WEBVTT

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As First Lady, she was an
active part of the administration, serving as

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the President's personal emissary to Latin American
countries and even sitting in on cabinet meetings.

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Intense negotiations continue on a possible deal
to release dozens of hostages held by

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Hamas. Appearing on NBC's Meet the
Press, Deputy National Security Advisor John Finer

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says it's the closest they've come to
reaching an agreement. Finer caution that nothing

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has been finalized, but some of
the hostages could be released in in exchange

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for a pause in the fighting.
The Thanksgiving travel rushes officially underway. Triple

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A estimates about fifty five and a
half million people will travel fifty miles or

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more from home for the holiday.
Tammy Triheo, NBC News Radio KILLISTINAKCAA Lowolinda

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and one O six point five FM
K two ninety three CF Marino Valley.

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The information economy has arrived. The
world is teeming with innovation as new business

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models reinvent every industry industry. Inside
Analysis is your source of information and insight

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about how to make the most the
exciting new era. Learn more and inside

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analysis dot com. Inside Analysis dot
Com and now here's your host, thro

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Eric Kavanaugh. All Right, ladies
and gentlemen, it's time but the only

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coast to coast radio show that's all
about the information economy. It's called Inside

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Analysis. Your host here, Eric
Kavanaugh, and I am so excited to

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have an all star cast today.
We've got the Malcolm Chisholm, who's got

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the receipts on LinkedIn promotions. We've
got the data Scott Taylor, who of

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Meta Meta Consulting. He's amazing,
and less but not least, we have

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Kidstration Data Kate. She knows where
you live, she knows where your data

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lives. She will find you.
I suggest you not to make her unhippie.

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Unhippie. That's what my Russian boss
used to always say about the people

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who he had stiffed on bills.
They are unhippie. That was his favorite

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term. We're talking all things data. Today's to be a fantastic show and

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let's not waste any time. I
saw this hilarious quote from one of my

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favorite people in the business, Steve
Lucas said data is the new sand I

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thought that was hysterical because everyone talks
about data is the new oil. You

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have to refine it. It doesn't
aval with your find Oh okay, we

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know all that. But data is
the new sand. It's like just gritty

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gets on your shoes, winds up
in your car. You can't ever vacuum

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it all the way out. It's
just forever with you, little tiny bits

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of sand. But it makes sense
because if you refine it, you can.

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If you have a last blower,
for example, maybe you could do

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something with that and swip that sand. But let's throw it over to Kate

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and Stretch of Dedicated. Tell us
a bit about yourself and what you think

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about the importance of data. Yes, absolutely, thanks for having me on

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the show. Hello everybody. I'm
Kate Strashn. I can't do the accent

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as good as aerage cant for Russia, but I am the founder of Dedicated.

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It's a media company that's focused on
helping data, analytics, machine learning

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and AI companies reach their audience on
link. Then I help out with brand

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awareness. I've got my own show
called Dedicated on air, which is why

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I love Eric's background. That says
on air reminds me of my show as

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well. And you know, Eric, I wanted to point out when you

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were talking about sand. For some
reason, I started thinking of how data

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is the new water, because I've
heard of that, right, all the

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pipelines and cleaning the water and making
sure it all makes sense. Then I

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thought of mixing sand and water.
But then I remembered that I recently put

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together a video where I actually built
a sand castle to demonstrate the importance of

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data infrastructure. It was for one
of my clients. They wanted to talk

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about how if you build on poor
infrastructure, then all your apps go to

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crap basically, and it just doesn't
work well. And so I built like

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a really bad sand castle, and
then I scrapped it and built a much

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better one. I had so much
fun, and my kids are like,

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you get to do this for work, and I'm like, yeah, this

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is what I do for a living. And I explained it to my family.

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My brother's like, I still don't
know what you do. And I'm

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like, well, I got paid
to make a sand castle today, and

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he's like, can you just keep
making them? Like I'm like, no,

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it doesn't work that way. It's
all about the concept. But yeah,

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data, I think data is extremely
important. I think your question was

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what I think about data data is
literally my life. I even have a

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license plate on my car that literally
just says data, which gets a lot

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of attention from passerbys because they start
asking like, why do you like data

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so much? So it's it's been
fun. That's good stuff. And of

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course you help people get the word
out about their own personal brands. So

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you're passionate about brand, You've got
to find job branding yourself, by the

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way, dedicated, I mean,
come on, that's good stuff, right,

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dedicated? Yes, thank you,
thank you. Yeah, I love

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I love talking about personal branding.
I've got a course out on LinkedIn Learning

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on that topic as well. Or
people. I think it's like a forty

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five minute course of everything I know
about personal branding. I compacted it really

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really tight into a very short course. But yeah, I think personal brand

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is extremely important in what in any
area of data, data management, data

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analytics, data science. Now AI, right, it's all about standing out,

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especially with AI sort of doing all
of our work for us. A

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lot of times, the more personal
touch we can put on our work,

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the better. That's interesting. And
you had mentioned before the show that we'd

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be remiss where we can not mention
generative AI. So you've now hinted it

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generative AI, and I think the
key to success with AI is going to

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be wait for it, data,
right, your good quality data. In

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fact, let me throw this at
you and see what you think. I

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believe that AI, in a particular
large language models represent a second chance for

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data. And what I mean by
that is the first chance we did data

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warehousing and analytics and business intelligence and
visualization and all that stuff. We moved

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it around. We use master data
management to try to reconcile systems. We've

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done all these different things, essentially
torturing the data to make it say what

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we want it to say. But
it's been very expensive. There has been

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certainly value for that. But I
think that this inflection point we're at right

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now is not small. I think
this is a very significant transformation and that

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AI will give a second chance to
data, by which I mean, if

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you feed your AI model, your
corporate AI model, I think every big

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company is going to have one.
With the most trusted, carefully curated data,

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you're going to get a very good
result, and if you don't,

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you're just going to get some random
nonsense. What do you think? Yeah,

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I was going to say it gives
it a second chance, and hopefully

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companies and people take the second chance
to feed it clean data and don't lose

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focus and don't just focus on,
oh, look what we can do with

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AI. Now. I think going
back to data management and it's important,

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and making sure that you focus on
data quality and data governance, and then

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that you're feeding this large language model
good cookies, not crappy cookies, not

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junk food, and making sure you
actually clean it all up. Going back

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to the sandcastle analogy. You know, if I use sand with twigs and

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random stuff and wrappers and cigarette butts
in there, the sandtastle wouldn't be pretty,

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it wouldn't hold up well versus if
I used high quality sand. So

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sand and data could be a thing. Actually, you got me thinking,

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I like that. I like that, and sand doesn't need any branding.

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I think everyone knows what sand is. We've all walked on the beach.

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But to the point of my one
body on the LinkedIn platform, they said,

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you can create beautiful artwork with sand
if you are very careful about what

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you're doing to it. But again, like with data, it has to

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have purpose, it has to have
contacts. I think part of the challenge

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here, and I'll throw this at
you and then I'll maybe bring in the

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whisperer the comment on it. But
you think about the old days of data

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warehousing and how old habits die hard, and you had to strip out a

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lot of contexts in order to get
the little bits of transactional data through thin

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pipes to slow processors and expensive storage. Well, now the pipes are fat,

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the processors are blazing speed, you
can parallelize them. Storage is cheap,

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So every thing has changed in terms
of the data gain, which is

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the big is why we went to
the data lake. But again we thought,

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oh, we're going to put it
on in one place, which is

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probably a bad idea. I think
data should live where it is best used

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and accessed as needed. But what
do you think about this evolution and how

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old habits die hard? How do
you get your clients to make those old

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habits die Yeah, I think we're
still evolving, and I think we are

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moving in the right direction, and
the closer we can get to keeping that

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business context of that data as we
allow access to the data is extremely important.

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So I'm all about data literacy,
data accessibility, data democratization, making

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sure people have the right access.
But I think with the change in the

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progress in the data warehousing and cloud
now we make it easier for people to

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actually access not just the data,
but data in the right context for the

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right individual with personalization. So I
think we're definitely moving in that right direction.

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That's good. Last thing I'll throw
at you, can you to find

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data mesh data mesh? No good
answer needs you that what you have?

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My dog upstairs is trying to define
data right now and he's not very happy

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about it. I have two German
shepherds, and of course they chose this

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moment to chime in because they're trying
to figure out what data mesh is all

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about as well. Well, let's
let's hand it over to Scott Taylor,

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the proverbial data whisper of Meta Meta. And you've been talking about data and

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the business value of data for a
long time, and the deal with data

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Mesh is you want the business groups
to govern their own data. To help

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with that process, But tell us
a bit about your thoughts and the value

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of data on the data mesh.
You hear about the data mesh, hear

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about the data fabric. Eric I
asked you, what about the data spanks,

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the data spanks. Thanks like the
clothing to tighten that integration. Scott

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Taylor, the data whisperer here,
I help people calm data down. That's

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what data whisperings all about. I'm
thirty something years in the data management space

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and now I'm just a content creator, part of Kate's posse of the data

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avengers as well, out there trying
to get people excited and fired up about

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the data management part of the space. You're talking about feeding stuff into data

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second chance. That's a nice way
to put it, But are we just

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talking about the same thing We've always
talked about. Garbage in, garbage out.

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Put the bad data into llms,
you get hallucinations. So put garbage

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into even Jennai, you can get
garbage out at scale now, which is

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really well right Ben. Actually so
one of my good buddies, Eugene Burke,

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who's been on the show a few
times Digital Strategies Group, we're doing

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a bit of a stealth project now. He had the greatest line I think

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about this limitation of large language models. He said, they don't have an

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epistemological barrier, which means for non
philosophers out there, they don't know what

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they don't know, and it reminds
me of I won't say which country,

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but there are certain countries where I
was told don't ask people on the street

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for directions because they'll be too embarrassed
and they'll just tell you something you know

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that they don't know. And that's
kind of like what these large language models

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do. If you don't have a
good embedding strategy, if you don't populate

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either a vector database or your own
model itself with enough curated data, and

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if you don't train it properly,
then guess what, it's just going to

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make things up, Like when it
said I've written three books, and I

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was like, are you seeing the
future? So you're right that we need

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to curate carefully. But that's always
been the mission. It's always been the

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case. There's nothing new about that. There's nothing new about the need for

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a well structured, expertly stewarded,
wonderfully governed data that you know you're gonna

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bring Malcolm on here has got metadata, it's got master data, it's got

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reference data, mdm rdm RIM,
PIM, damn. All these foundational activities

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that enterprises must commit to if they
want to leverage data across their organization.

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And what enterprise doesn't want to leverage
data across their organization. So these are

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the same principles we've been dealing with. We got to shine them up,

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We gotta rephrase them a little bit
to get people's attention. But it's just

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maddening sitting in the data space for
so many decades. Again, I go

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back pre to k seeing the same
story over and over again with different characters.

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Well, I think part of the
challenge is that you have this long

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tale of legacy software and hardware and
mindset by the way, and it's very

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difficult. So when new technologies come
in, you try to move over to

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the new technology, but you still
have this long tale of legacy to contend

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with. And in fact, we
want to promote the Data Universe conference.

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It is coming April ten and eleventh
to New York to the Javit Center,

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which the Javit Centerism does not like. He's not a fan of the Javit

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Center. We'll find that out in
the next segment. Well, I'll be

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there, Ka, It'll be there. We'll let Malcolm decide whether he wants

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to be there not. Given his
it's going to be a party his feeling

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about that location, but I think
it's going to be a blast. Yeah,

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well, so I wrote it.
I wrote an abstract. I don't

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know if it'll get approved or not
yet, but it said the the fourth

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word is in parentheses forever in your
technical debt. That's what I wrote about.

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Oh and that's pretty cute to Kate
laughs, so she likes it.

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The headline basically leads into the abstract, which has something like what's the definition

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of a legacy system? And the
answer is any system in production. The

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joke is that the moment you drive
off the lot, you are accruing technical

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debt, Like literally the second you
leave the lot, tacnical debt is like

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bare debt. That's a good way
to think about it. Yes, well,

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you look at like doctor's offices.
I mean they still want me to

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fill out pieces of paper on a
on a notepad. I'm like, are

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you kidding me? I have to
write my name seven times? Like you

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can you just you know, could
you just give me the iPad? And

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then they give it the iPad And
it's like eight hundred and seventy five questions.

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It's like the people know anything about
surveys, Like when you get to

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question five and twenty one, the
quality of the answer goes way down because

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no one wants to write that stuff. But we can. We can port

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that to the data world and say
ask just enough questions to get just what

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you need and move on. What
do you think? I think so?

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Yeah, it's just I hate that
at doctor's offices as well, filling it

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and over and over again. It's
like, haven't they kept anything? Don't

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they have any legacy data about me? Yeah? They do. It's enough

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folder that they put in the in
the filing cabin in the back there.

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Somebody here. You have some interesting
folders too, including folders on stratchedy me.

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I have folders, Yes, I've
got all kinds of interesting stuff here.

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Do you want to talk about the
puppets yet? Or you want to

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bring Malcolm on first? Let's bring
in a puppet real quick, show your

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puppets so we can see them.
I think there is. This is about

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my character the cdo the Chief Dog
Officer, and he partners up with If

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you haven't seen the data puppets on
YouTube, you got to see them out

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there. There's a preview out there. Journey to the Center of the single

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version of the truth the greatest data
story ever told, starting the CEO,

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the Chief Dog Officer and also appearing
the CEO Chief Elephant Officer played by Kate.

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There's a CMO. Guess what he
is? A mouse? A CFO

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is a fish? I mean,
just stuff rights itself. How has nobody

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else come up with that? Before? They hire a cat sultant from meol

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Kinsey who borrows their watch to tell
them what time it is. Borrows their

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watch. We've got a whole crowd
of business users who will be out there

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trying to do but take a look
at that. It's crazy stuff. I'm

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having a ball. This is what
I'm doing now, Data puppets events,

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the funnest possible stuff And similar to
Kate, if you can get paid for

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making sand castles or doing puppets,
then welcome to our world. He gave

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me a segue to bring in a
fantastic lyric by coy Le Roy or Coyle

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Ray. I think her name is. She says time is money, so

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I spent it on a watch.
Hold on, It's like what That's pretty

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clever And that's the semantic side of
the equation. We do want words to

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go along with all these numbers.
I mean the numbers obviously are the transactions

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and how many widgets we're selling and
so on and so forth, or clicks

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and all this kind of fun stuff. But the words are the semantics.

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That's probably gonna be a good segue
to bring Malcolm Chisn't into the second segment

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here. But maybe we'll just finish
up with Scott, and maybe I'll go

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ahead and throw a curveball and bring
stra back into the conversation Gates the puppets.

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Why you play with games with puppets
with your data? What's the question?

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Your accent is very difficult there.
She doesn't take puppet questions. She

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doesn't. I'm not allowed to speak
about puppets for our contract with Scott.

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That's that's his thing. We can
it's my area, all right. This

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is I had a comment when you
were talking about the doctor's office and the

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iPad. So they do have iPad
now, or they send you a link

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on your phone. But the worst, most, most most annoying part is

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when you have to fill out Let's
say I recently had an appointment for my

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kids doctor check up, and they
ask for the kids date of birth at

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least eight times on eight different within
one form, like I filled it out.

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You can't. You don't have the
systems to just why. It's so

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compata model issue. It's a system's
integration issue. It's a data model,

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right. I mean I remember with
some of the smart people online they figured

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out, Hey, if we have
someone enter the zip code, we can

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dynamically look up the town that they're
in. I'm like, who's thinking,

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huh, who's thinking put the zip
code there? It's like the people like

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usay figured this out. You call
in, they say, I see you're

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donallly from a number in our system. If that's the phone you usually used

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to be a press one, Like, yes, that's right, you know

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who I am. I don't have
to tell five people in a row.

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This is Eric Cavanaugh. I'm calling
about this. Oh yes, yes,

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that's called enter integration. That's from
a lot twenty two years ago. Enterprise

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integration. We've come a long way, but data is front and center.

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I suppose data should lead the charge. Let the data talk to you,

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let the data tell you what's going
on. I mean, that's a standard

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thing to say in our industry.
But folks, we're just gonna have a

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lot of fun here. On our
show today talking to of course, Kate

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Stretch Dedicated. I'll never be able
to say your name normally, just so

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you know, because I now know
like you know, and you laughed.

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So come on. The guy gets
a girl to laugh, just keep saying

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that stuff. Keep her laughing.
That's the joke. Going to have you

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introduced me at like conferences. Now, I say my last fact to bring

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Homer Simpson back into it. One
time on an episode he made he got

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Marge mad and then he made her
laugh. He goes, ahh, I

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made you laugh. I win.
He want away. Oh we're not all

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that bad. Don't touch that doll. Folks will be right back. You

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were listening to Inside Analysis. Welcome
back to Inside Analysis. Here's your host,

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Eric Tavanaugh's all right, folks,
take us to the future. Indeed,

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you're on Inside Analysis talking to an
all star cast. Scott Taylor,

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the data whisperer, kid Stratch of
Dedicated and next up, one of my

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favorite people in the business. He's
just rocking and rolling on the LinkedIn platform

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these days with thought provoking posts about
receipts. He has all the receipts and

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he knows about the metadata on the
receipt one. Malcolm Chisholm of Data Millennium,

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Welcome back to DM radio. Tell
us about the receipts post that got

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three hundred thousand views. Thank you
so well. That was you know,

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something I always wanted to do,
which was I mean, you know,

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how do you bring home data to
people and the different types of data?

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Well, do data analysis of a
receipt. We all deal with receipts,

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I mean just normally throw them away
or even refuse to accept them. But

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if you look at them, there's
a lot of data on it, and

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some of it, frank I don't
know what it means quite frankly. It's

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all encoded, but a lot of
it you can make out, and then

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you can classify its meta data.
It's transaction data, it's mass to data,

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it's reference data, and it's you
know, a useful way, useful

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tool for teaching people about data.
I didn't think it would go very far,

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but it like took off and went
viral, which is quite bizarre.

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So and it's still going strong out
there in the linkedinn sphere. So there

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you go. Gave me hope that
stuff actually can go viral because I've learned

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some hard lessons over the years that
a lot of what goes viral was paid

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to go viral, so to speak. Yeah, I don't think you paid

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any money to promote that, right, that was no, No, I

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paid no money. Well, I
mean except for the lettuce and whatever else

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was I bought. It cost me
about six bucks something like that to get

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the receipt. So you know,
the local supermarket and the folks of the

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supermarket were very happy because it served
us three hundred thousand views of unpaid advertising

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for them too, So there you
go. But I thought it was very

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clever because you were looking at the
receipt and you pointed out, here's metadata

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about the product, here's metadata about
the skew. All this information is very

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important information, which is a window
into the operational system that is actually generating

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the receipt right right, and all
systems it's a composite that's all ending up

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in the receipt of all this data. So you can think about you would

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have to have different systems to handle
different bits to it, but they're all

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coming together there. So it's a
view of data, you know, and

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something you know quite common, but
it's it's it shows the importance of data

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yeah, and trying to understand where
things come from. The thing I like

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about it is that it is a
reflection of what is coming from the inside.

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There are all these systems that are
tracking the price of products, of

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course, the date and things of
this nature. If you have your your

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code or your you know, your
rewards number, for example, I'm like

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Bill Burr, I don't really trust
the rewards programs. They're always changing their

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minds and pulling the string on things. But there is some value and all

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that stuff, and it allows you
to kind of see what's inside, you

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know. I remember years and years
ago I worked on a project where we

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connected to Fanny May's desktop underwriting system. We're the first company ever to do

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that, and I remember that the
developers having a hard time, and I

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thought to myself, you know,
in the office they use a system,

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a desktop underwriter. It's an application. And if I just did something like

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you know, prints to PDF or
something. I think PDFs are around back

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then, but it's year two thousand. I was like, look, let's

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just print out what it needs,
and that those would be the fields you

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would look to capture through this web
form. So it's like, how the

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hell hard is that it's just the
data stream. Now you have to securely

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connect to something, but in terms
of the data itself, and that's what

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we did and it freaking worked.
I'm like, well, duh, why

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don't you pay me a thousand dollars
an hour or more? And just for

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those that are no desktop underwriter is
a way to get mortgage applications approved so

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that the government will securitize them and
put them into mortgage backed security. So

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00:23:11.839 --> 00:23:17.039
it's pretty important part of our economy
actually, So yeah, that was that's

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a big deal. So you know, if you own a house, you've

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been probably been through it. And
even if you don't know, yeah,

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it's it's painful. I remember the
collateralized debt obligations and what was Bucket's comment,

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don't believe the expirying formulas or something
like that. It's like, watch

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out for that kind of stuff.
Those are the days. Yeah, But

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the important thing is the data that's
in these systems. And I'm curious to

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00:23:44.759 --> 00:23:47.920
know your thoughts. You've been around
for a number of years here that have

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00:23:48.000 --> 00:23:52.960
seen highs and lows and movements like
hadob coming along, everyone getting excited and

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now of course AI but to our
point, earlier. AI is not going

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to do much good if you don't
have really high quality data, right,

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00:24:00.400 --> 00:24:03.039
Yeah, I mean talking about the
historical I always thought, you know,

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00:24:03.079 --> 00:24:06.440
the slogan of it ought to be, we build the legacy systems of the

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future, you know, so that
people truly understand the value that they add.

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That said, I mean, okay, AI data is going to be

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00:24:17.480 --> 00:24:21.039
important, but it's a different take
on it. It's going to because these

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00:24:21.079 --> 00:24:26.000
things work off of sentence form you're
going to have to get It's much more

357
00:24:26.039 --> 00:24:33.119
about getting unstructured data, as we
would call it into a shape that you

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can push through in data preparation into
the into the you know, AI black

359
00:24:41.200 --> 00:24:47.799
hole beast basically, and that data
preparation, having looked into it, is

360
00:24:47.839 --> 00:24:52.200
an enormous number of steps. There's
all kinds of things you've got to do,

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and there's things that AI doesn't do
in terms of normal data management practices,

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00:24:57.039 --> 00:25:00.680
like data attention. You forget about
it once it goes in. That's

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00:25:00.720 --> 00:25:03.759
it. It's not you know,
it's like it's like Las Vegas. It's

364
00:25:03.759 --> 00:25:07.799
well, it comes out again,
but I mean it's you can't make it

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00:25:07.799 --> 00:25:11.839
forget. There's no forgetting mechanism so
if you put something in like PII,

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it's not like you can scrub it. There's no bleach for AI. So

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that's a problem. But this enormous
set of steps to break up things into

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maybe propositional format. Yes it'll read
some structural data, but mostly it's getting

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it into that format. And then
on the other side of it, it's

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not like the thing in Star Trek
where you ask it anything and it'll just

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tell you whatever, and it'll be
like this super brain. It's it's going

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to have to be coupled to use
cases, and those use cases are going

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to have to be expressed as prompts. So you've got these prompt libraries that

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will turn you know, the gibberish
question that Malcolm asks the AI into the

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closest prompt and give it the answer
associated with that prompts together question answer their

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So all of that is a huge
amount of data management, data preparation,

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and you better get it right because
what I have found in the past,

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maybe this doesn't apply to AI is
but with documentation is that as soon as

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somebody finds a bit of documentation,
they don't trust like one percent of some

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00:26:21.640 --> 00:26:25.079
threshold like that, they don't trust
any of it. Okay, So if

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00:26:25.079 --> 00:26:32.000
you get garbage answers out of AI, then it's going to make people thinking.

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00:26:32.000 --> 00:26:34.559
And although they probably you know,
use the day, it's probably conditioned

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to think that AI is, you
know, going to solve all their problems.

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00:26:37.799 --> 00:26:44.279
They'll maybe be a bit more generous
with it than documentation, but they

385
00:26:44.440 --> 00:26:47.599
won't, you know, they'll come
to a point where they don't trust any

386
00:26:47.599 --> 00:26:52.119
of it if this isn't done correctly
right well, that that is actually a

387
00:26:52.160 --> 00:26:55.559
pretty interesting point, and there is
a trust issue that we're going to have

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00:26:55.599 --> 00:26:59.359
to get around it. There's also
a mathematical issue, because as I understand

389
00:26:59.400 --> 00:27:03.359
it, what these models have done
is, first of all, they vectorize,

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00:27:03.480 --> 00:27:07.480
by which they mean they take words
and turn them into numbers, into

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00:27:07.519 --> 00:27:12.160
a raise of numbers, and then
they're doing statistical analysis on the numbers.

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00:27:12.519 --> 00:27:15.880
And then when you sort of decode
it on the other end, it turns

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00:27:15.920 --> 00:27:21.480
it from numbers back into letters.
So you do have this very interesting transformation

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00:27:22.039 --> 00:27:26.160
like reminds me of asking, remember
asking, or it switches from letters and

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00:27:26.480 --> 00:27:33.680
sentences and paragraphs and so forth to
numeric values and then back. So the

396
00:27:33.759 --> 00:27:37.319
thing that kind of strikes me is
the other shoot to drop. Here is

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00:27:37.319 --> 00:27:40.440
going to be the cost of this
stuff now? Right now, there are

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00:27:40.519 --> 00:27:45.200
billions of dollars going in by Microsoft
and Google another guys to train these things,

399
00:27:45.640 --> 00:27:48.319
and companies are all going to want
to train their own models. But

400
00:27:48.400 --> 00:27:51.480
I don't think we really have any
idea what the actual cost is going to

401
00:27:51.519 --> 00:27:53.279
be to the user. And I
think that's going to be another shoot to

402
00:27:53.359 --> 00:27:56.640
drop. But what do you think
about them? Well, right, I

403
00:27:56.680 --> 00:28:00.279
mean I think that it depends on
the apps that are made from them.

404
00:28:00.559 --> 00:28:04.160
So you know, I'm looking to
this right now because I don't want to

405
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use one of the hosted ones because
why should I give it my intellectual property?

406
00:28:10.400 --> 00:28:11.440
So I'll get one of the ones
I can have on prem Well,

407
00:28:11.480 --> 00:28:15.960
it's going to cost me about five
thousand dollars to buy the equipment, you

408
00:28:15.960 --> 00:28:21.160
know, just to be able to
run the you know, the base version

409
00:28:21.440 --> 00:28:25.319
of one of these. But then
I could do things like, you know,

410
00:28:25.880 --> 00:28:32.000
create something around a curated set of
knowledge where people would understand it,

411
00:28:32.240 --> 00:28:37.680
do that and give you answers to
that. But I think a more interesting

412
00:28:38.119 --> 00:28:45.039
set of use cases to me is
one is knowledge integration. So you know,

413
00:28:45.039 --> 00:28:48.839
we talk about data integration like you
know, you're talking MDM like integrating

414
00:28:48.880 --> 00:28:52.240
customer data earlier. Okay, but
we've got knowledge as well, so I

415
00:28:52.319 --> 00:28:57.000
could theoretically think about nobody likes being
a data steward, So let's have an

416
00:28:57.000 --> 00:29:03.880
AI one. So this AI data
steward is told who looks after you know,

417
00:29:03.000 --> 00:29:07.720
these reference data tables, these code
tables. And then it's also let's

418
00:29:07.759 --> 00:29:14.960
say, coupled to the HR system, and it recognizes the feed from the

419
00:29:15.160 --> 00:29:19.599
HR system, so it says,
okay, Eric is or Malcolm is the

420
00:29:19.680 --> 00:29:25.039
data steward for the country codes.
Okay, that's good to know. And

421
00:29:25.079 --> 00:29:29.039
then Malcolm gets fired because he's useless
and is replaced by Eric. But that's

422
00:29:29.039 --> 00:29:33.519
an announcement from HR. Now today
you'd have to go into a data catalog

423
00:29:33.640 --> 00:29:37.519
and manually some puschmucks data slash data
steward has to update it. But now

424
00:29:37.559 --> 00:29:45.279
we can integrate these various unstructured data
sources and it'll realize who is the true

425
00:29:45.319 --> 00:29:48.519
data steward of the country code.
So we've got that as a possibility to

426
00:29:48.519 --> 00:29:52.519
get all this integration of knowledge,
which is something a little bit different to

427
00:29:52.519 --> 00:29:56.960
the structured data integration which is so
that's a good one and having these you

428
00:29:56.000 --> 00:30:03.960
know data steward bot really you know
agents out there doing this work for us

429
00:30:03.359 --> 00:30:07.839
so that we don't have to do
all the donkey work which nobody wants to

430
00:30:07.880 --> 00:30:10.000
do. We keep telling them that
they have to do it. No,

431
00:30:10.279 --> 00:30:14.640
no, they don't want to do
it, so and I don't blame them.

432
00:30:14.880 --> 00:30:17.400
So those are there's a lot of
promise there too, if we can

433
00:30:17.440 --> 00:30:21.559
get this to work properly well,
and you know, let me throw this

434
00:30:21.640 --> 00:30:23.559
curveball et you and then I'll bring
in the other guests and throw it at

435
00:30:23.559 --> 00:30:26.480
them as well, or some variation
of it, and maybe a hard slider.

436
00:30:27.079 --> 00:30:34.119
When I think about the power of
these models to upon prompt grab information

437
00:30:34.240 --> 00:30:37.880
from all these different sources and fuse
it together to give you some narrative,

438
00:30:37.920 --> 00:30:44.079
which is what they're doing, I
wonder to myself think about all of the

439
00:30:44.480 --> 00:30:48.799
heavy lifting that's done even still today
for data warehouse and extract transform load extract

440
00:30:48.880 --> 00:30:53.079
load transform, all these batch windows, which in large organizations are in the

441
00:30:53.200 --> 00:30:57.079
hundreds, if not thousands, And
you have to know that it's not all

442
00:30:57.200 --> 00:31:00.920
change data capture. There's just a
lot of bulk movement of data picking it

443
00:31:00.960 --> 00:31:03.799
up for naked over the year,
pick it back up again, fork lifted

444
00:31:03.880 --> 00:31:08.000
over there. That's I think that's
going to be looked at as a tremendous

445
00:31:08.000 --> 00:31:14.160
waste of time in the new world. But the key is cand these models

446
00:31:14.400 --> 00:31:18.240
short circuit all that and just get
me the answer I want somewhat reliably,

447
00:31:18.319 --> 00:31:19.480
so I don't have to move all
that stuff around anymore. What do you

448
00:31:19.519 --> 00:31:23.519
think, I think you're on something
that I think that one of the impetuses

449
00:31:23.599 --> 00:31:29.599
if you look away these the AI
audents are coming from is to get rid

450
00:31:29.599 --> 00:31:33.000
of code, is to get rid
of data. Engineers don't need them anymore.

451
00:31:33.000 --> 00:31:37.160
We don't want them. They're a
huge expense, so could we replace

452
00:31:37.200 --> 00:31:41.359
them. So you've got you know, copilot and things like that. You've

453
00:31:41.400 --> 00:31:47.880
got assistance to generate code, But
why don't let it do the code generation?

454
00:31:48.000 --> 00:31:49.759
If you could just show it the
data, okay, and do exactly

455
00:31:49.799 --> 00:31:52.880
what you're saying. I mean,
how complicated is it? I mean,

456
00:31:53.000 --> 00:32:00.119
etl is you know, the thetail
products are, or traditionally have been a

457
00:32:00.240 --> 00:32:04.119
replacement for the more expensive kind of
programmer. This is just taking that evolution

458
00:32:04.200 --> 00:32:08.319
a step further. So then then
you know, if you could like just

459
00:32:08.359 --> 00:32:14.039
get rid of the you know,
legions and legions of coders that are out

460
00:32:14.119 --> 00:32:16.920
there. You know it's not good
for them, But and replace them with

461
00:32:17.039 --> 00:32:22.279
some skilled people who are driving this
and who you know, constrain the AI

462
00:32:22.279 --> 00:32:25.440
and let it generate the code based
on what it's seeing in the day.

463
00:32:25.640 --> 00:32:31.839
That would be that would be a
massive you know, efficiency gain in the

464
00:32:31.880 --> 00:32:39.119
economy. Yeah wow, well and
uh yeah, I mean I asked myself,

465
00:32:39.119 --> 00:32:43.079
we have a show planned next year, what's not in the cross heres

466
00:32:43.640 --> 00:32:46.680
of this stuff? And you know
the fact is we have lots and lots

467
00:32:46.680 --> 00:32:52.720
of legacy processes, legacy mindsets,
legacy systems. But so just give you

468
00:32:52.720 --> 00:32:58.480
one example real quick before the break. Think about MRI scans. My wife

469
00:32:58.519 --> 00:33:01.119
who has to deal with MS which
is a real pain. She has to

470
00:33:01.160 --> 00:33:06.279
go get these MRIs every once in
a while. Those files are massive.

471
00:33:06.359 --> 00:33:07.640
I mean you have you can fit
them on a CD ye on, but

472
00:33:07.680 --> 00:33:13.640
it's a huge file. If you
vectorize something like that, number one,

473
00:33:13.720 --> 00:33:17.440
it's a form of compression. But
number two, it facilitates statistical analysis.

474
00:33:17.920 --> 00:33:24.920
So isn't that a use case for
for vectorization to vectorize graphic images big ones

475
00:33:24.960 --> 00:33:30.160
into numbers which is much smaller and
allows statistical analysis. What do you think,

476
00:33:30.880 --> 00:33:35.039
Well, I think we're we' I
don't. I think you're right,

477
00:33:35.079 --> 00:33:38.720
But I think we're part of the
way there already with the image processing features

478
00:33:38.759 --> 00:33:44.559
that we have that are available in
current libraries which are pre AI. So

479
00:33:44.640 --> 00:33:47.799
a lot of that can be done
already. I've seen it done, you

480
00:33:47.799 --> 00:33:52.559
know, building data pipelines to do
just that, like has the has this

481
00:33:52.079 --> 00:33:58.319
you know, tumor progress regress whatever, So so we go a lot of

482
00:33:58.319 --> 00:34:01.559
that, So that would be taking
step further. Why not? Yeah,

483
00:34:01.599 --> 00:34:07.359
I mean I think again, what
isn't in the crosshairs of these new technologies,

484
00:34:07.400 --> 00:34:09.400
you know? And I think also
you you're in the process. We

485
00:34:09.480 --> 00:34:15.800
could have a you know, we
could have an AI illusion of a host

486
00:34:16.199 --> 00:34:20.800
who could ask the questions. Yeah, you know, you can get you

487
00:34:20.840 --> 00:34:24.079
can get Jen Jena AI to ask
to you know, give you questions.

488
00:34:24.760 --> 00:34:29.679
And you couple that with one of
the you know, the video generates the

489
00:34:29.719 --> 00:34:35.400
things that they have and they are
you got a new personality. Yeah.

490
00:34:35.440 --> 00:34:38.119
I think in terms of its storytelling, it's still pretty formula. But it's

491
00:34:38.280 --> 00:34:42.039
you know, yeah, you have
to figure it's learning, it's going to

492
00:34:42.119 --> 00:34:44.360
learn, it's going to get better
and better at these things, and you're

493
00:34:44.360 --> 00:34:49.960
going to have your choices. Maybe
maybe they'll plateau. Yeah, hopefully we'll

494
00:34:50.000 --> 00:34:52.119
see. Well, don't touch the
doubt books. We'll be right back.

495
00:34:52.159 --> 00:35:02.840
You are listening to Inside Analysis.
Welcome back to Inside Analysis. Here's your

496
00:35:02.920 --> 00:35:10.840
host, Eric Tabanata. All right, folks, back here on Inside Analysis,

497
00:35:10.880 --> 00:35:15.239
talking all about all things data.
Time to refine. You have to

498
00:35:15.280 --> 00:35:19.039
refine the data to get value from
it, just like oil, just like

499
00:35:19.159 --> 00:35:22.480
water. I heard the water analogy
at a conference this past week, and

500
00:35:22.639 --> 00:35:25.119
I was joking, we're all thirsty. Look at the oceans. They're huge,

501
00:35:25.159 --> 00:35:28.559
but you can't drink that water.
You got to desalinate it. You

502
00:35:28.599 --> 00:35:30.079
got to clean it out. You
got to get the fishes out of there,

503
00:35:30.079 --> 00:35:32.039
you got to get all kinds of
fun stuff out of there. So

504
00:35:32.079 --> 00:35:37.199
we understand that refined data. But
I'll throw it over to Kate's stretch of

505
00:35:37.320 --> 00:35:44.480
data caated to to share with us
her thoughts on on best practices going forward.

506
00:35:44.559 --> 00:35:46.639
We have a second chance for data. I think our audience has agreed

507
00:35:46.679 --> 00:35:51.800
with me on that. What's your
advice for companies to build their own AI

508
00:35:51.960 --> 00:35:54.920
model with refined data? How do
you get that process started. Yeah,

509
00:35:55.039 --> 00:35:58.639
you know, when you said best
practices, I was actually going to take

510
00:35:58.639 --> 00:36:04.559
it into the direction of data privacy
because something Malcolm shared earlier when he was

511
00:36:04.559 --> 00:36:08.239
talking about receipts reminded me of a
story I had from a couple of months

512
00:36:08.239 --> 00:36:12.239
ago. I went clothes shopping,
So raise your hand if you've been clothes

513
00:36:12.239 --> 00:36:15.639
shopping in the past few months,
right in store, not online. And

514
00:36:15.679 --> 00:36:21.000
I think we share so much information
online that when I don't really do in

515
00:36:21.000 --> 00:36:23.280
store shopping. But when I got
there, they said, do you want

516
00:36:23.280 --> 00:36:27.719
to put your email or phone number
into the system? Literally every store?

517
00:36:27.760 --> 00:36:30.480
And I'm like, no, I
don't want to put my information in.

518
00:36:30.559 --> 00:36:32.000
I just don't like it. I
don't like all the spam. It was

519
00:36:32.039 --> 00:36:35.800
fine, and then I get to
one store. I'm not going to name

520
00:36:35.840 --> 00:36:37.639
and shame them, but they're like, okay, do you want to receive

521
00:36:37.760 --> 00:36:40.400
After our whole back and forth of
are you sure you don't want to sign

522
00:36:40.440 --> 00:36:44.239
up, I'm like yes, I'm
sure, fine, Okay, So they're

523
00:36:44.280 --> 00:36:45.679
like, okay, so in order
to get a receipt, you're going to

524
00:36:45.760 --> 00:36:50.440
need to give us a phone number
or an email address. So I'm like,

525
00:36:50.480 --> 00:36:52.760
okay, Well, I guess I
won't get a receipt then, and

526
00:36:52.880 --> 00:36:55.239
so they come back and they're like, well, you can't return your stuff

527
00:36:55.239 --> 00:36:59.320
with that or receipt and these were
shoes for my kids. So I'm like,

528
00:36:59.360 --> 00:37:00.719
okay, well, unfit, I'm
gonna have to come back. So

529
00:37:00.760 --> 00:37:05.239
they sort of put me in this
position where I won't be able to return

530
00:37:05.280 --> 00:37:09.079
it, but they're sort of forcing
me into sharing my data. When you

531
00:37:08.800 --> 00:37:12.800
when you were talking about receipts welcome, that was all I could think about.

532
00:37:12.840 --> 00:37:15.360
I'm like, yeah, we have
all this information that they're collecting,

533
00:37:15.840 --> 00:37:21.800
but sometimes I just didn't want that
to happen. I ended up not getting

534
00:37:22.239 --> 00:37:24.880
my receipt. But I think one
of the best practices that I can think

535
00:37:24.920 --> 00:37:29.559
of for organizations and data is making
sure that the people that are taking data

536
00:37:29.599 --> 00:37:36.239
from feel comfortable enough sharing that and
sort of allowing them to opt out of

537
00:37:36.239 --> 00:37:38.480
that process if that's something that they
want to do. Well. You bring

538
00:37:38.519 --> 00:37:43.960
up a very interesting point, which
is getting forced into using a certain system,

539
00:37:44.360 --> 00:37:46.519
and this I think is the dark
side of digital transformation. You know,

540
00:37:46.519 --> 00:37:52.800
I went to a hotel in Austin, Texas last October and there was

541
00:37:52.880 --> 00:37:54.400
no one at the desk there was
no one to talk to to give me

542
00:37:54.440 --> 00:38:00.239
my keys or anything. It was
all this this online process of you in

543
00:38:00.280 --> 00:38:04.280
your phone and I had to like
show it a picture of my driver's license

544
00:38:04.360 --> 00:38:07.679
to capture that. I'm like,
how orewellinging is this weird situation? And

545
00:38:07.679 --> 00:38:09.719
it was a real pain in the
rear. I'm not gonna lie. It

546
00:38:09.800 --> 00:38:14.320
was like you really can't afford to
have one person sitting there and like get

547
00:38:14.320 --> 00:38:17.000
paid twenty bucks an hour to take
information, like that's how far we're going

548
00:38:17.039 --> 00:38:20.039
to go? Or I'll try to
be you, Kate, and then we'll

549
00:38:20.039 --> 00:38:23.199
get the other gentleman in on this. The McDonald's chaosks where they want you

550
00:38:23.320 --> 00:38:27.360
to go and find your food.
Look, if you just do the guarden

551
00:38:27.440 --> 00:38:30.239
variety, something okay, But if
you have special orders, it's like,

552
00:38:30.679 --> 00:38:32.920
oh my god, hold on this
menu, over to that menu and this

553
00:38:32.960 --> 00:38:37.559
other now wait which I lost it. It's just a total nightmare. There

554
00:38:37.559 --> 00:38:39.840
are some things that should not be
digitally transformed. Real quick, Kate,

555
00:38:39.880 --> 00:38:43.239
what do you think? Yeah,
so real quick, I'll tell you.

556
00:38:43.280 --> 00:38:46.039
I think there are preferences. Some
people, probably about half the population,

557
00:38:46.119 --> 00:38:51.000
would want to avoid eye contact and
people. Yeah, and they would love

558
00:38:51.000 --> 00:38:53.960
it. So I think just providing
the option for someone like you who wants

559
00:38:53.960 --> 00:38:58.159
that social interaction of like, here
you check my idea, I don't want

560
00:38:58.159 --> 00:39:01.760
to scan this. And then I
think just just long story short, have

561
00:39:01.920 --> 00:39:06.159
both options, because I know that
could speed things up if there's a really

562
00:39:06.239 --> 00:39:08.119
long line and you've got that one
person who just maybe is new and does

563
00:39:08.199 --> 00:39:12.000
know what they're doing, or well
system crashes. You have all these other

564
00:39:12.039 --> 00:39:15.920
options of people doing it themselves.
But I personally like, if I'm going

565
00:39:15.960 --> 00:39:19.079
to get do food shopping, I'm
not going to do the self checkout when

566
00:39:19.119 --> 00:39:22.800
I see even if there's a line. I just why there are people a

567
00:39:22.880 --> 00:39:25.400
right there who are going to do
it for you, Where my mom would

568
00:39:25.519 --> 00:39:29.920
insist on self checkout, even if
it's a cart full of stuff. I'm

569
00:39:29.920 --> 00:39:31.760
like, you might you might as
well work here now you're just you're doing

570
00:39:31.760 --> 00:39:36.480
it all, so there shouldn't at
least a discount for that service. Yeah,

571
00:39:36.480 --> 00:39:37.960
Bilber makes that exact joke. It's
like, why don't I just check

572
00:39:37.960 --> 00:39:40.000
it out myself and I get a
discount? Or what's going on? I

573
00:39:40.039 --> 00:39:44.559
will? Yeah, exactly when did
I sign a dive an Intel insurance plan?

574
00:39:44.639 --> 00:39:46.519
What's going on here? Oh why
am I working for you? Well,

575
00:39:46.519 --> 00:39:51.400
Scott Taylor, I'm sure you don't
mind hitting all the different buttons in

576
00:39:51.400 --> 00:39:52.880
the menus. I don't. I
don't mind that. I guess my big

577
00:39:52.960 --> 00:39:57.639
question is based on the conversation we
just had. Kate, did the shoes

578
00:39:57.679 --> 00:40:01.079
fit your kids? The shoes fit
I did not have to return, but

579
00:40:01.119 --> 00:40:07.039
I'm not going back to this.
Hew. Okay, that's good. I

580
00:40:07.119 --> 00:40:12.239
did. I had the opposite experience
recently at a panera where they had a

581
00:40:12.280 --> 00:40:15.039
bunch of things on the menu and
I was sitting there, going, I

582
00:40:15.039 --> 00:40:17.480
have to ask these people what's in
this salad? Like three different salads,

583
00:40:19.079 --> 00:40:22.400
And they had a kiosk and they
had the nutrition information and they listened to

584
00:40:22.400 --> 00:40:23.719
every single item that was in there. Was like, all right, that's

585
00:40:23.719 --> 00:40:29.360
a lot easier. So yes,
if you have a choice, but sometimes

586
00:40:29.360 --> 00:40:31.360
you feel like not because I don't
want to have eye contact with somebody.

587
00:40:31.519 --> 00:40:35.920
But it just might be a smoother
experience to go this way rather than that

588
00:40:35.960 --> 00:40:40.039
way. So as much as these
things can augment our existence, that's good.

589
00:40:40.079 --> 00:40:45.079
But when it starts to replace it
as an either or or that ridiculous

590
00:40:45.079 --> 00:40:51.039
story you're talking about kate of demanding
certain data. Otherwise you don't get proof

591
00:40:51.159 --> 00:40:54.920
of the transaction we just had.
That's what it's crazy. That's I was

592
00:40:55.000 --> 00:41:01.760
wonder as well, because I don't
know. But if you know, if

593
00:41:01.800 --> 00:41:05.760
you spend some time and you're not
from Russia, you're from Tajika standard,

594
00:41:05.719 --> 00:41:08.440
I guess, so it's not not
the same. You'll probably just say don't

595
00:41:08.440 --> 00:41:10.880
shop here if you don't like it, right, but I don't know,

596
00:41:12.079 --> 00:41:15.119
don't shop her if we can't get
your data. Yeah, pretty much,

597
00:41:15.360 --> 00:41:19.840
that's that's pretty strangel Malcolm. I'll
throw it over to you for Yeah,

598
00:41:17.679 --> 00:41:23.519
I would. I would try that
my favorite Soviet rule, which is the

599
00:41:23.599 --> 00:41:28.519
rules are there to be avoided.
And you want my emails, okay,

600
00:41:28.639 --> 00:41:32.760
say a at gmail dot com.
Okay, so I'm just lye or you

601
00:41:32.760 --> 00:41:36.559
know, we just said you were
the most honest man a moment ago.

602
00:41:36.679 --> 00:41:39.400
You've upset the apple then no,
no, no, no breaking now you

603
00:41:39.519 --> 00:41:44.320
heard it here first, just ripped
it wide open. In this kind of

604
00:41:44.400 --> 00:41:50.519
ethical dilemma, you're forcing me to
do something against my will, So this

605
00:41:50.599 --> 00:41:52.360
is what you'll get. Well,
well, Malcolm, then you you still

606
00:41:52.400 --> 00:41:57.760
you won't get the receipt which give
will print it out and give it you

607
00:41:57.760 --> 00:42:00.599
even that print. Yeah, they
don't have only email. Okay, in

608
00:42:00.639 --> 00:42:05.760
that case, what would It's a
single point of failure. So this is

609
00:42:05.800 --> 00:42:09.480
like when they had a colonial pipeline
ransomware a few was a couple of years

610
00:42:09.559 --> 00:42:14.599
back where they said, they said, well, okay, the system screw

611
00:42:14.719 --> 00:42:17.159
up. Well why didn't you go
back to managing this pipeline, you know,

612
00:42:17.320 --> 00:42:21.960
the mechanical way that you used to
do it, because it's quite old.

613
00:42:21.960 --> 00:42:23.599
And the guy said, well,
everybody knew how to do that is

614
00:42:23.639 --> 00:42:30.079
either dead or retired, and so
you know we are. You can't read

615
00:42:30.159 --> 00:42:34.480
maps, you don't want to make
eye contact with anybody or talk to anybody.

616
00:42:35.719 --> 00:42:43.599
Okay, this is okay until something
happens with this great shaky edifice of

617
00:42:43.679 --> 00:42:49.280
technology that makes our lives easier in
the sense that we don't have to think

618
00:42:49.480 --> 00:42:53.119
very much or engage in too much
physical labor anymore. Yeah, but what

619
00:42:53.159 --> 00:42:58.440
happens when you need to so you've
got no backup, You're you know,

620
00:42:58.480 --> 00:43:04.159
you're making your civilization extremely vulnerable.
So we will we will see. Well

621
00:43:04.159 --> 00:43:06.280
we were talking about in the break, right, So you have Jena I

622
00:43:06.320 --> 00:43:08.800
create the questions for an interview that
you have, Jena, I create the

623
00:43:08.880 --> 00:43:13.639
answers for the interview that you have
it, watched the show and generate a

624
00:43:13.800 --> 00:43:20.360
summary do we need as part of
the show. There are bots that are

625
00:43:20.360 --> 00:43:23.280
accounted as visitors, as viewers to
my website. Look at all the viewers

626
00:43:23.320 --> 00:43:28.159
I have. Yeah, I had
a nasty spell, an experience. I

627
00:43:28.199 --> 00:43:32.840
don't know, but I was contacted
in by over LinkedIn by it was a

628
00:43:32.920 --> 00:43:37.679
legitimate company. I'm pretty sure it
was a bot that was texting me on

629
00:43:37.760 --> 00:43:44.880
LinkedIn messages and then somebody another bot
on email, and I think we ought

630
00:43:44.880 --> 00:43:46.840
to at least have transparency, say
Hi, I'm a bot, I'm emailing

631
00:43:46.880 --> 00:43:52.639
you on behalf of you know,
blah blah blah company. There's no there

632
00:43:52.679 --> 00:43:55.400
is no transparency. It's like I
had to prove I had to go through

633
00:43:55.400 --> 00:44:00.519
this LinkedIn certification where I did have
to do facial recognition stuff to get LinkedIn

634
00:44:00.559 --> 00:44:06.320
to believe I was a human,
whereas no bot has to declare themselves to

635
00:44:06.400 --> 00:44:09.880
be a bot on LinkedIn and doesn't
have to prove that they're not. But

636
00:44:10.320 --> 00:44:14.199
I had. I had to prove
I wasn't. I had to prove I

637
00:44:14.239 --> 00:44:16.559
was human. So that kind of
irked me. Is that a rou Is

638
00:44:16.599 --> 00:44:22.920
there a need for like a reverse
Turing test? M. Yeah, you're

639
00:44:22.960 --> 00:44:27.119
onto something though, because you get
these phone calls too, like Hi,

640
00:44:27.199 --> 00:44:30.199
it's Cindy, I'm calling you about
the new online program. Like okay,

641
00:44:30.679 --> 00:44:34.559
I'm sure there was a Cindy who
recorded that at some point in time,

642
00:44:34.559 --> 00:44:37.000
but you are not on the phone. No, No, there's not even

643
00:44:37.039 --> 00:44:44.159
a Cindy. It's totally generated.
True. Oh, that's just bizarre.

644
00:44:44.239 --> 00:44:46.800
I think I like opening up one
chat bot and then getting into a conversation

645
00:44:46.880 --> 00:44:51.119
with the other chat bot. So
you get two chat bots talking to each

646
00:44:51.159 --> 00:44:55.119
other. Yeah, and you still
don't get your order through. But it's

647
00:44:55.119 --> 00:44:59.440
fun. It's you know, plenty
of time with my hands in between puppet

648
00:44:59.440 --> 00:45:01.960
takes. Yeah. At a certain
point, customer service does come into play

649
00:45:02.360 --> 00:45:06.480
and you will remember, as Kate
will, not to go get that store

650
00:45:06.519 --> 00:45:09.320
anymore. But I have to shout
out to old radio Shack because in the

651
00:45:09.599 --> 00:45:15.800
nineteen eighties radio Shack had the foresight
to ask you for information. They had

652
00:45:15.800 --> 00:45:19.679
a little form you'd fill out when
you bought your batteries or whatever it was.

653
00:45:19.760 --> 00:45:22.480
It was actually very annoying, but
in retrospect. I was like,

654
00:45:22.519 --> 00:45:24.960
wow, they were very forward looking, but they weren't. There was a

655
00:45:24.960 --> 00:45:29.280
comedian who did a bit during that
time about having to give your because they

656
00:45:29.320 --> 00:45:32.519
forced they were really militant about getting
your phone number. They were remember this,

657
00:45:32.599 --> 00:45:37.119
and you need your phone number to
buy batteries. It's a corporate dictate.

658
00:45:37.519 --> 00:45:39.280
Yeah. How's how's radio shack doing
these days? By the way,

659
00:45:39.360 --> 00:45:43.639
so well, folks who've been listening
to the only coast to coast radio show

660
00:45:43.920 --> 00:45:46.760
here in America about the infation economy. It's called Inside Analysis. Talk to

661
00:45:46.800 --> 00:45:53.400
you next time. All right,
folks, time for the podcast bonus segment.

662
00:45:53.480 --> 00:45:59.960
We said you've gone and we can
talk about it in different ways.

663
00:46:00.360 --> 00:46:02.480
We will get the information from it. I love Russians like I just love

664
00:46:02.519 --> 00:46:06.679
the Russian accent. That just cracks
me up. Hey, you're bringing me

665
00:46:06.719 --> 00:46:14.719
back to my bow ankle days here
with your with yours And they finally,

666
00:46:14.880 --> 00:46:17.360
and what's Natasha's last name? And
it's talking about trivia since the bonus you

667
00:46:17.400 --> 00:46:24.559
know Natasha Boris and Natasha what is
Natasha Fatal? It's Natasha. Yeah,

668
00:46:24.800 --> 00:46:28.840
So I like, did not see
me look anything up for that one?

669
00:46:29.000 --> 00:46:32.960
I know that one look trivia pursuit
days. You are a walking, talking,

670
00:46:34.039 --> 00:46:44.639
large language model. I've certainly hallucinated
given my Berkeley background. That's pretty

671
00:46:44.679 --> 00:46:47.119
funny. I like that. Well, speaking of hallucinations, I mean,

672
00:46:47.920 --> 00:46:52.519
goodness, gracious, we do have
to get down to business here and get

673
00:46:52.599 --> 00:46:57.719
serious about these AI models. And
this is my prediction that every major corporation

674
00:46:57.840 --> 00:47:00.360
is going to have their own AI
model. Many of them will working at

675
00:47:00.400 --> 00:47:01.880
it already, a lot of them
aren't. In fact, you'll probably have

676
00:47:02.000 --> 00:47:07.360
multiple models where I get multiple data
warehouses and I get back to is anything

677
00:47:07.440 --> 00:47:09.000
not in the crosshairs here? I'll
throw it first to Scott and then over

678
00:47:09.039 --> 00:47:15.000
to Malcolm. You know, if
you launch one of your own models,

679
00:47:15.000 --> 00:47:20.880
your private, single instance, if
you will single tenant AI model and start

680
00:47:20.920 --> 00:47:23.400
feeding it with your data, you're
going to be able to get interesting stuff

681
00:47:23.440 --> 00:47:29.400
from that. From a senior executive
standpoint, you'd be able to ask who's

682
00:47:29.440 --> 00:47:30.840
working on this right now? Up? Up, it's just going to pull

683
00:47:30.840 --> 00:47:34.800
this stuff in. It's kind of
crazy how well it works. I don't

684
00:47:34.840 --> 00:47:37.880
fully understand it. I don't think
anyone does. But Scott, I'll throw

685
00:47:37.880 --> 00:47:42.960
it over to you. I really
think that every last bit of legacy technology

686
00:47:43.519 --> 00:47:45.440
is to some extent in the crosshairs. Now, what do you think,

687
00:47:45.719 --> 00:47:51.639
don't you hope so everybody complains about
so much of this stuff, you hope

688
00:47:51.639 --> 00:47:54.519
that it gets transformed in a way. Meanwhile, more than you know,

689
00:47:55.039 --> 00:47:59.719
half the world, if not more, is still tied to Excel and things

690
00:47:59.760 --> 00:48:02.079
like that where they got to go
kind of do their own little skunk works.

691
00:48:02.639 --> 00:48:07.159
But you know, I hear about
what I kind of want to remind

692
00:48:07.239 --> 00:48:12.280
people of is all this stuff's cool, But how's it driving the business regardless?

693
00:48:12.320 --> 00:48:16.280
How is this enabling the strategic intentions
of your enterprise? How is it

694
00:48:16.360 --> 00:48:21.360
helping you your company get to where
it needs to go? And that's still

695
00:48:21.440 --> 00:48:27.039
the nature of business, right,
human interaction, bringing value to your relationships

696
00:48:27.599 --> 00:48:31.360
through your brands at some form of
scale, right, that still has to

697
00:48:31.440 --> 00:48:37.039
happen, right, And and and
I think you know, while Malcolm was

698
00:48:37.079 --> 00:48:38.320
talking about coders, I mean,
it wasn't that long ago, right that

699
00:48:38.360 --> 00:48:40.239
they were saying, okay, what
you got to do. Everybody's got to

700
00:48:40.320 --> 00:48:44.840
learn to code, And now it's
you know, that's the equivalent of we

701
00:48:44.880 --> 00:48:46.920
all have to learn how to use
a slide role. So just you know,

702
00:48:47.280 --> 00:48:51.840
having the creative aspects. And what
I would counsel anybody going to the

703
00:48:51.840 --> 00:48:55.400
space is make sure you, you
know, you beef up your creativity interesting

704
00:48:55.559 --> 00:49:00.920
capabilities there because that is going to
be tough to to replace. Yeah,

705
00:49:01.039 --> 00:49:06.719
that's despite what's going on in Hollywood
and that you know, discussions and during

706
00:49:06.760 --> 00:49:09.079
the strike about the use of AI
and that sort of thing. Uh,

707
00:49:09.159 --> 00:49:14.000
it seems to be okay to tell
a kind of mediocre story, but it

708
00:49:14.039 --> 00:49:16.559
can't. Hasn't come up with great
stories yet, has it? Yeah?

709
00:49:16.719 --> 00:49:19.679
I don't know, Man, this
is interesting, Malcolm. I'll throw it

710
00:49:19.679 --> 00:49:22.360
over to you. You've been tracking
this industry for a long time. There

711
00:49:22.440 --> 00:49:28.280
are countless inefficiencies in how we manage
data, how we move data, what

712
00:49:28.320 --> 00:49:31.599
we do with data. I think
these new models, these new AI models

713
00:49:31.639 --> 00:49:38.519
are going to be truly revolutionary in
terms of information persistence and retrieval. But

714
00:49:38.639 --> 00:49:42.760
we do have to be careful because
they do make stuff up. So how

715
00:49:42.760 --> 00:49:45.559
do you know, how do you
build an epistemological barrier into these things?

716
00:49:45.599 --> 00:49:49.599
I don't know. Do you have
any ideas? Now? I'm not sure,

717
00:49:49.639 --> 00:49:52.639
but I would I think one of
the things that we we're going to

718
00:49:52.719 --> 00:49:58.559
have is a bit difficult to conceive
of. Is it's going to replace legacy.

719
00:49:58.599 --> 00:50:02.840
I mean, what we have is
an accumulating layer after layer after layer

720
00:50:02.880 --> 00:50:07.079
of legacy technology. There's going to
be a new one on top of that.

721
00:50:07.320 --> 00:50:10.440
It's kind of the law of primitive
survivals. You've still got you know,

722
00:50:10.559 --> 00:50:15.440
I looked at jobs for cobol programmers
on LinkedIn a couple of days ago.

723
00:50:15.840 --> 00:50:21.800
You've got people, you know,
still writing cobalt and it's not necessarily

724
00:50:21.800 --> 00:50:24.519
going away. So we're getting this
layered architecture built up with these things on

725
00:50:24.559 --> 00:50:29.719
top of it, which is becoming
increasingly difficult to manage. It's not like

726
00:50:30.000 --> 00:50:32.239
we've had this before and you generation
your technology is supposed to replace everything else.

727
00:50:32.440 --> 00:50:36.320
It doesn't. It just becomes another
layer. Look, you know what's

728
00:50:36.320 --> 00:50:42.639
happened to her do But that's legacy
now. So I think that's part of

729
00:50:42.679 --> 00:50:45.719
the The other part is this is
different in some ways. It addresses a

730
00:50:45.760 --> 00:50:50.039
different set of use cases, and
as you know what Scott was saying in

731
00:50:50.159 --> 00:50:52.880
terms of the value it brings,
I don't know, but I think it's

732
00:50:52.960 --> 00:50:59.719
like one of these moments where everybody's
paralyzed with fear again that if they're competitor

733
00:50:59.760 --> 00:51:01.079
to get hold of this thing and
does something with it, they're going to

734
00:51:01.079 --> 00:51:04.960
be driven out of business. So
we got to do something too, and

735
00:51:05.039 --> 00:51:08.800
we'll spend money on it up until
the point where we've disproven the technology that

736
00:51:08.880 --> 00:51:13.280
it isn't a threat to us.
Okay, like you know, the books

737
00:51:13.280 --> 00:51:20.360
and mortar crowd then if you remember, rebelled against the dot com people back

738
00:51:20.400 --> 00:51:23.400
in the early two thousand once the
dot com bubble burst, and they took

739
00:51:23.440 --> 00:51:29.280
a pretty terrible revenge on them.
Now everything's been sure since then, but

740
00:51:29.840 --> 00:51:34.239
it kind of feels a little bit
to me like that again, Well,

741
00:51:34.239 --> 00:51:37.840
everything's got a label now right slapped
on it now with Jennai. It's just

742
00:51:37.960 --> 00:51:44.960
like you know now with extra Zest, it just becomes this productization for a

743
00:51:45.000 --> 00:51:47.360
lot of these technology companies. You
know, they ripped the data Mesh label

744
00:51:47.400 --> 00:51:51.599
off their box and put Jennai on
it because that's the hottest thing now.

745
00:51:51.639 --> 00:51:54.039
So there's it's hard not to be
cynical about some of this stuff if you've

746
00:51:54.039 --> 00:51:59.559
been in the space for long enough. Wow, what a fantastic show here,

747
00:51:59.599 --> 00:52:00.760
Folks who want to be on the
show, send an email info at

748
00:52:00.800 --> 00:52:05.440
Inside Analysis dot com. We'll talk
to you next time you've been listening to

749
00:52:05.559 --> 00:52:13.760
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learn more about hosting a show on the

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00:57:52.159 --> 00:57:57.039
best station in the nation, or
call our CEO for details too. Eight

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00:57:57.119 --> 00:58:04.239
one five nine nine ninety eight hundred
NBC News Radio. I'm Chris Caragio.

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00:58:04.360 --> 00:58:07.400
Former First Lady Rosalind Carter, the
wife of the nation's thirty ninth president,

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has died. The Quarter Center announced
she passed away this afternoon at her home

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in Plains, Georgia. As First
Lady, she was an active part of

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the administration, serving as the President's
personal emissary to Latin American countries and even

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00:58:20.320 --> 00:58:24.440
sitting in on cabinet meetings. She
focused her advocacy on performing arts and improving

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mental health programs, and not even
death can steal the love between the longest

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00:58:29.679 --> 00:58:34.159
married presidential couple in US history.
Rebecca Hughes has more. We just developed

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this really good partnership that has pasted
fell on time. Rosalind Carter married James

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Earl Carter Junior, better known as
Jimmy, on July seventh, in nineteen

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forty six, while he served in
the Navy. The couple met because Rosalind

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was a friend of his sister Ruth, and they even kissed on the first

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00:58:46.840 --> 00:58:50.960
date. The strong marriage saw ups
and downs, including conflict when the pair

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decided to write a book. According
to them both during a talk at the

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Aspen Institute, it was the worst
experience of my life. There's a vibe

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that and that's why we're still married
to The couple raised three sons and a

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daughter. After politics, they volunteered
with charities like Habitat for Humanity, founded

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00:59:07.400 --> 00:59:09.960
the Carter Center, and became leaders
in their church. Jimmy once told reporters

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his marriage to Rosalind is the most
important thing in my life, and the

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00:59:14.440 --> 00:59:17.719
ninety nine year old former president released
a statement today saying that Rosalin was his

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00:59:17.800 --> 00:59:22.800
equal partner in everything he ever accomplished. Rosalind was ninety six. The Iten

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00:59:22.880 --> 00:59:27.519
Freeway in Los Angeles will be open
for the Monday morning commute. California Governor

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Gavenussen provided that update this morning,
saying the structural integrity assessment indicated the damage

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wasn't as bad as originally thought.
Crews also worked around the clock to complete

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repairs early on the busy stretch of
freeway after a major fire last Saturday.

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00:59:40.280 --> 00:59:45.840
Iceland is bracing for a major volcanic
eruption. All thirty eight hundred presidents have

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been forced to evacuate from the small
town of Grindavic after a huge crack in

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00:59:50.119 --> 00:59:53.760
the earth began to spread last week. The seismic activity triggered hundreds of earthquakes

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00:59:53.760 --> 00:59:58.800
and tremors in the region. I'm
Chris Caragio, NBC News Radio Listine the

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00:59:58.840 --> 01:00:01.840
CASEAA lowl lie to ED one O
six point five FM, K two ninety

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01:00:01.840 --> 01:00:06.800
three CF, Burrito Valley. What
is the Del Walmsley Radio Show? Welcome

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01:00:06.880 --> 01:00:08.480
to the Del Walmsley Radio Show,
where the high fends and the Hell