Speaker 1: Welcome to tech Stuff, a production from I Heart Radio.
Speaker 1: Hey there, and welcome to tech Stuff. I'm here host
Speaker 1: Jonathan Strickland. I'm an executive producer with I Heart Radio.
Speaker 1: And how the tech are you. It's Friday. It's time
Speaker 1: for a classic episode. This episode is called the Evils
Speaker 1: of Data Tracking and it originally published on December six,
Speaker 1: two thousand fifteen, and Ben Johnson, another podcaster and journalist,
Speaker 1: joined the show for this discussion. Keep in mind, you
Speaker 1: know this was done back in the issues with data
Speaker 1: tracking have only gotten worse since then. It has not
Speaker 1: improved in any stretch except for the various agencies that
Speaker 1: are tracking data, whether those are corporations or governments. So um,
Speaker 1: fun times, but enjoy this classic episode. I got to
Speaker 1: chance to be a guest host on a show you
Speaker 1: host called code Breaker. Yes, And on code Breaker when
Speaker 1: we talked, I made a reference to Shakespeare. Yes. Now
Speaker 1: my question for you is are you ever confused with
Speaker 1: the seventeenth century British playwright Ben Johnson? Oh man, all
Speaker 1: the time opens, all the time embarrassing. Yeah, I know
Speaker 1: I've been. You know, I have been to Stratford upon Avon.
Speaker 1: I have done that whole thing and and and I
Speaker 1: used to you know, I never get confused for that
Speaker 1: Ben Johnson. But in the past, at least people made
Speaker 1: jokes about steroids because of the other Ben Johnson, Nadian Runner,
Speaker 1: and I used to have a T shirt that said
Speaker 1: I'm not on steroids, but thanks for asking, which was
Speaker 1: a kind of inside joke among my friends, but it
Speaker 1: was you know, I've never been no, I've never been
Speaker 1: that poetic, I've never been that good at sonnets. So
Speaker 1: usually I'm not not confused. It's really funny because I'm
Speaker 1: in in an upcoming episode of tech Stuff. I'm going
Speaker 1: to have my friend i As actar from c net
Speaker 1: on and his his Twitter handle is at i as,
Speaker 1: so he gets a lot of hip hop and rap questions.
Speaker 1: So yeah, it's like me not so much. I'm okay
Speaker 1: with that. But the reason I have been on today
Speaker 1: is because we're doing something kind of a complimentary episode
Speaker 1: to the Codebreaker episode I was on, which was all
Speaker 1: about data tracking and data mining, and to kind of
Speaker 1: talk about what that is, what it's used for, what
Speaker 1: is the best case scenario, like what why do we
Speaker 1: want it? And what are some really really good reasons
Speaker 1: why we may not want it. Uh, despite the fact
Speaker 1: that at this point it's here to stay. I think
Speaker 1: a spoiler alert, data tracking is not going away, right
Speaker 1: until the sort of posted a post apocalyptic world descents,
Speaker 1: until we're all in um fallout for world or whatever
Speaker 1: it is, or or we reach singularity and we're sharing
Speaker 1: the same consciousness anyway, right, yeah, and then it doesn't
Speaker 1: matter who buys what. There's no such thing as an
Speaker 1: individual at that point. So until then we're stuck with it.
Speaker 1: So data tracking and data mining these are terms that
Speaker 1: kind of relate to another buzz term that you've probably
Speaker 1: heard over and over again, which is big data. Um.
Speaker 1: And the reason why we're even talking about this at
Speaker 1: all is because information has value, but it's hard to
Speaker 1: pin down exactly what the value is because it may
Speaker 1: be very different from one person to another, or between
Speaker 1: the individual whose data it is and some other organization
Speaker 1: or a chain of organizations, and the whole thing gets
Speaker 1: really messy. And uh, yeah, we'll be talking about information
Speaker 1: and information about information like metadata. I mean this it
Speaker 1: becomes a rabbit hole that you can easily get lost
Speaker 1: in as you start to try and unravel everything and
Speaker 1: figure out, Okay, well, what's the heart of this story.
Speaker 1: And to be fair, data treking is something that's been
Speaker 1: going on for ages. It's not new. It's just that
Speaker 1: the Internet and the tools we use today allow us
Speaker 1: to generate, gather, and sift through more information than we've
Speaker 1: ever been able to do before. It's kind of a
Speaker 1: catch all phrase, right, Jonathan. It's it's this kind of
Speaker 1: catch all phrase for for computers in software to build
Speaker 1: a really comprehensive um data profile about you in some ways,
Speaker 1: but it's something that credit card companies and banks and
Speaker 1: advertisers have endeavored to do for decades exactly. I mean,
Speaker 1: and this can be done for very simple, quote unquote
Speaker 1: innocent reasons. Like one of the examples I like to
Speaker 1: say is you might have a shop and you in
Speaker 1: your shop, you sell ice cream, and you pay attention
Speaker 1: to see which flavors of ice cream sell the best,
Speaker 1: and that informs you of which flavors of ice cream
Speaker 1: you need to stock up on. You are tracking the
Speaker 1: data of sales. In this case, you're not necessarily looking
Speaker 1: at individuals. You don't care who it is that buys
Speaker 1: the vanilla, but if everyone's buying vanilla, you want to
Speaker 1: make sure you have more vanilla than the other flavors
Speaker 1: that aren't selling as well. So that's one that's like
Speaker 1: the very simplest version of data tracking there where you
Speaker 1: know you've scrubbed the identity of the individuals. That's not
Speaker 1: even that doesn't even come up. It's not important. But
Speaker 1: it can go all the way to the other extreme,
Speaker 1: where you want to create targeted advertising that is going
Speaker 1: to appeal to each specific person that visits a particular
Speaker 1: site or uses a particular service, and the whole point
Speaker 1: of that is to attempt to get them to buy
Speaker 1: something or to enroll in something. It's it's the goal
Speaker 1: of all advertising. It's to get you to act in
Speaker 1: a certain way. And the idea is that, well, if
Speaker 1: we can narrow the target down to the most likely candidates,
Speaker 1: will get a better return on our efforts. And this
Speaker 1: is theoretically good for the user, right Jonathan. Theoretically it's
Speaker 1: it's sort of like, we want to give you the
Speaker 1: thing that you really want, and we want to give
Speaker 1: it to you in the most efficient way and in
Speaker 1: the most sort of sympatico way with with what you
Speaker 1: are interested in doing already. Um, but it can sort
Speaker 1: of like data tracting to me means so many things, Jonathan, right.
Speaker 1: It means your online browsing behavior, or the things you
Speaker 1: choose to buy, the platforms, the software you use, the
Speaker 1: things you download, your name, age, race, sexual orientation, when
Speaker 1: you're sick, what what you're sick with, your eating habits,
Speaker 1: your television watching habits, your fears, hope streams right when
Speaker 1: you're awake, when you're asleep. I mean, like, we we're
Speaker 1: talking about a world now where where we we want
Speaker 1: these wearables. They can give us real time data feedback
Speaker 1: on how we are doing throughout the day. Maybe you
Speaker 1: are an extreme type a personality and you are trying
Speaker 1: to schedule your day so that when you're hitting those
Speaker 1: peak moments of productivity, that's when you're tackling the most
Speaker 1: important tasks to you, and you're wearing wearables, you're getting
Speaker 1: that feedback and you're understanding all this stuff. That data
Speaker 1: may also be going to other places and there may
Speaker 1: not be any use for it right now. Like right now,
Speaker 1: as you're generating that data, that's fine, no one's looking
Speaker 1: at it. But down the road that might not be
Speaker 1: the case for various reasons, some of which are terrifying.
Speaker 1: But but you know you were talking about the different
Speaker 1: types of data. There are actually three kind of broad
Speaker 1: categories we can classify the data that we are creating
Speaker 1: that that various entities are really interested in. There's there's
Speaker 1: volunteered data, and that's the stuff we share, right that's
Speaker 1: when we go on facebooks exactly, that's my tweets, your tweets,
Speaker 1: your your YouTube's, your facebooks, your Pinterests, it's it's the
Speaker 1: stuff where we are participating in the conversation and generating
Speaker 1: that information. We're not just doing that for the people
Speaker 1: that we are trying to impress, or our friends or
Speaker 1: sometimes our enemies if we want to rub their noses
Speaker 1: and stuff because they think they're so big. But that's
Speaker 1: that's the stuff that we are actively sharing. Then there's
Speaker 1: the observed data. That's the stuff that companies and other
Speaker 1: entities can can figure out about us just by watching
Speaker 1: our behavior. So this would be kind of the stuff
Speaker 1: we buy. The browsing habits we have where we log
Speaker 1: in from. Like, if you are doing most of your
Speaker 1: browsing on a mobile device, you better bet their their
Speaker 1: companies out there that want to know that. They want
Speaker 1: to know that you access stuff on your phone. More
Speaker 1: than you do on a laptop or desktop. That's important information.
Speaker 1: And then there's the inferred data, and that's the stuff
Speaker 1: that companies guests is relevant to you based upon the
Speaker 1: information they gather from the other two types of sources.
Speaker 1: And it's it's this collection that's important. And you know
Speaker 1: I mentioned about value and how value is different for
Speaker 1: different people. So the Telegraph did a survey UH in
Speaker 1: which they asked people what they felt their personal information
Speaker 1: was worth to them personally, like, what is your identity
Speaker 1: worth to you? Uh? Based upon the kind of information
Speaker 1: to get shared around these entities? Can you make a
Speaker 1: ballpark guess? I'll tell you this. It's it's it's less
Speaker 1: than ten thousand dollars, but it's more than five dollars. Man, man,
Speaker 1: I'm gonna go with I'm gonna go with twenty bucks
Speaker 1: or no, no, no no, wait, wait, hundred bucks. You know
Speaker 1: it's probably because we share so much ben that we
Speaker 1: value it so little. But the average person five thousand dollars,
Speaker 1: Oh well, that's good that that makes and that makes
Speaker 1: the average person a lot smarter than me. Well, to
Speaker 1: be fair, it was three thousand, two pounds sterling. But
Speaker 1: I did the conversion fair enough, well done, Yeah and uh.
Speaker 1: And they also mentioned the fact that, um that while
Speaker 1: they were doing this, they they found that there was
Speaker 1: a disparity that that women in the survey tended to
Speaker 1: answer more frequently that their personal information was priceless, that
Speaker 1: there was no price you could put on it, that
Speaker 1: they would feel comfortable selling it okay, and men were
Speaker 1: less likely to do that. They also said an interesting
Speaker 1: data point, if you will, Yeah, I also mentioned that
Speaker 1: older people were less likely to want to sell their
Speaker 1: information than younger people, which kind of young younger people
Speaker 1: are just giving it away. Yeah they are, I mean
Speaker 1: they you know, we're seeing that more and more with
Speaker 1: the the the very cavalier behavior of certain executives like Zuckerberg,
Speaker 1: who have said that privacy is dead. No, privacy is
Speaker 1: no longer a thing. That seems to be kind of
Speaker 1: the message that younger generations have not only absorbed, but
Speaker 1: adopted at least to some extent. Say you and I
Speaker 1: s old kajers. So we claim, yeah, no, if your
Speaker 1: generation X or older, you're like, get your dirty, gruvy
Speaker 1: hands off my personal information, you damn millennial right so
Speaker 1: now here. So that's how much the person values their
Speaker 1: personal information in the thousands of dollars range. Okay, I
Speaker 1: know what you're gonna do next, Right, You're gonna give
Speaker 1: me the average company's value on our data. What's going
Speaker 1: to happen? That is exactly what's gonna happen. So here's
Speaker 1: the deal. Your personal information to a company at best,
Speaker 1: like at its peak for average amount of information. I'm
Speaker 1: not talking about very specialized stuff like if you were
Speaker 1: talking about that wearable example. I was saying earlier that
Speaker 1: would that would be worth more because it's more comprehensive data.
Speaker 1: But you're average stuff like you're the stuff you might
Speaker 1: find on a Facebook profile page. It is worth point
Speaker 1: zero zero five dollars. So half a cent is how
Speaker 1: much your data is work at the peak average is
Speaker 1: closer to point zero zero zero five dollars. To know that, I'm,
Speaker 1: you know, a boring, middle aged white guy who's probably
Speaker 1: going to see the Star Wars movie. Probably, I mean
Speaker 1: I would have paid you for definitely, Okay, I mean
Speaker 1: it's seven days away, dude, It is seven days away
Speaker 1: as we record. That can so so at any rate? Yeah, exactly,
Speaker 1: Like the companies like, that's how much your information is
Speaker 1: worth to them. So to you, it's in the thousands
Speaker 1: of dollars. To them, it doesn't even equal a penny
Speaker 1: for the but in the aggregate, right, it's gonna be
Speaker 1: worth so much more. Well, exactly because because when a
Speaker 1: company is buying information, they're not buying one person's information.
Speaker 1: They're buying bundles of thousands upon thousands of people's information.
Speaker 1: Then it's that company's job to either sell that information
Speaker 1: off to someone else. These are data brokers. This is
Speaker 1: all they do. That's that's their job. That's their business.
Speaker 1: They buy and sell info. They don't make stuff, they
Speaker 1: don't provide any services. They aggregate data and sell it
Speaker 1: off to other people. This is a fascinating part of
Speaker 1: this whole thing to me, Jonathan, because it's like they're
Speaker 1: these companies are really I mean, they're they're kind of
Speaker 1: like in the shadows, you know, they're they're they're like
Speaker 1: companies with like weird names that that that have you know,
Speaker 1: data centers in in in the middle of nowhere, and
Speaker 1: they're really hard to find. And you you know, I
Speaker 1: mean you and I probably both remember the days of
Speaker 1: like do not call me at home, you know, getting
Speaker 1: getting that phone call from marketers the pre do not
Speaker 1: call list days, right, And we don't really have a
Speaker 1: do not call list for data yet. Right. We have
Speaker 1: some tools, but we don't really have that. And I
Speaker 1: feel like we're in this kind of um, I don't
Speaker 1: know gold rush on data exactly. We don't have We
Speaker 1: don't have the power to say here is what you
Speaker 1: can use my data to do, and here is what
Speaker 1: you cannot use my data to do. Uh. You know,
Speaker 1: if you're lucky you will be or if you're very
Speaker 1: um you know, careful and you're and you're paying really
Speaker 1: close attention, then you might use services where you're looking
Speaker 1: at the user agreement and looking to see do they
Speaker 1: have a policy? Do they do they specifically state they
Speaker 1: will not share your information with third parties. Um, you
Speaker 1: know you can find some of those, but honestly, that's
Speaker 1: not the way bus this works. For most of these companies.
Speaker 1: You are the product, right, like like Google when the
Speaker 1: service is free. Yep, you're the one product. That's that's
Speaker 1: what Google, your Google's product, your Facebook product, you are
Speaker 1: what they are selling to other other interested parties. And uh,
Speaker 1: even if you look at ones that say that, oh,
Speaker 1: don't worry, we scrub all the identifiable stuff from your day.
Speaker 1: Animize your data if you will. Yeah, that, Um, that
Speaker 1: don't work so good, Ben, it doesn't work very good.
Speaker 1: There have been studies about this in the last few years, right,
Speaker 1: I think there was a study that came out of
Speaker 1: the UK that suggested, um that really in order to
Speaker 1: identify a person, um, you really only needed like one
Speaker 1: or two pieces of information about them. Right. Do you
Speaker 1: remember that study that came out in the last I
Speaker 1: don't know year or so. It was like, I remember
Speaker 1: that one. And there's one that Latania Sweeney Harvard she
Speaker 1: did one or she proved that with three points of data,
Speaker 1: which was gender, birth date, and ZIP code, she could
Speaker 1: identify of US residents. So so nearly of all people
Speaker 1: living in the United States she could identify just with
Speaker 1: those three pieces of information. That One of the things
Speaker 1: I like to stress to people is your name, which
Speaker 1: you try to protect when you are when you think
Speaker 1: you're being anonymous on the Internet and you use a handle,
Speaker 1: so you're not using your name like I use my
Speaker 1: name everywhere as my handle. That's just but most of
Speaker 1: the time, but a lot of people like, no, I'm
Speaker 1: going to use an anonymous handle. Your name is the
Speaker 1: least important thing about you. I hate to break it
Speaker 1: to you that because honestly, most of these companies don't care.
Speaker 1: Just like I was saying, the shop owner with the
Speaker 1: ice cream doesn't care who you are. Most of these
Speaker 1: companies don't really care who you are. They care about
Speaker 1: what you do. Yeah, and it's really interesting too, Like Google,
Speaker 1: for instance, leverages large amounts of money and even some
Speaker 1: of their best algorithmic computing power to make sure that
Speaker 1: advertisers and marketers, for instance, don't engage in dishonest practices
Speaker 1: or break Google's own rules about tracking data. And even
Speaker 1: for Google, this is becoming like a herculean task. We
Speaker 1: I talked to in for Cobreaker, I talked to some
Speaker 1: people at Columbia's, some Columbia grad students who are studying this,
Speaker 1: and it's really interesting to see what gets pulled from
Speaker 1: even like emails that you might send in Gmail, for instance. Yeah, yeah,
Speaker 1: this is this is kind of um, kind of terrifying.
Speaker 1: Also if you if you think about because you think
Speaker 1: of email as being this this uh, private means of
Speaker 1: communication that you know, just as you wouldn't expect someone
Speaker 1: else to read your snail mail. That would be a
Speaker 1: violation of trust to find out that stuff is popping
Speaker 1: up based on things that you've typed. Like if you've
Speaker 1: written an email recently and then something that is related
Speaker 1: to that email starts seemingly to pop up in places
Speaker 1: you weren't expecting, you might think at first that that's
Speaker 1: a weird coincidence. But if this happens a few times, like, Okay,
Speaker 1: this is not a coincidence. What is going on? Yeah,
Speaker 1: it's really interesting. And I think that's something, Jonathan, that
Speaker 1: so many people can relate to, right, this idea of like,
Speaker 1: oh I looked at those shoes once, or I looked
Speaker 1: at uh, you know, whatever, the whatever item that you
Speaker 1: might buy online once and that advertisement follows you around forever. Right, Yeah,
Speaker 1: you had that great moment and codebreaker in fact, about
Speaker 1: the sandals. Yes, the sandals that followed Molly around. Every
Speaker 1: time I logged into Facebook for a straight week, they
Speaker 1: would pop up. And I was so scared because I
Speaker 1: was like, oh my god, I'm gonna be sitting next
Speaker 1: to my boyfriend and I'm gonna log onto Facebook and
Speaker 1: these sandals are gonna come up, and it's gonna reopen
Speaker 1: the wound. And he's gonna know that I was sending
Speaker 1: them to people asking for an opinion. Did you change
Speaker 1: your behavior when you were hanging out with him? Yeah,
Speaker 1: Like if we were loafing around, I would open so
Speaker 1: many tabs and none of them would be Facebook. This
Speaker 1: is a really funny story. What does it make you
Speaker 1: think about? It made me wish that I could go
Speaker 1: back to the days where Facebook was just showing me
Speaker 1: things that I wanted to buy and not things that
Speaker 1: I wanted to run away from. That's that's such a
Speaker 1: I mean, I've seen the same sort of stuff, and
Speaker 1: I've seen advertisements that it's odd, like things that do
Speaker 1: not interest me at all. They're just completely off base
Speaker 1: and I can't It's one of those things where I think, well,
Speaker 1: maybe now, it's just that the system hasn't gathered enough
Speaker 1: information about me to get a good feel, right, so
Speaker 1: I'm seeing stuff. It's like, this is the shotgun approach.
Speaker 1: I'm throwing everything I can and hopefully stuff will work.
Speaker 1: We'll be back to talk more about the evils of
Speaker 1: data tracking after these messages. Systems like Facebook, for example,
Speaker 1: where you can actually go in to an ad and
Speaker 1: say I am not interested in this, you know, then
Speaker 1: maybe you'll end up getting a different selection of stuff,
Speaker 1: but sometimes you'll just get different versions of the same thing.
Speaker 1: You already said, that's not that's not me man. I
Speaker 1: appreciate the muscles on this dude, but I Am not
Speaker 1: going to be that guy. So I'm into I'm into
Speaker 1: my little pony. But I I think I've outgrown it.
Speaker 1: I look my collection, My collection can only be so complete.
Speaker 1: That's you know, there's you gotta draw the line after
Speaker 1: like series five, I gotta say I'm done. I have
Speaker 1: no more shelf space. Um yeah, this is this is
Speaker 1: one of those those amazing stories, like the story about
Speaker 1: the sandals. Just just one of those things that has
Speaker 1: happened to pretty much anyone who has been active online
Speaker 1: over the last couple of years. And it's probably one
Speaker 1: of those stories I would imagine that people start to share,
Speaker 1: not realizing that this is something that other people have experienced. Yeah, yeah, right,
Speaker 1: it is. It feels like a very personal experience, but
Speaker 1: it's something that seems to happen to everyone, and it
Speaker 1: was interesting to see. Relatively recently, Facebook, I believe um
Speaker 1: made it easier for people to control how much of
Speaker 1: their ex girlfriend or boyfriend or significant others information surfaced
Speaker 1: on their own timeline. Did you see that story that
Speaker 1: came out relatively recently. He was interesting this idea that like,
Speaker 1: you know, you know, even the tech companies they're really
Speaker 1: really good at data tracking at this point, are still
Speaker 1: trying to figure out when it goes wrong and how
Speaker 1: to help the user in some cases, um make it
Speaker 1: a more kind of custom experience, right. And and we
Speaker 1: should also stress that that the overwhelming majority of companies
Speaker 1: out there do not want it to go wrong, because
Speaker 1: that's that's counterproductive to what the end end goal is,
Speaker 1: at least for the person that actually bought the advertisement
Speaker 1: and the person who owns the place where that ad
Speaker 1: is being shown in the case of targeted advertising, at
Speaker 1: least like they don't want that to go badly. But
Speaker 1: in other cases, you know, they don't want to go
Speaker 1: badly either, that that's counterproductive. But it is possible for
Speaker 1: this to go wrong even if everyone is trying to
Speaker 1: use it to to people's benefit. And on Codebreaker, you
Speaker 1: had a heartbreaking story about about a father who shared
Speaker 1: some information not not knowing, not even realizing that that
Speaker 1: information was somehow going to make its way into a
Speaker 1: database and then came back to haunt him. Yeah, this
Speaker 1: guy Mike Say, and and this is happened a few
Speaker 1: years back. He you know, unfortunately, um, his his one
Speaker 1: of his children, his daughter died in a car accident.
Speaker 1: And he went to buy some uh you know, he
Speaker 1: went to buy some some actually some some um some
Speaker 1: stuff for photos, like he was putting together some photo albums,
Speaker 1: and he went to buy some frames for those photos.
Speaker 1: And the place that he called to do that, um,
Speaker 1: he just in passing mentioned uh that his daughter had
Speaker 1: died in this car accident. And the person that he
Speaker 1: was talking to actually took down that information. And it's
Speaker 1: still not clear how it how all of this happened,
Speaker 1: but apparently they took down the information and that information
Speaker 1: ended up getting sold to a data broker that's sold
Speaker 1: the information from the company he originally called to Office Max,
Speaker 1: and then Office Max sent a letter to him. Um
Speaker 1: there was addressed to owner of the house or Mike
Speaker 1: Say daughter died in a car accident. Um. And that's
Speaker 1: when it goes really really wrong, right. The picture frames
Speaker 1: that was was buying with through my children. In the
Speaker 1: bottom it had Ashley's name and her birth and death
Speaker 1: date and that she loved them, and you know what
Speaker 1: I mean, that's how she the conversation came about that
Speaker 1: what is this for? And she took you know, the
Speaker 1: information that would go on the bottom and put that,
Speaker 1: you know, on whatever to get it stamp. But they
Speaker 1: had nothing to it. Didn't say killed in car crashing
Speaker 1: out of that. That was all in her own. And
Speaker 1: this is a totally separate company from Office Max. And
Speaker 1: somehow Office Max got the information. They bought the information
Speaker 1: from a data group, so that information was sold from
Speaker 1: things remembered to a data group that collects data and
Speaker 1: then taken and sold to Office Max in an Office
Speaker 1: Max used without looking at it. What an an emotional
Speaker 1: uh punch to the gut to see something like that.
Speaker 1: And and honestly, you know, I mean, you know, Office
Speaker 1: Max did not intend that. There's no sane reason anyone
Speaker 1: would do that. So it's clearly an epic mistake that
Speaker 1: that could result in true emotional trauma, even if it's
Speaker 1: just a momentary thing. You don't want any person to
Speaker 1: experience that kind of thing. And uh, it was a
Speaker 1: pure accident, I imagine based upon way back in the day,
Speaker 1: I used to work for consultants and when when I
Speaker 1: worked for consultants, one of the things I had to
Speaker 1: do was arranged giant mailing lists and print out tons
Speaker 1: and tons and tons of mailing labels. And so you're
Speaker 1: you're working with these big spreadsheets there possibly thousands of
Speaker 1: names long. So I'm guessing what happened was you had
Speaker 1: the data entered into some sort of spreadsheet, and probably
Speaker 1: the spreadsheet was converted at least once, and in that conversion,
Speaker 1: a tab that had been or you know, a cell
Speaker 1: that had just been labeled miscellaneous got put into address
Speaker 1: instead of miscellanys or title or something along those lines.
Speaker 1: And that's when it went from one point where it
Speaker 1: was a piece of data, which, as as Mike said,
Speaker 1: like why do people even need to know this? No
Speaker 1: one needs to know this. This is this is not
Speaker 1: a relevant piece of information. It should never have gone
Speaker 1: into a database. But for that to then accidentally get
Speaker 1: transposed into a cell that is going to show up
Speaker 1: on a mailing label is incredibly it feels incredibly callous, yeah,
Speaker 1: and invasive. And it also, you know, it reminds you that,
Speaker 1: I mean, it reminds us all that the way we
Speaker 1: view ourselves isn't always the way we're viewed by the world, right,
Speaker 1: and and and and that's an interesting aspect of this too,
Speaker 1: This idea that basically a data profile of most users
Speaker 1: on the Internet is being slowly put together over time,
Speaker 1: and and you might be we all might be really
Speaker 1: curious to know, um or disappointed to know um, what
Speaker 1: that data profile looks like from the other side. Now,
Speaker 1: then let me ask you this. I'm going to paint
Speaker 1: a picture for you of a dystopia. Okay, that's that's
Speaker 1: sadly not difficult to imagine based on our current events.
Speaker 1: All Right, so I'm sure you heard. I mean, I'm
Speaker 1: absolutely certain you heard about the app people p E
Speaker 1: E p l E. Right, Yes, this was the one
Speaker 1: that was supposed to be the yelp for people where
Speaker 1: you would be able to rate other human beings and
Speaker 1: so then other human beings could see your rating and
Speaker 1: could see ratings of other people. And it just seemed
Speaker 1: like it was going to lead to fallout. For that's
Speaker 1: what literally an idea, an idea whose time should have
Speaker 1: never come exactly. Now, let's take that same basic concept,
Speaker 1: but instead of you rating people, imagine that you get
Speaker 1: to look up what that person's data profile is worth
Speaker 1: compared to your own because guess what you could do
Speaker 1: that you could build something where you start to look
Speaker 1: at the the robustness of a data profile, at least
Speaker 1: for a certain give and set of transactions. Because keep
Speaker 1: in mind, there are a lot of data brokers out there,
Speaker 1: and your profile in one data broker's database may be
Speaker 1: very different from another because it may be less or
Speaker 1: more complete or from totally different sources than the other one.
Speaker 1: But that would be a weird world where you say, wow,
Speaker 1: my life is worth this much to a potential marketer,
Speaker 1: and my friends life is worth twice as much. What
Speaker 1: am I doing wrong in my life where I'm worth less?
Speaker 1: I mean, it's this isies are looking at us. Yeah,
Speaker 1: and it is scary. I mean, I think it's also
Speaker 1: fair to say that this is as scary as it
Speaker 1: is to imagine that it is. It does also have
Speaker 1: some connection to reality. I mean, when you go into
Speaker 1: a bank and apply for a loan um, you're also
Speaker 1: being sort of sized up right and over time, Um,
Speaker 1: you know, we've we've we've seen some actually some really
Speaker 1: of big problems arise from how banks look at people
Speaker 1: and look at people's ability to pay things back. And
Speaker 1: how credit card companies do this, how your net worth
Speaker 1: is calculated, how you're um, you know, your your likelihood
Speaker 1: of paying a loan back is calculated. And but at
Speaker 1: the same time, over time, there have been some safeguards
Speaker 1: put in against discrimination, right, and a lot of people
Speaker 1: have actually talked about this idea of data discrimination over
Speaker 1: time and how um we really need to think about
Speaker 1: as we go forward and as more and more data
Speaker 1: gets collected on on on users over time. I mean,
Speaker 1: people that were born you know, in the last ten
Speaker 1: years are going to have a very different life on
Speaker 1: the internet than even you or I have had, right,
Speaker 1: I mean I wasn't on the internet until I was
Speaker 1: a teenager. So it's interesting to think about it. And
Speaker 1: it's also you know, you would you would hope some
Speaker 1: people are talking about this idea of like, you know,
Speaker 1: if you all are perceived to be a a person
Speaker 1: of a certain race, a certain education level, tax bracket
Speaker 1: like that the tax bracket, then you might actually see
Speaker 1: a web that is different from the web that someone
Speaker 1: else with a different perceived data profile would see. Yeah,
Speaker 1: this is this is like a dark twisted version of
Speaker 1: what the semantic web is supposed to be. The semantic
Speaker 1: web is supposed to learn about you and then respond
Speaker 1: to what you want, so that you don't have to
Speaker 1: play the game of the web search or the web browser.
Speaker 1: You just say what you want and it returns exactly
Speaker 1: what you want, possibly from multiple sources, so you're not
Speaker 1: even going to a web page in the semantic web necessarily.
Speaker 1: It may be that it's pulling things from various sources
Speaker 1: and presenting you kind of a united view. That's one
Speaker 1: realization of what those semantic web would look like. This
Speaker 1: this is similar to that, but a very dark version
Speaker 1: where it's not responding to what you want, it's responding
Speaker 1: to what the other side wants based upon what they
Speaker 1: think they can get out of you. Yeah, and that's
Speaker 1: a really scary thing. I mean, there's also some potential
Speaker 1: good from that can come from this, right, Um, And
Speaker 1: and you know, other people are talking about this idea
Speaker 1: of you know, a time at which maybe people actually
Speaker 1: start to you know, big data is more valuable. Big data,
Speaker 1: you know, it's all perceived to be more valuable to
Speaker 1: companies and marketers and and and and certain organizations in
Speaker 1: the aggregate. Right, So what if we actually reached a
Speaker 1: time where, um, not. I won't say like data gets
Speaker 1: unionized necessarily, but like, what if you reach the time
Speaker 1: where users actually banded together and did collective bargating on
Speaker 1: behalf of their data in order to say, hey, Okay,
Speaker 1: my data maybe only half of a penny value, but
Speaker 1: when I get together with this large group of other
Speaker 1: users that i'm that I'm aligned with, it's actually worth
Speaker 1: a lot more. And I want to align myself with
Speaker 1: this large group and then sell that um and actually
Speaker 1: gain some agency in that way and gain maybe some
Speaker 1: some money. You're you're like creating a brand new career
Speaker 1: a data agent, like agent. Yeah, agent, they're like Dave
Speaker 1: Walkers really wouldn't if you want to go the vampire route, Yeah,
Speaker 1: that's that. That's I I honestly had never considered that,
Speaker 1: but that is a really compelling idea, especially when you
Speaker 1: think even if you go on a route where you're like,
Speaker 1: all right, we're not doing this to make money, We're
Speaker 1: doing this so that we're only opting into the things
Speaker 1: that we agree with that we find acceptable. One of
Speaker 1: the other things about data tracking that I think is
Speaker 1: problematic is that sure, uh, you might be getting stuff
Speaker 1: that's reacting to your behavior but a lot of that
Speaker 1: is going to be based on guesswork. So for example,
Speaker 1: if I have posted a lot about hiking, it maybe
Speaker 1: that I get a lot of advertisements for outdoor supply
Speaker 1: stores like camping gear, that kind of stuff. And it
Speaker 1: may be that that doesn't fully overlap with my interests.
Speaker 1: That's not terrible or whatever, but it also could mean
Speaker 1: that I start to have a narrower selection of things
Speaker 1: presented to me, which in one way is good, but
Speaker 1: it also means it takes away the possibility of discovery. Yeah,
Speaker 1: this is a huge I mean, this is a really
Speaker 1: interesting idea and a really interesting topic, right. I mean,
Speaker 1: Amazon is is kind of thinking about, I think trying
Speaker 1: to do this. I mean, there there are companies that
Speaker 1: would would love to be able to basically when you
Speaker 1: go to their homepage they basically say, or not that
Speaker 1: we would ever necessarily do that in the future, but
Speaker 1: you you, whatever, you you engage with their platform, they
Speaker 1: would say, here's the three products you can choose from today. Yeah, right, right,
Speaker 1: And that does, as you say, really reduce this idea
Speaker 1: of discovery. And this is you know, this happens in
Speaker 1: the media world to write this idea of like UM
Speaker 1: and in the social media world, this idea of UM
Speaker 1: being able to curate so in such a you know,
Speaker 1: a focused way to the individual user also makes it
Speaker 1: so that user doesn't I don't know, read the New
Speaker 1: York Times and learn anything about what goes on outside
Speaker 1: of their own interest. Here it becomes it becomes like
Speaker 1: the ultimate echo chamber, where where you know you're you're
Speaker 1: you're stuck there seeing the same stuff and maybe you
Speaker 1: don't even realize that we're we're heading to like this
Speaker 1: is almost a Terry Gilliam film what we're describing at
Speaker 1: this right Like we're heading to Brazil right now, let's
Speaker 1: head let's say I'm ready for that movie. I mean,
Speaker 1: I don't want to be in the reality that it depicts.
Speaker 1: I don't want to be I don't want it to
Speaker 1: be reality. But I would totally watch that movie. Yeah,
Speaker 1: me too. When we're talking big data, when we're talking
Speaker 1: about all the information that's going out there, and we
Speaker 1: mentioned the fact that companies are buying profiles in the
Speaker 1: tens or hundreds of thousands or more, here's why. Let
Speaker 1: me give you a few statistics on how much information
Speaker 1: is hitting the Internet on a per minute basis. This
Speaker 1: is based off of research that was conducted by a
Speaker 1: data company called Domo, and this was published in August
Speaker 1: of two thousand fifteen. So keep in mind these numbers
Speaker 1: are already old. But every single minute Amazon receives four
Speaker 1: thousand three unique visitors. Well, we're going to pause the
Speaker 1: data tracking conversation for another quick break. So Amazon is
Speaker 1: one of those companies that's really good at doing this
Speaker 1: focused data tracking that you know, this is where you
Speaker 1: buy a product and it says, hey, people who bought
Speaker 1: this product also bought these other things, and then you think, oh, right, right,
Speaker 1: I should get that. It's like it's like the perfect
Speaker 1: impulse by shelf and it's customized for every single purchase. Right,
Speaker 1: I do need batteries and dog biscuits. How did you know? Well,
Speaker 1: there's there's there's there's this other level two, uh that
Speaker 1: that people are starting to imagine when you think about
Speaker 1: companies like nest Um, and you think about smart televisions
Speaker 1: that actually interact with your refrigerator. And one of the
Speaker 1: most convincing sort of imagined scenarios that was ever presented
Speaker 1: to me in recent years was this idea that essentially,
Speaker 1: over time, your refrigerator learns that you go for ice cream.
Speaker 1: You go in for that vanilla ice cream, Um, right
Speaker 1: around eight pm, and um, it tells your television that.
Speaker 1: And then in the world of streaming that you know,
Speaker 1: more and more people are doing on their actual television
Speaker 1: instead of having it connected to a you know, a
Speaker 1: cable channel. Um, you actually get at eight forty five,
Speaker 1: you get an advertisement for ice cream, which reminds you
Speaker 1: to go and eat some ice cream, and then you
Speaker 1: end up eating more ice cream, and then you end
Speaker 1: up buying This is the world where the appliances are
Speaker 1: working against you. Yes, they're not telling you they eat
Speaker 1: a kale set, or maybe they are. We Yeah, that's
Speaker 1: also possible, Like if you have if you have this
Speaker 1: thing where you're going out and buying kale salads and
Speaker 1: then you're watching a lot of you know, food type stuff,
Speaker 1: and your you might end up getting, Hey, here's a
Speaker 1: quick recipe, and here are the fourteen ingredients you don't
Speaker 1: have that you need to go to Whole Foods and buy. Um. Yeah,
Speaker 1: it's a world. I host a show. I host another
Speaker 1: show called forward Thinking, and in that show, it's it's
Speaker 1: an optimistic view of the future and I have often
Speaker 1: looked at this side from the optimist side, and it
Speaker 1: is similar to what you were saying about every single
Speaker 1: person's Web experience could be unique to them based upon
Speaker 1: these factors. Uh. And and there's a way of doing
Speaker 1: that where it is not kind of the scary, oppressive
Speaker 1: type of way. But I I talk about how with
Speaker 1: the Internet of things and with this connectivity of different appliances,
Speaker 1: it's not that far of a stretch to imagine not
Speaker 1: just the Web, your experience of reality could get to
Speaker 1: a point where a lot of it is catered specifically
Speaker 1: to you. Yeah, and there could be a lot of
Speaker 1: good things that come out of that, right, um and
Speaker 1: and and and I totally believe in that possibility. I mean,
Speaker 1: like like we like you said, I think so, um
Speaker 1: so thoughtfully when you came in and and and uh
Speaker 1: talked about this stuff with me on Codebreaker, was was
Speaker 1: basically this idea that look, it's um, I'm not going
Speaker 1: to pull out the quote so eloquently as you did,
Speaker 1: but look, this technology can it's us that makes it
Speaker 1: good or bad. Right we we we have the power
Speaker 1: theoretically to to make all of this stuff and make
Speaker 1: data tracking work for us. As users and work for
Speaker 1: us as organizations and companies. I mean, I think a
Speaker 1: lot about the possibility of data tracking being really good
Speaker 1: in the future where our climate is changing, um and
Speaker 1: and the fact that we really understand and on a
Speaker 1: global level, a lot more about our climate than we
Speaker 1: did even thirty years ago thanks to an increased level
Speaker 1: of data tracking, right, And that is an important point
Speaker 1: to make, Ben, is that data tracking does not necessarily
Speaker 1: mean it's about just people. Although people are an important
Speaker 1: factor when you're looking even at climate change, because you
Speaker 1: want to see, well, how is this affecting actual human
Speaker 1: beings as well as the environment in general. You need
Speaker 1: to know the whole picture. But data tracking is really
Speaker 1: it's it's agnostic as far as what the data actually represents.
Speaker 1: Like we're talking about data about everything, you know, anything
Speaker 1: that you can imagine that can be quantified in the
Speaker 1: form of data that is being tracked by someone for
Speaker 1: some reason. Some of those reasons are good, some of
Speaker 1: those reasons are way not good. Yeah, But but it's
Speaker 1: not all just about people. And it's interesting too to
Speaker 1: think about I wonder what you think about this, But
Speaker 1: it's interesting too to think about just how I think
Speaker 1: a lot of it feels out of control at this
Speaker 1: point for the user, it feels like we don't fully
Speaker 1: understand it. We don't fully understand how it's being used.
Speaker 1: Um and what data about us is being tracked as
Speaker 1: users and UM and you know, government, I mean, the
Speaker 1: FTC in the past few years has become much more
Speaker 1: involved in this idea of helping consumers protect themselves against
Speaker 1: you know, abusive forms of data traction um if you will,
Speaker 1: um and and there are a lot of qui and
Speaker 1: and and also it's fair to say that I think
Speaker 1: a lot of tech companies have endeavored to put not
Speaker 1: only in terms of service agreements, but in the actual
Speaker 1: tools that they give to the user when they manage
Speaker 1: their own accounts, as you know, to put the power
Speaker 1: in the user's hands to control this. But you know,
Speaker 1: let's be honest, right, I mean, you and I don't
Speaker 1: necessarily read all of the terms of services. You're telling me.
Speaker 1: You don't read every end user license agreement. You don't.
Speaker 1: You can't recite the is you know. It's it's tough.
Speaker 1: It's tough. I can't remember the last time, honestly, I
Speaker 1: cannot remember the last time I actually read a LA
Speaker 1: because who who has time for that? I want to
Speaker 1: use your shiny new toy. I don't care that I've
Speaker 1: given up all my privacy. And if as long as
Speaker 1: I get to play with the shiny new toy, if
Speaker 1: my b B eight remote control droid is secretly telling
Speaker 1: Disney exactly how to make me buy more BB eight
Speaker 1: remote control droids, so be it. Um, it all comes
Speaker 1: back to Star Wars. Well, I mean, you know, the
Speaker 1: force is strong in my family. My father has it,
Speaker 1: I have it, my sister has it. So uh, you
Speaker 1: know this is this has been a great conversation, great
Speaker 1: talk about this topic. And uh you know again, if
Speaker 1: you guys have it checked out Codebreaker, you definitely need
Speaker 1: to remedy that immediately. It is a phenomenal series that
Speaker 1: covers various topics. And this is your first season, right,
Speaker 1: This is our first season, Jonathan, and we're looking forward
Speaker 1: to having you back on for our second season which
Speaker 1: is coming soon. Um. But but yeah, it's been a
Speaker 1: lot of fun and and it was it was great
Speaker 1: fun to talk to you on Codebreaker and um and
Speaker 1: have you be a part of the show, and we
Speaker 1: hope you'll you'll keep listening. Absolutely, and guys, you also
Speaker 1: have to know this thing about code breaker, because it's
Speaker 1: a gimmick that's so good. I was mad at myself
Speaker 1: for never thinking about it. This is it was brilliant.
Speaker 1: You you produced, You had all of the shows produced
Speaker 1: and ready to go, and you hid codes in the
Speaker 1: episodes and if you're able to break the code, you
Speaker 1: can listen to the next episode before it publishes. Yes,
Speaker 1: well we should start, you know, I mean, you know
Speaker 1: I'm ready to have we embedded a secret code in
Speaker 1: this episode. John. You know, I like to say yes
Speaker 1: just to drive people crazy, but I'm not gonna do that.
Speaker 1: I was just so proud of myself for for breaking
Speaker 1: the the Morse code in the You did an episode
Speaker 1: about internet porn, which is an incredible episode. Probably not
Speaker 1: safe for you to listen to on speakers if you have,
Speaker 1: not safe for work or for little ones, obviously for
Speaker 1: for obvious reasons. But it's a real honest look at
Speaker 1: what is not only a big industry online but legitimately
Speaker 1: it can make or break certain types of on the
Speaker 1: edge technologies like if you wonder why Blu ray one
Speaker 1: over h D, d v D. It's one of the
Speaker 1: big reasons I mean you know, it's it's crazy to
Speaker 1: think about that. If you wonder why the a v
Speaker 1: N conference overlapped with c ES for so many years.
Speaker 1: This is why it's an incredible episode. And I managed
Speaker 1: to actually break the code and I was probably a
Speaker 1: little more proud of myself than I should have been. Well,
Speaker 1: now you should, I mean you should be proud. It's
Speaker 1: Morris code. It's it's it's not used, um, you know,
Speaker 1: very very widely anymore by by at least most people.
Speaker 1: But it's, uh, it's always a good thing to know. Yeah,
Speaker 1: it turns out, you know, once you once you commit
Speaker 1: that scouts stuff to memory and have and have a
Speaker 1: web page opened up that tells you what the dots
Speaker 1: and dashes being because I didn't remember. But but it's
Speaker 1: still it's it's a it's a great way to engage
Speaker 1: the listeners on top of this interesting take you have
Speaker 1: on the different topics. So I just make sure you
Speaker 1: go and check that out. Ben, Thank you so much
Speaker 1: for agreeing to be part of this. This was I
Speaker 1: think a really fun, kind of complimentary piece to your
Speaker 1: Codebreaker episode, which again, uh, I really enjoyed you guys.
Speaker 1: Let me listen to it before I was even I
Speaker 1: recorded the final segment on that episode and I got
Speaker 1: to listen to everything up to that point, and I
Speaker 1: was just I was like, are you sure you want me?
Speaker 1: You're bat and clean up man, you are our close up.
Speaker 1: We love glad, We're glad I could could be of service.
Speaker 1: It was an honor so well, it was great to
Speaker 1: have you. It was great to have you on and
Speaker 1: when we're big fans of tech stuff, so we're like, um,
Speaker 1: it was thank you so much for for having me
Speaker 1: and for for talking to me about this stuff. And
Speaker 1: I think, I don't know, man, I you know, I
Speaker 1: end up feeling you know, I think so many of
Speaker 1: us end up heeling lost right about when it when
Speaker 1: it comes to data tracking. And my only hope is
Speaker 1: that is that I mean not to say that they're
Speaker 1: should be an app for that, but you know, I'm
Speaker 1: my only hope is that we will at some point
Speaker 1: reach a reach a stage where it's easier for the
Speaker 1: user to to really have control over what they're sharing
Speaker 1: and and and also users are more more educated about
Speaker 1: what they're sharing. Um. And I think I think we're all,
Speaker 1: you know, we're all a little bit on the hook
Speaker 1: for for not being more educated about what we're sharing.
Speaker 1: But we're also you know, we also need better tools
Speaker 1: to understand how we are sharing our data and how
Speaker 1: it's being trapped and we and we definitely need to
Speaker 1: hold government accountable for making sure they create and foster
Speaker 1: departments that are specialized in informing other bodies of government
Speaker 1: about these things, keeping up to date, because as we know,
Speaker 1: I mean, we're we're still seeing an era where the
Speaker 1: people who are in positions of power often they have
Speaker 1: limited to no experience with a lot of the things
Speaker 1: that are affecting us on a daily basis. Oh man,
Speaker 1: those those those uh the Supreme Court transcripts. Man, it's
Speaker 1: a sad world. So if we're gonna like, we can't
Speaker 1: wait until the millennials are in the Supreme Court for
Speaker 1: one thing, I'm gonna be dead by then. So I
Speaker 1: would like to see things improve before that. But that's
Speaker 1: that's one of those real challenges is that we have
Speaker 1: a different population and positions of power who they may
Speaker 1: have every desire to help us make sure that these
Speaker 1: things are being used in a responsible way, a non
Speaker 1: predatory and an ethical way, but they have, you know,
Speaker 1: to them, it's it's like if you were to present
Speaker 1: me with a page written in Greek, I would have
Speaker 1: no idea what to do with that. So what technology
Speaker 1: moves faster than policy? Right? Always has and and and
Speaker 1: and and I think it's it's as true with data
Speaker 1: tracking as anything else. And it's just, um, it's a
Speaker 1: reality that we all have to deal with. But it's
Speaker 1: it's it's something that hopefully will will change over time.
Speaker 1: And it's not not not before, not before a lot
Speaker 1: of stuff happens that many people may regret. So it's
Speaker 1: it's always good to be thinking about it. Yeah. Absolutely,
Speaker 1: That wraps up the classic episode of the evils of
Speaker 1: data tracking. Big thanks again to Ben Johnson from Two
Speaker 1: Bells and fifteen joining the show. That was a pleasure.
Speaker 1: Hope we can sometimes get them back on the show.
Speaker 1: And yeah, that's it for this classic episode of tech Stuff.
Speaker 1: If you have suggestions, you can always reach out to
Speaker 1: me either through the I Heart Radio app, using a
Speaker 1: little microphone on the tech stuff page, to leave me
Speaker 1: a voicemail, or to send me a message on Twitter.
Speaker 1: The handle for the show is tech Stuff H s
Speaker 1: W and I'll talk to you again really soon. Text
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