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Analysis is your source of information and insight about how to make the most of
this exciting new era. Learn more at inside analysis dot com, Inside Analysis
dot com. And now here's your host, Eric Kavanaugh. All Right,
ladies and gentlemen, it's time for the only coast to coast radio show that's
all about the information economy. It's called Inside Analysis. Your host here,
Eric Kavanaugh, and I am so excited to have an all star cast today.
We've got the Malcolm Chisholm, who's got the receipts on LinkedIn promotions.
We've got the data Scott Taylor, who of Meta Meta Consulting. He's amazing
and less not at least, we have Kate Stretch Data. Kate, she
knows where you live, she knows where your data lives. She will find
you. I suggest you not to make her unhippy. Unhippy. That's what
my Russian boss used to always say about the people who he had stiffed on
bills. They are unhippy. That was his favorite term. We're talking all
things data today. It's going to be a fantastic show, and let's not
waste any time. I saw this hilarious quote from one of my favorite people
in the business, Steve Lucas said data is the new sand. I thought
that was hysterical because everyone talks about data is the new oil. You have
to rEFInd it. It doesn't have valent. You're find Oh, okay,
we know all that, but data's the new sand. It's like just gritty
gets on your shoes, winds up in your car. You can't ever vacuum
it all the way out. It's just forever with you, little tiny bits
of sand. But it makes sense because if you refine it, you can
If you have a last blower, for example, maybe you can do something
with that, with that sand. But let's throw it over to Kate and
stretch dedicated. Tell us a bit about yourself and what you think about the
importance of data. Yes, absolutely, thanks for having me on the show.
Hello everybody, I'm Kate Stoshna. I can't do the accent as good
as Eric cant for Russia, but I am the founder of Dedicated. It's
a media company that's focused on helping data, analytics, machine learning, and
AI companies reach their audience on LinkedIn. I help out with brand awareness.
I've got my own show called Dedicated on air, which is why I love
Eric's background. That says on air reminds me of my show as well.
And you know, Eric, I wanted to point out when you were talking
about sand for some reason, I started thinking of how data is the new
water because I've heard of that, right, all the pipelines and cleaning the
water and making sure it all makes sense. Then I thought of mixing sand
and water. But then I remembered I recently put together a video where I
actually build a sand castle to demonstrate the importance of data infrastructure. It was
for one of my clients. They wanted to talk about how if you build
on for infrastructure and all your apps go to crap basically and it just doesn't
doesn't work well. And so I built like a really bad sand castle,
and then I scrapped it and built a much better one. I had so
much fun, and my kids are like, you get to do this for
work, and I'm like, yeah, this is what I do. For
a living, and I explained it to my family. My brother's like,
I still don't know what you do and I'm like, well, I get
paid to make a sand castle today, and he's like, can you just
keep making them? And I'm like, no, it doesn't work that way.
It's all about the concept. But yeah, data, I think data
is extremely important. I think your question was what I think about data.
Data is literally my life. I even have a license plate in my car
that literally just says data, which gets a lot of attention from passersbys because
they start asking like, why do you like data so much? So it's
it's been fun, that's good stuff. And of course you help people get
the word out about their own personal brands, so you're passionate about brand.
You've got a fine job branding yourself, by the way. Dedicated. I
mean, come on, that's good stuff, right, dedicated? Yes,
thank you, thank you. Yeah. I love I love talking about personal
branding. I've got a course out on LinkedIn Learning on that topic as well
for people. I think it's like a forty five minute course of everything I
know about personal branding. I compacted it really really tight into a very short
course. But yeah, I think personal brand is extremely important in what in
any area of data, data management, data analytics, data science, now
AI, right, it's all about standing out, especially with AI sort of
doing all of our work for us. A lot of times, the more
personal touch we can put on our work, the better. That's interesting.
And you had mentioned before the show that we'd be remiss where we can not
mention generative AI. So you've now hinted it generative AI. And I think
the key to success with AI is going to be wait for it, data,
right, you're good quality data. In fact, let me sure this
at you and see what you think. I believe that AI, in a
particular large language models represent a second chance for data. And what I mean
by that is the first chance. We did data warehousing and analytics and business
intelligence and visualization, all that stuff. We moved it around. We use
master data management to try to reconcile systems. We've done all these different things,
essentially torturing the data to make it say what we want it to say.
But it's been very expensive. There has been certainly value for that.
But I think that this inflection point we're at right now is not small.
I think this is a very significant transformation, and that AI will give a
second chance to data, by which I mean if you feed your AI model,
your corporate AI model, and I think every big company is going to
have one with the most trusted, carefully curated data, you're going to get
a very good result. And if you don't, you're just going to get
some random nonsense. What do you think? Yeah, I was going to
say it gives it a second chance, and hopefully companies and people take the
second chance to feed it clean data and don't lose focus and don't just focus
on look what we could do a I know, I think going back to
data management and it's important, and making sure that you focus on data quality
and data governance, and then that you're feeding this large language model good cookies,
not crappy cookies, not junk food, and making sure you actually clean
it all up. And going back to the sand castle analogy. If you
know, if I use sand with twigs and random stuff and wrappers and cigarette
butts in there, the sand castle wouldn't be pretty, it wouldn't it wouldn't
hold up well. Versus if I used high quality sand, so sand and
data could be a thing. Actually, you got me thinking, I like
that I like that, and sand doesn't need any branding. I think everyone
knows what sand is. We've all walked on the beach. But to the
point of my one body on the LinkedIn platform, they said, you can
create beautiful artwork with sand if you are very careful about what you're doing to
it. But again, like with data, it has to have purpose,
it has to have contacts. And I think part of the challenge here,
I'll throw this at you and then'll maybe bring in the Westburg the comment on
it. But you think about the old days of data warehousing and how old
habits die hard and you had to strip out a lot of context in order
to get the little bits of transactional data through thin pipes to slow processors and
expensive storage. Well now the pipes are fat, the processors are blazing speed,
you can parallelize them. Storage is cheap. So everything has changed in
terms of the data game, which is the big is why we went to
the data lake. But again we thought, oh, we're going to put
it on in one place, which is probably a bad idea. I think
data should live where it is best used and accessed as needed. But what
do you think about this evolution and how old habits die hard? How do
you get your clients to make those old habits die Yeah, I think we're
still evolving, and I think we are moving in the right direction, and
the closer we can get to keeping that business context of that data as we
allow access to the data is extremely important. So I'm all about, you
know, data literacy, data accessibility, data democratization, making sure people have
the right access. But I think with the change in the progress in the
data warehousing and cloud now we make it easier for people to actually access not
just the data, but data in the right context for the right individual with
personalization. So I think we're definitely moving in that right direction. That's good.
Lasting I'll throw at you, can you define data mesh? Data mesh?
No good answer needs you what you have to say? My dog upstairs
is trying to define data right now, and he's not very happy about it.
I have two German shepherds, and of course they chose this moment to
chime in because we are trying to figure out what data mesh is all about
as well. Well, let's let's hand it over to Scott Taylor, the
proverbial data whistpar of meta, meta and You've been talking about data and the
business value of data for a long time, and they deal with data mesh
is you want the business groups to govern their own data. To help with
that process, but tell us a bit about your thoughts and the value of
data on the data mesh. You hear about the data mesh here about the
data fabric. Eric I asked you what about the data spanks? Help could
tighten the data spanks? Thanks like the clothing, tighten that integration. Scott
Taylor, the data whisperer here, I help people calm data down. That's
what data whispering is all about. I'm thirty something years in the data management
space and now I'm just a content creator, part of Kate's posse of the
data a Vengers as well out there trying to get people excited and fired up
about the data management part of the space. You're talking about feeding stuff into
data second chance. That's a nice way to put it, But are we
just talking about the same thing We've always talked about. Garbage in, garbage
out. Put the bad data into llms, you get hallucinations. So put
garbage into jenn Ai. You can get garbage out at scale now, which
is really well right, I'm actually so one of my good buddies, Eugene
Burke, who's been on the show a few times Digital Strategies Group, we're
doing a bit of a stealth project now. He had the greatest line I
think about this limitation of large language models. He said, they don't have
an epistemological barrier, which means for non philosophers out there, they don't know
what they don't know. And it reminds me. I won't say which country,
but there are certain countries where I was told don't ask people in the
street for directions because they'll be too embarrassed and they'll just tell you something you
know that they don't know. And that's kind of like what these large language
models do. If you don't have a good embedding strategy, if you don't
populate either a vector database or your own model itself with enough curated data,
and if you don't train it properly, then guess what, it's just going
to make things up. Like when it said I've written three books, and
I was like, are you seeing the future? So you're right that we
need to curate carefully. But that's always been the mission. It's always been
the case there's nothing new about that. There's nothing new about need for a
well structured, expertly stewarded, wonderfully governed data that you know you're gonna bring.
Malcolm on here has got metadata, it's got master data, it's got
reference data, mdm rdm RIM, PIM, damn. All these foundational activities
that enterprises must commit to if they want to leverage data across their organization.
And what enterprise doesn't want to leverage data across their organization. So these are
the same principles we've been dealing with. We got to shine them up,
we got to rephrase them a little bit to get people's attention. But it's
just maddening sitting in the data space for so many decades. Again, I
go back pre to K seeing the same story over and over again with different
characters. Well, I think part of the challenge is that you have this
long tail of legacy software and hardware and mindset, by the way, and
it's very difficult. So when new technologies come in, you try to move
over to the new technology, but you still have this long tail of legacy
to contend with. And in fact, we want to promote the Data Universe
conference, it is coming April tenth and eleventh to New York to the the
Javit Center, which I Javit Centritism does not like. He's not a fan
of the Javit Center. I'll find that out in the next segment. Well,
I'll be there, Kate will be there. We'll let Malcolm decide whether
he wants to be there or not. Given is going to be a party
his feeling about that location. But I think it's going to be a blast.
Yeah. Well, so I wrote I wrote an abstract. I don't
know if it will get approved or not yet, but it's said the fourth
word is in parentheses forever in your technical debt. That's what I read about.
Oh and that's pretty cue to Kate laughs, so she likes it.
The headline basically leads into the abstract, which has something like what's the definition
of a legacy system? And the answer is any system in production. The
joke is that the moment you drive off the lot, you are accruing technical
debt, like literally the second you leave the lot, tactical debt is like
bare death. That's a good way to think about it. Yes, well,
you look at like doctor's offices, I mean they still want me to
fill out pieces of paper on a on a notepad. I'm like, are
you kidding me? I have to write my name seven times? Like you
can you just you know, could you just give me the iPad? And
then they give it the iPad and it's like eight hundred and seventy five questions.
It's like, do you people know anything about surveys? Like when you
get to question five hundred and twenty one, the quality of the answer goes
way down because no one wants to write that stuff right. But we can.
We can port that to the data world and say ask just enough questions
to get just what you need and move on. What do you think?
I think so? Yeah, it's just I hate that a doctor's offices as
well, filling it and over and over again. It's like, haven't they
kept anything? Don't they have any legacy data about me? Yeah? They
do. It's en a folder that they put in the in the filing cabin
in the back there some rdy here. You have some interesting folders too,
including folders on stretchedy. I have folders. Yes, I've got all kinds
of interesting stuff here. Do you want to talk about the puppets yet?
Or you want to bring Malcolm on. First, let's bring in a puppet
real quick, show your puppets so we can see them. This is about
my character, the CDO, the Chief Dog Officer, and he partners up
with If you haven't seen the Data Puppets on YouTube, you got to see
him out there. There's a preview out there. Journey to the Center of
the single version of the Truth, the greatest data story ever told, starting
the CDEO, the Chief Dog Officer and also appearing the CEO and Chief Elephant
Officer played by Kate. There's a CMO. Guess what he is? A
mouse? A CFO is a fish? I mean, just stuff rights itself.
How has nobody else come up with that? Before? They hire a
cat sultant from meol Kinsey who borrows their watch to tell him what time it
is. Watch. We've got a whole crowd of business users who will be
out there trying to do but take a look at that. It's crazy stuff.
I'm having a ball. This is what I'm doing now, Data Puppets
events, the funnest possible stuff and similar decay. If you can get paid
for making sand castles or doing puppets, then welcome to our world. You
gave me a segue to bring in a fantastic lyric by coy Leroy or Coyle
Ray I think her name is. She says time is money, so I
spent it on a watch. Hold on, It's like what that's pretty clever,
And that's the semantics side of the equation. We do want words to
go along with all these numbers. I mean, the numbers obviously are the
transactions and how many widgets we're selling and so on and so forth, or
clicks and all this kind of fun stuff. But the words are the semantics.
That's probably gonna be a good segue to bring Malcolm chi isn't into the
second segment here, But maybe we'll just finish up with Scott, and maybe
I'll go ahead and throw a curveball and bring Stretch back in the conversation Gates
the puppets. Why you play with games with puppets with your data? What's
the question? Your accent is very difficult there. She doesn't take puppet questions.
She doesn't. I'm not allowed to speak about puppets for our contract with
Scott. That's that's his thing. We can it's my area, all right.
But I had a comment when you were talking about the doctor's office and
the iPad. So they do have iPad now, or they send you a
link on your phone. But the worst, most, most most knowing part
is when you have to fill out Let's say I recently had an appointment for
my kids doctor checkup and they ask for the kids date of birth at least
eight times on eight different within one form. Like I filled it out.
You can't. You don't have the systems to just why it's so compat the
model. It's a system's integration issue. It's a data model, right.
I mean I remember with some of the smart people online they figured out,
hey, if we have someone enter the zip code, we can dynamically look
up the town that they're in. I'm like, who's thinking, huh,
who's thinking put the zip code there? It's like the people like USAA figured
this out. You call in, they say, I see you're donally from
a number in our system. If that's the phone you usually used to be
a press one, Like, yes, that's right, now you know who
I am. I don't have to tell five people in a row. This
is Eric Cavanaugh. I'm calling about this. Oh yes, yes, that's
called enterprise integration. That's from a lot twenty two years ago, enterprise integration.
We've come a long way, but data is front and center. I
suppose data should lead the charge. Let the data talk to you, let
the data tell you what's going on. I mean, that's a standard thing
to say in our industry. But folks, we're just going to have a
lot of fun here on our show today. Talking to of course, Kate
Stretch. I'm dedicated. I'll never be able to say your name normally,
just so you know, because I now know like you know, and you
laughed. So come on, a guy gets the growth laugh, keep saying
that stuff. Keep her laughing. That's the joke. And then have you
introduced me at like conferences? Now back to bring Homer Simpson back into it.
One time on an episode he made he got marg mad and then he
made her laugh. He goes, ah h, I made you laugh.
I win. He walked away. Oh we're not on that bad. Don't
touch that doll. Folks will be right back. You were listening to Inside
Analysis. Welcome back to Inside Analysis. Here's your host, Eric Tabanaugh.
All right, folks, take us to the future. Indeed, you're on
Inside Analysis talking to an all star cast. Scott Taylor, the data whisperer,
Keate Stratch of Dedicated and next up one of my favorite people in the
business. He's just rocking and rolling on the LinkedIn platform these days with thought
provoking posts about receipts. He has all the receipts and he knows about the
metadata on the receipt one. Malcolm Chisholm of Data Millennium, welcome back to
DM radio. Tell us about the receipts post that got three hundred thousand views.
Thank you so well, that was, you know, something I always
wanted to do, which was I mean, you know, how do you
bring home data to people and the different types of data? Well, do
data analysis of a receipt. We all deal with a receipts, I mean
just normally throw them away or even refuse to accept them. But if you
look at them, there's a lot of data on it, and some of
it I frankly, I don't know what it means. Quite frankly. It's
all encoded, but a lot of it you can make out and then you
can classify and it's met to data. It's transaction data, it's mass to
data, it's reference data. And it's you know, useful way, useful
tool for teaching people about data. I didn't think it would go very far,
but it like took off and went viral, which is quite bizarre.
So and it's still going strong out there in the LinkedIn sphere, So there
you go. Gave me hope that stuff actually can go viral because I've learned
some hard lessons over the years that a lot of what goes viral was paid
to go viral. So yeah, I don't think you paid any money to
promote that, right, That was no, No, I paid no money.
Well, I mean except for the lettuce and whatever else was I bought.
It cost me about six bucks something like that to get the receipt,
so you know, the local supermarket and the folks of the supermarket were very
happy because it served as three hundred thousand views of unpaid advertising for them too,
So there you go. But I thought it was very clever because you
were looking at the receipt and you pointed out, here's metadata about the product,
here's metadata about the skew. All. This information is very important information,
which is a window into the operational system that is actually generating the receipt,
right right, and all systems. It's a composite that's all ending up
in the receipt of all this data. So you can think about you would
have to have different systems to handle different bits to it, but they're all
coming together there. So it's a view of data, you know, and
something you know quite common, but it's it's it shows the importance of data,
yeah, and trying to understand where things come from. The thing I
like about it is that it is a reflection of what is coming from the
inside. There are all these systems that are tracking the price of products,
of course, the date and things of this nature. If you have your
your magic code or your you know, your rewards number for example, I'm
like Bill Burr, I don't really trust the rewards programs. They're always changing
their minds and pulling the string on things. But there is some value and
all that stuff, and it allows you to kind of see what's inside,
you know. I remember years and years ago I worked on a project where
we connected to Fanny May's deshtop underwriting system. We're the first company ever to
do that, and I remember that the developers having a hard time, and
I thought to myself, you know, in the office. They use a
system, a desktop underwriter. It's an application and if I just did something
like you know, print to PDF or something. I think pds are around
back then, but it's year two thousand. I was like, look,
let's just print out what it needs and that those would be the fields you
would look to capture through this web form. So it's like, how the
hell hard is that? It's just the data stream Now you have to securely
connect to something but in terms of the data itself, and that's what we
did, and they freaking it worked. I'm like, well, duh,
why don't you pay me a thousand dollars an hour or more? And just
for those that are no Desktop Underwriter is a way to get mortgage applications approved
so that the government will securitize them and put them into mortighbacked security. So
it's pretty important part of our economy actually, So yeah, that was that's
a big deal. So even if you own a house, you've been probably
been through it, and even if you don't know, yeah, it's it's
painful. I remember the collateralized debt obligations and what was Bucket's comment, don't
believe the experience formulas or something like that. It's like watch out for that
kind of stuff. Yeah, those are the days. Yeah. But the
important thing is the data that's in these systems. And I'm curious to know
your thoughts. You've been around for a number of years here that have seen
highs and lows and movements like Haddo coming along, everyone getting excited and now
of course AI. But to our point earlier, AI is not going to
do you much good if you don't have really high quality data, right,
Yeah, I mean talking about the historical I always thought, you know,
slogan of it ought to be we build the legacy systems of the future,
you know, so that people truly understand the value that they add. That
said, I mean, okay, AI data is going to be important,
but it's a different take on it is going to because these things work off
of sentence form you're going to have to get It's much more about getting unstructured
data as we would call it, into a shape that you can push through
in data preparation into the into the you know, AI black hole beast basically,
and that data preparation, having looked into it, is an enormous number
of steps. There's all kinds of things you've got to do. And there's
things that AI doesn't do in terms of normal data management practices, like data
attention. You forget about it once it goes in. That's it. It's
not you know, it's like it's like Las Vegas. It's well, it
comes out again, but I mean it's you can't make it forget. There's
no forgetting mechanism. So if you put something in like PII, it's not
like you can scrub it. There's no bleach for AI. So that's a
problem. But this enormous set of steps to break up things into maybe propositional
format. Yes it'll read some structural data, but mostly it's getting it into
that format. And then on the other side of it. It's not like
the thing in Star Trek where you ask it anything and it'll just tell you
whatever, and it'll like this super brain. It's it's going to have to
be coupled to use cases, and those use cases are going to have to
be expressed as prompts. So you've got these prompt libraries that will turn,
you know, the gibberish question that Malcolm asks the AI into the closest prompt
and give it the answer associated with that prompts together question answer pair. So
all of that is a huge amount of data management, data preparation, and
you better get it right. Be goes what I have found in the past.
Maybe this doesn't apply to ai IS, but with documentation is that as
soon as somebody finds a bit of documentation they don't trust like one percent of
some threshold like that, they don't trust any of it. Okay, So
if you get garbage answers out of AI, then it's going to make people
thinking. And although they probably you know, use the day, it's probably
conditioned to think that AI is you know, going to solve all the problems.
So maybe be a bit more generous with it than documentation, but they
won't, you know, they'll come to a point where they don't trust any
of it if this isn't done correctly right. Well, that is actually a
pretty interesting point, and there is a trust issue that we're going to have
to get around it. There's also a mathematical issue, because as I understand
it, what these models have done is first of all, they vectorize,
by which they mean they take words and turn them into numbers, into a
raise of numbers, and then they're doing statistical analysis on the numbers, and
then when you sort of decode it on the other end, it turns it
from numbers back into letters. So you do have this very interesting transformation like
reminds me of asking, remember asking, Or it switches from letters and sentences
and paragraphs and so forth to numeric values and then back. So the thing
that kind of strikes me is the other shoot to drop here is it's going
to be the cost of this stuff now. Right now, there are billions
of dollars going in by Microsoft and Google other guys to train these things,
and companies are all going to want to train their own models. But I
don't think we really have any idea what the actual cost is going to be
to the user. And I think that's going to be another shoot to drop.
But what do you think about them? Well, right, I mean
I think that it depends on the apps that are made from them. So
you know, I'm looking to this right now because I don't want to use
one of the hosted ones because why should I give it my intellectual property?
So I'll get one of the ones I can have on prem Well, it's
going to cost me about five thousand dollars to buy the equipment, you know,
just to be able to run the you know, the base version of
one of these. But then I could do things like, you know,
create something around a curated set of knowledge where people would understand it will do
that and give you answers to that. But I think a more interesting set
of use cases to me is one is knowledge integration. So you know,
we talked about data integration like you know you're talking at MDM, like integrating
customer data earlier. Okay, but we've got knowledge as well, So I
could theoretically think about nobody likes being a data steward, So let's have an
AI one. So this AI data steward is told who looks after you know,
these reference data tables, these code tables. And then it's also let's
say, coupled to the HR system, and it recognizes the feed from the
HR system, so it says, okay, Eric is or Malcolm is the
data steward for the country code. Okay, that's good to know. And
then Malcolm gets fired because he's useless and is replaced by Eric. But that's
an announcement from HR. Now today you'd have to go into a data catalog
and manually some pushchmucks data slash data steward has to update it. But now
we can integrate these various unstructured data sources and it'll realize who is the true
day's steward of the country coast. So we've got that as a possibility to
get all this integration of knowledge, which is something a little bit different to
the structured data integration. See, so that's a good one. And having
these you know, data steward bots really you know agents out there doing this
work for us so that we don't have to do all the donkey work which
nobody wants to do. We keep telling them they have to do it.
No, no, they don't want to do it, so and I don't
blame them. So those are there's a lot of promise there too. If
we can get this to work properly, well, and you know, let
me throw this curve ball at you, and then I'll bring in the other
guests and throw it at them as well, or some variation of it,
and maybe a hard slider. When I think about the power of these models
to upon prompt grab information from all these different sources and fuse it together to
give you some narrative, which is what they're doing. I wonder to myself
think about all of the heavy lifting that's done even still today for data warehouse
and extract transform load extract load transform, all these batch windows, which in
large organizations are in the hundreds, if not thousands. And you have to
know that it's not all change data capture. There's just a lot of bulk
movement of data picking it up fork maker over here, pick it back up
again, fork lifted over there. That's I think that's going to be looked
at as a tremendous waste of time in the new world. But the key
is can these models short circuit all of that and just get me the answer
I want somewhat reliably so I don't have to move all that stuff around anymore.
What do you think I think you're on something that I think that one
of the impetuses if you look away the AI audiences that coming from is to
get rid of codeer is to get rid of data. Engineers don't need them
anymore, we don't want them. They're a huge expense, So could we
replace them. So you've got you know, copilot and things like that.
You've got assistance to generate code, But why not let it do the code
generation. If you could just show it the data, okay, and do
exactly what you're saying. I mean, how complicated is it? I mean
ETL is you know, the the ETL products are, or traditionally have been
a replacement for the more expensive kind of programmer. This is just taking that
evolution a step further. So then then you know, if you could like
just get rid of the you know, legions and legions of coders that are
out there, you know it's not good for them, but and replace them
with some skilled people who are driving this and who you know, constrain the
AI and let it generate the code based on what it's seeing in the data.
That would be that would be a massive you know, efficiency gain in
the economy. Yeah. Well, well and the yeah, I mean,
I asked myself, we have a show planned next year. What's not in
the crosshairs of this stuff? And you know the fact is we have lots
and lots of legacy processes, legacy mindsets, legacy systems. So just give
you one example real quick before the break, think about MRI scans. My
wife who has to deal with MS, which is a real pain. She
has to go get these MRIs every once in a while. Those files are
massive. I mean, you have you can fit them on a cdond,
but it's a huge file. If you vectorize something like that, number one,
it's a form of compression. But number two, it facilitates statistical analysis.
So isn't that a use case for vectorization to vectorize graphic images big ones
into numbers, which is much smaller and allows statistical analysis. What do you
think? Well, I think we're we' I don't. I think you're right,
But I think we're part of the way there already with the image processing
for features that we have that are available in current libraries which are pre AI.
So a lot of that can be done already. I've seen it done,
you know, building data pipelines to do just that, like has the
has this you know, tumor progressed regress whatever. So so we've got a
lot of that, So that would be taking a step further. Why not?
Yeah, I mean I think again, what isn't in the crosshairs of
these new technologies, you know, and I think all you you're in the
process. We could have a you know, we could have an AI illusion
of a host who could ask the questions you know you can get you can
get Jeni jen a Ai to ask to you know, give you questions.
And you couple that with one of the you know, the video generator for
things that they have and they are you've got a new personality. Yeah.
I think in terms of its storyteller, it's still pretty formula, but it's
you know, you have to figure it's learning. It's going to learn.
It's going to get better and better at these things, and you're going to
have your choices. Maybe maybe they'll plateau. Yeah, hopefully we'll see,
well they'll touch the doubt. Folks will be right back. You are listening
to Inside Analysis. Welcome back to Inside Analysis. Here's your host, Eric
Tabanaugh. All right, folks, back here on Inside Analysis, talking all
about all things data. Time to refine. You have to refine the data
to get value from it, just like oil, just like water. I
heard the water analogy at a conference this past week and I was joking,
we're all thirsty. Look at the oceans. They're huge, but you can't
drink that water. You got to desalidate it. You got to clean it
out, You got to get the fishes out of there, You got to
get all kinds of fun stuff out of there. So we understand that refined
data. But I'll throw it over to Kate's that of dedicated to share with
us her thoughts on on best practices going forward. We have a second chance
for data. I think our audience has agreed with me on that. What's
your advice for companies to build their own AI model with refined data? How
do you get that process started? Yeah, you know, when you said
best practices, I was actually going to take it into the direction of data
privacy because something Malcolm shared earlier when we was talking about receipts reminded me of
a story I had from a couple of months ago. I went clothes shopping,
So raise your hand if you've been clothes shopping in the past few months,
right in store, not online. And I think we share so much
information online that when I don't really do in store shopping. But when I
got there, they said, do you want to put your email or phone
number into the system? Literally every store, and I'm like, no,
I don't want to put my information in. I just don't like it.
I don't like all the spam. It was fine, and then I get
to one store I'm not going to name and shame them, but they're like,
okay, do you want to receive After our hole back and forth of
are you sure you don't want to sign out, like yeah, I'm sure,
fine, okay, so they're like, okay, so in order to
get a receipt, you're going to need to give us a phone number or
an email address. So I'm like, okay, well, I guess I
won't get a receipt then, and so they come back and they're like,
well, you can't return your stuff with that a receipt. And these were
shoes for my kids, So I'm like, okay, well, if they
don't fit, I'm gonna have to come back. So they sort of put
me in this position where I won't be able to return it, but they're
sort of forcing me into sharing my data. When you were talking about receipts
Welcome, that was all I could think about, Like, Yeah, we
have all this information that they're collecting, but sometimes I just didn't want that
to happen. I ended up not getting my receipt. But I think one
of the best practices that I can think of for organizations and data is making
sure that the people they're taking data from feel comfortable enough sharing that and sort
of allowing them to opt out of that process if that thing that they want
to do well. You bring up a very interesting point, which is getting
forced into using a certain system, and this some thing is the dark side
of digital transformation. You know. I went to a hotel in Austin,
Texas last October and there was no one at the desk. There was no
one to talk to to give me my keys or anything. It was all
this this online process of using your phone and I had to like show it
a picture of my driver's license to capture that. I'm like, how arwellian
is this weird situation? And it was a real pain in the rear.
I'm not gonna lie. It was like you really can't afford to have one
person sitting there and like get paid twenty bucks an hour to take information,
Like that's how far we're going to go? Or I'll throw to Kate and
then we'll get the other gentleman in on this. The McDonald's kiosks where they
want you to go and find your food. Look, if you just do
the guarden variety something, okay, But if you have special orders, it's
like, oh my god, hold on this menu, over to that menu
and this other now wait which I lost it. It's just a total nightmare.
There are some things that should not be digitally transformed. Real quick,
key, what do you think? Yeah, so real quick, I'll tell
you. I think there are preferences. Some people, probably about half the
population, would want to avoid eye contact and people, and they would love
it. So I think just providing the option for someone like you who wants
that social interaction of like, here you check my idea, I don't want
to scan this, And then I think just just long story short, have
both options because I know that could speed things up if there's a really long
line and you've got that one person who just maybe is new and there's know
what they're doing, or one system crashes. You have all these other options
of people doing it themselves. But I personally like, if I'm going to
get do food shopping, I'm not going to do the self checkout when I
see even if there's a line. I just why there are people a right
there who are going to do it for you. Where my mom would insist
on self checkout, even if it's a cart full of stuff, I'm like
you might you might as well work here. Now you're just you're doing it
all, so there should be at least a discount for that service. Shoper
makes that joke. It's like, why don't I just check it out myself
and I get a discount? Or what's going on? I will? Yeah,
exactly when did I sign up to dive an Intel insurance plan? What's
going on here? Oh? Why am I working for you? Well,
Scott Taylor, I'm sure you don't mind hitting all the different buttons in the
menus. I don't, I don't mind. I guess my big question is
based on the conversation we just had. Kate, did the shoes fit your
kids? Yes, the shoes fit. I did not have to return,
but I'm not going back to this youw okay? That's good? Looks like
radio did. I had the opposite experience recently at a panera where they had
a bunch of things on the menu and I was sitting there, going,
I have to ask these people what's in this salad? Like three different salads,
and they had a kiosk and they had the nutrition information and they listened
to every single item that was in there was like, all right, that's
a lot easier. So yes, if you have a choice, but sometimes
you feel like not because I don't want to have eye contact with somebody.
But it just might be a smoother experience to go this way rather than that
way. So as much as these things can augment our existence, that's good.
But when it starts to replace it as an either or or that ridiculous
story you're talking about, Kate of demanding certain data otherwise you don't get proof
of the transaction we just had. That's if that's even crazy. That's I
was wondering if it's legal as well, because maybe I don't know. But
if you know, if you spend some time and you're not from Russia,
you're from Tatika's standard, I guess, so it's not not the same.
You'll probably just say don't shop here if you don't like it, right,
but I don't know, don't shop here if we can't get your data.
Yeah, pretty much, that's pretty strangel malth Im. I'll throw it over
to you for Yeah, I would. I would try my favorite Soviet rule,
which is the rules of there to be avoided. And you want my
emails, okay, say at gmail dot com. Okay, so I'm just
lye or you know, we just said you were the most honest man a
moment ago. You've upset then no, no, no, no breaking Now
you heard it here first open in this kind of ethical dilemma, you're forcing
me to do something against my will. So this is what you're going to
get. Well, now then you you still you won't get the receipt which
they will give you. They will print it out and give it you even
that they don't have only email. Okay, in that case, what would
do it was a single point of failure. So this is like when they
had a colonial pipeline ransomware a few a couple of years back, where they
said they said, well, okay, the system screw up. Well,
why didn't you go back to managing this pipeline, you know, the mechanical
way that you used to do it, because it's quite old. And the
guy said, well, everybody knew how to do that is either dead or
retired, and so you know we are. You can't read maps, you
don't want to make eye contact with anybody or talk to anybody. Okay,
this is okay until something happens with this great shaky edifice of technology that makes
our lives easier in the sense that we don't have to think very much or
engage in too much physical labor anymore. Yeah, but what happens when you
need to so you've got no backup, You're you know, you're making your
civilization extremely vulnerable. So we will we will see. Well we're talking about
in the break right. So you have Jena I create the questions for an
interview that you have jen AAI create the answers for the interview that you haven't
watched the show, and generate a summary do we need as part of the
show. There bots, they're accounted as visitors, as viewers to my website.
Look at all the viewers I have. Yeah, I had a nasty
spell, an experience. I don't know, but I was contacted in by
over LinkedIn by It was a legitimate company. I'm pretty sure it was a
bot that was text me on LinkedIn messages and then somebody another bot on email.
And I think we ought to at least have transparency. Say Hi,
I'm a bot, I'm emailing you on behalf of you know, blah blah
blah company. There's no there is no transparency. It's like I had to
prove I had to go through this LinkedIn certification, where I did have to
do facial recognition stuff to get LinkedIn to believe I was a human, whereas
no bot has to declare themselves to be a bot on LinkedIn and doesn't have
to prove that they're not. But I had. I had to prove I
wasn't. I had to prove I was human. So that kind of irked
me. Is there a need for like a reverse Turing test? And yeah,
you're onto something though, because you get these phone calls too, like
Hi, it's Cindy, I'm calling you about the new online program. Like
okay, I'm sure there was a Cindy who recorded that at some point in
time, but you are not on the phone. N No, there's not
even a Cindy. It's totally generated. True. Oh that's just bizarre.
I think I like opening up one chat bot and then getting into a conversation
with the other chat bot, So you get two chat bots talking to each
other. Yeah, and you still don't get your order through. But it's
fun. It's you know, plenty of time with my hands in between puppet
takes. Yeah. At a certain point, customer service does come into play,
and you will remember as Kate will not to go get that store anymore.
But I have to shout out to old Radio Shack because in the nineteen
eighties, Radio Shack had the foresight to ask you for information. They have
a little form you'd fill out when you bought your batteries or whatever it was.
It was actually very annoying, But in retrospect, I was like,
wow, they were very forward looking. But they weren't. There was a
comedian who did a bit during that time about having to give your because they
forced They were really militant about getting your phone number. They were remember this,
and you need your phone number to buy batteries. It's a corporate dictate.
Yeah. How's how's radio Shack doing these days? By the way,
so well, folks who've been listening to the only coast to coast radio show
here in America about the infationied economy. It's called Inside Analysis. Talk to
you next time. All right, folks, time for the podcast bonus segment.
We said, stretch and we can talk about her in different ways.
We will get the information from it. I love Russians like I just love
the Russian accent. It just cracks me up. Hey, you're bringing me
back to my bow ankle days here with your with your that's right order?
And they finally and what's Natasha's last name? And it's talking about trivia since
the bonus? You know Natasha Boris and Natasha No, what is Natasha fatal?
It's Natasha? Like yeah, so I like, did not see me
look anything up for that one? I know that one. Look Trivia Pursuit
days. You are a walking, talking, large language model. I've certainly
hallucinated given my Berkeley background. That's pretty funny. I like that. Well,
speaking of hallucinations, I mean, goodness, gracious, we do have
to get down to business here and get serious about these AI models. And
this is my prediction that every major corporation is going to have their own AI
model. Many of them were working at it already, a lot of them
aren't. In fact, you'll probably have multiple models where I did multiple data
warehouses. And I get back to is anything not in the crosshairs here?
I'll throw at first to Scott and then over to Malcolm. You know,
if you launch one of your own models, your private, single instance,
if you will single tenants AI model and start feeding it with your data,
you're going to be able to get interesting stuff. From that, from a
senior executive standpoint, you'd be able to ask who's working on this right now?
It's just going to pull this stuff in. It's kind of crazy how
well it works. I don't fully understand it. I don't think anyone does,
but Scott'll throw it over to you. I really think that every last
bit of legacy technology is to some extent in the crosshairs. Now, what
do you think. Don't you hope so everybody complains about so much of this
stuff, you hope that it gets transformed in a way. Meanwhile, more
than you know half the world, if not more, it's still tied to
Excel and things like that, where they've got to go kind of do their
own little skunk works. But you know, I hear about what I kind
of want to remind people of is all this stuff's cool. But how's it
driving the business regardless? How is this enabling the strategic intentions of your enterprise?
How is it helping you your company get to where it needs to go?
And that's still the nature of business, right, human interaction, bringing
value to your relationships through your brands at some form of scale, right,
that still has to happen, right, and and I think, you know,
while Malcolm was talking about coders, I mean, it wasn't that long
ago, right that they were saying, okay, what you got to do.
Everybody's got to learn to code, and now it's you know, that's
the equivalent of we all have to learn how to use a slide role.
So just you know, having the creative aspects. And what I would counsel
anybody going to the space is make sure you, you know, you beef
up your creativity interesting capabilities there because that is going to be tough to to
replace. Yeah, that's despite what's going on in Hollywood and then you know,
discussions and during the strike about the use of AI and that sort of
thing. Uh, it seems to be okay to tell a kind of mediocre
story, but it can't. Hasn't come up with great stories yet, has
it. Yeah? I don't know, man, this is interesting, Malcolm.
I'll throw it over to you. You've been tracking this industry for a
long time. There are countless inefficiencies in how we manage data, how we
move data, what we do with data. I think these new models,
these new AI models are going to be truly revolutionary in terms of information persistance
and retrieval. But we do have to be careful because they do make stuff
up. So how do you know, how do you build an epistemological barrier
into these things? I don't know. Do you have any ideas now?
I'm not sure, but I would. I think one of the things that
we we're going to have that is a bit difficult to conceive of, is
it's going to replace legacy. I mean, what we have is an accumulating
layer after layer after layer of legacy technology. There's going to be a new
one on top of that. It's kind of the law of primitive survivals.
You've still got you know, I looked at jobs for Cobolt programmers on LinkedIn
a couple of days ago. You've got people, you know, still writing
Cobolt and it's not necessarily going away. So we're getting this layered architecture built
up with these things on top of it, which is becoming increasingly difficult to
manage. It's not like we've had this before, and you generation, your
technology is supposed to replace everything else. It doesn't. It just becomes another
layer. You know, what's happened to her? Do that's legacy now?
So I think that's part of it. The other part is this is different
in some ways. It addresses a different set of use cases. And as
you know what Scott was saying in terms of the value it brings, I
don't know, but I think it's like one of these moments where everybody's paralyzed
with fear again that if their competitor gets hold of this thing and does something
with it, they're going to be driven out of business. So we got
to do something too, and we'll spend money on it up until the point
where we've disproven the technology that it isn't a threat to us. Okay,
like you know the books and mortar crowd. Then if you remember rebelled against
the dot com people back in the early two thousand once the dot com bubble
burst, and they took a pretty terrible revenge on them. Now everything's been
cured since then, but it's it kind of feels a little bit to me
like that again, Well, everything's got a label now right slapped on it.
Now with jen it's just like you know now with Extra Zest. It
just becomes this practization for a lot of these technology companies, you know,
they rip the data mesh label off their box and put GENAI on it because
that's the hottest thing now. So there's it's hard not to be cynical about
some of this stuff. If you've been in the space for long enough.
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It's downtown waiting for you. She has not a thing that really has
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a sweary NBC News Radio. I'm Chris Karragio. A young Israeli American child
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