August 27, 2024 • 27 mins
Join us on Real Talk on Talent as we explore the complex world of data in human resources and talent acquisition. Dina and Hilary discuss the pitfalls of using data without context and the frustrations of balancing quantitative metrics with qualitative insights. We’ll illustrate the importance of a holistic approach to data that marries analytics with intuition for more effective decision-making.
Tune in as we navigate the ongoing challenge of maintaining clean and organized data, the risks of excessive data collection, and the crucial balance between privacy and utility.
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Links & Mentions:
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➡︎ Hubspot Database Decay
➡︎ How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did
➡︎ Mass IT outage hits airports
Paris Summer Olympics Fun Facts:
➡︎ Flavor Flavor Sponsors Women’s Water Polo Team
➡︎ Bob the Cap Catcher
➡︎ Paris Olympics Ratings
➡︎ Behind the creation of the Paris Olympic posters
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Connect with our Team of Huemans:
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➡︎ Website: https://www.hueman.com/
➡︎ Podcast: https://www.youtube.com/@huemanps/podcasts
➡︎ LI: https://www.linkedin.com/company/hueman-people-solutions
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#hueman #talentacquisition #recruiting #recruitmentprocess #rpo
Don't forget to subscribe to the Hueman Resources Podcast Channel for more valuable insights on talent acquisition, recruiting, and workforce planning and management.
Visit Hueman.com to learn more about our recruiting services.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 2 (00:06):
Welcome to Real Talk on Talent, a human resources
podcast where we talk abouttalent acquisition, recruiting
and all things hiring.
Hi, hi, dina, how are you?
I'm good, welcome back.
Hey, thank you, great to behere.
Are you all springy in themiddle of summer?
You know, I like my pink, Ilike my pink.
(00:29):
I'm here for it.
Yeah, which we need some ofthis, because I would like to
today to just complain, oh, youknow what Will you join me on
this journey?
Speaker 3 (00:39):
Let's do it.
Speaker 2 (00:47):
I'm so for um, yeah, and, but it can be productive
complaining.
It's purposeful.
Purposeful if we're pointingout potential issues or red
flags, because today is data.
This is perfect, which and Ithink last time when we talked
about data we had like clowingreviews.
We're like, we love data, lovereporting love it, we love it,
we do.
Speaker 3 (01:00):
Let's talk about when it goes bad, though, the worst.
Yes, it is so true, though Imean it definitely goes bad.
Speaker 2 (01:07):
Yeah, it can go bad, it can go because data is
there's so much like we talkabout big data and how like we
went through this whole era oflike the more data, the better
get it, splice it, have it.
But there are some realpitfalls there are absolutely
Collecting, using understanding,can be a real pain.
Speaker 3 (01:29):
So yeah, I mean, we know we love it, but we also
hate it.
Speaker 2 (01:33):
Kate, you want to start Give me?
Give me a pet peeve when itcomes to data.
Speaker 3 (01:37):
OK, just one?
Well, we'll start with one.
Ok, so first data withoutcontext.
Ok, tell me more.
Just don't just throw somethingat me and expect me to
understand how this fits intothe bigger picture.
So a lack of analysis.
So a lack of analysis.
We know this about data.
Anybody can splice the data tomake it tell the story that they
(01:58):
want.
Speaker 2 (01:59):
for the most part, Well, because if you have enough
data, you can to your point,you can pull out the pieces that
tell different stories.
Speaker 3 (02:06):
Yes, so I'll just pulling it back to what we're
here to talk about, or what thispodcast is.
Speaker 1 (02:12):
Complaining about data.
Speaker 3 (02:13):
Complaining about data.
No, like human resources, ohyeah, okay, just to be like.
Oh well, our turnover rates 27%, okay, okay, is that good, is
that bad?
Speaker 1 (02:23):
How's that?
Speaker 3 (02:23):
compare what's industry.
Give me context.
Yeah, there are moments Justlike don't just give me a data
point, I need context around.
Or like not having a baseline.
Speaker 2 (02:32):
Baseline, like where are you today, where were you
yesterday, where were you a weekago?
Where do you want to be, likeyeah.
Speaker 3 (02:39):
All those things.
Speaker 2 (02:39):
I also kind of going along with that.
I really get frustrated whenyou are arguing data versus
anecdotal opinions and I hadthis whole experience once where
I spent weeks, literally weeks,arguing with a company.
(03:00):
It was kind of arguing, butthey-.
Speaker 3 (03:03):
Debating is what we call that.
Speaker 2 (03:05):
I was telling them they were wrong and they didn't
agree with me Hillary wasn't onthe debate team.
That was not you were.
I needed you there because ifyou'd been there it wouldn't
have taken so long.
But it was so frustratingbecause we helped set up a new
ATS and then the hiring managerstarted complaining that they
weren't getting enough.
They weren't getting candidatesanymore, and so I went I said
(03:27):
okay, well, tell me.
They said well, we used to posta job and get hundreds of
applies for hard to fillpositions.
And I went back to all thehistorical data.
I was like that's not true.
One, it's not true.
And two, you're actuallygetting better candidates,
better, like the data shows thatit's there.
And they're like well, no, ourhiring managers aren't happy.
And I was like I don't knowwhat to tell you, because if
(03:48):
here are the numbers, I pulledthem straight from the job
boards and you even get in weekswhere they literally like no,
yeah, no, this isn't true.
And I was like I don't know.
Speaker 3 (04:01):
So you know.
It's so interesting that yousay that.
Because, so you know, it's sointeresting that you say that?
Because data often overlooksthe qualitative feelings, the
softer feelings, the moresubjective feelings, so to speak
If you were, if you were, andso people will often lean on how
they feel as an out for whatdata is.
(04:22):
So, with data, it is importantthat you have a champion and
somebody who is there to beanalytical, but you do also have
to factor in the people part.
Speaker 2 (04:33):
You do and it kind of reminds me of, I think, last
time.
Or you talked about the gutcheck.
Like, gut check, yeah, you usethe data to gut check, or you
use data to validate or disprovethe gut check, because there is
value in saying like, okay,what's my human intuition,
what's my experience telling me?
But if you get to a point whereit's like no, I reject all of
(04:57):
the data you're showing me, thenit's like that's my frustration
.
One plus one does not equal two.
Why am I here?
Yeah, why am I here For thispodcast?
No, in that conversation.
Speaker 3 (05:07):
I know, I know I get it, I get it.
Yeah, anyway, you know that's awhole different scenario it is.
We'll talk about how to sell topeople your narrative.
I'm surprised, that's kind ofyour thing.
You're the marketing person.
Speaker 2 (05:21):
That.
That's why I was so frustratedbecause I was like look.
Speaker 3 (05:23):
Anyway.
Speaker 2 (05:24):
Yeah, one thing as a marketer is we do have to.
There's this constant balancingact of like you collect data
and you analyze data.
Speaker 3 (05:34):
Yeah, what do you do with?
Speaker 2 (05:35):
that?
What do you do with it?
And so over the past couple ofyears, as more countries and
more states have put in privacylike right to privacy, right to
forget laws and stuff, that hasbeen something that's been, as a
professional, a littlefrustrating, but at the same
time, personally, I love it.
Okay, tell me why?
Because I do really enjoy thefeeling of anonymity even though
(05:59):
in our world.
Is there really?
Speaker 3 (06:01):
true anonymity, but believe it.
Speaker 2 (06:03):
One of my favorite stories on like the misuse of
data is there's a example withTarget and I'm not throwing them
under the bus Like this is verywell known.
It's a great case study.
But the story goes that afather got really ticked off at
Target because Target started tosend his teenage daughter like
(06:24):
baby coupons for like diapersand strollers and he's like, and
he goes.
He goes and complains to target.
He said what are you doing?
I have, you know, I have ateenage daughter.
Why are you sending her all ofthese baby related things?
That's completely inappropriate.
We'll come to find out.
His daughter was pregnant andbecause of her buying patterns
(06:45):
and Target actually has they'vesaid it's something like if you
start buying a certain type ofvitamin and unscented lotion and
like one other thing they foundthat that buying behavior tends
to three, six months down theroad, start to buy all of the.
So Target knew this because oftheir data related to buying
(07:06):
patterns.
So they used very targetedmarketing, perhaps a little too
targeted.
Speaker 3 (07:12):
Way too targeted, way too targeted yeah.
Speaker 2 (07:14):
And so it's.
I think it's one of the mostfascinating stories on.
Oh no, that's reallyinteresting, not just like what
do you do with data, but likethe stories.
You who would have thoughtthat's crazy, like three
completely unrelated things thatthey found were one of the
first trigger points for movinginto a pregnant person.
Speaker 3 (07:32):
Yeah, very interesting.
Speaker 2 (07:34):
So be careful with data.
I guess is where I'm going onthat.
Speaker 3 (07:36):
Or don't sign up for any of the memberships where
they track what you're buying.
They track what you buy anyway.
I like to pretend anonymity.
I like my coupons too.
I will say Good deal, Like areyou a member?
No, oh, you should be.
Hey, they track what I'm buying.
Speaker 1 (07:50):
So they always send me great coupons.
Speaker 2 (07:54):
So one of the things because I studied the Target
case study in school and talkedabout it many times in my
professional career what they donow and this might be tall tale
, but I pretend it's truth is,instead of sending, like, all
baby coupons, now they'll havetheir standard, but they'll
start to insert more babyrelated coupons, so you don't
(08:14):
know you're being targetedbecause people, they want the
convenience of customizationwithout feeling like you know
everything about them.
Speaker 3 (08:24):
The illusion of privacy.
Listen.
We love the illusion of privacyListen.
Speaker 2 (08:26):
we love the illusion of privacy Listen we just sell
our data to get things for freelike Gmail, oh yeah.
Speaker 3 (08:32):
Totally free?
Totally fine?
Yeah, that's fine.
And then serve me up the adsfor what I want yeah.
And then I get mad at Instagram.
I'm like dang it, instagram,you're making me shop too much,
but that's the thing, but that'sthe thing?
Speaker 2 (08:43):
Okay, my own fault.
Okay, what about you, weirdest?
Speaker 3 (08:48):
targeting you've had.
So I keep getting stuff forprivate school for children.
Okay, I do not have children, Ihave dogs.
Speaker 1 (08:57):
I was going to say does Buddy need to go to private
school?
Speaker 3 (08:59):
My mutt needs to go to private school and I'm like,
why am I?
Continuously getting pieces forprivate school education.
Maybe they're trying to get youto go back to school.
Maybe I don't think they wantme in an elementary school,
though, teacher yeah, maybe Idon't have the patience for that
.
Speaker 2 (09:15):
No.
Speaker 3 (09:16):
Yeah, that is kind of a weird one.
Yeah, I get that, do you think?
Speaker 2 (09:19):
of anything in your life that would have set that
off.
Speaker 3 (09:22):
I mean my guess is somebody is just doing like very
generic, unthoughtfuldemographic screening and going
oh, this is a woman in her lateto mid mid 30s.
Speaker 2 (09:33):
Just own it Dina.
Speaker 3 (09:35):
You're not a millennial.
We could do the math this is awoman in her early 40s.
She's probably has kids goingto wherever.
Speaker 2 (09:40):
Yeah, that's a good point.
Speaker 3 (09:41):
And I'm like oh, be more thoughtful, okay so that's
a question, so be morethoughtful.
Speaker 2 (09:46):
So you're grumpy that they're not more thoughtful,
exactly.
Speaker 3 (09:49):
Why isn't my husband getting that piece of direct
mail?
Speaker 2 (09:51):
That's what I want to know that is a great question.
Don't send it to me.
Speaker 3 (09:56):
That's a better question than the age one.
Don't send it to me, send it tohim, send it to him.
You know what I startedthinking about on the data thing
.
So you know I know before wewere talking about AI and we
were talking about some of thebiases with it, and now I'm not
a machine, so I don't understandmachine learning.
But what I want to know is whatare the different factors that
(10:21):
are going into all of theconsiderations?
Like what is every data pointthat it's looking at when it's
deciding whether or not acandidate is good for you?
Speaker 2 (10:32):
I mean, we don't have time to get into that, I know,
but like is there like.
Speaker 3 (10:36):
I mean again, I don't know machine learning, I don't
understand this you know AI, butis it like.
Speaker 2 (10:41):
All the algorithms, so AI and machine learning and
like they're all differentlevels and variations of it,
right?
So if we're going to thinkabout the I'm going to speak to
the customization of anindividual within like a
recruiting process, is that kindof where you're thinking on
that?
Okay, maybe, okay, so there area couple of ways that it will
(11:06):
look and make those educateddecisions.
Very often it's like the targetexample.
It's like if you see this kindof behavior or a data point,
what is the most common outcomeor behavior that will come after
that and so there's a levelwhere you can say individuals
who have this on their resume orwho visit your website five
times in a week are more likely.
(11:28):
There's that predictivebehavior, that of saying if they
take these behaviors, they'remore likely to take those
behaviors.
So you should prioritize youroutreach, your sourcing, your
conversation or, going back tothe customization, then you can
serve up something that says ifsomeone has visited your website
five times in the past week,they're probably closer to the
(11:50):
decision-making stage.
So start serving up reallyspecific content and so it'll
make decisions based on thatbehavior piece.
There's also the whole idea ofpixel tracking and cookies to
say there are a lot of companieswhere they'll say we'll put a
pixel on your website and thenwe will track where your users
(12:12):
are elsewhere.
So I actually used this before,where I had access to a
dashboard.
That said, I actually used thisbefore, where I had access to a
dashboard.
That said, the people that cometo your website tend to be in
this age demographic.
They tend to watch these typesof TV shows, they tend to drive
these types of cars Like whatcars they drove?
Yeah, all from the behavior oftheir online.
(12:33):
And I've kind of diverted fromyour original question.
Speaker 3 (12:35):
No, no, but that's super interesting.
Speaker 2 (12:36):
But if you have that, then all of a sudden you can
say, okay, well, if someone is a41-year-old, thank you.
Like married woman who recentlypurchased a larger car.
Speaker 3 (12:53):
Ah, mm-hmm.
Speaker 2 (12:55):
Maybe she needs to ship her kids off to private
school, and that's kind oftaking the data and doing like a
okay persona.
Yeah, adjustment.
So there's the behavior andthere's the persona.
Okay, interesting veryinteresting.
Speaker 3 (13:06):
At what point, like does the data go bad?
Speaker 2 (13:09):
so you know, like that's what.
What about?
I'm gonna ask, put it back toyou from a recruiting
perspective yeah, that that'swhat I was thinking about.
Speaker 3 (13:17):
So you know, one thing that we do is we have
people who've applied for goshany job we've had posted for who
knows how long.
So we've got a massive database.
Most companies do, and you cango back in and you can source
for candidates based on keywords, kind of like your basic
Boolean strings.
But, like, at some point thedata is just too old, it just
it's not relevant anymore.
(13:39):
What somebody did 12 years agoisn't really applicable for what
I'm looking for, I think.
Hubspot.
Speaker 2 (13:43):
I read this a year ago I think, so it's probably
changed and I may have thenumber wrong.
Whitney, let's find the rightone.
Correction section potentiallythat every year or every six
months your database depreciatesby like 30%.
(14:03):
So in that time frame, 30% ofyour information is no longer
relevant.
So I guess that's why you haveto maintain your database, you
have to keep it clean, you haveto audit what's in there.
Maybe that's.
The other thing that's annoyingis when people are working off
of poor data.
Speaker 3 (14:22):
Yes, so love that.
You say that I was doing aHubSpot cleanup earlier today.
Okay, and I'm sitting theregoing at it.
I'm like, oh my gosh, I justcan't take it.
It's such a mess.
Speaker 2 (14:32):
It's such a mess.
So there really absolutelyorganized and clean.
Speaker 3 (14:35):
Flawless, super clean , a plus to all of our users.
No, no, not at all, not at all.
Myself included.
Yeah, okay, that's why you weredoing the cleanup.
Speaker 2 (14:49):
Yeah, exactly, Exactly.
That is actually not a petpeeve.
I am super proud of anytimesomeone goes into a system and
they're like I'm just going todo a little bit of cleanup at a
time.
Oh so kudos to you.
Speaker 3 (14:54):
Thank you so pet peeve of mine is dirty data.
Like I will, I will get thisand I will be like and I'll call
like an all hands meeting andI'll be like are you aware that
you do not have the first andlast name, you only have their
email address.
That is not acceptable.
So that's how you look at it islike you do the visual check
yes, I do the visual check andI'm like how many missing pieces
are we have?
(15:14):
You know?
I mean just simple things likehere on data.
You never want to send out anemail to hello.
First name.
Speaker 2 (15:23):
Don't do it.
Yes, oh, you know what?
Honestly, don't do it.
The bane of a marketer'sexistence Like hello, friend,
yes, yeah, or then what reallygrinds my gears is then it's my
fault as a marketer, oh yeah,that's my.
Other thing is like when itcomes to data and data usage and
customization, it's a teameffort.
Speaker 1 (15:47):
It is a team effort, I will give human.
Speaker 2 (15:48):
Tons of credit for this.
Yeah, sales and marketing worktogether flawlessly.
We truly are just trying toprovide a great experience to
everyone who interacts with us.
I concur, and so don't blame meif the data is bad, if I don't
own the data.
Yeah, I'm trying here.
I get it.
I get it.
I'm trying to customize withoutnot being creepy.
Speaker 3 (16:07):
So you know what?
So here's another thing.
This is where I think companiesget themselves into trouble.
It's trying to get too muchdata and data that they're not
going to use.
And we'll actually we'll takethis back to a recruitment
perspective.
Going back to an application,like you know people, some
people have super tediousapplications where you have to
complete ABC who's your cousin,joe's first reference and what
was your second car?
(16:27):
No, the worst.
Speaker 2 (16:28):
Oh, sorry, side note.
Please upload your resume.
Great, upload my resume Now.
Fill out every single field ofall the information that you
just uploaded on your resume.
Speaker 3 (16:39):
Yeah, so like why do you need all this?
What are you actually doingwith this information?
So that is a pet peeve of mineis when people just ask for more
information than is necessaryand they're getting data that
they are not going to use foranything.
Speaker 2 (16:56):
Well, so let me ask you this, because we talked
about this.
We talked about, like,throttling the pipeline I think
it was our last processdiscussion where it's like you
increase the burden on yourcandidates to throttle it.
Should you give context as towhy you're asking for certain
types of information in anapplication?
Speaker 3 (17:18):
No, no, no, okay, no, that just makes the application
longer.
Speaker 2 (17:23):
Well, I'm just thinking you were like saying
what are you doing with all thisdata?
Are you meaning from thecandidate side or the employer
side?
So no, I mean.
Speaker 3 (17:29):
I'm saying first of all, employers, just don't ask
for irrelevant information fromyour candidates.
You don't need to do that.
Speaker 2 (17:34):
That is fair, even if we're trying to throttle, you
know you don't need to do that.
That is fair, Even if we'retrying to throttle.
Speaker 3 (17:37):
You know you don't need to do that.
There's just some pieces ofinformation that.
Does it really matter where Iwent to high school at Like?
Does it Well?
Speaker 2 (17:47):
if I want to judge you Does it?
Let me tell you something.
It depends.
Speaker 3 (17:51):
It was the same school that sent me a mailer for
my faux child.
Speaker 2 (18:01):
Are you serious?
No, no, I'm not.
I was like full circle here.
Speaker 3 (18:03):
That is great customization.
Public school, florida publicschool yeah.
Speaker 2 (18:06):
No, but that's a great point, Because if you're
asking for all this data andthen you're not keeping it clean
you're not keeping it clean,Then you're just making it messy
.
Speaker 3 (18:15):
And then what you're doing is you're actually putting
yourselves at risk too, like,think of, like, the impacts of a
data breach, you know.
Speaker 2 (18:20):
Oh my gosh.
So which are happening more andmore?
Speaker 3 (18:23):
No so, but there truly is a risk of you know, the
more data you get, the moreresponsible you are for securing
that data, and so you know whyask for information that you are
not going to use.
Speaker 2 (18:35):
So, and this is that you are not going to use, so,
and this is going off of that.
At what point do we give up theillusion of privacy and just say
selective access to my data?
Yeah, right, because whenyou're born, you're given a
social security number and, like, I still have my paper one.
(18:56):
I don't know if they still givethose out anymore.
I don't know either, still givethose out anymore.
I don't know either.
I have mine as well, but that Iguess it's like everything's
are being digitized and like ourwhole world is digital.
Yeah, so like, at what point dowe, like I I don't you know
what I mean like I'm a bigbeliever in privacy and right to
date like I, and that'sliterally an american right that
(19:19):
we believe I'm also a big fanof blissful ignorance too, and
that's kind of where I'm likeyeah security firm a few weeks
ago that grounded like a millionplanes
yeah, yeah, that was not from anattack, though.
That was poor coding, you're.
You're right, though, butthat's my point is saying we're
(19:41):
kind of living in this worldwhere, yeah, we believe in all
of this like data protection.
We still live in big data.
Our whole world exists becauseof data.
I don't know if that's a petpeeve or not.
It's just an interestingconsideration when we think
about data and privacy, andindividuals?
Speaker 3 (19:57):
It definitely is.
I did enjoy the times when Icould have a conversation in my
house with my husband and notget an ad for it on my cell
phone an hour later, but at thesame time I understand that I've
given that up because I haveAlexa and I have Google and I
talk to them and I tell themwhat to do, and I know my Siri's
listening to me, yeah, so youknow.
Speaker 2 (20:18):
I mean and see, and I do not have any of those, like
I don't have any smart speakersand I don't have Siri turned on,
like all the time, I still getthose ads.
Yeah, yeah, yeah, it's becausethere's somebody in, like all
the time I still get those ads.
Speaker 3 (20:31):
Yeah, yeah, yeah, mm-hmm, it's because there's
somebody in your closet, youjust don't know about them Dina.
Speaker 1 (20:36):
Dina I've got your data.
Speaker 2 (20:39):
I've got your data.
Speaker 3 (20:40):
Honestly, I would Surf up this ad guys.
I'm like as long as.
Speaker 1 (20:43):
Dina can hang out with me you can have my data,
it's fine.
Speaker 2 (20:52):
Last thing, I um, I I uh last thing, I'll say on this
, because I think that we'reprobably coming up on time.
No, I would say so, is it?
Um, it reminds me of parks andrecreation.
Okay, you watch that show.
I have you got ron swanson.
His like tipping point in thefinal season is like his whole
thing is his own, like dataprivacy, and it's like to the
extreme.
And it gives me so much joywhen he's just like living off
the grid and I'm like that.
That'd be nice man, I tell you.
Speaker 3 (21:12):
But also not.
So to be clear, I often I liketo watch the um doomsday movies,
just whatever they are you?
Know, apocalypse, all thosethings um, I would not make it
off the grid I am not making it.
I am not prepared for that typeof living.
Speaker 2 (21:26):
So I could, I think you could, but my thing is
there's a surviving and there'sa thriving, yeah, and I'm
willing to trade off my data forsome of those.
I don't know if it's right tocall it like a personal luxury,
but there is like existing inthe world the way we are today
doing the job I do.
I have certain social mediaaccounts that I want to delete
(21:49):
so badly.
I don't, can't.
Speaker 3 (21:54):
No For my job.
No, I get it.
Speaker 2 (21:54):
And that's, I get a trade off, and that's the,
that's my thriving.
There we go.
Cool, wonderful.
Any last thoughts on data.
Speaker 3 (22:01):
Or we started with pet peeves and then we just yeah
, we just rolled, so just don'tsend me stuff, because I'm a
woman of a certain age.
Speaker 1 (22:08):
Honestly.
Speaker 3 (22:09):
Please refine it a little bit more.
Get some originality.
People Send it to my husband.
Speaker 2 (22:15):
Honestly yes.
So I think if you're going tocustomize, there's certain
assumptions I'm comfortable withyou making but that is one
that's Just, it's so passe.
I will say, though, from amargin of error standpoint
you're never going to get thatExceptional that is you are
exceptional.
You are the exceptional to therule.
(22:37):
There we go, love it, love itWhit.
Oh, hot takes for hot topics.
Speaker 3 (22:49):
Well.
Speaker 1 (22:50):
I'm not sure about a hot take, but, um, the hot topic
obviously the uh olympic summergames just wrapped up in paris.
Speaker 2 (22:57):
So celine dion queen, forever I cried, love, I'm not
gonna lie.
Speaker 1 (23:02):
Um, I'm just gonna rattle off a few fun facts that
people may not have known, andum, we're gonna wrap it up with
a fun fact from Hillary.
Speaker 2 (23:12):
I like how we started , like what are you annoyed
about?
And now we're like hoorayOlympics, yeah.
Speaker 1 (23:19):
So fun fact number one is the opening ceremony for
the Paris Olympics drew nearly29 million viewers.
Speaker 3 (23:28):
So that's half as many as are listening to this.
So that's pretty cool.
Speaker 2 (23:32):
Okay, got it uh where's your data set on that
one, dina?
I'd love this is an emotionaldata response, well it's also
nearly half as many viewers asthe 2021 olympics that only drew
18 million viewers.
Speaker 3 (23:49):
Okay, interesting Twice as many.
Speaker 1 (23:52):
Twice as many.
Correction section I don't know.
Speaker 2 (23:57):
So people are a lot more interested in the Paris
opening ceremonies than in Tokyo.
Correct, interesting CelineDion.
Speaker 1 (24:06):
Yeah, that is.
Speaker 2 (24:07):
I mean honestly.
I think that's your answer.
Speaker 3 (24:09):
That dress.
It was a big deal yeah.
Speaker 1 (24:12):
Number two, after Team USA's Emma Weber lost her
swim cap during the women's 100meter breaststroke.
Speaker 2 (24:20):
That's the worst feeling as a swimmer.
Speaker 1 (24:22):
Oh no, this is great.
A hero came to call and he hasnow been dubbed Bob the Cap
Catcher.
He became a national icon.
Speaker 3 (24:31):
I miss I missed that.
Oh, I'll tell you, bob is likewe were thinking of having bob
come and speak here to thecompany how did no one tell me
about bob?
Oh my god, bob is such a bigdeal.
Speaker 1 (24:45):
I am excited yeah straight face um so you know how
the olympic winners they get alittle box with their medal.
Well, apparently inside is aposter designed by the parisian
(25:06):
illustrator who did the main art.
Um, it took him over 2 000hours to create that main poster
.
Speaker 2 (25:13):
That's a long time.
But here's the other thing themedals actually have a piece of
the Eiffel Tower in it.
Interesting, was that on yourlist?
No, but thank you, so I wasreading about it where, as they
do maintenance work, pieces ofthe Eiffel Tower will come off
and so they actually have bitsof the Eiffel Tower in the
medals Very cool.
(25:34):
Well, why don't?
Speaker 1 (25:35):
you end us off Hilary with how Flavor Flav made a
debut at the Paralympics.
Speaker 3 (25:41):
Wait, I don't want to let you end on that, because I
just want to do two things veryquick, okay, okay, shout out to
Team Rhythmic Gymnastics andshout out to Synchronized
Swimming.
Speaker 2 (25:51):
Out to Team Rhythmic Gymnastics and shout out to
Synchronized Swimming Bestsports ever Done.
I do really enjoy those,although I will say my favorite
sport to watch is I think thiswas from the last Olympics Snoop
.
Speaker 3 (26:06):
Dogg doing commentary for dressage, because I love
the horses.
Yeah, dressage is.
Speaker 2 (26:09):
Dressage Snoop Dogg, but um.
So I found this out and it islike my new favorite olympic
fact is that flaviflavepersonally sponsors the entire
women's water polo team.
I mean freaking awesome.
Like he found out that theywere working multiple jobs while
trying to train.
Yeah, he was like no, you guysare like literally the best in
(26:29):
the world.
So there are pictures of him.
If you look from from the, fromthe Olympics in Paris, he's got
his big like clock necklace butit's all like women's water
polo branded.
Oh my God, it's amazing,fantastic.
Speaker 3 (26:38):
I love it.
Thank you, Flava Flav.
Oh, if you want to be a gueston here, you can.
He's already a guest.
He's coming.
Speaker 2 (26:43):
He is.
Speaker 3 (26:45):
Okay, good, god, we've got a packed agenda.
Man, things have really pickedup here 58 million viewers,
flavor, flav, it's true.
Bob the Cap Catcher.
Speaker 1 (26:56):
Hillary and I are going to do.
Speaker 3 (26:57):
Celine Dion is coming , lady Gaga, gaga, pet peeve.
We're weird, yeah, or like Idon't know, delightful Gem.
I think both yeah, something inbetween you, I don't know
delightful gem.
Speaker 2 (27:11):
I think both.
Yeah, Something in between.
You know what, though?
Data does this to you?
Speaker 3 (27:15):
It really does.
Speaker 2 (27:16):
It gets you really excited about life.
It really does Opens up a lotof possibilities.
Speaker 3 (27:19):
It does, yeah, it's a good time, great times, tina,
speaking of great times, greattimes, uh-huh.
Thank you, oh, hillary, mypleasure, thank you Okay.
Speaker 2 (27:29):
Till next time, till we meet again.
Okay, bye.
Welcome to Real Talk on Talent, a human resources
podcast where we talk abouttalent acquisition, recruiting
and all things hiring.
Hi, hi, dina, how are you?
I'm good, welcome back.
Hey, thank you, great to behere.
Are you all springy in themiddle of summer?
You know, I like my pink, Ilike my pink.
(00:29):
I'm here for it.
Yeah, which we need some ofthis, because I would like to
today to just complain, oh, youknow what Will you join me on
this journey?
Speaker 3 (00:39):
Let's do it.
Speaker 2 (00:47):
I'm so for um, yeah, and, but it can be productive
complaining.
It's purposeful.
Purposeful if we're pointingout potential issues or red
flags, because today is data.
This is perfect, which and Ithink last time when we talked
about data we had like clowingreviews.
We're like, we love data, lovereporting love it, we love it,
we do.
Speaker 3 (01:00):
Let's talk about when it goes bad, though, the worst.
Yes, it is so true, though Imean it definitely goes bad.
Speaker 2 (01:07):
Yeah, it can go bad, it can go because data is
there's so much like we talkabout big data and how like we
went through this whole era oflike the more data, the better
get it, splice it, have it.
But there are some realpitfalls there are absolutely
Collecting, using understanding,can be a real pain.
Speaker 3 (01:29):
So yeah, I mean, we know we love it, but we also
hate it.
Speaker 2 (01:33):
Kate, you want to start Give me?
Give me a pet peeve when itcomes to data.
Speaker 3 (01:37):
OK, just one?
Well, we'll start with one.
Ok, so first data withoutcontext.
Ok, tell me more.
Just don't just throw somethingat me and expect me to
understand how this fits intothe bigger picture.
So a lack of analysis.
So a lack of analysis.
We know this about data.
Anybody can splice the data tomake it tell the story that they
(01:58):
want.
Speaker 2 (01:59):
for the most part, Well, because if you have enough
data, you can to your point,you can pull out the pieces that
tell different stories.
Speaker 3 (02:06):
Yes, so I'll just pulling it back to what we're
here to talk about, or what thispodcast is.
Speaker 1 (02:12):
Complaining about data.
Speaker 3 (02:13):
Complaining about data.
No, like human resources, ohyeah, okay, just to be like.
Oh well, our turnover rates 27%, okay, okay, is that good, is
that bad?
Speaker 1 (02:23):
How's that?
Speaker 3 (02:23):
compare what's industry.
Give me context.
Yeah, there are moments Justlike don't just give me a data
point, I need context around.
Or like not having a baseline.
Speaker 2 (02:32):
Baseline, like where are you today, where were you
yesterday, where were you a weekago?
Where do you want to be, likeyeah.
Speaker 3 (02:39):
All those things.
Speaker 2 (02:39):
I also kind of going along with that.
I really get frustrated whenyou are arguing data versus
anecdotal opinions and I hadthis whole experience once where
I spent weeks, literally weeks,arguing with a company.
(03:00):
It was kind of arguing, butthey-.
Speaker 3 (03:03):
Debating is what we call that.
Speaker 2 (03:05):
I was telling them they were wrong and they didn't
agree with me Hillary wasn't onthe debate team.
That was not you were.
I needed you there because ifyou'd been there it wouldn't
have taken so long.
But it was so frustratingbecause we helped set up a new
ATS and then the hiring managerstarted complaining that they
weren't getting enough.
They weren't getting candidatesanymore, and so I went I said
(03:27):
okay, well, tell me.
They said well, we used to posta job and get hundreds of
applies for hard to fillpositions.
And I went back to all thehistorical data.
I was like that's not true.
One, it's not true.
And two, you're actuallygetting better candidates,
better, like the data shows thatit's there.
And they're like well, no, ourhiring managers aren't happy.
And I was like I don't knowwhat to tell you, because if
(03:48):
here are the numbers, I pulledthem straight from the job
boards and you even get in weekswhere they literally like no,
yeah, no, this isn't true.
And I was like I don't know.
Speaker 3 (04:01):
So you know.
It's so interesting that yousay that.
Because, so you know, it's sointeresting that you say that?
Because data often overlooksthe qualitative feelings, the
softer feelings, the moresubjective feelings, so to speak
If you were, if you were, andso people will often lean on how
they feel as an out for whatdata is.
(04:22):
So, with data, it is importantthat you have a champion and
somebody who is there to beanalytical, but you do also have
to factor in the people part.
Speaker 2 (04:33):
You do and it kind of reminds me of, I think, last
time.
Or you talked about the gutcheck.
Like, gut check, yeah, you usethe data to gut check, or you
use data to validate or disprovethe gut check, because there is
value in saying like, okay,what's my human intuition,
what's my experience telling me?
But if you get to a point whereit's like no, I reject all of
(04:57):
the data you're showing me, thenit's like that's my frustration
.
One plus one does not equal two.
Why am I here?
Yeah, why am I here For thispodcast?
No, in that conversation.
Speaker 3 (05:07):
I know, I know I get it, I get it.
Yeah, anyway, you know that's awhole different scenario it is.
We'll talk about how to sell topeople your narrative.
I'm surprised, that's kind ofyour thing.
You're the marketing person.
Speaker 2 (05:21):
That.
That's why I was so frustratedbecause I was like look.
Speaker 3 (05:23):
Anyway.
Speaker 2 (05:24):
Yeah, one thing as a marketer is we do have to.
There's this constant balancingact of like you collect data
and you analyze data.
Speaker 3 (05:34):
Yeah, what do you do with?
Speaker 2 (05:35):
that?
What do you do with it?
And so over the past couple ofyears, as more countries and
more states have put in privacylike right to privacy, right to
forget laws and stuff, that hasbeen something that's been, as a
professional, a littlefrustrating, but at the same
time, personally, I love it.
Okay, tell me why?
Because I do really enjoy thefeeling of anonymity even though
(05:59):
in our world.
Is there really?
Speaker 3 (06:01):
true anonymity, but believe it.
Speaker 2 (06:03):
One of my favorite stories on like the misuse of
data is there's a example withTarget and I'm not throwing them
under the bus Like this is verywell known.
It's a great case study.
But the story goes that afather got really ticked off at
Target because Target started tosend his teenage daughter like
(06:24):
baby coupons for like diapersand strollers and he's like, and
he goes.
He goes and complains to target.
He said what are you doing?
I have, you know, I have ateenage daughter.
Why are you sending her all ofthese baby related things?
That's completely inappropriate.
We'll come to find out.
His daughter was pregnant andbecause of her buying patterns
(06:45):
and Target actually has they'vesaid it's something like if you
start buying a certain type ofvitamin and unscented lotion and
like one other thing they foundthat that buying behavior tends
to three, six months down theroad, start to buy all of the.
So Target knew this because oftheir data related to buying
(07:06):
patterns.
So they used very targetedmarketing, perhaps a little too
targeted.
Speaker 3 (07:12):
Way too targeted, way too targeted yeah.
Speaker 2 (07:14):
And so it's.
I think it's one of the mostfascinating stories on.
Oh no, that's reallyinteresting, not just like what
do you do with data, but likethe stories.
You who would have thoughtthat's crazy, like three
completely unrelated things thatthey found were one of the
first trigger points for movinginto a pregnant person.
Speaker 3 (07:32):
Yeah, very interesting.
Speaker 2 (07:34):
So be careful with data.
I guess is where I'm going onthat.
Speaker 3 (07:36):
Or don't sign up for any of the memberships where
they track what you're buying.
They track what you buy anyway.
I like to pretend anonymity.
I like my coupons too.
I will say Good deal, Like areyou a member?
No, oh, you should be.
Hey, they track what I'm buying.
Speaker 1 (07:50):
So they always send me great coupons.
Speaker 2 (07:54):
So one of the things because I studied the Target
case study in school and talkedabout it many times in my
professional career what they donow and this might be tall tale
, but I pretend it's truth is,instead of sending, like, all
baby coupons, now they'll havetheir standard, but they'll
start to insert more babyrelated coupons, so you don't
(08:14):
know you're being targetedbecause people, they want the
convenience of customizationwithout feeling like you know
everything about them.
Speaker 3 (08:24):
The illusion of privacy.
Listen.
We love the illusion of privacyListen.
Speaker 2 (08:26):
we love the illusion of privacy Listen we just sell
our data to get things for freelike Gmail, oh yeah.
Speaker 3 (08:32):
Totally free?
Totally fine?
Yeah, that's fine.
And then serve me up the adsfor what I want yeah.
And then I get mad at Instagram.
I'm like dang it, instagram,you're making me shop too much,
but that's the thing, but that'sthe thing?
Speaker 2 (08:43):
Okay, my own fault.
Okay, what about you, weirdest?
Speaker 3 (08:48):
targeting you've had.
So I keep getting stuff forprivate school for children.
Okay, I do not have children, Ihave dogs.
Speaker 1 (08:57):
I was going to say does Buddy need to go to private
school?
Speaker 3 (08:59):
My mutt needs to go to private school and I'm like,
why am I?
Continuously getting pieces forprivate school education.
Maybe they're trying to get youto go back to school.
Maybe I don't think they wantme in an elementary school,
though, teacher yeah, maybe Idon't have the patience for that
.
Speaker 2 (09:15):
No.
Speaker 3 (09:16):
Yeah, that is kind of a weird one.
Yeah, I get that, do you think?
Speaker 2 (09:19):
of anything in your life that would have set that
off.
Speaker 3 (09:22):
I mean my guess is somebody is just doing like very
generic, unthoughtfuldemographic screening and going
oh, this is a woman in her lateto mid mid 30s.
Speaker 2 (09:33):
Just own it Dina.
Speaker 3 (09:35):
You're not a millennial.
We could do the math this is awoman in her early 40s.
She's probably has kids goingto wherever.
Speaker 2 (09:40):
Yeah, that's a good point.
Speaker 3 (09:41):
And I'm like oh, be more thoughtful, okay so that's
a question, so be morethoughtful.
Speaker 2 (09:46):
So you're grumpy that they're not more thoughtful,
exactly.
Speaker 3 (09:49):
Why isn't my husband getting that piece of direct
mail?
Speaker 2 (09:51):
That's what I want to know that is a great question.
Don't send it to me.
Speaker 3 (09:56):
That's a better question than the age one.
Don't send it to me, send it tohim, send it to him.
You know what I startedthinking about on the data thing
.
So you know I know before wewere talking about AI and we
were talking about some of thebiases with it, and now I'm not
a machine, so I don't understandmachine learning.
But what I want to know is whatare the different factors that
(10:21):
are going into all of theconsiderations?
Like what is every data pointthat it's looking at when it's
deciding whether or not acandidate is good for you?
Speaker 2 (10:32):
I mean, we don't have time to get into that, I know,
but like is there like.
Speaker 3 (10:36):
I mean again, I don't know machine learning, I don't
understand this you know AI, butis it like.
Speaker 2 (10:41):
All the algorithms, so AI and machine learning and
like they're all differentlevels and variations of it,
right?
So if we're going to thinkabout the I'm going to speak to
the customization of anindividual within like a
recruiting process, is that kindof where you're thinking on
that?
Okay, maybe, okay, so there area couple of ways that it will
(11:06):
look and make those educateddecisions.
Very often it's like the targetexample.
It's like if you see this kindof behavior or a data point,
what is the most common outcomeor behavior that will come after
that and so there's a levelwhere you can say individuals
who have this on their resume orwho visit your website five
times in a week are more likely.
(11:28):
There's that predictivebehavior, that of saying if they
take these behaviors, they'remore likely to take those
behaviors.
So you should prioritize youroutreach, your sourcing, your
conversation or, going back tothe customization, then you can
serve up something that says ifsomeone has visited your website
five times in the past week,they're probably closer to the
(11:50):
decision-making stage.
So start serving up reallyspecific content and so it'll
make decisions based on thatbehavior piece.
There's also the whole idea ofpixel tracking and cookies to
say there are a lot of companieswhere they'll say we'll put a
pixel on your website and thenwe will track where your users
(12:12):
are elsewhere.
So I actually used this before,where I had access to a
dashboard.
That said, I actually used thisbefore, where I had access to a
dashboard.
That said, the people that cometo your website tend to be in
this age demographic.
They tend to watch these typesof TV shows, they tend to drive
these types of cars Like whatcars they drove?
Yeah, all from the behavior oftheir online.
(12:33):
And I've kind of diverted fromyour original question.
Speaker 3 (12:35):
No, no, but that's super interesting.
Speaker 2 (12:36):
But if you have that, then all of a sudden you can
say, okay, well, if someone is a41-year-old, thank you.
Like married woman who recentlypurchased a larger car.
Speaker 3 (12:53):
Ah, mm-hmm.
Speaker 2 (12:55):
Maybe she needs to ship her kids off to private
school, and that's kind oftaking the data and doing like a
okay persona.
Yeah, adjustment.
So there's the behavior andthere's the persona.
Okay, interesting veryinteresting.
Speaker 3 (13:06):
At what point, like does the data go bad?
Speaker 2 (13:09):
so you know, like that's what.
What about?
I'm gonna ask, put it back toyou from a recruiting
perspective yeah, that that'swhat I was thinking about.
Speaker 3 (13:17):
So you know, one thing that we do is we have
people who've applied for goshany job we've had posted for who
knows how long.
So we've got a massive database.
Most companies do, and you cango back in and you can source
for candidates based on keywords, kind of like your basic
Boolean strings.
But, like, at some point thedata is just too old, it just
it's not relevant anymore.
(13:39):
What somebody did 12 years agoisn't really applicable for what
I'm looking for, I think.
Hubspot.
Speaker 2 (13:43):
I read this a year ago I think, so it's probably
changed and I may have thenumber wrong.
Whitney, let's find the rightone.
Correction section potentiallythat every year or every six
months your database depreciatesby like 30%.
(14:03):
So in that time frame, 30% ofyour information is no longer
relevant.
So I guess that's why you haveto maintain your database, you
have to keep it clean, you haveto audit what's in there.
Maybe that's.
The other thing that's annoyingis when people are working off
of poor data.
Speaker 3 (14:22):
Yes, so love that.
You say that I was doing aHubSpot cleanup earlier today.
Okay, and I'm sitting theregoing at it.
I'm like, oh my gosh, I justcan't take it.
It's such a mess.
Speaker 2 (14:32):
It's such a mess.
So there really absolutelyorganized and clean.
Speaker 3 (14:35):
Flawless, super clean , a plus to all of our users.
No, no, not at all, not at all.
Myself included.
Yeah, okay, that's why you weredoing the cleanup.
Speaker 2 (14:49):
Yeah, exactly, Exactly.
That is actually not a petpeeve.
I am super proud of anytimesomeone goes into a system and
they're like I'm just going todo a little bit of cleanup at a
time.
Oh so kudos to you.
Speaker 3 (14:54):
Thank you so pet peeve of mine is dirty data.
Like I will, I will get thisand I will be like and I'll call
like an all hands meeting andI'll be like are you aware that
you do not have the first andlast name, you only have their
email address.
That is not acceptable.
So that's how you look at it islike you do the visual check
yes, I do the visual check andI'm like how many missing pieces
are we have?
(15:14):
You know?
I mean just simple things likehere on data.
You never want to send out anemail to hello.
First name.
Speaker 2 (15:23):
Don't do it.
Yes, oh, you know what?
Honestly, don't do it.
The bane of a marketer'sexistence Like hello, friend,
yes, yeah, or then what reallygrinds my gears is then it's my
fault as a marketer, oh yeah,that's my.
Other thing is like when itcomes to data and data usage and
customization, it's a teameffort.
Speaker 1 (15:47):
It is a team effort, I will give human.
Speaker 2 (15:48):
Tons of credit for this.
Yeah, sales and marketing worktogether flawlessly.
We truly are just trying toprovide a great experience to
everyone who interacts with us.
I concur, and so don't blame meif the data is bad, if I don't
own the data.
Yeah, I'm trying here.
I get it.
I get it.
I'm trying to customize withoutnot being creepy.
Speaker 3 (16:07):
So you know what?
So here's another thing.
This is where I think companiesget themselves into trouble.
It's trying to get too muchdata and data that they're not
going to use.
And we'll actually we'll takethis back to a recruitment
perspective.
Going back to an application,like you know people, some
people have super tediousapplications where you have to
complete ABC who's your cousin,joe's first reference and what
was your second car?
(16:27):
No, the worst.
Speaker 2 (16:28):
Oh, sorry, side note.
Please upload your resume.
Great, upload my resume Now.
Fill out every single field ofall the information that you
just uploaded on your resume.
Speaker 3 (16:39):
Yeah, so like why do you need all this?
What are you actually doingwith this information?
So that is a pet peeve of mineis when people just ask for more
information than is necessaryand they're getting data that
they are not going to use foranything.
Speaker 2 (16:56):
Well, so let me ask you this, because we talked
about this.
We talked about, like,throttling the pipeline I think
it was our last processdiscussion where it's like you
increase the burden on yourcandidates to throttle it.
Should you give context as towhy you're asking for certain
types of information in anapplication?
Speaker 3 (17:18):
No, no, no, okay, no, that just makes the application
longer.
Speaker 2 (17:23):
Well, I'm just thinking you were like saying
what are you doing with all thisdata?
Are you meaning from thecandidate side or the employer
side?
So no, I mean.
Speaker 3 (17:29):
I'm saying first of all, employers, just don't ask
for irrelevant information fromyour candidates.
You don't need to do that.
Speaker 2 (17:34):
That is fair, even if we're trying to throttle, you
know you don't need to do that.
That is fair, Even if we'retrying to throttle.
Speaker 3 (17:37):
You know you don't need to do that.
There's just some pieces ofinformation that.
Does it really matter where Iwent to high school at Like?
Does it Well?
Speaker 2 (17:47):
if I want to judge you Does it?
Let me tell you something.
It depends.
Speaker 3 (17:51):
It was the same school that sent me a mailer for
my faux child.
Speaker 2 (18:01):
Are you serious?
No, no, I'm not.
I was like full circle here.
Speaker 3 (18:03):
That is great customization.
Public school, florida publicschool yeah.
Speaker 2 (18:06):
No, but that's a great point, Because if you're
asking for all this data andthen you're not keeping it clean
you're not keeping it clean,Then you're just making it messy
.
Speaker 3 (18:15):
And then what you're doing is you're actually putting
yourselves at risk too, like,think of, like, the impacts of a
data breach, you know.
Speaker 2 (18:20):
Oh my gosh.
So which are happening more andmore?
Speaker 3 (18:23):
No so, but there truly is a risk of you know, the
more data you get, the moreresponsible you are for securing
that data, and so you know whyask for information that you are
not going to use.
Speaker 2 (18:35):
So, and this is that you are not going to use, so,
and this is going off of that.
At what point do we give up theillusion of privacy and just say
selective access to my data?
Yeah, right, because whenyou're born, you're given a
social security number and, like, I still have my paper one.
(18:56):
I don't know if they still givethose out anymore.
I don't know either, still givethose out anymore.
I don't know either.
I have mine as well, but that Iguess it's like everything's
are being digitized and like ourwhole world is digital.
Yeah, so like, at what point dowe, like I I don't you know
what I mean like I'm a bigbeliever in privacy and right to
date like I, and that'sliterally an american right that
(19:19):
we believe I'm also a big fanof blissful ignorance too, and
that's kind of where I'm likeyeah security firm a few weeks
ago that grounded like a millionplanes
yeah, yeah, that was not from anattack, though.
That was poor coding, you're.
You're right, though, butthat's my point is saying we're
(19:41):
kind of living in this worldwhere, yeah, we believe in all
of this like data protection.
We still live in big data.
Our whole world exists becauseof data.
I don't know if that's a petpeeve or not.
It's just an interestingconsideration when we think
about data and privacy, andindividuals?
Speaker 3 (19:57):
It definitely is.
I did enjoy the times when Icould have a conversation in my
house with my husband and notget an ad for it on my cell
phone an hour later, but at thesame time I understand that I've
given that up because I haveAlexa and I have Google and I
talk to them and I tell themwhat to do, and I know my Siri's
listening to me, yeah, so youknow.
Speaker 2 (20:18):
I mean and see, and I do not have any of those, like
I don't have any smart speakersand I don't have Siri turned on,
like all the time, I still getthose ads.
Yeah, yeah, yeah, it's becausethere's somebody in, like all
the time I still get those ads.
Speaker 3 (20:31):
Yeah, yeah, yeah, mm-hmm, it's because there's
somebody in your closet, youjust don't know about them Dina.
Speaker 1 (20:36):
Dina I've got your data.
Speaker 2 (20:39):
I've got your data.
Speaker 3 (20:40):
Honestly, I would Surf up this ad guys.
I'm like as long as.
Speaker 1 (20:43):
Dina can hang out with me you can have my data,
it's fine.
Speaker 2 (20:52):
Last thing, I um, I I uh last thing, I'll say on this
, because I think that we'reprobably coming up on time.
No, I would say so, is it?
Um, it reminds me of parks andrecreation.
Okay, you watch that show.
I have you got ron swanson.
His like tipping point in thefinal season is like his whole
thing is his own, like dataprivacy, and it's like to the
extreme.
And it gives me so much joywhen he's just like living off
the grid and I'm like that.
That'd be nice man, I tell you.
Speaker 3 (21:12):
But also not.
So to be clear, I often I liketo watch the um doomsday movies,
just whatever they are you?
Know, apocalypse, all thosethings um, I would not make it
off the grid I am not making it.
I am not prepared for that typeof living.
Speaker 2 (21:26):
So I could, I think you could, but my thing is
there's a surviving and there'sa thriving, yeah, and I'm
willing to trade off my data forsome of those.
I don't know if it's right tocall it like a personal luxury,
but there is like existing inthe world the way we are today
doing the job I do.
I have certain social mediaaccounts that I want to delete
(21:49):
so badly.
I don't, can't.
Speaker 3 (21:54):
No For my job.
No, I get it.
Speaker 2 (21:54):
And that's, I get a trade off, and that's the,
that's my thriving.
There we go.
Cool, wonderful.
Any last thoughts on data.
Speaker 3 (22:01):
Or we started with pet peeves and then we just yeah
, we just rolled, so just don'tsend me stuff, because I'm a
woman of a certain age.
Speaker 1 (22:08):
Honestly.
Speaker 3 (22:09):
Please refine it a little bit more.
Get some originality.
People Send it to my husband.
Speaker 2 (22:15):
Honestly yes.
So I think if you're going tocustomize, there's certain
assumptions I'm comfortable withyou making but that is one
that's Just, it's so passe.
I will say, though, from amargin of error standpoint
you're never going to get thatExceptional that is you are
exceptional.
You are the exceptional to therule.
(22:37):
There we go, love it, love itWhit.
Oh, hot takes for hot topics.
Speaker 3 (22:49):
Well.
Speaker 1 (22:50):
I'm not sure about a hot take, but, um, the hot topic
obviously the uh olympic summergames just wrapped up in paris.
Speaker 2 (22:57):
So celine dion queen, forever I cried, love, I'm not
gonna lie.
Speaker 1 (23:02):
Um, I'm just gonna rattle off a few fun facts that
people may not have known, andum, we're gonna wrap it up with
a fun fact from Hillary.
Speaker 2 (23:12):
I like how we started , like what are you annoyed
about?
And now we're like hoorayOlympics, yeah.
Speaker 1 (23:19):
So fun fact number one is the opening ceremony for
the Paris Olympics drew nearly29 million viewers.
Speaker 3 (23:28):
So that's half as many as are listening to this.
So that's pretty cool.
Speaker 2 (23:32):
Okay, got it uh where's your data set on that
one, dina?
I'd love this is an emotionaldata response, well it's also
nearly half as many viewers asthe 2021 olympics that only drew
18 million viewers.
Speaker 3 (23:49):
Okay, interesting Twice as many.
Speaker 1 (23:52):
Twice as many.
Correction section I don't know.
Speaker 2 (23:57):
So people are a lot more interested in the Paris
opening ceremonies than in Tokyo.
Correct, interesting CelineDion.
Speaker 1 (24:06):
Yeah, that is.
Speaker 2 (24:07):
I mean honestly.
I think that's your answer.
Speaker 3 (24:09):
That dress.
It was a big deal yeah.
Speaker 1 (24:12):
Number two, after Team USA's Emma Weber lost her
swim cap during the women's 100meter breaststroke.
Speaker 2 (24:20):
That's the worst feeling as a swimmer.
Speaker 1 (24:22):
Oh no, this is great.
A hero came to call and he hasnow been dubbed Bob the Cap
Catcher.
He became a national icon.
Speaker 3 (24:31):
I miss I missed that.
Oh, I'll tell you, bob is likewe were thinking of having bob
come and speak here to thecompany how did no one tell me
about bob?
Oh my god, bob is such a bigdeal.
Speaker 1 (24:45):
I am excited yeah straight face um so you know how
the olympic winners they get alittle box with their medal.
Well, apparently inside is aposter designed by the parisian
(25:06):
illustrator who did the main art.
Um, it took him over 2 000hours to create that main poster
.
Speaker 2 (25:13):
That's a long time.
But here's the other thing themedals actually have a piece of
the Eiffel Tower in it.
Interesting, was that on yourlist?
No, but thank you, so I wasreading about it where, as they
do maintenance work, pieces ofthe Eiffel Tower will come off
and so they actually have bitsof the Eiffel Tower in the
medals Very cool.
(25:34):
Well, why don't?
Speaker 1 (25:35):
you end us off Hilary with how Flavor Flav made a
debut at the Paralympics.
Speaker 3 (25:41):
Wait, I don't want to let you end on that, because I
just want to do two things veryquick, okay, okay, shout out to
Team Rhythmic Gymnastics andshout out to Synchronized
Swimming.
Speaker 2 (25:51):
Out to Team Rhythmic Gymnastics and shout out to
Synchronized Swimming Bestsports ever Done.
I do really enjoy those,although I will say my favorite
sport to watch is I think thiswas from the last Olympics Snoop
.
Speaker 3 (26:06):
Dogg doing commentary for dressage, because I love
the horses.
Yeah, dressage is.
Speaker 2 (26:09):
Dressage Snoop Dogg, but um.
So I found this out and it islike my new favorite olympic
fact is that flaviflavepersonally sponsors the entire
women's water polo team.
I mean freaking awesome.
Like he found out that theywere working multiple jobs while
trying to train.
Yeah, he was like no, you guysare like literally the best in
(26:29):
the world.
So there are pictures of him.
If you look from from the, fromthe Olympics in Paris, he's got
his big like clock necklace butit's all like women's water
polo branded.
Oh my God, it's amazing,fantastic.
Speaker 3 (26:38):
I love it.
Thank you, Flava Flav.
Oh, if you want to be a gueston here, you can.
He's already a guest.
He's coming.
Speaker 2 (26:43):
He is.
Speaker 3 (26:45):
Okay, good, god, we've got a packed agenda.
Man, things have really pickedup here 58 million viewers,
flavor, flav, it's true.
Bob the Cap Catcher.
Speaker 1 (26:56):
Hillary and I are going to do.
Speaker 3 (26:57):
Celine Dion is coming , lady Gaga, gaga, pet peeve.
We're weird, yeah, or like Idon't know, delightful Gem.
I think both yeah, something inbetween you, I don't know
delightful gem.
Speaker 2 (27:11):
I think both.
Yeah, Something in between.
You know what, though?
Data does this to you?
Speaker 3 (27:15):
It really does.
Speaker 2 (27:16):
It gets you really excited about life.
It really does Opens up a lotof possibilities.
Speaker 3 (27:19):
It does, yeah, it's a good time, great times, tina,
speaking of great times, greattimes, uh-huh.
Thank you, oh, hillary, mypleasure, thank you Okay.
Speaker 2 (27:29):
Till next time, till we meet again.
Okay, bye.
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