WEBVTT

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History. I'm Chris Karagio, NBC
News Radio, NBC News on CACAA Lomlada

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sponsored by Teamsters Local nineteen thirty two, protecting the Future of working Families Teamsters

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The information economy has a rid. The

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world is teeming with innovation as new
business models reinvent every industry industry. Inside

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Analysis is your source of information and
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this exciting new eraic learn more at
inside analysis dot com, Inside Analysis dot

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com. And now here's your host, Eric Kavanaugh. M all right,

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folks, welcome to the future.
Indeed, your host here, Eric Kavanaugh,

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and the only Coast to coast radio
show all about the information economy,

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Inside Analysis, Folks. I'm very
excited to have two experts on the call

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today for what is arguably the new
center of gravity in the data world,

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and that's the data catalog. So
what is a data catalog. For those

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who've been around for a few years, you may know data dictionaries. For

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example, Wikipedia, you go to
Wikipedia. A lot of companies have wikis

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these days. They're kind of like
catalogs. It's a portal that has definitions

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about things. It has information about
projects and entities within the organization. But

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of course the whole point is you
want to use something like a data catalog

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to help your under help your organization
understand what the data means. That may

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sound like it's simple, but when
you start getting into the weeds a bit,

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you realize how complex it can be, especially for a complex business model,

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maybe for an insurance company or a
healthcare company, financial services, but

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even retail, even manufacturing. There
are countless terms that are used in various

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ways, and if you have a
data catalog, you can help define those.

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And what you really want is a
collaborative effort. You want people from

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all around the organization getting involved in
this, not just a handful of IT

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people try to chase down the business
leaders who know what things mean. That's

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how data catalogs have been built over
the years. It's not a very effective

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mechanism because the IT people are constantly
trying to track down the business people who

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maybe are not that easy to get
a hold of. And with us today,

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we've got a couple experts. We
were talking to Aaron Wilson of Athena

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Solutions and Kiribasu of Metaphor. Metaphor
is a vendor. They have a data

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catalog that they sell, which is
very interesting stuff. Look them up.

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Metaphor data is where you can find
them. They're on LinkedIn, They're on

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Twitter now called X I suppose.
But let's kind of dive in, so

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Aaron, I'll bring you in first
from Athena Solutions. You're in the business

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of helping companies understand their data.
Connect the dots. That's really what data

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catalogs do, right They connect the
definitional dots of information in your organization and

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help you understand what all that information
means right erin. Yeah, absolutely,

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I mean the new generation of tools, I mean, I can't I don't

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think it can be overstated. You
know what an advantage these tools have over

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prior generations of metadata management tools bring
in, you know, and it's the

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efforts that the companies have gone through
with the more traditional methods, the data

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dictionaries and various tools that have been
used in the past. It's difficult to

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get off the ground, and it's
difficult to get users really engaged and into

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the governance process. And I think
what we're seeing is these tools, you

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know, they make it a lot
easier to sort of democratize the process.

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And you made a really good point
there. And your colleague Alazaichen on a

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show I did with her a couple
of months ago, and this really interesting

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comment that really stuck with me.
She said, one beauty of a data

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catalog is it forces the business to
pay attention. Right. That's when you

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have engagements because they know, they
understand what they do all day, will

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have some report that goes to senior
executives maybe or to the board, or

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to someone else, even out to
end users for example, and they understand

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that those definitions are wrong, that
report's going to be wrong. So when

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they understand what you're working on is
this data catalog which is going to drive

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and govern how information gets shared and
reported, that's that makes them pay attention.

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Right. Well, definitely, I
think that. Yeah. I mean,

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nobody knows generally speaking, the data
better than the people who are using

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it, right. But you know, I think the problem that we've had

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in the past, as I've said, is that you know, the tools

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were it was difficult to get involved. It was difficult to jump right in

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to a conversation about data definitions.
Just as an example, it was difficult

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to get an up to date data
lineage when people wanted to see one.

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So I think that we're actually you
know, the slide that I used in

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my presentation was was you know,
data dictionary, the need was there,

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what the technology wasn't. But I
think we're actually at a point where you

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know, the technology is there.
Yeah, I think that's really important.

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And Cirri Itasu from Metaphor will bring
you in. You've been in this business

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for a while, in the data
world, and you know there's tremendous value

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in data, but only if people
understand it. And I'll throw another quote

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from Allah at you and just get
your commentary on this. Said, in

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order for data to be an asset, it must be understood. What do

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you think about that? Curt Absolutely, And in fact i'd add to that,

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and understood by whom yea or the
data team understands or business sort of

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like the analysts populations understand. But
from our perspective, you know, we

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care to make sure that everyone in
that supply chain, right from the people

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who are producing it all the way
to someone who's just looking at a report,

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can really take it in, really
understand the things, simply be effective,

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be you know, sort of really
add to that value if you will,

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for having that data, of using
the data in the first place.

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Yeah, And you know, I
just think as I'm an old school publisher,

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so I've been in print production where
you would print eighty five thousand magazines

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and then realize that there's a typo
in the first paragraph of the lead article

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and you're just like, oh no, and that's not the world we live

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in anymore. But you think about
data definitions in a printed document, well,

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guess what things change? Do terms
come around, the definitions change.

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So the fact that we have this
digital world now where the repository is malleable,

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it can be changed over time,
and it can be up to date

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such that anyone of logs in any
time sees the latest information. I think

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that's I mean, it's a simple
thing in a way. But that's one

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of the reasons why these catalogs are
now so much more effective and so much

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more usable is because they are dynamic, They can change quickly and everyone can

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get access to the latest version,
right Kritt, Yeah, absolutely. You

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know one example that was just gooding
you earlier is one of the many things

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that we do within our tool is
allow you to basically persist institutional knowledge,

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meaning conversations that are happening on Slack
or teams about like the evolution of data,

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evolution of metrics, et cetera,
and literally within one click sort of

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persisted within the catalog and the moment
it is like literally within the next few

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milliseconds, few seconds, depending,
it will show up within the catalog.

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Right, So the next time someone
is asking for that term, even if

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they're completely separate, you know,
completely different silo, they immediately know what's

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the most recent version of whatever it
is that people were talking about or were

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looking for. So yeah, right, and that's you know, that gets

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to collaboration and the importance of collaboration
that you know, it's really kind of

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intense the different world we live in
today compared to even five years ago,

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I mean for some companies even last
week, right, because as you know,

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there's a very long tail of software
and processes in businesses, and we're

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all inside this industry, so we're
up to date with the latest developments,

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but a lot of companies are not. And so the stark disparity between an

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old world system where there's like a
printed document that one person prints and if

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he's on vacation, then you know, you can't even get access to it

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to today, where you have this
dynamic, sort of living, breathing catalog

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of definitions that anyone can take part
in. That's a huge disparity, and

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I almost think that the mindset change
is one of the hardest for the business

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to wrap their heads around. You
know, we don't have to do it

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the old way, guys, you
can now do it this new way,

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and look how nice it is.
What do you think about that, Curt?

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Yeah, you know one quote that
comes to mind a customer told us

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a wild back. He basically said, the best catalog is one when there's

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no catalog at all. And what
he's really meaning is that, you know,

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exactly like you mentioned. You know, the the IT team, for

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example, might have their own point
of view on how things are going,

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but the reality of what users are
doing on a day to day basis where

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they're doing it, no one really
knows unless it's a very very well managed

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sort of organization. For most average
organizations, different sort of centers of knowledge

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just keep emerging and going, you
know, coming and going, so to

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speak. And that's one of the
things that we really try to tap into.

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Right you know, how could we
get someone who has zero experience with

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the catalog. Heck, they don't
even they really should not even need to

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know what the heck a catalog is. They get value out of the catalog.

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But on the other side, from
the producer side, you have now

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great visibility into what people are asking. It's like a direct you know,

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being a product manager for example,
it's like having a very very direct window

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into exactly what a customer is doing
and they're asking for pretty much in real

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time if you think about it.
Yeah, and that's so important because you

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know, let's just kind of quickly
get into organizational dynamics and how we've traditionally

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done things, and how you do
things is you have meetings, right like

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if the marketing team comes in,
the sales team, the management team,

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all these spokes to come in and
you go through the meeting one at a

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time. That is a very old
school way of doing things. You can

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have much fewer meetings now if you
embrace this kind of approach because guess what,

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it's just like it's adding almost a
meta layer on top of existing communication

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channels like slack you mentioned, or
email. If you can capture email inside

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of an intranet, or a portal
for example, you can capture with your

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technology if it's connected properly, obviously, you can capture all these conversations about

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the business. And then by using
these large language models, by using the

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sort of gen AI components, very
quickly spin up answers in natural language for

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what's happening in the company, So
you don't have to call someone, you

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don't even have to email someone.
You're just in the system. You ask

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it a question, you'll see who's
been doing one and where that's like the

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ultimate collaboration. What do you think
you're in? Yeah? Absolutely, you

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know. The one quote that comes
to mind or a meme I guess was

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this could have been this meeting,
could have been an email health. That's

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exactly what's happening is so many companies
are obviously, you know, whether it's

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in slack or teams, they're having
discussions about data, really evolving the data

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as they go along in a data
day basis, and the ability to be

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able to take that and you know, basically persist that, like you mentioned,

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with large language models, but with
one important caveat right, like this

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is not a very generic Hey,
here's your chat chat bought or you know

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whatever it is, a lot of
these companies do like here, go talk

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to chat GPT and figure it out. Well, it's not really that we

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of course, we use these large
language models, but more importantly, we've

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tapped into all the other content that
there is, right, all the business

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glossteries, all the technical metadata that
we have, and so the answers coming

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out of the system are highly,
highly contextualized and relevant to what you're doing,

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and not just chat GPT, you
know, hallucinating, which is a

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problem. I mean, and that's
the challenge of just using a generic tool,

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as powerful as it may be,
something like chat GPT. It's incredibly

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powerful, it's great for text generation, but there are people who misuse the

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tool all the time. I even
saw a guy, a very single level

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person I think from Meta, who
is ragging on chat GPT, and he

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said, somebody came up with the
crap ratio, which is a clever idea.

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I get at the crap ratio of
like how good is the material you

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get back is good or as a
crap But he gave it a very complex

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sort of mathematical question, and I'm
thinking, dude, that's a misuse of

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the tool. It's not what it's
designed to do. Now, I'm guessing

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that there are going to be better
and better reasoning engines baked into these over

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time. That's probably going to happen, But right now it's just for text

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generation. So you always have to
be careful about how you use the tool

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and understand the purpose of the tool. You're not going to be using a

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data catalog to create graphics for your
magazine, for example. It's just not

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the right use, so it is
important to understand the use. But to

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your point here, it'll throw it
back to you just for comment here in

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context, that kind of collaborative information
is extremely valuable and it allows you to

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save time. It allows for that
meeting to just be an email, right,

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Yeah, absolutely, And you know, to your point, I think

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this is one of the reasons why
it's really important for companies to know what

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they're getting into with regards to sort
of adopting chat GPT, because you know,

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the EXAC sort of mandate will come
down, Hey we should do chat

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GPT to get better, but it's
going to be some weird chat bought experience

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which really isn't work, And so
that's why we spend a huge amount of

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time really trying to make sure the
marrying of the metadata that currently exists,

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making really activating that through a language
model is the is the way to go

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right, So using the right tools
is absolutely critical in this equation. Yeah,

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and I'll throw it over to Aaron
to kind of comment on this.

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Aaron, specificity matters. Context really
matters with these kinds of technologies to these

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kinds of use cases. And to
hear its point, if you are capturing

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the metadata and managing that intentionally maybe
in a RAG model for example, as

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long as you're leveraging that and the
fact set from your information and you're just

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using the AI engine to spin up
text, that's when you're going to get

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pretty good results. You're not going
to get too many hallucinations when you're within

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context doing what the business does.
What do you think erin Well, I

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certainly think that, you know,
based on the demos that I've seen,

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you know, I've seen a little
bit more of Metaphor than than than he

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showed here today, although I thought
it was a really good introductory demo and

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the way that Metaphor does it is
really impressive. But essentially they're using the

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communicative ability of generative AI, but
they are enforcing some context. So in

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other words, you're you're asking the
data catalog. You can either act,

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you can also add, if you
want confirmation from an answer that you think

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might be a little bit wonky,
you can jump right in and ask a

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subject matter expert, you know.
And again being able to do it without

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actually using you know, an actual
just using for example, Slack or whatever

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is it's a pretty compelling advantage.
Yeah, because it sits on top.

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That's what gets me so excited.
And you know, maybe I'll throw this

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one over to you again. Aaron
I was talking with the gentleman, very

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interesting guy, Neil Hunch. He
runs the company called Silicon Foundry out of

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Silic out of San Francisco, and
we did a show talking about a bunch

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of different aspects of it. Was
actually around supply chain, but we were

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talking about information sharing and Jenai and
some of these new engines. And you

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know, the summarization capabilities of Jenai
are really impressive. If you haven't used

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them, folks, you have got
to use this. Take a big long

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document forty pages for example, loaded
into chat GPT and ask it to summarize,

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and ask it to summarize around different
threads and give me a voice for

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the board, give me a summary
for the IT team. It's really good

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at doing that stuff. And what
got me excited is he said that organizations

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are using it to leverage that eighty
percent of data in the company that is

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unstructured. Well, let me tell
you SharePoint was you know, kind of

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going to do that. And there
are some other ways elastic search, there

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are other ways that we've used technologies
to kind of get at that data.

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But there's nothing like I've ever seen
with these technologies. And that means that

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strange things are happening here in the
world. But a real quick Aaron comment

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on that, we've got a minute
left for the break. Yeah, I

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mean that illustrates one of the one
point that I'm actually curious to know a

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little bit more from Carrott, which
is that you know, there's a back

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end to the governance process where you
know, everybody wants to see governance teams

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produce some sort of report, you
know, and or you know, a

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summary something that they can tangibly say, here's here's our product, here's you

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know, how well we're governing our
data. And it sounds like with the

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tools generate AI. You can generate
those kinds of products, those kinds of

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reports, you know, so that
the governance team now can actually present you

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something tangible. Yeah, go ahead, real quick, thirty seconds. I

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was just going to say, yeah, we are absolutely doing things like that,

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and in fact, we're trying to
push it further where you don't even

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have to ask the questions, you
know, Can we just tell you what

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you need to know. That's a
little road map. Yea, we get

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to that soon. Yeah. But
I mean, seriously, folks, what

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we're talking about is the ability to
ingest, synthesize, and then articulate key

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points about your business dynamically all day
long, all day every day. I

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mean, that is like having a
virtual assistant who knows everything that you need

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to know, just available at your
fingertips to tell you what's going on.

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Well, folks, don't touch up
now, we'll be right back. You're

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listening to the only coast to coast
radio show all about the information economy.

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It's called Inside Analysis. Welcome back
to Inside Analysis. Here's your host,

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Eric Tavanaugh, and take this to
show. All right, folks, you

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can see why I love that song
for our show. Take us to the

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future. Maybe that's Black Bananas as
the band, if you want to look

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them up. And I used my
quote earlier today, one of my favorite

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quotes that we use for our TV
show Future Proof by William Gibson, who

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once said that the future is here
already, it's just not evenly distributed.

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I love that show. I love
that concept. It's just brilliant and it's

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true. So we're diving in.
We're talking to Aaron Wilson of Atena Solutions

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and Kiri Basu from Metaphor today about
data catalogs. And as I've said,

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data catalogs, in my opinion,
are the new center of gravity, and

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in many ways they are like a
metaphor for business. So if you think

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about what a metaphor is supposed to
be used for, it's to help you

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understand complex concepts. We come up
with metaphors to describe things so that you

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can figure out what someone really means
by something, and that's what metaphors do.

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So in many ways, the data
catalog is like a metaphor for business,

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and key it. I'll throw this
one over to you and I understand

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your solution and how it works.
So I've got a pretty good grasp on

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that. But I'm just thinking here, the more time and effort people put

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into it, the more access your
engine has two slack messages, to emails,

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even video calls that can be automatically
transcribed. These days, you've got

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technologies like I think Gong is one
of them, and there's Otter, and

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there's a bunch of these others that
automatically transcribe your meetings and give you summaries

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afterwards. It's like whiz bang,
wow, hello, where have you been

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on my life? When you can
capture all that, you are incrementally building

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institutional knowledge day after day right here. Writ absolutely. You know. The

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thought that comes to mind is so
many people have said this, where AI

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and LM's are really at that,
they're at the Sega console moment right now.

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It's so much more that happening.
You know. Multimodal models are already

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very very impressive, but the directions
that they're going are going to be absolutely

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amazing. And so yeah, we're
constantly keeping an eye out on what's the

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latest, greatest and being obviously very
cautious about how do we bring it in.

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Just like I mentioned earlier, we
want to make sure it's not just

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hey, here's a portal to an
LM, Like no, no, here's

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a portal to an LM through the
context of your data. Yeah, and

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maybe Aeron will throw out over to
you. You think about some sort of

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an layered architecture and up here's all
the information that's being shared. And in

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between, you've got this data catalog, and then you've got databases and things

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like this underneath, and that data
catalog is it's like a lens through which

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you can view the world, like
a colleidoscope or something, but it's a

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lens that allows you to understand the
definitions. And ideally you want it to

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be connected to your reporting and your
analytics, your business intelligence, your whatever

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you're using to analyze your data.
Because if a definition changes of let's say

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customer, so we've figured out some
new way to define customer and we're no

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longer including some category that we did
include missing. Little changes like that can

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have a great impact on reporting and
can give you bad numbers like duplicates.

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For example, if you've figured out, oh no, these are dupes.

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Now you have to get all these
duplicates out, your total number of customers

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go from one thousand to eight hundred
and fifty. That's going to change the

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numbers of your revenue per customer,
of your categorization of customers, all these

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kinds of things. That's why it's
important to have this collaborative effort around the

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data catalog. What do you think
erin, Well, yeah, absolutely,

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I mean you've brought up a couple
of different issues there that the point to

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how the new technology is so useful. I mean, one of which we're

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talking about here is lineage, right, and the idea of being able to

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correct errors quickly. I myself have
been through in many of the people on

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the call, I'm sure I have
been through, you know, the difficulty

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of trying to trace back a data
error and you know, find out then

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you know, how many reports were
impacted. There's always the big question it

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was just this one reporter. We've
we do have a you know, a

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whole nightmare of clients that were impacted. But the lineage tools, to just

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name one, are extremely you know, to be able to get your hands

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on lineage that that's accurate, you
know, instead of looking at charts that

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you don't even know how old they
are. I mean, it's it's it's

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it's extremely valuable. Yeah, Kira
it, I'll throw that over to you,

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because there are some of these issues
that we have been contending with for

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years, one of which is out
data reports for example. Now we have

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all sorts of really cool technologies around
observability that show us when data feeds are

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not working properly, and they give
alerts to end users, people who are

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delivering reports, for example, how
there's a problem here, let me go

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check and fix it before the reporters
do or whatever. They're cool things that

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you could do. But the lineage
and the timelineans if things are very important

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because something changes at a certain point, do you have the capacity to roll

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back to other versions and see things
or how does that work? Yeah?

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So, well, a couple of
things. We don't actually touch your data,

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so we're only looking at meddata,
so where we're sort of incurring things

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out of metadata and presenting to you. Yeah. Absolutely, We do show

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version histories of things that you could
certainly go back and see when changes have

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occurred, so we can help fix
all of those kind of things or certainly

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get you much much better insights into
how to fix it by pinpointing the right

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places where these kind of things happen. Yeah, and that helps because,

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as Aaron suggested, some changes made, maybe it was made incorrectly. All

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of a sudden, the reports start
adding up. And this is why business

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analysts ask questions, right, this
is why they're looking at some way there's

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something wrong with this looking into In
the old days, it was like,

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good luck figuring out what went wrong
if you don't have the documentation. But

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now you really can figure out what
went wrong. And that's all very positive

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for being able to sort of true
the wheels of business, if you will,

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and ensure the accuracy and the relevance
of the data. What do you

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think you're right? Yeah? Absolutely, So you know, the way we

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think about it is ours is sort
of a merging of three different graphs,

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if you will, right, Like, there's the technological graphs, so connections

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between systems. There's the business graph, which is you know, glossary terms,

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how they relate to objects within tables, et cetera. And then,

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as we call it, the social
graph, right like, how are people

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using, what are they talking about, how are they you know, interacting

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with the data, et cetera.
So we bring all of these things together,

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and so to your point, when
someone needs to go back and say,

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Okay, what happened at this point
in time. It's not just here

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are the technical changes that have happened, but oh, by the way,

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here are the conversations that were happening
about this data which led to this result.

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And here you go. Right,
So it's a much much more fuller

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picture of what happened and when.
Yeah, that's amazing because you can learn

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and I'm guessing sing and curate kurtb
Fro, I'm wrong, I'm guessing that.

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Especially over time, you can also
get a very good feel for who

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knows which subject matter better than others. You could probably ask it who really

359
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understands this skew in our manufacturing environment, or who understands this region. Those

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kinds of questions can probably be answered
very accurately. Now, if someone's using

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this kind of technology, right,
absolutely, I would say this. You

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know, certainly a lot of governments
people would want to specifically go out and

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mark an individual or two as being
the expert. But then the natural expertise

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that has just arisen because there are
people talking about it, etc. You

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could easily pinpoint these kind of clusters
if you will, of knowledge. Yeah,

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Aaron, I'm going to throw that
over to you. That's a really

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big deal. Like think from the
perspective of a senior executive who is in

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here to try to understand what's going
on in the organization. Maybe there's a

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recent merger or acquisition or something,
and they're trying to wrap their head around

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who knows this or who knows that? To be able to ask a question

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of a data catalog like that,
who really understands our business in the Southeast

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region or who really understands this product
line and get some dynamically generated answer based

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upon conversations happening in the company.
Is it me or is that just a

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massively cool deal? Yeah, I
think it's potentially a huge advantage. It's

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going to be a shift, though, and I think that's one of the

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things I'd like to ask Curate about, is that now you sort of democratize

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data and then it's no longer this
top down as Eric you pointed out,

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you know, maybe a senior manager
who goes to his manager, who goes

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to you know, goes to his
next report down the line, this is

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where is this data come from?
Now you have it's been democratized, but

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it's it's a real it's I would
imagine that companies undergo a real, uh

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sort of cognitive shift in terms of
the way they operate. And I'm curious

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to actually know what he has seen, you know, when they introduce these

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types of tools. Yeah. Absolutely. You know, if you think about

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every governance role that's out there,
you look at their job descriptions, it's

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about like chasing down governance problems and
making sure tagging is right, et cetera.

387
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Pretty much everyone hates doing the manual
labor involved in doing that stuff,

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but with technologies like ours, it
allows them to actually be about governance right,

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Like they can step back all the
way and say, Okay, big

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00:27:26.880 --> 00:27:30.880
picture, how am I solving the
problems that I'm trying to that my company

391
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is having. That the real role
of governance sort of really shines through with

392
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the power of these technologies to take
out the mundane that everyone has to do

393
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otherwise and make it that much more
effective. Yeah, and I think that

394
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that gets one of my favorite soapbox
topics, which is morale. And I

395
00:27:48.160 --> 00:27:52.720
throw this speck over to you,
career. Once you understand what the changes

396
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that this kind of technology brings,
which are significant, but once you wrap

397
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your head around that and understand it. I think it's great for morale because

398
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the things that kill morale are spinning
your wheels, not being able to get

399
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answers to things, not being able
to change what needs to be changed,

400
00:28:11.240 --> 00:28:14.680
not knowing who is responsible, not
knowing who I can reach out to to

401
00:28:14.720 --> 00:28:18.839
get answers. All these things are
solved at least to some degree with this

402
00:28:18.960 --> 00:28:22.720
approach. So I have to think
that improves morale because by and large,

403
00:28:22.720 --> 00:28:26.279
if people feel like they're getting somewhere, they're getting something done, they're making

404
00:28:26.359 --> 00:28:30.160
progress, that's excellent for morale.
It's the opposite that gets them depressed.

405
00:28:30.160 --> 00:28:34.079
What do you think her absolutely?
I mean, I think I mentioned this

406
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earlier. The code that we hear
from most of our customers, especially who

407
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spend a lot of time on legacy
catalogs, is you know, catalogs are

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when data goes to die, and
that is fundamentally one of the issues where

409
00:28:48.839 --> 00:28:53.759
people don't care about working on these, you know, especially antiquated systems.

410
00:28:53.799 --> 00:28:57.599
The UIs are terrible, all those
kind of things. And now this is

411
00:28:57.640 --> 00:29:02.640
a whole new varadigm where heck,
you don't even need to be trained on

412
00:29:02.680 --> 00:29:04.839
the catalog to get value from it. So, yeah, absolutely, morale

413
00:29:04.920 --> 00:29:11.200
makes a huge difference in that well
and in training too, So you think

414
00:29:11.200 --> 00:29:17.240
about upskilling training people. A catalog
again, properly used, properly installed,

415
00:29:17.279 --> 00:29:22.400
et cetera, is a great training
vehicle because it is an access point to

416
00:29:22.519 --> 00:29:27.839
information about business processes, about terminology. I mean you talked curate about understanding

417
00:29:27.880 --> 00:29:33.440
the relationships between systems, and that's
a very important thing as well. And

418
00:29:33.480 --> 00:29:40.480
again this generative AI concept, these
technologies can do tremendous things in explaining things,

419
00:29:40.559 --> 00:29:42.960
explaining how these two systems interconnect,
right, go ahead, cirrit.

420
00:29:44.799 --> 00:29:47.920
Yeah, so you know, I
think this is where from a from a

421
00:29:47.960 --> 00:29:52.480
product perspective, the point of view
that we take is JENNAI is one of

422
00:29:52.519 --> 00:29:56.240
the tools in our tool get right. Like the other really really critical one

423
00:29:56.400 --> 00:30:00.519
is in fact, the user experience
and the user interface in general. My

424
00:30:00.559 --> 00:30:03.359
philosophy really is like the moment you
have to make a context switch and go

425
00:30:03.400 --> 00:30:10.079
and look up a document or documentation, etc. You mix the point completely.

426
00:30:10.200 --> 00:30:15.039
So to have an experience, whether
it's magical through AI or it's a

427
00:30:15.160 --> 00:30:18.440
very procedural thing, but having it
be so intuitive, so simple to use

428
00:30:18.960 --> 00:30:22.640
is one of the baselines for us
as we design any kind of capabilities,

429
00:30:23.400 --> 00:30:26.839
because we want to make sure every
user gets value out of the catalog.

430
00:30:29.119 --> 00:30:30.920
Yeah, and you know, that's
an excellent point. And this kind of

431
00:30:30.960 --> 00:30:36.920
gets back to another theme that I
have about the UI and the point of

432
00:30:37.000 --> 00:30:41.039
interaction with the business. And what
I think is very compelling about what you

433
00:30:41.119 --> 00:30:45.559
folks had metaphor has done is that
you are leveraging the places where they're already

434
00:30:45.559 --> 00:30:51.559
going. You're leveraging Slack because you're
indexing what goes across Slack. You're leveraging

435
00:30:51.640 --> 00:30:55.920
email. If they get access to
the email, whatever channel of communication people

436
00:30:55.960 --> 00:31:00.119
are already using, you're taking advantage
of that, so you're not forcing people

437
00:31:00.160 --> 00:31:03.960
to go into some separate environment to
ask questions. To your point, I

438
00:31:03.960 --> 00:31:07.440
think most people know that when you're
bouncing around from this app to that app

439
00:31:07.440 --> 00:31:14.759
to some other apps, it's disruptive. It is it's a discontinuing sort of

440
00:31:15.319 --> 00:31:18.640
event, and it is disruptive to
the brain. I mean not that you

441
00:31:18.680 --> 00:31:21.480
can't do it. You can't,
but it's just it's like a hurdle you

442
00:31:21.519 --> 00:31:23.920
have to jump across, and you've
kind of knocked all those hurdles down.

443
00:31:25.000 --> 00:31:26.799
Isn't that about right here? Yeah, absolutely, I mean, you know,

444
00:31:29.039 --> 00:31:33.720
I challenge people to find or if
anyone has less than I don't know,

445
00:31:33.920 --> 00:31:37.880
twenty tabs open in there, for
example, I'd like to meet them.

446
00:31:38.519 --> 00:31:44.359
But yeah, I mean, why
introduce fifty other you know, browser

447
00:31:44.400 --> 00:31:48.319
Windows, you have to go searching
through stuff, switching context, you know,

448
00:31:48.440 --> 00:31:52.160
hard switching context. Whereas the actual
problem you were trying to solve,

449
00:31:52.160 --> 00:31:53.920
what's completely different. It has to
do with business, but now you have

450
00:31:53.960 --> 00:31:57.200
to go through all the details.
So that's exactly one of the reasons why

451
00:31:57.799 --> 00:32:00.759
you know, slacked. We want
to make sure that we can give the

452
00:32:00.799 --> 00:32:07.440
answers you want right there with all
the context and all the stuff behind it,

453
00:32:07.519 --> 00:32:12.240
right. Yeah, Well, it's
a big time saver, first of

454
00:32:12.279 --> 00:32:15.880
all, and it maintains that continuity. Know, Aeron, I'll throw this

455
00:32:15.920 --> 00:32:17.640
over to you. We've got about
a couple of minutes before this segment is

456
00:32:17.720 --> 00:32:23.000
up. Analysis is a thought process
and it really should be fluid in order

457
00:32:23.039 --> 00:32:27.559
to be effective. And what I
mean by that is if you're bouncing around

458
00:32:27.559 --> 00:32:30.200
from one app to another, that's
not very fluid. Right. Those are

459
00:32:30.559 --> 00:32:35.240
sort of truncated moments or disruptive moments
when you're bouncing around, and you want

460
00:32:35.279 --> 00:32:38.240
to have that true conversation with your
data. We've talked about that for thirty

461
00:32:38.319 --> 00:32:43.319
years in this industry, but now
you really can have the conversation with your

462
00:32:43.400 --> 00:32:46.240
data. If it's connected to the
information systems, if it has a data

463
00:32:46.279 --> 00:32:51.319
catalog layer which is making sense of
things, and if you're able to use

464
00:32:51.319 --> 00:32:54.240
this GENAI stuff real quick, you
could have a conversation with your business data.

465
00:32:54.279 --> 00:32:58.839
What do you think, erin,
I completely agree. I mean,

466
00:33:00.000 --> 00:33:02.519
I think you can't. You can't
overstate the value of kind of eliminating that

467
00:33:02.640 --> 00:33:07.039
layer right of having to go to
another tool separately. You know, I've

468
00:33:07.880 --> 00:33:10.680
I personally, I've done it,
and that's the way most you know,

469
00:33:10.920 --> 00:33:15.519
of the previous generation of these tools
have worked. But to be able to

470
00:33:15.960 --> 00:33:21.200
just you know, interact directly on
teams, on slack, that type of

471
00:33:21.200 --> 00:33:24.279
thing, and then if you do
need to provide you know, sample data

472
00:33:24.279 --> 00:33:27.720
and talk about the problem or jump
on a call or something like that,

473
00:33:27.720 --> 00:33:30.559
you can also do those things too, but without that extra layer of going

474
00:33:30.599 --> 00:33:34.640
to your you know, your governance
tool as it were. That that's exactly

475
00:33:34.720 --> 00:33:37.079
right, folks. People want to
work in one environment. They want to

476
00:33:37.079 --> 00:33:38.680
be on the phone or in a
zoom call or whatever doing their thing.

477
00:33:38.720 --> 00:33:42.680
They want to have to jump all
around, but don't touch that doll.

478
00:33:42.759 --> 00:33:46.400
Folks. We're going to be right
back. You're listening to Inside Analysis.

479
00:33:52.039 --> 00:34:00.880
Welcome back to Inside Analysis. Here's
your host, Eric Tavanaugh Show Fact.

480
00:34:00.960 --> 00:34:05.799
Here on Inside Analysis, we're talking
to Aaron Wilson of Athena Solutions and Curates

481
00:34:06.319 --> 00:34:09.880
of Metaphor. They are a data
catalog company, Metaphor Data, and Aaron,

482
00:34:09.920 --> 00:34:14.519
you had a question for key Rits
to go ahead. Yeah, what

483
00:34:14.559 --> 00:34:19.679
I was curious about from Curate's perspective
is, you know, we have data

484
00:34:19.679 --> 00:34:22.960
governments, governance teams. You know, most companies to deal with data have

485
00:34:23.519 --> 00:34:30.199
a data governance team, and this
type of functionality, you know, with

486
00:34:30.280 --> 00:34:35.360
a product like Metaphor, really I
would think completely would reshape, you know,

487
00:34:35.519 --> 00:34:37.639
kind of their daily existence because I'm
looking at it from the perspective of

488
00:34:37.719 --> 00:34:45.079
so much time spent on information gathering
right and curating, and you know,

489
00:34:45.280 --> 00:34:51.360
with with a tool, with a
modern data catalog tool, this probably shifts

490
00:34:51.400 --> 00:34:54.920
them more into making. It probably
allows them to come up with policy quicker

491
00:34:55.159 --> 00:34:58.519
and a number of other things now
that they've kind of got a lot of

492
00:34:58.559 --> 00:35:00.960
that mundane work out of a way, But I'm curious to know what perspectives

493
00:35:01.000 --> 00:35:06.239
you've gotten from clients. Yeah,
so I think you know, it's across

494
00:35:06.239 --> 00:35:08.880
the spectrum. Obviously, I'll give
you the high and the low side.

495
00:35:08.880 --> 00:35:13.880
So on the on the low side
or the very simplistic sort of side,

496
00:35:13.960 --> 00:35:19.199
we certainly have customers where yes,
governance teams are there and they have,

497
00:35:19.480 --> 00:35:22.880
you know, sort of a vision
of what they do, but the reality

498
00:35:22.079 --> 00:35:28.360
is that the company on the whole
might not be fully aligned with that vision.

499
00:35:28.440 --> 00:35:30.960
Right, Like the governance person wants
to get something done, but the

500
00:35:30.000 --> 00:35:32.559
rest of the company is still trying
to get there, et cetera. So

501
00:35:32.599 --> 00:35:37.559
we certainly have like it's helpful in
that sort of realm because it, like

502
00:35:37.639 --> 00:35:39.280
you said, allows them to step
out of the Monday and say, okay,

503
00:35:39.440 --> 00:35:43.360
big picture, here are the types
of things I can actually get done

504
00:35:43.760 --> 00:35:45.920
now that I don't have to do
the manual work that I would otherwise have

505
00:35:46.000 --> 00:35:49.599
to, you know, do it
on a day to day basis. So

506
00:35:50.000 --> 00:35:53.719
that's one side. The other side, absolutely we have customers where they came

507
00:35:53.760 --> 00:35:58.039
in effectively saying, oh, yeah, I need to be a governance data

508
00:35:58.039 --> 00:36:00.960
governance person and you know, I
through all the documentation on the internet and

509
00:36:01.000 --> 00:36:04.880
it says, these are the twenty
things I have to do, and oh,

510
00:36:04.880 --> 00:36:07.960
by the way, now I have
to do like half a thing,

511
00:36:07.239 --> 00:36:10.599
right, or I can think much
more big picture, and so a lot

512
00:36:10.599 --> 00:36:15.360
of times we find organizations who are
much much more further advanced and mature,

513
00:36:15.480 --> 00:36:21.039
so they're already on their data meash
or data product, one of those sort

514
00:36:21.079 --> 00:36:23.199
of journeys. They've already built out
some of those kind of things. They

515
00:36:23.199 --> 00:36:28.519
can take a much bigger view and
they're effectively redefining governance. They are talking

516
00:36:28.880 --> 00:36:32.280
much higher picture. They're talking about
like here's the data product and here's I'm

517
00:36:32.320 --> 00:36:37.199
going to evolve It. Nothing to
do with the like the menial labor that

518
00:36:37.239 --> 00:36:39.400
you have to do. You're focusing
on the data. You're focusing on the

519
00:36:39.400 --> 00:36:45.559
actual problem and the outcomes that the
company cares about. That's awesome. I

520
00:36:45.599 --> 00:36:49.320
mean, really you think about Okay, ours right, maybe I'll throw it

521
00:36:49.360 --> 00:36:52.480
over to you, Aaron. I
remember when this whole concept of objectives and

522
00:36:52.559 --> 00:36:57.599
key results came out as opposed to
just numbers and things. Because business is

523
00:36:57.719 --> 00:37:00.119
very unwieldy when you can get right
down to it, and you want to

524
00:37:00.159 --> 00:37:05.159
be striving towards things, but not
just basic metrics, right because if you're

525
00:37:05.199 --> 00:37:07.519
just going for metrics, what happens
they turn into vanity metrics. So the

526
00:37:07.639 --> 00:37:12.760
objectives and key results I think is
pretty important stuff. And a data catalog

527
00:37:12.840 --> 00:37:15.920
is a great conduit to help you
get there, to help you define reasonable

528
00:37:15.960 --> 00:37:21.800
okay ours and know if you've gotten
there right erin well, it definitely would

529
00:37:21.840 --> 00:37:24.840
be I think what it would do. Would it would open the door to

530
00:37:25.000 --> 00:37:31.000
making you know nothing against KPIs.
You know, I'm sure organizations will you

531
00:37:31.000 --> 00:37:34.639
know, always have a use for
those, but you could probably make them

532
00:37:34.679 --> 00:37:37.719
a heck of a lot better by
being able to get your hands on maybe

533
00:37:37.760 --> 00:37:42.480
additional data. I mean, dashboards
will be easy. It'll be easier to

534
00:37:42.480 --> 00:37:45.079
come up with a new data point
and understand it and incorporate it into a

535
00:37:45.119 --> 00:37:49.719
dashboard and make it that much more
useful because you can get your hands on

536
00:37:49.760 --> 00:37:53.480
the data and understand it better.
And you might find that users from all

537
00:37:53.519 --> 00:37:58.880
over the organization can participate in that
process, whereas it may it may not

538
00:37:58.920 --> 00:38:01.960
have been the case earlier mm hmm. And that's the key point again to

539
00:38:01.960 --> 00:38:06.840
get back to collaboration, krat I'll
throw it back over to you. Anyone

540
00:38:06.960 --> 00:38:08.760
can log into this, anyone.
I mean, obviously they have to have

541
00:38:08.840 --> 00:38:15.039
permission to do so. But having
teams from different parts of the organization be

542
00:38:15.199 --> 00:38:20.079
able to make their comments about things, especially if folks in the field who

543
00:38:20.079 --> 00:38:22.280
would say, hey, guys,
I noticed something and that's not correct,

544
00:38:22.320 --> 00:38:27.840
and you fix this all of a
sudden, it's like you're fusing what we're

545
00:38:27.960 --> 00:38:32.239
to spirit channels. They think support, for example, versus email versus slack.

546
00:38:32.400 --> 00:38:35.920
Right, the guys in the support
and the girls in the support area,

547
00:38:36.000 --> 00:38:38.079
they're the the coal face, as
they say, dealing with customers and

548
00:38:38.119 --> 00:38:43.239
they're going to see stuff, and
historically it's been very difficult for that feedback

549
00:38:43.280 --> 00:38:45.920
to make it all the way up
the IT ladder to the person who has

550
00:38:45.000 --> 00:38:49.639
the authority to change something. And
that's just out the window now right.

551
00:38:50.400 --> 00:38:53.239
Absolutely. I'll give you a great
example of this. We have several customers

552
00:38:53.239 --> 00:38:58.960
who've been through this recently where you
know, their old school sort of pointer

553
00:38:58.960 --> 00:39:01.400
of view and doing government was like, let's get coverage, so let's make

554
00:39:01.440 --> 00:39:05.599
sure that I don't know, one
hundred percent of our data is documented,

555
00:39:06.000 --> 00:39:08.480
and let's be honest, most documentation
in that case was like one sentence,

556
00:39:08.519 --> 00:39:12.719
Oh, this table does blah,
and that's the end of that. So

557
00:39:12.760 --> 00:39:15.119
we've sort of changed that around.
What we've basically said was like, hey,

558
00:39:15.159 --> 00:39:19.400
look through all the comments that you're
having, you know, questions coming

559
00:39:19.400 --> 00:39:22.559
in through Slack or support channels,
et cetera, and then basically answer your

560
00:39:22.559 --> 00:39:28.119
top twenty five questions, meaning you
literally write it out verbatim. See if

561
00:39:28.159 --> 00:39:31.480
the system has an answer. If
you don't create, here are buttons and

562
00:39:31.599 --> 00:39:36.400
guess what. Here's help with AI
which will help you generate that right answer

563
00:39:36.480 --> 00:39:40.039
as well. And basically within a
couple of hour window, you could answer

564
00:39:40.400 --> 00:39:45.760
the top twenty five questions and every
single variation, infinite variations, because that's

565
00:39:45.760 --> 00:39:49.760
where the LN really shines. You
know, I asked for how do you

566
00:39:49.800 --> 00:39:53.239
define revenue? Your version of that
statement, how is revenue defined? Doesn't

567
00:39:53.280 --> 00:39:57.119
matter, We'll get you the answer. We know how to get to that

568
00:39:57.159 --> 00:40:01.280
point. So that has been like
a huge change and how like the old

569
00:40:01.280 --> 00:40:07.559
way of doing things have has completely
been supplanted by a very outcome oriented way

570
00:40:07.800 --> 00:40:10.800
to get to the answers that you
need. Yeah, and real quick,

571
00:40:10.800 --> 00:40:15.239
and we can maybe get into this
in the podcast bonus segment a bit deeper,

572
00:40:15.280 --> 00:40:16.639
and we've got a couple of minutes
left here, but I'm going to

573
00:40:16.679 --> 00:40:22.880
throw us back to this concept of
an abstraction layer that can absorb from all

574
00:40:22.880 --> 00:40:29.199
these existing channels. Right. One
of the challenges of deploying a new technology

575
00:40:29.239 --> 00:40:30.800
is that it's a new app,
it's a separate app, it's a separate

576
00:40:30.840 --> 00:40:34.440
log and all this stuff you have
to go and do and then you know,

577
00:40:34.679 --> 00:40:37.000
historically set up pipelines and things.
But the fact that you're pulling from

578
00:40:37.039 --> 00:40:44.559
all these existing channels and allowing for
all that to be baked into this data

579
00:40:44.599 --> 00:40:49.719
catalog that I think is one of
the real key differentiators because now you're not

580
00:40:49.840 --> 00:40:52.320
forcing people out of slack, you're
not forcing people out of email, You're

581
00:40:52.320 --> 00:40:55.559
allowing them to work where they work
and live where they live. You're just

582
00:40:55.760 --> 00:41:00.719
adding this layer of value to it, which is a reconciling layer, a

583
00:41:00.800 --> 00:41:04.039
definitional layer. What do you think, he rit, Yeah, absolutely,

584
00:41:04.079 --> 00:41:07.440
I mean it is. Yeah.
The way I think about it is,

585
00:41:07.000 --> 00:41:10.599
you know, the stuff that we
do in addition with the AI sort of

586
00:41:10.599 --> 00:41:15.320
capabilities, it becomes kind of like
the great equalizer of SUTs. So it's

587
00:41:15.360 --> 00:41:19.639
no longer that you have just the
data people who are the experts. And

588
00:41:20.079 --> 00:41:22.239
I mean, of course people are
experts, we're not denying that, but

589
00:41:22.519 --> 00:41:30.280
the availability of that expertise is instantaneous
to someone who's you know, not in

590
00:41:30.559 --> 00:41:34.920
that role, and so absolutely it's
it's abstraction layer. But it's more than

591
00:41:34.960 --> 00:41:37.800
that, it's an equalizer for for
people. Yeah, that's I'll ask you

592
00:41:37.840 --> 00:41:40.320
to comment on that, Aaron.
I think, I mean, really it's

593
00:41:40.360 --> 00:41:43.559
kind of a slam duck, right, if you can afford to do this

594
00:41:43.639 --> 00:41:45.280
and you can get it done in
your organization, it's kind of a slam

595
00:41:45.400 --> 00:41:50.920
duck to do something like this.
What do you think erin for sure?

596
00:41:52.079 --> 00:41:54.079
And one of the points that you
kind of touched on there is the fact

597
00:41:54.079 --> 00:42:00.440
that the ability for a product,
you know, for a modern day catalog

598
00:42:00.519 --> 00:42:05.599
of product and not just work with
the data assets, but also through the

599
00:42:05.639 --> 00:42:08.559
distribution channels. I find that particularly, I mean, I just think that's

600
00:42:08.599 --> 00:42:14.000
really cool, the fact that you
can monitor and track how often is somebody

601
00:42:14.000 --> 00:42:17.280
talking about this particular data series,
right, who's talking about it? What

602
00:42:17.320 --> 00:42:21.239
reports is it appearing in? I
mean, I guess that's the you know,

603
00:42:21.280 --> 00:42:24.559
it's part of this concept that people
are calling active metadata, this idea

604
00:42:24.639 --> 00:42:28.840
that you know you have a feedback
loop. Now you know what your relevant

605
00:42:28.920 --> 00:42:31.119
data points are, much more than
you're used to under the old environment.

606
00:42:32.599 --> 00:42:37.280
Yeah, and just then use ability
side of the equation. Who's using this?

607
00:42:37.519 --> 00:42:39.760
What are they talking about? You
think about bubble what do they called

608
00:42:39.800 --> 00:42:44.639
word bubbles? Where you see which
terms are most popular. These are all

609
00:42:44.760 --> 00:42:51.480
useful constructs to help guide the attention
of the executive or help guide the attention

610
00:42:51.639 --> 00:42:54.280
of the frontline worker. I mean
that's the other thing is customer service.

611
00:42:54.320 --> 00:42:58.440
If someone's on the call, if
you can, you can patch this into

612
00:42:58.440 --> 00:43:01.679
a customer service center. Think about
it. In the call. That person

613
00:43:01.760 --> 00:43:06.840
needs to have whatever information is available
and relevant right now. You want it

614
00:43:06.960 --> 00:43:09.960
very quickly. You can't be waiting, especially if the customer is angry calling

615
00:43:10.039 --> 00:43:13.599
up all upset. They came be
like, oh my systems are running slow

616
00:43:13.639 --> 00:43:16.039
today, Sorry sir. You know
nobody likes to hear that. But these

617
00:43:16.119 --> 00:43:22.400
kinds of information flows can be extremely
valuable for anyone in the organization, whether

618
00:43:22.400 --> 00:43:25.280
you're on the front lines, whether
you're middle management, whether you're an executive.

619
00:43:25.639 --> 00:43:30.719
If you give partners access for example. The point is it's it's a

620
00:43:30.880 --> 00:43:35.480
knowledge repository. And I'll go back
to that comment I made earlier about the

621
00:43:35.559 --> 00:43:40.159
gentleman from Silicon Foundry that the aha
moment in my head was knowledge management.

622
00:43:40.360 --> 00:43:45.719
Remember that guy is like twenty five
years ago, before even business intelligence became

623
00:43:45.760 --> 00:43:49.679
a thing, it was knowledge management
and it kind of went nowhere because there

624
00:43:49.719 --> 00:43:53.159
just wasn't the compute power. There
weren't the technologies available to really make sense

625
00:43:53.199 --> 00:43:57.960
of all that stuff. And now
that is all here. That whole paradigm

626
00:43:58.000 --> 00:44:02.199
has shifted to where you can do
actual, real tangible knowledge management with you

627
00:44:02.400 --> 00:44:07.679
in your organization. Folks. That
is not a small thing. We're finally

628
00:44:07.719 --> 00:44:10.599
going to be able to truly leverage
this eighty percent of corporate data which is

629
00:44:10.719 --> 00:44:15.159
unstructured. But podcast bone A segment
is coming up next, folks. You

630
00:44:15.199 --> 00:44:22.679
are listening to Inside Analysis. Okay, folks, time for the podcast boning

631
00:44:22.719 --> 00:44:27.800
A segment here on Inside Analysis talking
to Aaron Wilson of Athena Solutions and Kirt

632
00:44:27.840 --> 00:44:30.599
Basu of Metaphor. Aaron is a
consultant. If you need some consulting help,

633
00:44:30.639 --> 00:44:34.559
give a call curate. He's the
vendor if you need a new tool,

634
00:44:34.599 --> 00:44:37.000
and I think everyone does. You
got to get a data catalog,

635
00:44:37.079 --> 00:44:39.639
folks. This is good stuff called
ty writ and his team parent. I'll

636
00:44:39.639 --> 00:44:44.599
throw it over to you. Metadata
management. You know, I remember,

637
00:44:45.280 --> 00:44:49.719
gosh, twenty years ago talking to
a client about his metadata repository, and

638
00:44:49.880 --> 00:44:52.400
I was such a babe in the
woods. It was like twenty four years

639
00:44:52.400 --> 00:44:54.039
ago in fact, I said,
I'm like, you know, this is

640
00:44:54.159 --> 00:44:58.719
I keep talking to these vendors and
they all have their own metadata catalogs.

641
00:44:58.719 --> 00:45:01.559
But wouldn't it be good if there
were like one metadata catalog that we all

642
00:45:01.639 --> 00:45:05.960
kind of adhere to, and you
know, and that's like an ontology,

643
00:45:06.039 --> 00:45:07.320
right, I'm like a little babe
in the woods out here, and he

644
00:45:07.440 --> 00:45:09.920
like kind of shakes his day's like, well, yeah, that would be

645
00:45:10.039 --> 00:45:13.920
good, I guess. But it's
just not really the way things work out

646
00:45:13.920 --> 00:45:15.480
there in the real world. But
we're kind of getting close to that.

647
00:45:15.519 --> 00:45:20.119
But maybe just talk real quick,
Eric about metadata management, why it's so

648
00:45:20.159 --> 00:45:23.440
important, and how things are changing
now about how you do that. Sure,

649
00:45:24.639 --> 00:45:29.360
I mean, I think it's no
accident that you know, some of

650
00:45:28.960 --> 00:45:31.920
the bigger companies that are really data
intensive, and I mean, you know,

651
00:45:31.960 --> 00:45:35.400
if you look at metaphors origins.
You know, coming out of the

652
00:45:35.440 --> 00:45:40.159
data team at LinkedIn, they were
under enormous pressure, probably more pressure than

653
00:45:40.199 --> 00:45:45.239
most right to make, to get
valuable insights out of an enormous quantity of

654
00:45:45.280 --> 00:45:50.519
data. And you can't really do
it effectively if you don't have any concept

655
00:45:50.559 --> 00:45:54.760
of metadata management. You have to
have commonly agreed upon data definitions, you

656
00:45:54.840 --> 00:46:00.280
have to be able to get your
hands on lineage quickly. So I think

657
00:46:00.400 --> 00:46:04.679
the old way of doing things.
You know, you need to bring clarity

658
00:46:04.760 --> 00:46:06.679
to these things, and you don't
have a whole lot of time to do

659
00:46:06.719 --> 00:46:08.480
it. You don't really have a
lot of time for a governance team to

660
00:46:09.000 --> 00:46:13.760
assemble, for example, you know, a collection of terms or a data

661
00:46:13.760 --> 00:46:17.360
dictionary. It's it's tremendously valuable to
be able to kind of get to the

662
00:46:17.360 --> 00:46:22.039
point when it comes to metadata management. Yeah, good point. I'll throw

663
00:46:22.079 --> 00:46:25.119
it over to you. Here it
and maybe talk about how metadata management has

664
00:46:25.280 --> 00:46:32.039
changed, because I mean it used
to be a fairly uh what manual process

665
00:46:32.119 --> 00:46:36.360
and only a couple people touched it. They had to go, like I

666
00:46:36.360 --> 00:46:37.840
said earlier, to go talk to
the business try to figure stuff out.

667
00:46:38.079 --> 00:46:43.119
Now it could be much more dynamic, and it's very useful in terms of

668
00:46:43.199 --> 00:46:46.920
understanding who's doing what, which metadata
is being accessed, what's the important metadata

669
00:46:46.960 --> 00:46:51.320
we got to watch out for.
All those things are now much more clear

670
00:46:51.400 --> 00:46:54.159
because we can see what's happening right
here. It absolutely so you know,

671
00:46:54.280 --> 00:46:58.840
I'll give you a simple example.
When you're searching for, like, what's

672
00:46:58.880 --> 00:47:02.239
the definition of revenue. Let's say
you plug that into our system. The

673
00:47:02.320 --> 00:47:07.639
answer you're going to get is not
just the actual definition of revenue. Of

674
00:47:07.679 --> 00:47:10.360
course, there's that you know,
it might be specified in a glossary somewhere,

675
00:47:10.440 --> 00:47:14.239
or could be in a dictionary,
depending on you know, what that

676
00:47:14.320 --> 00:47:17.039
metric is. But we will also
give you other context We'll give you all

677
00:47:17.039 --> 00:47:21.000
the others. Well, yeah,
here's the definition, but by the way,

678
00:47:21.039 --> 00:47:23.039
here are the you know, stated
owners of these, or here are

679
00:47:23.079 --> 00:47:28.800
the conversations happening about. So what
you get is a much much more richer

680
00:47:28.960 --> 00:47:32.360
contextual answer about what it is whatever
it is you're asking for. And so

681
00:47:34.000 --> 00:47:37.320
yeah, I mean, it's still
a human doing the interpretation, but now

682
00:47:37.360 --> 00:47:40.039
you've made that human into a basically
an analyst. Even if they were not,

683
00:47:40.159 --> 00:47:45.679
for example, right like they are
able to synthesize much much more interesting

684
00:47:45.760 --> 00:47:49.679
data to get to a conclusion that
they need to. Yeah, that's that's

685
00:47:49.719 --> 00:47:52.480
a really good way to put it. And you know, it's funny you

686
00:47:52.519 --> 00:47:58.480
would say that because our show is
called Inside Analysis and we our tagline is

687
00:47:58.519 --> 00:48:02.559
take the inside Track to Insight.
But our whole mission was to enable the

688
00:48:02.599 --> 00:48:07.440
audience to be an analyst. Like
that's our whole job in the world is

689
00:48:07.480 --> 00:48:13.519
to share information about tools, technologies, processes, people, to help people

690
00:48:13.719 --> 00:48:16.320
understand how to ask the right question, put it in context, and they

691
00:48:16.440 --> 00:48:20.800
make better decisions about the number one
the tech that they use, for sure,

692
00:48:21.039 --> 00:48:22.800
but the business and how they run
the business. So I think that's

693
00:48:22.840 --> 00:48:27.960
really beautiful that you just said that. You spoke to our whole Reison Detras

694
00:48:28.000 --> 00:48:31.119
as the French would say, so
good good, good work. Final comment,

695
00:48:31.840 --> 00:48:36.079
Aaron, final piece of advice about
how people should get started or do

696
00:48:36.119 --> 00:48:39.599
something with this, Well, yeah, I mean I would say that,

697
00:48:40.159 --> 00:48:45.920
you know, with companies that are
struggling with these types of issues, you

698
00:48:45.920 --> 00:48:49.400
know, this is an important decision
to make. This is you know what

699
00:48:49.679 --> 00:48:53.079
do I need? And this is
the title of one of our prior presentations.

700
00:48:53.119 --> 00:48:58.880
You know what data catalog do I
need a lot of considerations. One

701
00:48:58.920 --> 00:49:00.440
of them, though, I would
say this, this is that if a

702
00:49:00.480 --> 00:49:05.119
company has a reasonably good handle on, you know, their data challenges.

703
00:49:05.199 --> 00:49:07.280
And what's interesting is, you know, I would say ten fifteen years ago,

704
00:49:07.639 --> 00:49:12.760
most companies were consider themselves, by
their own admission, very much behind

705
00:49:12.800 --> 00:49:15.639
the curve. But now that we've
gotten you know, companies are further ahead

706
00:49:15.639 --> 00:49:21.920
in terms of data warehousing and integration. And then you still have you often

707
00:49:22.000 --> 00:49:24.320
left with the metadata problem. You
know, it's kind of what's left.

708
00:49:24.320 --> 00:49:28.199
It's kind of like, well,
we have good you know, we we

709
00:49:28.719 --> 00:49:31.360
have good time to market, we
have our data is better organized, but

710
00:49:31.440 --> 00:49:36.880
yet we don't have general agreement.
We have problems around definition and lineage and

711
00:49:36.880 --> 00:49:42.159
so forth. These these are the
kind of tools that companies may very well

712
00:49:42.199 --> 00:49:45.400
be ready for if they find that
those are their pain points. And that's

713
00:49:45.480 --> 00:49:47.760
part of what we do with Athena
is to come in and do an assessment

714
00:49:47.800 --> 00:49:51.239
and to say like, hey,
what are your pain points, and then

715
00:49:51.280 --> 00:49:54.880
we can hopefully line you up in
terms of vendors with the vendors that go

716
00:49:54.920 --> 00:50:00.639
to those strengths. Good stuff for
folks. Th Thank you so much for

717
00:50:00.679 --> 00:50:04.079
your time today for listening in thanks
to Kirrit Basu of Metaphor and Aaron Wilson

718
00:50:04.079 --> 00:50:07.559
of Athena Solutions. We do all
these webinars for the interviewing. Share it

719
00:50:07.559 --> 00:50:09.880
with your colleagues. One nice thing
about Zoom is the URL for the live

720
00:50:09.920 --> 00:50:14.480
show is the same for the archive. Relliant. Someone was thinking, that's

721
00:50:14.480 --> 00:50:17.320
an architectural decision. Someone made it
zoom a long time ago and it was

722
00:50:17.440 --> 00:50:22.559
fantastic because WebEx was not like that, it was a different URL. Someone

723
00:50:22.599 --> 00:50:24.760
says, you know what, let's
make it the same URL for usability,

724
00:50:24.840 --> 00:50:29.599
for consumption, all this great stuff. So check out Metaphor Online, Metaphor

725
00:50:29.679 --> 00:50:31.400
Data Data Catalog, and Athena Solutions. And with that we're going to bid

726
00:50:31.440 --> 00:50:35.559
you farewell. Folks. You've been
listening to Inside Analysis. Get all the

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facts all you need to know on
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NBC News Radio. I'm Chris Karragio,

815
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a fifty year old retired Pennsylvania volunteer
fire chief, has been idd as the

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deceased victim of former President Trump's would
be assassin, Governor Joshapiros book about Corey

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00:58:00.920 --> 00:58:04.960
Comparatory, who was shot and killed
at Trump's rally. Corey was a girl

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00:58:05.039 --> 00:58:12.000
dad. Corey was a firefighter.
Corey went to church every Sunday. Corey

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loved his community. Most especially,
Corey loved his family. His sister says

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Comparatory was killed while trying to shield
his family from the bullets. The US

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Secret Services facing intense scrutiny following the
assassination attempt. Lawmakers on both sides of

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the aisle are asking how a sniper
was able to get on a rooftop about

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00:58:30.239 --> 00:58:32.719
four hundred feet from the stage and
fire shots at Trump. There are also

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questions about the size of the rally
security perimeter and what efforts agents took to

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00:58:37.280 --> 00:58:43.079
sweep these shooters building. President Biden
said Trump had heightened security Service protection and

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a full review of the event will
take place, but Florida Congressman Michael Waltz

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says that he was told Trump had
the same type of coverage given to former

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00:58:50.840 --> 00:58:54.760
presidents Obama and Clinton and that wasn't
enough. Former President Trump has landed in

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00:58:54.800 --> 00:58:59.840
Milwaukee ahead of the Republican National Convention. This morning. On Truth Social he

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00:59:00.079 --> 00:59:02.320
said he planned to arrive a couple
of days late, but later announced he

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00:59:02.360 --> 00:59:07.599
had changed his mind. Trump said
he cannot allow a shooter or potential assassin

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00:59:07.679 --> 00:59:10.840
to force a change in his schedule
or anything else. The Daily Show is

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00:59:10.880 --> 00:59:15.400
canceling tapings in Milwaukee during the convention
following the assassination attempt. In a post

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00:59:15.400 --> 00:59:21.360
on x, the show cited logistical
issues and the evolving situation in Milwaukee is

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00:59:21.400 --> 00:59:25.000
the reason the Comedy Central News satire
show sold out multiple nights in the city.

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00:59:25.480 --> 00:59:30.280
Target shoppers can now leave their checkbooks
at home. As of tomorrow,

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the big retailer will no longer accept
personal checks as payment for purchases. It

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00:59:34.920 --> 00:59:37.679
says few shoppers still pay by check, and eliminating the payments would speed up

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00:59:37.760 --> 00:59:42.880
checkout. Target's main rival, Walmart, continues to accept checks. I'm Chris

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00:59:42.920 --> 00:59:50.519
Karagio, NBC News Radio, NBC
News on CACAA Lomlada, sponsored by Teamsters

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00:59:50.639 --> 00:59:54.920
Local nineteen thirty two, Protecting the
Future of Working Families Teamsters nineteen thirty two,

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00:59:55.000 --> 01:00:04.840
Dot Org. M hmmm h.
Thank you for tuning in to this

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01:00:05.000 --> 01:00:07.800
edition of Justice Watch with Attorney zulu
Wali. I am Attorney zulu Wali with

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01:00:07.840 --> 01:00:08.480
a Justice Watch