WEBVTT

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The information economy as a ride.
The world is teeming with innovation as new

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

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insight about how to make the most
of this exciting new era. Learn more

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and inside Analysis dot Comside Analysis dot
com. And now here's your host,

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through Eric Kavanaugh. All Right,
folks, hello, and welcome back once

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again to the only coast to coast
radio show in the US of A.

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And it's all about the information economy. It's called Inside Analysis. Yours to

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the Eric Kavanaugh here, and we're
with our good buddy Eve Mulkers today,

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the founder of seven W Data over
there in Belgium and I are kindred spirits

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trying to understand what's going on in
the data biz, and there's a lot

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going on. There's some news.
I heard our friends at Okaira have been

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acquired by Data Bricks, So I'm
glad to see a happy ending there for

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the Okira folks. That's actually a
very interesting space in and of itself.

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Even maybe I'll just kind of comment
on that and get your thoughts on it.

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But this concept of dynamic governance is
kind of interesting. So what the

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Okaia folks figured out is that to
manually handle all access to information systems like

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sensitive data sets, for example,
it's just too hard to do. It's

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going to take too long, and
it's a really menial task. It's just

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basically assigning privileges to someone. And
it's more complicated now than it has been

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in the past because there are lots
of different data sources and you can't just

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easily tie an access level to a
role. So a lot of times people

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would connect to active directory or eldapp
or some of these other solutions to ascertain

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okay, this is a director or
a manager that should only be seen by

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a VP. That stuff sounds like
it can solve the problem, but usually

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it's just not granular enough. And
so what Okia did, and there's also

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Immuda and previous Sarah, there's a
couple others. Now there's a whole space

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kind of opening up right now.
And what they decided was, look,

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we're going to come up with an
engine that will dynamically determine upon query time

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whether or not this person should see
this data, which is pretty clever.

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So Data Prince has now acquired it. From what I understand, Okira,

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it's okay, E r A.
But what do you think about this space?

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I think it really does speak to
the maturation of the industry that the

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old fashioned way of controlling access to
data systems manually is just not going to

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cut it these days. But what
are you thinking, Yeah, I joined

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that that division. Security has always
been a problem, an issue maintaining it.

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From my developer days, we always
wanted to have access to reduction data,

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for example, and then it's give
me the admin rights because well,

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you know, you trust me as
signed the security documents, so we're pretty

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fine on that, but maintaining it
because people enter companies, leave companies,

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you're working with partners, how does
that all tie into your ld up?

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Like you're saying, that's that's pretty
hard to maintain that, And with the

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variety of sources what you're getting,
how do you maintain that and pull that

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into a data product. So definitely
that's a market that has a lot of

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potential on getting that finally in a
certain way, right in an easy,

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maintainable way as such, because like
you say, before, was okay,

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director's day can see all of this
level of type of data. Financial sensitive

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data, personal data was only reserved
to HR departments, but how do you

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set up this matrix before you have
it finalized, it's already outdated and that

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was a big challenge on on maintaining
all these roles and responsibilities and rules in

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place. So I think, yeah, if you can do that and really

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at real time, it's always an
advantage to have that in a more agile

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way where you can say we deploy
a new source and the security is handled

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by various dynamic rules. Maybe you
have a fixed set of rules as well,

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but on the other hand, you
can define which makes sense that a

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financial director can see more financial data
than somebody else who doesn't have anything to

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see with that financial aspect, and
the saying goes for marketing or sales type

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of data. So yeah, I
think Data Bricks made a smart move on

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acquiring a CARA. If you see, they are kind of being positioned of

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the central data leak as such,
and they're making big kicks in moving forward

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on that. Yeah. Well,
you know, it's funny you think about

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governance as controlling access to systems by
people or from people, but really there's

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also the algorithms and so as AI
really takes off, and I don't think

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it's going to stop this time.
We've had a couple of AI winters over

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the many years because the ideas showed
up and even the algorithms, but we

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didn't have the processing power. It
wasn't inexpensive enough. And that's no longer

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the case. Now we have tons
and tons of processing power. It's very

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cheap and you have all this data, and as companies look to leverage AI,

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you have to be careful about which
data you give that algorithm access too,

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because you know it as models train. If you just open up your

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kimono and let it see anything,
there's going to be PII in there,

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there's going to be bad data.
I think it's really a case now where

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it's almost like we're throwing down the
gauntlet on data quality and on finally cleaning

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up the mass because and so data
catalogs are part of the storyline we've had

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to show recently with data dot World. We're gonna do a show soon with

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alation. Data catalogs came along and
really helped sort of identify and begin the

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process of defining business terms and creating
this dynamic business glossary for example, and

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educating the users. I think you
told me that data literacy is a hot

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topic on SEVENW. Data and a
data catalog will help improve data literacy because

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you're getting definitions of things and you're
also governing the data. But I think

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the real key is that you have
to have some kind of system that will

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scan your entire network and find every
data source and every data targets and begin

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to map out what that is.
Now, you do that in the security

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space. Like there's a company Extra
Hop, one of the coolest companies I've

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come across. They and this is
gosh, probably ten years ago that it

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took a briefing from them, but
I've taken briefings since, but not terribly

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recently. But what they do,
it's very interesting, is they will basically

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siphon off your network traffic and they
use a copy, so they create a

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copy of your network traffic, and
they use that copy to populate a landscape

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of all your systems, all the
data that's flowing from place to place,

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which is extremely valuable. And then
of course you have to go along and

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catalog these things and manage it.
But to me that's really important now because

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because of AI and because of the
pressure in the marketplace, etc. I

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think it's time that organizations really took
a hard look at what they have and

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make sure you've catalogued what it is. Where is the PII, what's your

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policy you're going to be around dealing
with that all that stuff. Really it's

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a must have. Now what do
you think? Yeah, the data catalogs

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to what we see Since five years
and some focus has been given to data

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catalogs and people start to understand and
information management to all the data catalog can

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bring to the understanding of your data. But you have the meta data level

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the data about the data, and
then you have as well more the business

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is loss of where you understand where
the data ties into your business strategy and

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your business processes. Now with a
low language, models and tools like for

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example, Cynoscope is one of those
unique special products that looks at the data

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in a different way. It looks
like if you look at it. You

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have these tools like you look at
your data on your disc and see the

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heat map of which files are have
the biggest size and type of data?

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Do I have more music on my
disc than compared to word documents? And

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Cynoscope is these kind of tools that
look at the data like an emma and

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eyes can from your body. But
for your data and so that helps you

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out identifying which is a sensitive data, which kind of data can an archive

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in such a way. And I
think a combination of a large language models

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and a tool like a sinoscope could
be a real great visualization understanding tool to

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build these smart type of data cantalogs
remembering the times when you were doing When

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I doing more business analyst roles,
it tooks ages before you understand the business,

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that you understand the rules in place, that you understand where and how

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to find the data and trust the
data what you're looking at. And by

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having a more dynamic and smart type
of data catalog this will be so helpful

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for businesses getting really insights out of
their data. And think it's it's just

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starting with the large language models that
will help building that understanding. Now,

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the challenge still is next to the
contextual type of the thouses that it needs

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to have a business specific list of
all the concepts in your industry. But

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I see a lot of companies popping
up as well that have that built already

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for the finance industry, for the
tech industry, and let's go along with

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a pharma and more specific type of
industries as well. So yeah, it's

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it's really exciting on how all of
this is developing right now. Well,

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and then there's this company Praxy Data. We just started doing some work with

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those folks, p r a XI
Data and this. So you've got Andrew

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On Andrew Turner. Andrew Turners a
long time expert in this field. He's

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a he's a real seasoned veteran.
And Then Andrew On has some great experience

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from over at Horton Works. He
ran a patchy atlas which was this metadata

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management technology basically, and they've figured
out some interesting stuff, one of which

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is that the expert curation is key
to their success. But they also focused

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on specific industries and specific business models
as a way to really expedite the process.

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So I think that makes a lot
of sense because you know, the

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lms, from what I can tell, they're filled with tremendous amounts of information,

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but it is pretty generic. I
mean, you can get into the

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weeds on some stuff pretty quickly.
But I think there's going to be there's

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going to need to be some way
to purely feed your corporate data into an

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LM and sort of like a single
tenant kind of situation. And I'm not

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sure if that's coming, as someone
told me that it is coming, that

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they're planning that open Ai, for
example, is planning to offer the sort

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of single tenant engine because now everyone's
just using the same engine, so all

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that content goes in there and you
lose control of things. And if you

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add code to that, then now
this model has code if as any proprietary

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have to be careful about that.
But I think that if you look at

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how powerful they are and what they're
able to create on demand very quickly,

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it's like, wow, I had
I had to chat GPT write a business

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plan for me, a three page
business plan, and I was really impressed

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with how good of a job it
did because it picked up on stuff that

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I had not mentioned, but we
had been talking about in the conversation moments

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earlier. So I'm like, I
don't think it hurt me by any means.

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But what's interesting is that it picked
up on a certain aspect of the

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industry because it's about a music music
concept that came up with this thing I

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called a place to Play, and
the ideas that you would have these community

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owned music venues where all the booking
and everything is done online, because the

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hard party. You're a music fan
yourself, right, you know. For

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musicians, the hard part is getting
onto a stage somewhere, in part because

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the people who run the venues can
be difficult to deal with, and of

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course there are other factors. Will
look at this. This is the prompt

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I gave it. I said,
to write a three page business plan for

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an array of crowdfunded, community owned
music venues where all the booking is done

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online and all the back office technology
is bundled for franchises. The organization is

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called a place to Play, and
the ideas to enable communities to own their

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local music venues, which will also
double as music studios and recreational facilities.

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Well, listen to what it did
here under the need. So it wrote

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executive summary and that was good the
the need. This is wow, he

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writes, this is chet GPT.
The live music industry has been severely impacted

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by the COVID nineteen pandemic, with
many music venues struggling to survive due to

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prolonged closures and restrictions. And here's
the interesting part, it writes. Additionally,

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traditional music venues are often only and
operated by large corporations or private investors

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limiting community engagement and control over these
spaces. I was like, oh my

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god, that's the message I wanted
to say. I didn't put it in

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the prompt. It just figured that
out right. You can get the excuse

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that the chet sput manship debt,
but that's exactly what I was thinking.

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If you see big concerts they're in
Beligum for example, the big festivals,

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that's the same thing happening over here. And you get some smaller groups that

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are within the same booking agency.
So a lot of the other artists are

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really wead out of this kind of
monopoly in acertive way. Yeah. Yeah,

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so I mean you look at that. I mean in the data industry

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and the analytics business, there is
a lot of rotwork that has to be

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done. I think chat GPT is
very good or generating content for tweets.

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For example, you've used it for
tweets right where you say give me ten

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tweets to talk about the power of
artificial intelligence for data and that that that

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that it comes up with stuff that
you wouldn't expect, and I think that's

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the pool part, right, Yeah, and the advantages that you can it's

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quality test mechanism in a certain way. You can throw out a few things

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and see who responds to what,
and that's helps in doing research as well,

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so other much you would have needed
other ways of testing it out,

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So you can just be ovocous and
try something and throw it out and see

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how the responses are online against a
certain statements and see how that works.

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And that helps you identifying as well, what are people looking for, what

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do they want to discuss, what
are the problems are there? And that's

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just an amazing productivity tool in using
into in such a way. But I

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was thinking where you're saying everybody puts
their data into chet gput into the single

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tenant instead of the multi tenant that
we've been talking to able as well,

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where they are training the models on
top of the company's data in that reserve

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tenant, in that private tenant.
And that's definitely I think aware where we

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are going having the algorithms tree on
a generic data set and then have it

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applied onto your onto your company data
in your private environment. Yeah. Well,

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and they can also scan through large
amounts of data, So I think

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the you know, one of the
more exciting things is that you could use

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this kind of technology in conjunction with
a data catalog. For example, you

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find me every case where I've got
PI a lot in my information landscape now

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and it goes and does it and
gives you a list here's all the stuff

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that we found. I mean,
it's really kind of crazy what's possible.

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But the key, I think is
to couple it with other technologies and other

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processes and just be careful about what
you're doing. Right. Yeah, it's

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like you say, careful of what
you're doing. You need to understand if

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you feed certain data in the large
language models, where is it going?

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And then we come again to a
certain to a topic observability and transparency.

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And that was already we've been discussing
that already five years ago, about the

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transparency of AI models, of just
statistical models. Understanding of we put something

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in it, something comes out.
But what is the rule and how did

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you come to decide this this this
particular thing, and how do you integret

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00:16:47.200 --> 00:16:49.759
it? And if you have that
understanding, then you can trust and the

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data and the algorithm. And that's
why we we we are going I think

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with these with these large language models. The complexity is that our trainoma well

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newest models on a few billion penalmeters, so that's going to be a difficult

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box of building that transferacy for the
business. Yeah, I mean basically it's

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not there at all, Like so
you can't be careful about using chat GPT

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for any kind of regulated industry because
it's just off the rails. I mean,

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they're you know, they're they're not
able to explain it as the bottom

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line. I mean I asked chat
GPT which database it uses, and it

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wouldn't tell me. And that's not
explainability. I mean explainability is you need

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to tell how it happened. So
I'm not sure why they've they've baked in

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this opacity, but that's clearly what
it is. I mean, they have

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a set of rules of things that's
not allowed to talk about, like how

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it works basically, which I mean
that is explainability. If you can't tell

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us how the darned thing works,
it is not explainable, right, Yeah,

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And I think we've been trying to
do that in the entire systems and

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and and systems before AI to get
to term. That's the explainability in there.

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by sixty five plus Medicare. Welcome
back to Inside Analysis. Here's your host.

282
00:22:10.319 --> 00:22:15.839
Eric's having off but nowly so.
It's talking to Eve Bunkers of seven

283
00:22:17.039 --> 00:22:21.480
W Data talking about large language models, the lms like chat GPT. Of

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course, Data Bricks rolled out their
own version Dolly two point zero. I

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00:22:25.079 --> 00:22:27.279
saw the other day. That happened
pretty quickly. Those folks have some good

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00:22:27.279 --> 00:22:32.559
developers and some good discipline for what
they're doing. And of course those shows

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00:22:32.559 --> 00:22:36.720
are coming up soon, the Big
Snowflakes Summit and the Data Breaks show,

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00:22:37.000 --> 00:22:40.480
which are at the same time in
different places, different cities. So you

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00:22:40.519 --> 00:22:42.319
know, a lot of the analysts
were complaining about that, but I read

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00:22:42.359 --> 00:22:45.400
a post from Aligadzi who said,
hey, we didn't try to do this

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00:22:45.480 --> 00:22:48.720
intentionally, just the way it happened. So there you go, and that

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00:22:48.880 --> 00:22:55.400
is really where everything is centering around
these days. Are these engines, these

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00:22:55.440 --> 00:22:57.720
analytical engines. Right, You've got
data bricks, you have Snowflake, You've

294
00:22:57.720 --> 00:23:00.720
got Tera data still out there,
there's Vertica. You know, there are

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00:23:00.720 --> 00:23:04.960
lots of these platforms. There's dremeio
for examples, another way of doing it.

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00:23:06.240 --> 00:23:08.359
But they're all trying to accomplish the
same thing, which is to allow

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you to get value from your data. And you know, this lakehouse concept

298
00:23:14.240 --> 00:23:17.839
is evolving. Now. We started
off with databasis, that we had data

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00:23:17.839 --> 00:23:21.319
warehouses, that we had data lakes. Now we have this federated sort of

300
00:23:21.400 --> 00:23:25.960
data lake at this lakehouse architecture.
It gets pretty confusing and I think,

301
00:23:26.480 --> 00:23:29.559
you know, pretty esoteric at a
certain point. The bottom line is you

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00:23:29.559 --> 00:23:33.240
want to be able to get value
from your data quickly. And now we

303
00:23:33.279 --> 00:23:37.079
have these low code data pipelines too. There's a company, Prophecy dot Io

304
00:23:37.200 --> 00:23:40.400
that we're doing some work with.
So you're seeing all of these sort of

305
00:23:40.440 --> 00:23:42.839
steps to go along the way.
But I mean, you've built pipelines before

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and you know that it takes time
and stuff breaks. So like any pipeline

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00:23:48.000 --> 00:23:51.400
you built, you're gonna have to
maintain, right, and like you have

308
00:23:51.440 --> 00:23:53.200
to and this gets it's almost like
the new batch window. What do you

309
00:23:53.240 --> 00:24:00.480
think, Yeah, it's it's still
the same problem and the problems only bigger

310
00:24:00.480 --> 00:24:03.759
in a certain way. Before it
was structured data. Now you have more

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00:24:03.799 --> 00:24:07.759
unstructured data. So understanding of what
you can what you can expect in your

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00:24:07.839 --> 00:24:11.920
data pipeline, that's an important part, and that's really only focused on the

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00:24:11.960 --> 00:24:18.440
technical part where you the better business
understanding what you have about your data.

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00:24:18.559 --> 00:24:22.440
That's why you can anticipate what you
can get in and how it should look

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00:24:22.480 --> 00:24:26.319
like to massage it for the data
scientists, for the business intelligence people,

316
00:24:26.680 --> 00:24:33.720
people building reports and so on and
so forth. So really having that data

317
00:24:33.839 --> 00:24:38.400
sagliness of your data, that's an
important skill. And the critical thinking alongside,

318
00:24:38.440 --> 00:24:44.160
where you can question why do we
have three values instead of four values

319
00:24:44.200 --> 00:24:48.759
of five values? What do the
values means? And really challenging the business

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00:24:48.839 --> 00:24:52.480
people that should have that knowledge in
a certain way that helps in building the

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00:24:52.519 --> 00:24:56.359
better pipelines. But on the other
hand, bringing their skill set to more

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people so they can do more with
the data in such a way. That's

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00:25:00.599 --> 00:25:07.720
a very important skill that we need
to teach to a lot of more people

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00:25:07.000 --> 00:25:12.039
to make really data insights and that
I mean a lot of this stuff you

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00:25:12.079 --> 00:25:17.559
could argue falls under the data literacy
moniker, right, like understanding what goes

326
00:25:17.599 --> 00:25:19.119
into a data pipeline, how to
build it, what you want out of

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00:25:19.119 --> 00:25:25.440
the other side. They are getting
easier to build. Five ten years ago,

328
00:25:25.720 --> 00:25:30.680
we had data pipelines, but they
were a bit more technically driven,

329
00:25:30.000 --> 00:25:33.839
so you had to have engineers build
these things. And the ideas that you

330
00:25:33.920 --> 00:25:38.160
want to be able to allow regular
business people to build their own stuff.

331
00:25:38.319 --> 00:25:41.759
I'm not sure if we're there yet, but we're at least getting close to

332
00:25:41.839 --> 00:25:45.000
being able to build these things.
And again, what's the purpose? What

333
00:25:45.039 --> 00:25:49.240
are you trying to understand? I
mean, you've had some experience trying to

334
00:25:49.240 --> 00:25:53.359
hack through all these pipelines in the
marketing world, and I think the challenge

335
00:25:53.359 --> 00:25:59.799
is that the bigger world out there
on LinkedIn and Twitter and Instagram, face

336
00:26:00.000 --> 00:26:03.640
Book and all these social engines,
it's very complex, the data, the

337
00:26:04.480 --> 00:26:08.160
rules are always changing for these guys, and like, this is what I've

338
00:26:08.559 --> 00:26:11.440
kind of determined here is that you
know, all of these engines, they're

339
00:26:11.480 --> 00:26:15.519
designed to make money and to keep
you engaged. That's the point they want

340
00:26:15.559 --> 00:26:21.920
you to stay on LinkedIn and just
go do stuff there to receive advertising or

341
00:26:21.960 --> 00:26:26.720
whatever. But because the rules keep
changing, it's a difficult thing to manage.

342
00:26:26.799 --> 00:26:30.880
Right, you have to maintain these
pipelines, see what's coming in,

343
00:26:32.039 --> 00:26:34.680
understand if there's anything of value.
You know, it's like sifting for gold,

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00:26:34.839 --> 00:26:37.039
right, I mean, you're what
you want to do is blow some

345
00:26:37.160 --> 00:26:41.319
dynamite on the side of the mountain
and then use your look, your sifters

346
00:26:41.359 --> 00:26:44.599
to find those gold nuggets. But
what do you think, Yeah, that's

347
00:26:44.640 --> 00:26:48.359
that's a different as what we see
with the model data platforms they have.

348
00:26:48.400 --> 00:26:55.720
That's that's collection catalog where the collection
developers day they take care of all this

349
00:26:55.880 --> 00:26:59.640
abspection where you say, if something
is changing on LinkedIn or any of the

350
00:26:59.640 --> 00:27:03.799
abayes, they take care of that
so you can have more an extracted way

351
00:27:03.799 --> 00:27:10.799
of implementing that ABI and things don't
get broken when you're developing the data pipelines.

352
00:27:11.160 --> 00:27:14.160
Things have become simpler, like you
say, with the low code and

353
00:27:14.200 --> 00:27:18.160
then low code platforms could I was
just thinking it compares to how back in

354
00:27:18.200 --> 00:27:23.880
the days business people were preparing what
you need to build as a developer.

355
00:27:25.079 --> 00:27:33.480
So they've been exploring the system,
building huge enormous improve performance, complex spl

356
00:27:33.559 --> 00:27:37.400
statements, and then gave it to
a developer that had to transform it into

357
00:27:37.400 --> 00:27:41.359
a data pipeline. So if you
can stick on the same platform from a

358
00:27:41.400 --> 00:27:45.079
business perspective and give them to the
people where they can build low code low

359
00:27:45.119 --> 00:27:52.480
code data pipelines to explore it in
a fast way and iterate and understand if

360
00:27:52.519 --> 00:27:56.720
the results is what they really meant
to have, and then talk to the

361
00:27:56.759 --> 00:28:02.160
people that the data engineers are data
architects that can really design that in the

362
00:28:02.200 --> 00:28:06.039
correct way with the guidelines, but
on the same platform. I think that's

363
00:28:06.079 --> 00:28:10.319
the way to go, but I
haven't seen it that much yet because the

364
00:28:10.359 --> 00:28:17.720
skill set of various people that varies
so much between between an engineer, between

365
00:28:18.440 --> 00:28:23.079
a business person somebody from marketing.
They all have different backgrounds and different and

366
00:28:23.119 --> 00:28:29.400
there are different tech savvy to build
this type of solutions. So that's that's

367
00:28:29.480 --> 00:28:36.279
the challenge to get a more homogenious
proof that can work together on that same

368
00:28:36.279 --> 00:28:40.480
platform in the future. But already, what I see very often that a

369
00:28:40.480 --> 00:28:44.680
lot of people are not able to
build a simple pivot table. Well I

370
00:28:44.799 --> 00:28:48.880
call it simple because it's it's it
became kind of general onlooking at data and

371
00:28:48.920 --> 00:28:53.640
aggregating it and trying to understand what
question do I want to see answered and

372
00:28:53.720 --> 00:28:59.160
why you pull the things together and
build the aggregates to build that understanding.

373
00:28:59.440 --> 00:29:03.359
But even in marketing platforms that I'm
using, they give me the clip true

374
00:29:03.440 --> 00:29:06.440
rate and then I think, wow, these look really insane, and then

375
00:29:06.440 --> 00:29:10.839
I do a different type of aggregation
and then think what type of data am

376
00:29:10.839 --> 00:29:15.400
I looking at? Because this is
so much lower? How are you building

377
00:29:15.400 --> 00:29:18.119
these numbers? What do they what
do they tell me? And I'm not

378
00:29:18.240 --> 00:29:22.400
able to understand that. I'm checking
with the help desk, but they're not

379
00:29:22.480 --> 00:29:26.440
able to explain that because in a
certain way, like you said, Eric,

380
00:29:26.519 --> 00:29:30.799
they're trying to to hide that for
me in a certain way. For

381
00:29:30.880 --> 00:29:33.720
me, it's the numbers I see
and need to trust the numbers and if

382
00:29:33.759 --> 00:29:38.519
you can tell me how they are
calculated, I have a hard time understanding

383
00:29:38.519 --> 00:29:42.240
the numbers and trusting the numbers.
Yeah, well that I mean, this

384
00:29:42.319 --> 00:29:48.279
is my point. And I've seen
when I've advertised on LinkedIn, when I've

385
00:29:48.319 --> 00:29:52.039
advertised on Twitter, when I've advertised
on Facebook and on YouTube. I mean,

386
00:29:52.039 --> 00:29:56.440
you get these crazy numbers LinkedIn is
probably the most conservative about them.

387
00:29:56.839 --> 00:30:00.359
But on Twitter, for example,
you throw ten bucks that's something, and

388
00:30:00.440 --> 00:30:04.319
you go from one hundred and thirty
impressions to four thousand impressions. It's like,

389
00:30:04.319 --> 00:30:08.240
okay, who are these people?
Like, how did you calculate this

390
00:30:08.359 --> 00:30:12.480
number? And you know what's interesting
to me is thinking, I feel like

391
00:30:12.960 --> 00:30:17.160
we need more transparency, Like,
first of all, and even from these

392
00:30:17.160 --> 00:30:21.720
email marketing engines, how are you
calculating? What is the mechanism of action

393
00:30:21.799 --> 00:30:23.759
for you to tell me that you
know the email was opened? Well,

394
00:30:23.799 --> 00:30:26.559
a lot of them were using the
pixel. There's a little pixel that gets

395
00:30:26.640 --> 00:30:30.519
called like okay, that pixel was
called. But then you have email clients

396
00:30:30.559 --> 00:30:36.599
that don't load images, right,
so they will intentionally not load the images.

397
00:30:36.640 --> 00:30:40.400
They're protecting you for that, And
I realize it gets complicated, It

398
00:30:40.400 --> 00:30:44.480
gets, you know, hard to
understand what's actually happening. It's hard to

399
00:30:44.519 --> 00:30:48.799
design these systems too. Right.
Then you've got like Google with Gmail coming

400
00:30:48.839 --> 00:30:52.759
up with their new rules of like
I've seen when that happens, they'll roll

401
00:30:52.839 --> 00:30:55.480
out some change and all of a
sudden, the numbers kind of go sideways

402
00:30:55.960 --> 00:30:59.880
for a period of time. But
I think we really do need transparency,

403
00:31:00.039 --> 00:31:03.279
and I don't know that we're going
to get it, but you know,

404
00:31:03.519 --> 00:31:06.480
you look at some of these numbers
from somebody social sites, not to name

405
00:31:06.519 --> 00:31:10.559
any particular names, but you got
to scratch your head like are you sure?

406
00:31:10.759 --> 00:31:14.319
Like what is this that i'man looking
at? It's this concept of vanity

407
00:31:14.519 --> 00:31:17.519
metrics, right, they're sort of
playing at the vanity metrics such that the

408
00:31:17.519 --> 00:31:19.880
marketing person go, look and see
this great result. But did anybody buy

409
00:31:19.960 --> 00:31:23.759
your stuff? Did anybody register for
the webinar? I mean there's a particular

410
00:31:23.799 --> 00:31:29.359
goal, and it's not just impressions, right, it's some action like clicking

411
00:31:29.440 --> 00:31:33.160
on something or buying something, and
man, it's hard, it's just hard

412
00:31:33.200 --> 00:31:37.119
to do. It's it's the critical
thinking. I think that that becomes important.

413
00:31:37.160 --> 00:31:41.640
And in such a way if you
say we can get you visibility and

414
00:31:41.720 --> 00:31:45.400
we get you a hundred thousands impressions, and you don't think further what you

415
00:31:45.480 --> 00:31:48.680
want to achieve with these hundred thousands
depressions, it's fine, Eric, you

416
00:31:48.799 --> 00:31:52.119
both the one hundred thousands depressions.
You paid me money and you're happy because

417
00:31:52.119 --> 00:31:55.759
you got to, but you forgot
to told me that you want to have

418
00:31:55.839 --> 00:32:00.240
a conversion of two percent, and
people buying ten K products, you're making

419
00:32:00.279 --> 00:32:05.400
twenty k. So that's an O
brainer. But it's like I have this

420
00:32:05.519 --> 00:32:08.880
this news letter platform and they say, I say, it's pretty strange.

421
00:32:08.920 --> 00:32:12.640
I see a high number of click
true rates and if I look at the

422
00:32:12.680 --> 00:32:16.960
Google analytics, it's a kind of
only ten percent of what you're telling me.

423
00:32:17.599 --> 00:32:21.519
Oh yeah, but very likely there
are bot clicks in there. Yes,

424
00:32:21.559 --> 00:32:23.440
I know they're bot clecks, but
how can I separate them? Because

425
00:32:23.480 --> 00:32:28.680
I don't care about the bot clecks. There are systems in places that look

426
00:32:28.720 --> 00:32:31.880
at your email and they click on
there to see if it's spend email or

427
00:32:31.880 --> 00:32:37.359
whatever, and that's how the systems
work. But that's the transparency where you

428
00:32:37.759 --> 00:32:42.880
were talking about that. It's only
when you dive into a certain domains around

429
00:32:42.920 --> 00:32:46.799
that data you start to understand how
the systems and the technology works, and

430
00:32:46.839 --> 00:32:51.480
then you can ask these questions.
So definitely we need a lot of people

431
00:32:51.559 --> 00:32:57.400
that can explain that and not only
selling solutions as such, really helping you

432
00:32:57.759 --> 00:33:00.039
what you want to achieve, and
then tie in that what you want to

433
00:33:00.079 --> 00:33:06.559
achieve building the solutions of finding the
data and building the transpellency and talk of

434
00:33:06.680 --> 00:33:09.279
that, so I think we're still
on that level, a long way from

435
00:33:09.319 --> 00:33:15.559
home getting there. Well, you
know, it's interesting from my perspective as

436
00:33:15.559 --> 00:33:19.119
a marketer, as a digital marketer, as an email marketer. You know,

437
00:33:19.160 --> 00:33:22.559
the last few years that all the
talk and all the rage has been

438
00:33:22.599 --> 00:33:25.960
around social media getting impressions and followers
and all that kind of stuff. And

439
00:33:27.079 --> 00:33:29.960
you know, from what I've seen, gosh, I mean, I would

440
00:33:29.960 --> 00:33:37.640
say that one thousand emails opt in
targeted emails are worth a hundred times a

441
00:33:37.720 --> 00:33:40.079
thousand Twitter followers, you know,
if not a thousand times. I mean,

442
00:33:40.839 --> 00:33:45.279
email is still a transactional system.
People trust email to get an email.

443
00:33:45.319 --> 00:33:49.759
They'll click on a link to look
at something. It's different than social.

444
00:33:49.839 --> 00:33:52.880
Social is much more shallow. It
is good for getting eyeballs content.

445
00:33:53.000 --> 00:33:58.480
But you know, we've been using
the daylights out of Twitter and LinkedIn for

446
00:33:58.720 --> 00:34:04.599
several years now and get less than
one percent of registrants for webinars from social

447
00:34:04.720 --> 00:34:08.199
less than one percent. And I've
noticed sometimes the people who registered via social

448
00:34:08.480 --> 00:34:13.360
have already registered by emails, so
it's like we're not getting get new people.

449
00:34:13.440 --> 00:34:15.400
We're getting people who did registered by
email who just happen to catch it

450
00:34:15.440 --> 00:34:22.320
on social So it's like, you
know, there's just these really weird divides

451
00:34:22.599 --> 00:34:27.559
in terms of perception versus reality.
And it's changing too, It's all constantly

452
00:34:27.639 --> 00:34:30.400
changing. So it's I mean there's
a lot of effort that's required. And

453
00:34:30.960 --> 00:34:34.679
when I look at what's happening now
in our industry and as you know,

454
00:34:35.159 --> 00:34:37.719
almost every company in our industry folks. And when I say ournistry, I

455
00:34:37.719 --> 00:34:43.480
mean data analytics, I mean information
architecture, data management, all the people

456
00:34:43.519 --> 00:34:49.000
who are helping you capture, process, analyze data. All those companies from

457
00:34:49.000 --> 00:34:52.639
the databasis to the data lake companies
and all the integration people. That whole

458
00:34:52.679 --> 00:34:57.519
industry just took like a ten percent
cut in terms of personnel. I mean

459
00:34:57.519 --> 00:35:00.119
there are companies. I know that
there's a you have four hundred people that

460
00:35:00.840 --> 00:35:06.000
fire twenty percent of their company,
the eighty out of four hundred. I

461
00:35:06.039 --> 00:35:09.719
mean, wow, that is a
significant hit. And to even know what

462
00:35:09.800 --> 00:35:13.639
other people were working on, right, well, what campaigns were you working

463
00:35:13.679 --> 00:35:15.400
on, what were you doing?
Like even figuring that out it's gonna be

464
00:35:15.400 --> 00:35:20.559
hard even if you use systems,
and you're supposed to use some kind of

465
00:35:20.559 --> 00:35:23.119
system to do this stuff. So
at least some of that knowledge is baked

466
00:35:23.119 --> 00:35:27.280
into the emails that have been sent, who received them, what the lists

467
00:35:27.320 --> 00:35:30.639
are, There's stuff like that.
But to me, it's a real sort

468
00:35:30.679 --> 00:35:34.559
of come to Jesus moment, as
they say, in our industry. And

469
00:35:34.639 --> 00:35:37.039
I think it's going to be interesting
to see who shakes out on the other

470
00:35:37.039 --> 00:35:42.119
side. But what do you think, Yeah, it's a dissipating very likely

471
00:35:42.119 --> 00:35:45.559
they're anticipating the large language models,
and you can ask what the twenty people

472
00:35:45.599 --> 00:35:50.920
that have been fired, what have
they been doing? What all? Maybe

473
00:35:50.960 --> 00:35:54.559
that's that's in their heads. But
it's already so so difficult to find information

474
00:35:54.599 --> 00:36:00.039
within our organization because there is a
lack of documentation because people are so precious.

475
00:36:00.119 --> 00:36:05.239
That's the big challenge. I don't
know. In States, it's apparently

476
00:36:05.280 --> 00:36:09.519
easier to fire twenty percent of your
labor force. Here over in Belgium it's

477
00:36:09.679 --> 00:36:15.800
it's much harder. You have to
be a bit more smart to do that,

478
00:36:15.880 --> 00:36:17.840
and even then you have the unions
that jump on there. That's a

479
00:36:17.880 --> 00:36:22.559
whole different dynamic. But I do
understand from time to time that entrepreneurs try

480
00:36:22.599 --> 00:36:28.760
to scale their business have a lot
of people which are kind of duplicate roles

481
00:36:28.840 --> 00:36:31.079
and so on. And that's the
way of what you say, we cut

482
00:36:31.079 --> 00:36:35.920
twenty percent of the workforce and that's
how we grow in a certain way.

483
00:36:36.159 --> 00:36:40.360
But I don't think that's that's anymore
in that that smart objective where you say

484
00:36:40.599 --> 00:36:45.599
we overdid it, we hided too
many people to be able to scale,

485
00:36:45.840 --> 00:36:49.719
and then turn it down. It's
a way of working with scaleops. But

486
00:36:50.679 --> 00:36:53.280
there is something else going on,
like you say, even they're looking at

487
00:36:53.360 --> 00:36:58.519
being acquired or they really only want
to cut costs, And that's that's the

488
00:36:58.599 --> 00:37:02.719
question we still have. What is
influencing the tech companies that are cutting down

489
00:37:04.280 --> 00:37:07.199
on so many people. And it's
strange. I mean, yesterday I looked

490
00:37:07.239 --> 00:37:12.639
at for example, and an AI
dout of this which holds the tools which

491
00:37:12.639 --> 00:37:17.679
are AI based. They're up to
four thousand tools based upon AI for marketing,

492
00:37:17.719 --> 00:37:23.960
for visualization, name anything, productivity
for more than four thousand tools,

493
00:37:23.960 --> 00:37:29.599
and I thought, I thought I
knew already about six hundred of these tools

494
00:37:29.599 --> 00:37:32.760
that are more than four thousand out
there. So it's insane how the technology

495
00:37:32.800 --> 00:37:37.280
companies are growing, and just small
companies as such. So I think what

496
00:37:37.440 --> 00:37:43.519
we saw with the data management space
a lot of rationalization which has happened over

497
00:37:43.599 --> 00:37:47.639
the last ten fifteen years already.
This will happen in the new technology companies

498
00:37:47.679 --> 00:37:52.159
as well. For folks. Don't
actually, that will be right back the

499
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That's eight five, five three two
five of eighteen twenty one express. Welcome

551
00:42:06.719 --> 00:42:44.800
back to Inside Analysis. Here's your
host, Eric Kavanaugh Flie. You don't

552
00:42:44.840 --> 00:43:19.760
make it's a experts but click buying
talent is it read a big deal right

553
00:43:19.800 --> 00:43:24.960
now? They're in the negotiations.
I think it's basically in the regulatory phase

554
00:43:25.000 --> 00:43:28.719
of the acquisition, just making sure
they can clear all those hurdles. I'd

555
00:43:28.760 --> 00:43:30.679
be surprised if it does not go
through. So that's probably going to go

556
00:43:30.760 --> 00:43:36.400
through, and that's going to change
the industry. So you look at where

557
00:43:36.400 --> 00:43:39.679
are the consumption points? Where are
people playing around with data? While you

558
00:43:39.719 --> 00:43:44.320
can play around with your data in
a data warehouse for example, or a

559
00:43:44.400 --> 00:43:49.119
data fabric as talent is able to
deliver, that's one way to do it.

560
00:43:49.119 --> 00:43:52.800
But then the consumption is really the
front end, right And what excites

561
00:43:52.840 --> 00:43:55.639
me about all of this fun stuff
is the discovery side of the equation.

562
00:43:55.719 --> 00:44:00.000
So I get very excited about discovery
and click is very good in the discovery

563
00:44:00.039 --> 00:44:04.119
department. So it's a company called
thought Spot. In fact, thought Spot

564
00:44:04.239 --> 00:44:08.519
is having their events. It's virtualed
again this year because of people not being

565
00:44:09.159 --> 00:44:14.599
really quite entirely ready to go back
to conferences, they are happening. Like

566
00:44:14.599 --> 00:44:15.639
I said, I want to click
world, So that conference is on.

567
00:44:16.000 --> 00:44:22.960
But thought Spot is another vendor that
has a very interesting approach to enabling discovery.

568
00:44:23.000 --> 00:44:25.519
And what they allow you to do
is just type in a natural language

569
00:44:25.599 --> 00:44:30.320
query and then it goes out that
it grabs some data and it gives you

570
00:44:30.440 --> 00:44:36.519
some sort of visualization to play with. Now, this automated components is everywhere

571
00:44:36.559 --> 00:44:38.639
these days. A lot of companies
are using this kind of tactic to get

572
00:44:38.639 --> 00:44:43.599
the ball rolling, basically, to
give you some view that you can then

573
00:44:43.679 --> 00:44:47.039
amend and augments and play around with
to get yourself a better understanding of what

574
00:44:47.199 --> 00:44:50.639
is going on out there. Well, there are lots of other places that

575
00:44:50.679 --> 00:44:52.760
you can go to play with data
right, and of course all these engines

576
00:44:53.159 --> 00:44:57.800
are running up against the big cloud
vendors and what they want to bring to

577
00:44:57.880 --> 00:45:00.800
the table. So you think about
the data ware housing world, Well,

578
00:45:00.800 --> 00:45:04.760
you've got Tera Data that's still still
doing very well. You've got, of

579
00:45:04.800 --> 00:45:07.920
course Snowflake that took off. Now
their stock prices taken a beating in this

580
00:45:08.800 --> 00:45:13.800
strange market place. But I can't
imagine Snowflake is going to go anywhere.

581
00:45:13.800 --> 00:45:16.440
But up you've got data Bricks,
You've got Dremio. There are lots of

582
00:45:16.440 --> 00:45:22.679
different ways that you can organize your
data and leverage these technologies. So there's

583
00:45:22.679 --> 00:45:24.519
a lot of competition. But if
you look at like a Google and big

584
00:45:24.599 --> 00:45:29.400
Query, Google is getting serious.
I heard that Google Cloud is making money

585
00:45:29.440 --> 00:45:31.960
for the first time, so that's
good news for the folks at Google.

586
00:45:32.079 --> 00:45:36.440
And let's face it, they've got
some pretty deep pockets even though Google is

587
00:45:36.519 --> 00:45:39.719
laying off people too this entire industry. Look at alter Ricks, for example.

588
00:45:39.800 --> 00:45:44.320
I know alter Ricks just had a
ten percent layoff. One of my

589
00:45:44.360 --> 00:45:47.519
good buddies got laid off there.
And what does all that mean? So

590
00:45:47.960 --> 00:45:52.480
when I'm looking at this industry and
seeing the number of players, well,

591
00:45:52.519 --> 00:45:55.159
what are you gonna get? You're
probably gonna get some consolidation. You're probably

592
00:45:55.199 --> 00:45:59.480
going to get some leaner times for
a lot of these vendors, because pretty

593
00:45:59.559 --> 00:46:04.960
much across the board in the data
and data management and analytics industries, we're

594
00:46:04.960 --> 00:46:08.760
seeing layoffs. Ten percent is a
pretty fair number. Twenty percent one company

595
00:46:08.800 --> 00:46:13.039
I know that was doing some pretty
cool work in the analytics space, very

596
00:46:13.119 --> 00:46:16.800
targeted analytics player. There are layoffs. So what does that mean? While

597
00:46:16.800 --> 00:46:21.840
it's going to mean a whole lot
of re orchestration figuring out who's doing what,

598
00:46:22.079 --> 00:46:23.440
Like, how do you even know
what people were doing? Now?

599
00:46:23.480 --> 00:46:28.079
If you use some kind of a
portal or you use some standard tools like

600
00:46:28.199 --> 00:46:31.800
HubSpot. Obviously for the marketing folks
is a good one. Email marketing technologies

601
00:46:31.920 --> 00:46:37.000
are all over the place these days, so a lot of the processes and

602
00:46:37.119 --> 00:46:39.440
content are captured in these systems,
and that way a new person can come

603
00:46:39.480 --> 00:46:43.800
along and kind of look through and
see what has been going out the door

604
00:46:43.840 --> 00:46:46.159
and figure out what they can do
better, how they can optimize things.

605
00:46:46.599 --> 00:46:51.760
I can promise that chat GPT is
going to have an impact. Chat GPT

606
00:46:52.119 --> 00:46:57.599
is a very compelling solution for marketing
language. Now you have to be careful

607
00:46:57.679 --> 00:47:01.159
because it does have hallucinations, as
they say. I find that rather amusing

608
00:47:01.360 --> 00:47:05.639
that we talk about chat GPT having
hallucinations, like it's you know, taking

609
00:47:05.719 --> 00:47:09.079
drugs or something. But it does. It creates a reality, right,

610
00:47:09.079 --> 00:47:14.320
because the thing to remember with large
language models is they are basically predictive engines

611
00:47:14.360 --> 00:47:20.159
that are trying to deliver the language
that you want based upon your prompt.

612
00:47:20.679 --> 00:47:22.440
So this gets to the concept of
a prompt, right, and you have

613
00:47:22.559 --> 00:47:29.199
to be very good at prompting something
like chat GPT if you want to get

614
00:47:29.239 --> 00:47:34.199
good value from it. Really the
question lies in the or the value lies

615
00:47:34.199 --> 00:47:37.440
in the nature of the question or
of the prompt or what you tell this

616
00:47:37.559 --> 00:47:39.880
thing to do. And the more
specific you are, the better the results

617
00:47:39.960 --> 00:47:44.559
you're going to get from using these
technologies. But it's a lot different from

618
00:47:44.599 --> 00:47:47.840
Google. Remember with Google, even
though Google has been changing lately, and

619
00:47:49.440 --> 00:47:54.159
you think about when you're querying Google's
indices to find a website, Well,

620
00:47:54.280 --> 00:47:58.400
one thing I noticed a couple of
years ago. Now is the Google started

621
00:47:58.800 --> 00:48:02.559
pointing you when you give a question
to some source that it is determined is

622
00:48:02.599 --> 00:48:07.760
reliable to give you a definition of
something. So a definition of an accounting

623
00:48:07.920 --> 00:48:10.320
term, for example, a definition
of a business term. You can just

624
00:48:10.519 --> 00:48:15.440
Google something and it'll go and give
you a quick view of what that definition

625
00:48:15.639 --> 00:48:19.639
is, and then it links,
of course to the page where it found

626
00:48:19.719 --> 00:48:22.519
that information. Well, that's a
little bit similar to what chat GBT is

627
00:48:22.519 --> 00:48:28.280
doing. But with chat GBT again, this this large language model that's trying

628
00:48:28.320 --> 00:48:32.119
to interpret your question and then deliver
text that it thinks you're gonna want to

629
00:48:32.119 --> 00:48:38.320
see. And for tweets, it's
great for LinkedIn sort of short posts.

630
00:48:38.360 --> 00:48:43.440
It's also very good. It's good
to get a start with something. It's

631
00:48:43.440 --> 00:48:46.920
good to kind of get a skeleton
or a rough draft, which then you're

632
00:48:46.920 --> 00:48:50.880
going to want to go through and
fine tune, just for kicks. I

633
00:48:50.960 --> 00:48:54.320
use chat GPT today to see about
generating an abstract for a show live this

634
00:48:54.360 --> 00:48:59.119
week on Data Match and Data Fabric
and it did a fairly decent job.

635
00:48:59.559 --> 00:49:07.480
And it's doing is it's leveraging this
repository of content and of relationships and models.

636
00:49:07.599 --> 00:49:12.320
Really, these large language models,
there are models in them that are

637
00:49:12.360 --> 00:49:15.880
trying to mimic what different kinds of
content look like, and it comes up

638
00:49:15.920 --> 00:49:20.960
with some pretty compelling stuff. I
mean, it's really remarkable what it can

639
00:49:21.039 --> 00:49:24.719
learn. But remember you're doing this
sort of as a ProForma or a rough

640
00:49:24.840 --> 00:49:30.639
draft of something which you can then
fine tune with details as an expert would

641
00:49:30.679 --> 00:49:34.960
need to in order to deliver highly
targeted content. So it's not just gonna

642
00:49:35.119 --> 00:49:37.000
do everything for you. In fact, over the weekend I had a bit

643
00:49:37.000 --> 00:49:43.119
of an epiphany and just thought to
myself, no job does itself. There's

644
00:49:43.159 --> 00:49:46.239
no job anywhere that's going to do
itself. But we have our tools to

645
00:49:46.320 --> 00:49:51.079
help us do the job. That
for sure we can get from these different

646
00:49:51.079 --> 00:49:54.880
technologies and chat GBT just like some
of these other large language models Dolly two

647
00:49:54.920 --> 00:50:00.400
point er from data Bricks, they
are designed again to to predict what you

648
00:50:00.480 --> 00:50:04.239
want to hear, what they think
you want to hear, and they're very

649
00:50:04.280 --> 00:50:07.599
good at that kind of thing.
So people are going to be using chat

650
00:50:07.599 --> 00:50:14.119
GPT I guarantee to develop skeletal blogs, skeletal articles, even business plans.

651
00:50:14.119 --> 00:50:16.880
I was musing earlier on the show
how I used it to write a business

652
00:50:16.920 --> 00:50:22.320
plan and it was pretty darn good. It comes up with interesting angles on

653
00:50:22.440 --> 00:50:24.800
things, and what's kind of cool
there is you can almost get a little

654
00:50:24.840 --> 00:50:29.199
taste of the zeitgeist, right,
so you can play around with stuff and

655
00:50:29.199 --> 00:50:34.679
just see what is the LM's view
of the world on this particular topic.

656
00:50:35.079 --> 00:50:38.000
Again, watch out for hallucinations,
but it's pretty useful stuff. And there

657
00:50:38.039 --> 00:50:40.679
are lots of other things we have
to watch out for these days. I

658
00:50:40.679 --> 00:50:45.880
mean, in a somewhat related manner. We have these bots on engines like

659
00:50:45.960 --> 00:50:49.880
Twitter. I'm starting to get some
more of them on LinkedIn. I think

660
00:50:49.920 --> 00:50:54.559
LinkedIn has some tighter protocols around logging
in and creating a profile and so forth.

661
00:50:54.559 --> 00:50:58.599
But there are fake profiles on LinkedIn. There are lots of fake bots

662
00:50:58.719 --> 00:51:00.840
on Twitter. And if you don't
believe me, you have a Twitter account,

663
00:51:01.000 --> 00:51:07.199
just and tweet about anything controversial,
anything political. Just say this person

664
00:51:07.360 --> 00:51:09.880
is great or that person is terrible. Choose your politician to fill in the

665
00:51:09.960 --> 00:51:15.599
blank and see what happens. Or
challenge someone and see what happens. You'll

666
00:51:15.800 --> 00:51:22.719
give this whole array of Twitter bots
liking your stuff, criticizing your stuff,

667
00:51:22.159 --> 00:51:25.360
tend to line up on two sides
of an issue and kind of go at

668
00:51:25.400 --> 00:51:29.440
each other. And they've gotten fairly
clever. In the recent past. That

669
00:51:29.599 --> 00:51:31.679
used to just be insults. So
someone would insult you, Oh, you're

670
00:51:31.679 --> 00:51:36.320
an idiot. Dotis on that and
saydaha, they're sweeping up the floor with

671
00:51:36.360 --> 00:51:38.800
this idiot today. That kind of
thing. And these are bots. I

672
00:51:38.920 --> 00:51:43.639
promise you, if you get one
of these coming at you asking you questions,

673
00:51:43.880 --> 00:51:46.800
ask them for their LinkedIn profile.
That's my trick Like that, I

674
00:51:46.920 --> 00:51:50.320
say, oh, let's let's connect
on LinkedIn. What's your LinkedIn profile?

675
00:51:50.320 --> 00:51:52.760
And need to get nothing because there's
no LinkedIn profile. Now someday they may

676
00:51:52.800 --> 00:51:55.880
get sophisticated enough to do that,
and this is a challenge with a generative.

677
00:51:57.159 --> 00:52:00.039
They are quite frankly, and I
think in our podcast Bonus Set and

678
00:52:00.119 --> 00:52:02.599
I'll talk about some of the interesting
conversations that we had at click World,

679
00:52:02.840 --> 00:52:07.320
which were absolutely fascinating. I mean, the folks over there at Click and

680
00:52:07.400 --> 00:52:10.519
some of their clients like Harmon,
Harmon, Cardon, like the radios.

681
00:52:10.960 --> 00:52:15.159
I learned a whole ton in a
short period of time by talking to those

682
00:52:15.159 --> 00:52:16.519
folks. They have a whole consulting
firm. I didn't know that, but

683
00:52:16.599 --> 00:52:22.519
Harmon Cardin is its own consultancy that
goes out and consults with clients and helps

684
00:52:22.519 --> 00:52:25.079
them build solution. Podcast bonus segment, Folks, send me an email,

685
00:52:25.119 --> 00:52:27.920
I do want to know what you
want to have on these shows. Info

686
00:52:28.000 --> 00:52:30.719
at Inside Analysis comes right to me. We'll be right back. You're listening

687
00:52:30.840 --> 00:52:45.679
to Inside Analysis KCAA and KCAA Internet
television, the station that leaves no listener

688
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and no viewer behind. This segment
sponsored by the generous support of the dream

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Two eight one five nine ninety eight
hundred. Listina kaa Lomelinda at one

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oh six point five FMK two ninety
three cf Brino Valley, NBC News Radio.

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I'm Chris Garagio. President Biden want
states to help strengthen background checks on

769
00:59:31.960 --> 00:59:36.880
gun buyers younger than twenty one to
slow the pace of mass shootings. Biden

770
00:59:36.960 --> 00:59:39.920
made the appeal today in an op
ed in USA Today. Biden wrote that

771
00:59:40.000 --> 00:59:44.559
he'll call for states to enact laws
that give the federal background check system access

772
00:59:44.599 --> 00:59:49.119
to all records that could prohibit someone
under age twenty one from purchasing a firearm.

773
00:59:49.559 --> 00:59:52.559
A congressional Democrat says it's up to
both parties to come together on immigration

774
00:59:52.679 --> 00:59:58.599
reform. We will support increase funding
for border patrol. We will support increased

775
00:59:58.639 --> 01:00:04.639
funding for immigration judges to quickly process
things. We will support better technology and

776
01:00:04.800 --> 01:00:08.320
security on the border. Speaking on
Fox News Sunday, California Representative Rocana called republic