything

672
00:41:40,880 --> 00:41:42,960
Speaker 1: that you can imagine that can be quantified in the

673
00:41:43,000 --> 00:41:47,239
Speaker 1: form of data that is being tracked by someone for

674
00:41:47,320 --> 00:41:50,319
Speaker 1: some reason. Some of those reasons are good, some of

675
00:41:50,360 --> 00:41:55,120
Speaker 1: those reasons are way not good. Yeah, But but it's

676
00:41:55,160 --> 00:41:59,080
Speaker 1: not all just about people. And it's interesting too to

677
00:41:59,120 --> 00:42:01,440
Speaker 1: think about I wonder what you think about this, But

678
00:42:01,520 --> 00:42:07,520
Speaker 1: it's interesting too to think about just how I think

679
00:42:07,560 --> 00:42:09,719
Speaker 1: a lot of it feels out of control at this

680
00:42:09,800 --> 00:42:13,040
Speaker 1: point for the user, it feels like we don't fully

681
00:42:13,120 --> 00:42:16,400
Speaker 1: understand it. We don't fully understand how it's being used.

682
00:42:16,800 --> 00:42:19,279
Speaker 1: Um and what data about us is being tracked as

683
00:42:19,400 --> 00:42:23,520
Speaker 1: users and UM and you know, government, I mean, the

684
00:42:23,560 --> 00:42:26,359
Speaker 1: FTC in the past few years has become much more

685
00:42:26,440 --> 00:42:31,800
Speaker 1: involved in this idea of helping consumers protect themselves against

686
00:42:32,120 --> 00:42:35,319
Speaker 1: you know, abusive forms of data traction um if you will,

687
00:42:35,960 --> 00:42:37,879
Speaker 1: um and and there are a lot of qui and

688
00:42:37,880 --> 00:42:40,120
Speaker 1: and and also it's fair to say that I think

689
00:42:40,120 --> 00:42:43,719
Speaker 1: a lot of tech companies have endeavored to put not

690
00:42:43,800 --> 00:42:47,160
Speaker 1: only in terms of service agreements, but in the actual

691
00:42:47,239 --> 00:42:50,400
Speaker 1: tools that they give to the user when they manage

692
00:42:50,440 --> 00:42:53,080
Speaker 1: their own accounts, as you know, to put the power

693
00:42:53,120 --> 00:42:56,120
Speaker 1: in the user's hands to control this. But you know,

694
00:42:56,520 --> 00:42:58,759
Speaker 1: let's be honest, right, I mean, you and I don't

695
00:42:58,840 --> 00:43:02,719
Speaker 1: necessarily read all of the terms of services. You're telling me.

696
00:43:03,080 --> 00:43:06,279
Speaker 1: You don't read every end user license agreement. You don't.

697
00:43:06,400 --> 00:43:11,160
Speaker 1: You can't recite the is you know. It's it's tough.

698
00:43:11,200 --> 00:43:14,040
Speaker 1: It's tough. I can't remember the last time, honestly, I

699
00:43:14,080 --> 00:43:17,080
Speaker 1: cannot remember the last time I actually read a LA

700
00:43:17,640 --> 00:43:22,000
Speaker 1: because who who has time for that? I want to

701
00:43:22,120 --> 00:43:25,640
Speaker 1: use your shiny new toy. I don't care that I've

702
00:43:25,680 --> 00:43:28,719
Speaker 1: given up all my privacy. And if as long as

703
00:43:28,760 --> 00:43:31,200
Speaker 1: I get to play with the shiny new toy, if

704
00:43:31,239 --> 00:43:34,480
Speaker 1: my b B eight remote control droid is secretly telling

705
00:43:34,560 --> 00:43:38,560
Speaker 1: Disney exactly how to make me buy more BB eight

706
00:43:38,560 --> 00:43:43,640
Speaker 1: remote control droids, so be it. Um, it all comes

707
00:43:43,640 --> 00:43:46,799
Speaker 1: back to Star Wars. Well, I mean, you know, the

708
00:43:46,840 --> 00:43:49,440
Speaker 1: force is strong in my family. My father has it,

709
00:43:49,640 --> 00:43:53,680
Speaker 1: I have it, my sister has it. So uh, you

710
00:43:53,719 --> 00:43:56,279
Speaker 1: know this is this has been a great conversation, great

711
00:43:56,320 --> 00:43:59,680
Speaker 1: talk about this topic. And uh you know again, if

712
00:43:59,719 --> 00:44:02,839
Speaker 1: you guys have it checked out Codebreaker, you definitely need

713
00:44:02,840 --> 00:44:07,480
Speaker 1: to remedy that immediately. It is a phenomenal series that

714
00:44:07,560 --> 00:44:10,839
Speaker 1: covers various topics. And this is your first season, right,

715
00:44:11,840 --> 00:44:14,840
Speaker 1: This is our first season, Jonathan, and we're looking forward

716
00:44:14,840 --> 00:44:17,560
Speaker 1: to having you back on for our second season which

717
00:44:17,600 --> 00:44:21,239
Speaker 1: is coming soon. Um. But but yeah, it's been a

718
00:44:21,239 --> 00:44:23,120
Speaker 1: lot of fun and and it was it was great

719
00:44:23,160 --> 00:44:26,680
Speaker 1: fun to talk to you on Codebreaker and um and

720
00:44:26,719 --> 00:44:29,640
Speaker 1: have you be a part of the show, and we

721
00:44:29,719 --> 00:44:33,440
Speaker 1: hope you'll you'll keep listening. Absolutely, and guys, you also

722
00:44:33,480 --> 00:44:37,040
Speaker 1: have to know this thing about code breaker, because it's

723
00:44:37,040 --> 00:44:40,840
Speaker 1: a gimmick that's so good. I was mad at myself

724
00:44:40,960 --> 00:44:45,320
Speaker 1: for never thinking about it. This is it was brilliant.

725
00:44:45,360 --> 00:44:48,720
Speaker 1: You you produced, You had all of the shows produced

726
00:44:48,719 --> 00:44:52,640
Speaker 1: and ready to go, and you hid codes in the

727
00:44:52,680 --> 00:44:56,480
Speaker 1: episodes and if you're able to break the code, you

728
00:44:56,520 --> 00:45:01,160
Speaker 1: can listen to the next episode before it publishes. Yes,

729
00:45:01,560 --> 00:45:03,759
Speaker 1: well we should start, you know, I mean, you know

730
00:45:05,080 --> 00:45:08,000
Speaker 1: I'm ready to have we embedded a secret code in

731
00:45:08,000 --> 00:45:11,359
Speaker 1: this episode. John. You know, I like to say yes

732
00:45:12,120 --> 00:45:16,400
Speaker 1: just to drive people crazy, but I'm not gonna do that.

733
00:45:16,560 --> 00:45:19,880
Speaker 1: I was just so proud of myself for for breaking

734
00:45:19,960 --> 00:45:24,200
Speaker 1: the the Morse code in the You did an episode

735
00:45:24,239 --> 00:45:29,239
Speaker 1: about internet porn, which is an incredible episode. Probably not

736
00:45:29,440 --> 00:45:32,960
Speaker 1: safe for you to listen to on speakers if you have,

737
00:45:34,120 --> 00:45:36,719
Speaker 1: not safe for work or for little ones, obviously for

738
00:45:36,719 --> 00:45:40,439
Speaker 1: for obvious reasons. But it's a real honest look at

739
00:45:40,440 --> 00:45:45,040
Speaker 1: what is not only a big industry online but legitimately

740
00:45:45,840 --> 00:45:51,480
Speaker 1: it can make or break certain types of on the

741
00:45:51,600 --> 00:45:56,680
Speaker 1: edge technologies like if you wonder why Blu ray one

742
00:45:56,840 --> 00:46:00,520
Speaker 1: over h D, d v D. It's one of the

743
00:46:00,560 --> 00:46:04,360
Speaker 1: big reasons I mean you know, it's it's crazy to

744
00:46:04,440 --> 00:46:08,440
Speaker 1: think about that. If you wonder why the a v

745
00:46:08,760 --> 00:46:13,719
Speaker 1: N conference overlapped with c ES for so many years.

746
00:46:14,840 --> 00:46:17,800
Speaker 1: This is why it's an incredible episode. And I managed

747
00:46:17,840 --> 00:46:20,719
Speaker 1: to actually break the code and I was probably a

748
00:46:20,760 --> 00:46:26,080
Speaker 1: little more proud of myself than I should have been. Well,

749
00:46:26,120 --> 00:46:28,040
Speaker 1: now you should, I mean you should be proud. It's

750
00:46:28,320 --> 00:46:31,759
Speaker 1: Morris code. It's it's it's not used, um, you know,

751
00:46:32,000 --> 00:46:35,320
Speaker 1: very very widely anymore by by at least most people.

752
00:46:35,360 --> 00:46:37,799
Speaker 1: But it's, uh, it's always a good thing to know. Yeah,

753
00:46:37,840 --> 00:46:40,560
Speaker 1: it turns out, you know, once you once you commit

754
00:46:40,640 --> 00:46:44,920
Speaker 1: that scouts stuff to memory and have and have a

755
00:46:44,960 --> 00:46:47,520
Speaker 1: web page opened up that tells you what the dots

756
00:46:47,520 --> 00:46:50,960
Speaker 1: and dashes being because I didn't remember. But but it's

757
00:46:50,960 --> 00:46:53,719
Speaker 1: still it's it's a it's a great way to engage

758
00:46:53,760 --> 00:46:58,239
Speaker 1: the listeners on top of this interesting take you have

759
00:46:58,360 --> 00:47:00,719
Speaker 1: on the different topics. So I just make sure you

760
00:47:00,800 --> 00:47:04,799
Speaker 1: go and check that out. Ben, Thank you so much

761
00:47:04,840 --> 00:47:06,960
Speaker 1: for agreeing to be part of this. This was I

762
00:47:06,960 --> 00:47:10,640
Speaker 1: think a really fun, kind of complimentary piece to your

763
00:47:10,800 --> 00:47:15,759
Speaker 1: Codebreaker episode, which again, uh, I really enjoyed you guys.

764
00:47:15,880 --> 00:47:18,279
Speaker 1: Let me listen to it before I was even I

765
00:47:18,560 --> 00:47:22,080
Speaker 1: recorded the final segment on that episode and I got

766
00:47:22,080 --> 00:47:24,680
Speaker 1: to listen to everything up to that point, and I

767
00:47:24,719 --> 00:47:26,879
Speaker 1: was just I was like, are you sure you want me?

768
00:47:28,040 --> 00:47:30,359
Speaker 1: You're bat and clean up man, you are our close up.

769
00:47:30,520 --> 00:47:33,600
Speaker 1: We love glad, We're glad I could could be of service.

770
00:47:33,640 --> 00:47:36,360
Speaker 1: It was an honor so well, it was great to

771
00:47:36,400 --> 00:47:38,080
Speaker 1: have you. It was great to have you on and

772
00:47:38,120 --> 00:47:41,440
Speaker 1: when we're big fans of tech stuff, so we're like, um,

773
00:47:41,960 --> 00:47:44,160
Speaker 1: it was thank you so much for for having me

774
00:47:44,200 --> 00:47:46,279
Speaker 1: and for for talking to me about this stuff. And

775
00:47:46,320 --> 00:47:49,200
Speaker 1: I think, I don't know, man, I you know, I

776
00:47:49,320 --> 00:47:52,200
Speaker 1: end up feeling you know, I think so many of

777
00:47:52,280 --> 00:47:54,879
Speaker 1: us end up heeling lost right about when it when

778
00:47:54,920 --> 00:47:57,480
Speaker 1: it comes to data tracking. And my only hope is

779
00:47:57,520 --> 00:48:00,000
Speaker 1: that is that I mean not to say that they're

780
00:48:00,080 --> 00:48:02,200
Speaker 1: should be an app for that, but you know, I'm

781
00:48:02,239 --> 00:48:04,360
Speaker 1: my only hope is that we will at some point

782
00:48:04,480 --> 00:48:08,279
Speaker 1: reach a reach a stage where it's easier for the

783
00:48:08,400 --> 00:48:12,520
Speaker 1: user to to really have control over what they're sharing

784
00:48:12,719 --> 00:48:17,280
Speaker 1: and and and also users are more more educated about

785
00:48:17,280 --> 00:48:20,440
Speaker 1: what they're sharing. Um. And I think I think we're all,

786
00:48:20,680 --> 00:48:22,640
Speaker 1: you know, we're all a little bit on the hook

787
00:48:22,680 --> 00:48:25,320
Speaker 1: for for not being more educated about what we're sharing.

788
00:48:25,360 --> 00:48:29,440
Speaker 1: But we're also you know, we also need better tools

789
00:48:29,640 --> 00:48:32,680
Speaker 1: to understand how we are sharing our data and how

790
00:48:32,680 --> 00:48:34,640
Speaker 1: it's being trapped and we and we definitely need to

791
00:48:34,719 --> 00:48:40,800
Speaker 1: hold government accountable for making sure they create and foster

792
00:48:41,960 --> 00:48:47,520
Speaker 1: departments that are specialized in informing other bodies of government

793
00:48:47,560 --> 00:48:51,160
Speaker 1: about these things, keeping up to date, because as we know,

794
00:48:51,239 --> 00:48:53,880
Speaker 1: I mean, we're we're still seeing an era where the

795
00:48:53,920 --> 00:48:57,239
Speaker 1: people who are in positions of power often they have

796
00:48:57,480 --> 00:49:00,680
Speaker 1: limited to no experience with a lot of the things

797
00:49:00,680 --> 00:49:04,120
Speaker 1: that are affecting us on a daily basis. Oh man,

798
00:49:04,280 --> 00:49:07,919
Speaker 1: those those those uh the Supreme Court transcripts. Man, it's

799
00:49:07,920 --> 00:49:11,560
Speaker 1: a sad world. So if we're gonna like, we can't

800
00:49:11,680 --> 00:49:15,080
Speaker 1: wait until the millennials are in the Supreme Court for

801
00:49:15,160 --> 00:49:18,920
Speaker 1: one thing, I'm gonna be dead by then. So I

802
00:49:18,960 --> 00:49:21,239
Speaker 1: would like to see things improve before that. But that's

803
00:49:21,320 --> 00:49:23,680
Speaker 1: that's one of those real challenges is that we have

804
00:49:24,239 --> 00:49:29,040
Speaker 1: a different population and positions of power who they may

805
00:49:29,200 --> 00:49:33,320
Speaker 1: have every desire to help us make sure that these

806
00:49:33,360 --> 00:49:36,120
Speaker 1: things are being used in a responsible way, a non

807
00:49:36,160 --> 00:49:41,279
Speaker 1: predatory and an ethical way, but they have, you know,

808
00:49:41,320 --> 00:49:43,560
Speaker 1: to them, it's it's like if you were to present

809
00:49:43,640 --> 00:49:46,000
Speaker 1: me with a page written in Greek, I would have

810
00:49:46,040 --> 00:49:49,560
Speaker 1: no idea what to do with that. So what technology

811
00:49:49,600 --> 00:49:53,520
Speaker 1: moves faster than policy? Right? Always has and and and

812
00:49:53,520 --> 00:49:55,680
Speaker 1: and and I think it's it's as true with data

813
00:49:55,719 --> 00:49:59,000
Speaker 1: tracking as anything else. And it's just, um, it's a

814
00:49:59,000 --> 00:50:01,080
Speaker 1: reality that we all have to deal with. But it's

815
00:50:01,160 --> 00:50:04,400
Speaker 1: it's it's something that hopefully will will change over time.

816
00:50:04,640 --> 00:50:09,160
Speaker 1: And it's not not not before, not before a lot

817
00:50:09,200 --> 00:50:12,040
Speaker 1: of stuff happens that many people may regret. So it's

818
00:50:12,120 --> 00:50:14,800
Speaker 1: it's always good to be thinking about it. Yeah. Absolutely,

819
00:50:16,120 --> 00:50:19,320
Speaker 1: That wraps up the classic episode of the evils of

820
00:50:19,400 --> 00:50:23,040
Speaker 1: data tracking. Big thanks again to Ben Johnson from Two

821
00:50:23,040 --> 00:50:25,760
Speaker 1: Bells and fifteen joining the show. That was a pleasure.

822
00:50:25,840 --> 00:50:28,600
Speaker 1: Hope we can sometimes get them back on the show.

823
00:50:29,480 --> 00:50:33,600
Speaker 1: And yeah, that's it for this classic episode of tech Stuff.

824
00:50:33,640 --> 00:50:36,440
Speaker 1: If you have suggestions, you can always reach out to

825
00:50:36,480 --> 00:50:39,320
Speaker 1: me either through the I Heart Radio app, using a

826
00:50:39,360 --> 00:50:41,719
Speaker 1: little microphone on the tech stuff page, to leave me

827
00:50:41,760 --> 00:50:44,440
Speaker 1: a voicemail, or to send me a message on Twitter.

828
00:50:44,640 --> 00:50:46,560
Speaker 1: The handle for the show is tech Stuff H s

829
00:50:46,760 --> 00:50:55,959
Speaker 1: W and I'll talk to you again really soon. Text

830
00:50:55,960 --> 00:50:59,400
Speaker 1: Stuff is an I Heart Radio production For more podcasts

831
00:50:59,440 --> 00:51:02,200
Speaker 1: from My heart Radio, visit the I heart Radio app,

832
00:51:02,320 --> 00:51:05,480
Speaker 1: Apple podcasts, or wherever you listen to your favorite shows.

833
00:51:10,000 --> 00:51:10,040
Speaker 1: H