KCAA: Inside Analysis with Eric Kavanagh (Sun, 14 Jul, 2024)

KCAA: Inside Analysis with Eric Kavanagh on Sun, 14 Jul, 2024

Episode Transcript

(00:00):
History. I'm Chris Karagio, NBCNews Radio, NBC News on CACAA Lomlada
sponsored by Teamsters Local nineteen thirty two, protecting the Future of working Families Teamsters
nineteen thirty two. Dot org.The information economy has a rid. The

(00:22):
world is teeming with innovation as newbusiness models reinvent every industry industry. Inside
Analysis is your source of information andinsight about how to make the most of
this exciting new eraic learn more atinside analysis dot com, Inside Analysis dot
com. And now here's your host, Eric Kavanaugh. M all right,

(00:47):
folks, welcome to the future.Indeed, your host here, Eric Kavanaugh,
and the only Coast to coast radioshow all about the information economy,
Inside Analysis, Folks. I'm veryexcited to have two experts on the call
today for what is arguably the newcenter of gravity in the data world,
and that's the data catalog. Sowhat is a data catalog. For those
who've been around for a few years, you may know data dictionaries. For

(01:10):
example, Wikipedia, you go toWikipedia. A lot of companies have wikis
these days. They're kind of likecatalogs. It's a portal that has definitions
about things. It has information aboutprojects and entities within the organization. But
of course the whole point is youwant to use something like a data catalog
to help your under help your organizationunderstand what the data means. That may

(01:30):
sound like it's simple, but whenyou start getting into the weeds a bit,
you realize how complex it can be, especially for a complex business model,
maybe for an insurance company or ahealthcare company, financial services, but
even retail, even manufacturing. Thereare countless terms that are used in various
ways, and if you have adata catalog, you can help define those.

(01:51):
And what you really want is acollaborative effort. You want people from
all around the organization getting involved inthis, not just a handful of IT
people try to chase down the businessleaders who know what things mean. That's
how data catalogs have been built overthe years. It's not a very effective
mechanism because the IT people are constantlytrying to track down the business people who
maybe are not that easy to geta hold of. And with us today,

(02:15):
we've got a couple experts. Wewere talking to Aaron Wilson of Athena
Solutions and Kiribasu of Metaphor. Metaphoris a vendor. They have a data
catalog that they sell, which isvery interesting stuff. Look them up.
Metaphor data is where you can findthem. They're on LinkedIn, They're on
Twitter now called X I suppose.But let's kind of dive in, so
Aaron, I'll bring you in firstfrom Athena Solutions. You're in the business

(02:38):
of helping companies understand their data.Connect the dots. That's really what data
catalogs do, right They connect thedefinitional dots of information in your organization and
help you understand what all that informationmeans right erin. Yeah, absolutely,
I mean the new generation of tools, I mean, I can't I don't

(02:58):
think it can be overstated. Youknow what an advantage these tools have over
prior generations of metadata management tools bringin, you know, and it's the
efforts that the companies have gone throughwith the more traditional methods, the data
dictionaries and various tools that have beenused in the past. It's difficult to

(03:21):
get off the ground, and it'sdifficult to get users really engaged and into
the governance process. And I thinkwhat we're seeing is these tools, you
know, they make it a loteasier to sort of democratize the process.
And you made a really good pointthere. And your colleague Alazaichen on a

(03:43):
show I did with her a coupleof months ago, and this really interesting
comment that really stuck with me.She said, one beauty of a data
catalog is it forces the business topay attention. Right. That's when you
have engagements because they know, theyunderstand what they do all day, will
have some report that goes to seniorexecutives maybe or to the board, or

(04:03):
to someone else, even out toend users for example, and they understand
that those definitions are wrong, thatreport's going to be wrong. So when
they understand what you're working on isthis data catalog which is going to drive
and govern how information gets shared andreported, that's that makes them pay attention.
Right. Well, definitely, Ithink that. Yeah. I mean,

(04:26):
nobody knows generally speaking, the databetter than the people who are using
it, right. But you know, I think the problem that we've had
in the past, as I've said, is that you know, the tools
were it was difficult to get involved. It was difficult to jump right in
to a conversation about data definitions.Just as an example, it was difficult

(04:46):
to get an up to date datalineage when people wanted to see one.
So I think that we're actually youknow, the slide that I used in
my presentation was was you know,data dictionary, the need was there,
what the technology wasn't. But Ithink we're actually at a point where you
know, the technology is there.Yeah, I think that's really important.

(05:08):
And Cirri Itasu from Metaphor will bringyou in. You've been in this business
for a while, in the dataworld, and you know there's tremendous value
in data, but only if peopleunderstand it. And I'll throw another quote
from Allah at you and just getyour commentary on this. Said, in
order for data to be an asset, it must be understood. What do
you think about that? Curt Absolutely, And in fact i'd add to that,

(05:31):
and understood by whom yea or thedata team understands or business sort of
like the analysts populations understand. Butfrom our perspective, you know, we
care to make sure that everyone inthat supply chain, right from the people
who are producing it all the wayto someone who's just looking at a report,
can really take it in, reallyunderstand the things, simply be effective,

(05:54):
be you know, sort of reallyadd to that value if you will,
for having that data, of usingthe data in the first place.
Yeah, And you know, Ijust think as I'm an old school publisher,
so I've been in print production whereyou would print eighty five thousand magazines
and then realize that there's a typoin the first paragraph of the lead article

(06:15):
and you're just like, oh no, and that's not the world we live
in anymore. But you think aboutdata definitions in a printed document, well,
guess what things change? Do termscome around, the definitions change.
So the fact that we have thisdigital world now where the repository is malleable,
it can be changed over time,and it can be up to date
such that anyone of logs in anytime sees the latest information. I think

(06:38):
that's I mean, it's a simplething in a way. But that's one
of the reasons why these catalogs arenow so much more effective and so much
more usable is because they are dynamic, They can change quickly and everyone can
get access to the latest version,right Kritt, Yeah, absolutely. You
know one example that was just goodingyou earlier is one of the many things

(06:58):
that we do within our tool isallow you to basically persist institutional knowledge,
meaning conversations that are happening on Slackor teams about like the evolution of data,
evolution of metrics, et cetera,and literally within one click sort of
persisted within the catalog and the momentit is like literally within the next few
milliseconds, few seconds, depending,it will show up within the catalog.

(07:19):
Right, So the next time someoneis asking for that term, even if
they're completely separate, you know,completely different silo, they immediately know what's
the most recent version of whatever itis that people were talking about or were
looking for. So yeah, right, and that's you know, that gets
to collaboration and the importance of collaborationthat you know, it's really kind of

(07:41):
intense the different world we live intoday compared to even five years ago,
I mean for some companies even lastweek, right, because as you know,
there's a very long tail of softwareand processes in businesses, and we're
all inside this industry, so we'reup to date with the latest developments,
but a lot of companies are not. And so the stark disparity between an

(08:03):
old world system where there's like aprinted document that one person prints and if
he's on vacation, then you know, you can't even get access to it
to today, where you have thisdynamic, sort of living, breathing catalog
of definitions that anyone can take partin. That's a huge disparity, and
I almost think that the mindset changeis one of the hardest for the business

(08:24):
to wrap their heads around. Youknow, we don't have to do it
the old way, guys, youcan now do it this new way,
and look how nice it is.What do you think about that, Curt?
Yeah, you know one quote thatcomes to mind a customer told us
a wild back. He basically said, the best catalog is one when there's
no catalog at all. And whathe's really meaning is that, you know,

(08:46):
exactly like you mentioned. You know, the the IT team, for
example, might have their own pointof view on how things are going,
but the reality of what users aredoing on a day to day basis where
they're doing it, no one reallyknows unless it's a very very well managed
sort of organization. For most averageorganizations, different sort of centers of knowledge

(09:09):
just keep emerging and going, youknow, coming and going, so to
speak. And that's one of thethings that we really try to tap into.
Right you know, how could weget someone who has zero experience with
the catalog. Heck, they don'teven they really should not even need to
know what the heck a catalog is. They get value out of the catalog.
But on the other side, fromthe producer side, you have now

(09:31):
great visibility into what people are asking. It's like a direct you know,
being a product manager for example,it's like having a very very direct window
into exactly what a customer is doingand they're asking for pretty much in real
time if you think about it.Yeah, and that's so important because you
know, let's just kind of quicklyget into organizational dynamics and how we've traditionally

(09:52):
done things, and how you dothings is you have meetings, right like
if the marketing team comes in,the sales team, the management team,
all these spokes to come in andyou go through the meeting one at a
time. That is a very oldschool way of doing things. You can
have much fewer meetings now if youembrace this kind of approach because guess what,
it's just like it's adding almost ameta layer on top of existing communication

(10:16):
channels like slack you mentioned, oremail. If you can capture email inside
of an intranet, or a portalfor example, you can capture with your
technology if it's connected properly, obviously, you can capture all these conversations about
the business. And then by usingthese large language models, by using the
sort of gen AI components, veryquickly spin up answers in natural language for

(10:39):
what's happening in the company, Soyou don't have to call someone, you
don't even have to email someone.You're just in the system. You ask
it a question, you'll see who'sbeen doing one and where that's like the
ultimate collaboration. What do you thinkyou're in? Yeah? Absolutely, you
know. The one quote that comesto mind or a meme I guess was
this could have been this meeting,could have been an email health. That's

(11:01):
exactly what's happening is so many companiesare obviously, you know, whether it's
in slack or teams, they're havingdiscussions about data, really evolving the data
as they go along in a dataday basis, and the ability to be
able to take that and you know, basically persist that, like you mentioned,
with large language models, but withone important caveat right, like this

(11:22):
is not a very generic Hey,here's your chat chat bought or you know
whatever it is, a lot ofthese companies do like here, go talk
to chat GPT and figure it out. Well, it's not really that we
of course, we use these largelanguage models, but more importantly, we've
tapped into all the other content thatthere is, right, all the business

(11:45):
glossteries, all the technical metadata thatwe have, and so the answers coming
out of the system are highly,highly contextualized and relevant to what you're doing,
and not just chat GPT, youknow, hallucinating, which is a
problem. I mean, and that'sthe challenge of just using a generic tool,

(12:05):
as powerful as it may be,something like chat GPT. It's incredibly
powerful, it's great for text generation, but there are people who misuse the
tool all the time. I evensaw a guy, a very single level
person I think from Meta, whois ragging on chat GPT, and he
said, somebody came up with thecrap ratio, which is a clever idea.
I get at the crap ratio oflike how good is the material you

(12:26):
get back is good or as acrap But he gave it a very complex
sort of mathematical question, and I'mthinking, dude, that's a misuse of
the tool. It's not what it'sdesigned to do. Now, I'm guessing
that there are going to be betterand better reasoning engines baked into these over
time. That's probably going to happen, But right now it's just for text
generation. So you always have tobe careful about how you use the tool

(12:50):
and understand the purpose of the tool. You're not going to be using a
data catalog to create graphics for yourmagazine, for example. It's just not
the right use, so it isimportant to understand the use. But to
your point here, it'll throw itback to you just for comment here in
context, that kind of collaborative informationis extremely valuable and it allows you to
save time. It allows for thatmeeting to just be an email, right,

(13:13):
Yeah, absolutely, And you know, to your point, I think
this is one of the reasons whyit's really important for companies to know what
they're getting into with regards to sortof adopting chat GPT, because you know,
the EXAC sort of mandate will comedown, Hey we should do chat
GPT to get better, but it'sgoing to be some weird chat bought experience
which really isn't work, And sothat's why we spend a huge amount of

(13:37):
time really trying to make sure themarrying of the metadata that currently exists,
making really activating that through a languagemodel is the is the way to go
right, So using the right toolsis absolutely critical in this equation. Yeah,
and I'll throw it over to Aaronto kind of comment on this.
Aaron, specificity matters. Context reallymatters with these kinds of technologies to these

(14:05):
kinds of use cases. And tohear its point, if you are capturing
the metadata and managing that intentionally maybein a RAG model for example, as
long as you're leveraging that and thefact set from your information and you're just
using the AI engine to spin uptext, that's when you're going to get
pretty good results. You're not goingto get too many hallucinations when you're within
context doing what the business does.What do you think erin Well, I

(14:30):
certainly think that, you know,based on the demos that I've seen,
you know, I've seen a littlebit more of Metaphor than than than he
showed here today, although I thoughtit was a really good introductory demo and
the way that Metaphor does it isreally impressive. But essentially they're using the
communicative ability of generative AI, butthey are enforcing some context. So in

(14:54):
other words, you're you're asking thedata catalog. You can either act,
you can also add, if youwant confirmation from an answer that you think
might be a little bit wonky,you can jump right in and ask a
subject matter expert, you know.And again being able to do it without
actually using you know, an actualjust using for example, Slack or whatever

(15:15):
is it's a pretty compelling advantage.Yeah, because it sits on top.
That's what gets me so excited.And you know, maybe I'll throw this
one over to you again. AaronI was talking with the gentleman, very
interesting guy, Neil Hunch. Heruns the company called Silicon Foundry out of
Silic out of San Francisco, andwe did a show talking about a bunch
of different aspects of it. Wasactually around supply chain, but we were

(15:37):
talking about information sharing and Jenai andsome of these new engines. And you
know, the summarization capabilities of Jenaiare really impressive. If you haven't used
them, folks, you have gotto use this. Take a big long
document forty pages for example, loadedinto chat GPT and ask it to summarize,
and ask it to summarize around differentthreads and give me a voice for

(16:00):
the board, give me a summaryfor the IT team. It's really good
at doing that stuff. And whatgot me excited is he said that organizations
are using it to leverage that eightypercent of data in the company that is
unstructured. Well, let me tellyou SharePoint was you know, kind of
going to do that. And thereare some other ways elastic search, there
are other ways that we've used technologiesto kind of get at that data.

(16:23):
But there's nothing like I've ever seenwith these technologies. And that means that
strange things are happening here in theworld. But a real quick Aaron comment
on that, we've got a minuteleft for the break. Yeah, I
mean that illustrates one of the onepoint that I'm actually curious to know a
little bit more from Carrott, whichis that you know, there's a back
end to the governance process where youknow, everybody wants to see governance teams

(16:45):
produce some sort of report, youknow, and or you know, a
summary something that they can tangibly say, here's here's our product, here's you
know, how well we're governing ourdata. And it sounds like with the
tools generate AI. You can generatethose kinds of products, those kinds of
reports, you know, so thatthe governance team now can actually present you

(17:06):
something tangible. Yeah, go ahead, real quick, thirty seconds. I
was just going to say, yeah, we are absolutely doing things like that,
and in fact, we're trying topush it further where you don't even
have to ask the questions, youknow, Can we just tell you what
you need to know. That's alittle road map. Yea, we get
to that soon. Yeah. ButI mean, seriously, folks, what

(17:30):
we're talking about is the ability toingest, synthesize, and then articulate key
points about your business dynamically all daylong, all day every day. I
mean, that is like having avirtual assistant who knows everything that you need
to know, just available at yourfingertips to tell you what's going on.
Well, folks, don't touch upnow, we'll be right back. You're
listening to the only coast to coastradio show all about the information economy.

(17:53):
It's called Inside Analysis. Welcome backto Inside Analysis. Here's your host,
Eric Tavanaugh, and take this toshow. All right, folks, you
can see why I love that songfor our show. Take us to the
future. Maybe that's Black Bananas asthe band, if you want to look

(18:15):
them up. And I used myquote earlier today, one of my favorite
quotes that we use for our TVshow Future Proof by William Gibson, who
once said that the future is herealready, it's just not evenly distributed.
I love that show. I lovethat concept. It's just brilliant and it's
true. So we're diving in.We're talking to Aaron Wilson of Atena Solutions
and Kiri Basu from Metaphor today aboutdata catalogs. And as I've said,

(18:38):
data catalogs, in my opinion,are the new center of gravity, and
in many ways they are like ametaphor for business. So if you think
about what a metaphor is supposed tobe used for, it's to help you
understand complex concepts. We come upwith metaphors to describe things so that you
can figure out what someone really meansby something, and that's what metaphors do.

(19:00):
So in many ways, the datacatalog is like a metaphor for business,
and key it. I'll throw thisone over to you and I understand
your solution and how it works.So I've got a pretty good grasp on
that. But I'm just thinking here, the more time and effort people put
into it, the more access yourengine has two slack messages, to emails,
even video calls that can be automaticallytranscribed. These days, you've got

(19:23):
technologies like I think Gong is oneof them, and there's Otter, and
there's a bunch of these others thatautomatically transcribe your meetings and give you summaries
afterwards. It's like whiz bang,wow, hello, where have you been
on my life? When you cancapture all that, you are incrementally building
institutional knowledge day after day right here. Writ absolutely. You know. The

(19:47):
thought that comes to mind is somany people have said this, where AI
and LM's are really at that,they're at the Sega console moment right now.
It's so much more that happening.You know. Multimodal models are already
very very impressive, but the directionsthat they're going are going to be absolutely

(20:08):
amazing. And so yeah, we'reconstantly keeping an eye out on what's the
latest, greatest and being obviously verycautious about how do we bring it in.
Just like I mentioned earlier, wewant to make sure it's not just
hey, here's a portal to anLM, Like no, no, here's
a portal to an LM through thecontext of your data. Yeah, and

(20:29):
maybe Aeron will throw out over toyou. You think about some sort of
an layered architecture and up here's allthe information that's being shared. And in
between, you've got this data catalog, and then you've got databases and things
like this underneath, and that datacatalog is it's like a lens through which
you can view the world, likea colleidoscope or something, but it's a
lens that allows you to understand thedefinitions. And ideally you want it to

(20:53):
be connected to your reporting and youranalytics, your business intelligence, your whatever
you're using to analyze your data.Because if a definition changes of let's say
customer, so we've figured out somenew way to define customer and we're no
longer including some category that we didinclude missing. Little changes like that can

(21:14):
have a great impact on reporting andcan give you bad numbers like duplicates.
For example, if you've figured out, oh no, these are dupes.
Now you have to get all theseduplicates out, your total number of customers
go from one thousand to eight hundredand fifty. That's going to change the
numbers of your revenue per customer,of your categorization of customers, all these
kinds of things. That's why it'simportant to have this collaborative effort around the

(21:37):
data catalog. What do you thinkerin, Well, yeah, absolutely,
I mean you've brought up a coupleof different issues there that the point to
how the new technology is so useful. I mean, one of which we're
talking about here is lineage, right, and the idea of being able to
correct errors quickly. I myself havebeen through in many of the people on

(21:59):
the call, I'm sure I havebeen through, you know, the difficulty
of trying to trace back a dataerror and you know, find out then
you know, how many reports wereimpacted. There's always the big question it
was just this one reporter. We'vewe do have a you know, a
whole nightmare of clients that were impacted. But the lineage tools, to just
name one, are extremely you know, to be able to get your hands

(22:22):
on lineage that that's accurate, youknow, instead of looking at charts that
you don't even know how old theyare. I mean, it's it's it's
it's extremely valuable. Yeah, Kirait, I'll throw that over to you,
because there are some of these issuesthat we have been contending with for
years, one of which is outdata reports for example. Now we have
all sorts of really cool technologies aroundobservability that show us when data feeds are

(22:45):
not working properly, and they givealerts to end users, people who are
delivering reports, for example, howthere's a problem here, let me go
check and fix it before the reportersdo or whatever. They're cool things that
you could do. But the lineageand the timelineans if things are very important
because something changes at a certain point, do you have the capacity to roll
back to other versions and see thingsor how does that work? Yeah?

(23:08):
So, well, a couple ofthings. We don't actually touch your data,
so we're only looking at meddata,so where we're sort of incurring things
out of metadata and presenting to you. Yeah. Absolutely, We do show
version histories of things that you couldcertainly go back and see when changes have
occurred, so we can help fixall of those kind of things or certainly

(23:30):
get you much much better insights intohow to fix it by pinpointing the right
places where these kind of things happen. Yeah, and that helps because,
as Aaron suggested, some changes made, maybe it was made incorrectly. All
of a sudden, the reports startadding up. And this is why business
analysts ask questions, right, thisis why they're looking at some way there's
something wrong with this looking into Inthe old days, it was like,

(23:53):
good luck figuring out what went wrongif you don't have the documentation. But
now you really can figure out whatwent wrong. And that's all very positive
for being able to sort of truethe wheels of business, if you will,
and ensure the accuracy and the relevanceof the data. What do you
think you're right? Yeah? Absolutely, So you know, the way we
think about it is ours is sortof a merging of three different graphs,

(24:17):
if you will, right, Like, there's the technological graphs, so connections
between systems. There's the business graph, which is you know, glossary terms,
how they relate to objects within tables, et cetera. And then,
as we call it, the socialgraph, right like, how are people
using, what are they talking about, how are they you know, interacting
with the data, et cetera.So we bring all of these things together,
and so to your point, whensomeone needs to go back and say,

(24:41):
Okay, what happened at this pointin time. It's not just here
are the technical changes that have happened, but oh, by the way,
here are the conversations that were happeningabout this data which led to this result.
And here you go. Right,So it's a much much more fuller
picture of what happened and when.Yeah, that's amazing because you can learn
and I'm guessing sing and curate kurtbFro, I'm wrong, I'm guessing that.

(25:02):
Especially over time, you can alsoget a very good feel for who
knows which subject matter better than others. You could probably ask it who really
understands this skew in our manufacturing environment, or who understands this region. Those
kinds of questions can probably be answeredvery accurately. Now, if someone's using
this kind of technology, right,absolutely, I would say this. You

(25:26):
know, certainly a lot of governmentspeople would want to specifically go out and
mark an individual or two as beingthe expert. But then the natural expertise
that has just arisen because there arepeople talking about it, etc. You
could easily pinpoint these kind of clustersif you will, of knowledge. Yeah,
Aaron, I'm going to throw thatover to you. That's a really

(25:48):
big deal. Like think from theperspective of a senior executive who is in
here to try to understand what's goingon in the organization. Maybe there's a
recent merger or acquisition or something,and they're trying to wrap their head around
who knows this or who knows that? To be able to ask a question
of a data catalog like that,who really understands our business in the Southeast

(26:10):
region or who really understands this productline and get some dynamically generated answer based
upon conversations happening in the company.Is it me or is that just a
massively cool deal? Yeah, Ithink it's potentially a huge advantage. It's
going to be a shift, though, and I think that's one of the

(26:30):
things I'd like to ask Curate about, is that now you sort of democratize
data and then it's no longer thistop down as Eric you pointed out,
you know, maybe a senior managerwho goes to his manager, who goes
to you know, goes to hisnext report down the line, this is
where is this data come from?Now you have it's been democratized, but
it's it's a real it's I wouldimagine that companies undergo a real, uh

(26:53):
sort of cognitive shift in terms ofthe way they operate. And I'm curious
to actually know what he has seen, you know, when they introduce these
types of tools. Yeah. Absolutely. You know, if you think about
every governance role that's out there,you look at their job descriptions, it's
about like chasing down governance problems andmaking sure tagging is right, et cetera.

(27:15):
Pretty much everyone hates doing the manuallabor involved in doing that stuff,
but with technologies like ours, itallows them to actually be about governance right,
Like they can step back all theway and say, Okay, big
picture, how am I solving theproblems that I'm trying to that my company
is having. That the real roleof governance sort of really shines through with
the power of these technologies to takeout the mundane that everyone has to do

(27:40):
otherwise and make it that much moreeffective. Yeah, and I think that
that gets one of my favorite soapboxtopics, which is morale. And I
throw this speck over to you,career. Once you understand what the changes
that this kind of technology brings,which are significant, but once you wrap
your head around that and understand it. I think it's great for morale because

(28:02):
the things that kill morale are spinningyour wheels, not being able to get
answers to things, not being ableto change what needs to be changed,
not knowing who is responsible, notknowing who I can reach out to to
get answers. All these things aresolved at least to some degree with this
approach. So I have to thinkthat improves morale because by and large,

(28:22):
if people feel like they're getting somewhere, they're getting something done, they're making
progress, that's excellent for morale.It's the opposite that gets them depressed.
What do you think her absolutely?I mean, I think I mentioned this
earlier. The code that we hearfrom most of our customers, especially who
spend a lot of time on legacycatalogs, is you know, catalogs are

(28:44):
when data goes to die, andthat is fundamentally one of the issues where
people don't care about working on these, you know, especially antiquated systems.
The UIs are terrible, all thosekind of things. And now this is
a whole new varadigm where heck,you don't even need to be trained on
the catalog to get value from it. So, yeah, absolutely, morale

(29:04):
makes a huge difference in that welland in training too, So you think
about upskilling training people. A catalogagain, properly used, properly installed,
et cetera, is a great trainingvehicle because it is an access point to
information about business processes, about terminology. I mean you talked curate about understanding

(29:27):
the relationships between systems, and that'sa very important thing as well. And
again this generative AI concept, thesetechnologies can do tremendous things in explaining things,
explaining how these two systems interconnect,right, go ahead, cirrit.
Yeah, so you know, Ithink this is where from a from a

(29:47):
product perspective, the point of viewthat we take is JENNAI is one of
the tools in our tool get right. Like the other really really critical one
is in fact, the user experienceand the user interface in general. My
philosophy really is like the moment youhave to make a context switch and go
and look up a document or documentation, etc. You mix the point completely.

(30:10):
So to have an experience, whetherit's magical through AI or it's a
very procedural thing, but having itbe so intuitive, so simple to use
is one of the baselines for usas we design any kind of capabilities,
because we want to make sure everyuser gets value out of the catalog.
Yeah, and you know, that'san excellent point. And this kind of

(30:30):
gets back to another theme that Ihave about the UI and the point of
interaction with the business. And whatI think is very compelling about what you
folks had metaphor has done is thatyou are leveraging the places where they're already
going. You're leveraging Slack because you'reindexing what goes across Slack. You're leveraging

(30:51):
email. If they get access tothe email, whatever channel of communication people
are already using, you're taking advantageof that, so you're not forcing people
to go into some separate environment toask questions. To your point, I
think most people know that when you'rebouncing around from this app to that app
to some other apps, it's disruptive. It is it's a discontinuing sort of

(31:15):
event, and it is disruptive tothe brain. I mean not that you
can't do it. You can't,but it's just it's like a hurdle you
have to jump across, and you'vekind of knocked all those hurdles down.
Isn't that about right here? Yeah, absolutely, I mean, you know,
I challenge people to find or ifanyone has less than I don't know,
twenty tabs open in there, forexample, I'd like to meet them.

(31:38):
But yeah, I mean, whyintroduce fifty other you know, browser
Windows, you have to go searchingthrough stuff, switching context, you know,
hard switching context. Whereas the actualproblem you were trying to solve,
what's completely different. It has todo with business, but now you have
to go through all the details.So that's exactly one of the reasons why
you know, slacked. We wantto make sure that we can give the

(32:00):
answers you want right there with allthe context and all the stuff behind it,
right. Yeah, Well, it'sa big time saver, first of
all, and it maintains that continuity. Know, Aeron, I'll throw this
over to you. We've got abouta couple of minutes before this segment is
up. Analysis is a thought processand it really should be fluid in order

(32:23):
to be effective. And what Imean by that is if you're bouncing around
from one app to another, that'snot very fluid. Right. Those are
sort of truncated moments or disruptive momentswhen you're bouncing around, and you want
to have that true conversation with yourdata. We've talked about that for thirty
years in this industry, but nowyou really can have the conversation with your

(32:43):
data. If it's connected to theinformation systems, if it has a data
catalog layer which is making sense ofthings, and if you're able to use
this GENAI stuff real quick, youcould have a conversation with your business data.
What do you think, erin,I completely agree. I mean,
I think you can't. You can'toverstate the value of kind of eliminating that
layer right of having to go toanother tool separately. You know, I've

(33:07):
I personally, I've done it,and that's the way most you know,
of the previous generation of these toolshave worked. But to be able to
just you know, interact directly onteams, on slack, that type of
thing, and then if you doneed to provide you know, sample data
and talk about the problem or jumpon a call or something like that,
you can also do those things too, but without that extra layer of going

(33:30):
to your you know, your governancetool as it were. That that's exactly
right, folks. People want towork in one environment. They want to
be on the phone or in azoom call or whatever doing their thing.
They want to have to jump allaround, but don't touch that doll.
Folks. We're going to be rightback. You're listening to Inside Analysis.

(33:52):
Welcome back to Inside Analysis. Here'syour host, Eric Tavanaugh Show Fact.
Here on Inside Analysis, we're talkingto Aaron Wilson of Athena Solutions and Curates
of Metaphor. They are a datacatalog company, Metaphor Data, and Aaron,
you had a question for key Ritsto go ahead. Yeah, what

(34:14):
I was curious about from Curate's perspectiveis, you know, we have data
governments, governance teams. You know, most companies to deal with data have
a data governance team, and thistype of functionality, you know, with
a product like Metaphor, really Iwould think completely would reshape, you know,

(34:35):
kind of their daily existence because I'mlooking at it from the perspective of
so much time spent on information gatheringright and curating, and you know,
with with a tool, with amodern data catalog tool, this probably shifts
them more into making. It probablyallows them to come up with policy quicker
and a number of other things nowthat they've kind of got a lot of

(34:58):
that mundane work out of a way, But I'm curious to know what perspectives
you've gotten from clients. Yeah,so I think you know, it's across
the spectrum. Obviously, I'll giveyou the high and the low side.
So on the on the low sideor the very simplistic sort of side,
we certainly have customers where yes,governance teams are there and they have,

(35:19):
you know, sort of a visionof what they do, but the reality
is that the company on the wholemight not be fully aligned with that vision.
Right, Like the governance person wantsto get something done, but the
rest of the company is still tryingto get there, et cetera. So
we certainly have like it's helpful inthat sort of realm because it, like
you said, allows them to stepout of the Monday and say, okay,
big picture, here are the typesof things I can actually get done

(35:43):
now that I don't have to dothe manual work that I would otherwise have
to, you know, do iton a day to day basis. So
that's one side. The other side, absolutely we have customers where they came
in effectively saying, oh, yeah, I need to be a governance data
governance person and you know, Ithrough all the documentation on the internet and
it says, these are the twentythings I have to do, and oh,

(36:04):
by the way, now I haveto do like half a thing,
right, or I can think muchmore big picture, and so a lot
of times we find organizations who aremuch much more further advanced and mature,
so they're already on their data meashor data product, one of those sort
of journeys. They've already built outsome of those kind of things. They
can take a much bigger view andthey're effectively redefining governance. They are talking

(36:28):
much higher picture. They're talking aboutlike here's the data product and here's I'm
going to evolve It. Nothing todo with the like the menial labor that
you have to do. You're focusingon the data. You're focusing on the
actual problem and the outcomes that thecompany cares about. That's awesome. I
mean, really you think about Okay, ours right, maybe I'll throw it

(36:49):
over to you, Aaron. Iremember when this whole concept of objectives and
key results came out as opposed tojust numbers and things. Because business is
very unwieldy when you can get rightdown to it, and you want to
be striving towards things, but notjust basic metrics, right because if you're
just going for metrics, what happensthey turn into vanity metrics. So the
objectives and key results I think ispretty important stuff. And a data catalog

(37:12):
is a great conduit to help youget there, to help you define reasonable
okay ours and know if you've gottenthere right erin well, it definitely would
be I think what it would do. Would it would open the door to
making you know nothing against KPIs.You know, I'm sure organizations will you
know, always have a use forthose, but you could probably make them

(37:34):
a heck of a lot better bybeing able to get your hands on maybe
additional data. I mean, dashboardswill be easy. It'll be easier to
come up with a new data pointand understand it and incorporate it into a
dashboard and make it that much moreuseful because you can get your hands on
the data and understand it better.And you might find that users from all
over the organization can participate in thatprocess, whereas it may it may not

(37:58):
have been the case earlier mm hmm. And that's the key point again to
get back to collaboration, krat I'llthrow it back over to you. Anyone
can log into this, anyone.I mean, obviously they have to have
permission to do so. But havingteams from different parts of the organization be
able to make their comments about things, especially if folks in the field who

(38:20):
would say, hey, guys,I noticed something and that's not correct,
and you fix this all of asudden, it's like you're fusing what we're
to spirit channels. They think support, for example, versus email versus slack.
Right, the guys in the supportand the girls in the support area,
they're the the coal face, asthey say, dealing with customers and
they're going to see stuff, andhistorically it's been very difficult for that feedback

(38:43):
to make it all the way upthe IT ladder to the person who has
the authority to change something. Andthat's just out the window now right.
Absolutely. I'll give you a greatexample of this. We have several customers
who've been through this recently where youknow, their old school sort of pointer
of view and doing government was like, let's get coverage, so let's make
sure that I don't know, onehundred percent of our data is documented,

(39:06):
and let's be honest, most documentationin that case was like one sentence,
Oh, this table does blah,and that's the end of that. So
we've sort of changed that around.What we've basically said was like, hey,
look through all the comments that you'rehaving, you know, questions coming
in through Slack or support channels,et cetera, and then basically answer your
top twenty five questions, meaning youliterally write it out verbatim. See if

(39:28):
the system has an answer. Ifyou don't create, here are buttons and
guess what. Here's help with AIwhich will help you generate that right answer
as well. And basically within acouple of hour window, you could answer
the top twenty five questions and everysingle variation, infinite variations, because that's
where the LN really shines. Youknow, I asked for how do you

(39:49):
define revenue? Your version of thatstatement, how is revenue defined? Doesn't
matter, We'll get you the answer. We know how to get to that
point. So that has been likea huge change and how like the old
way of doing things have has completelybeen supplanted by a very outcome oriented way
to get to the answers that youneed. Yeah, and real quick,

(40:10):
and we can maybe get into thisin the podcast bonus segment a bit deeper,
and we've got a couple of minutesleft here, but I'm going to
throw us back to this concept ofan abstraction layer that can absorb from all
these existing channels. Right. Oneof the challenges of deploying a new technology
is that it's a new app,it's a separate app, it's a separate

(40:30):
log and all this stuff you haveto go and do and then you know,
historically set up pipelines and things.But the fact that you're pulling from
all these existing channels and allowing forall that to be baked into this data
catalog that I think is one ofthe real key differentiators because now you're not
forcing people out of slack, you'renot forcing people out of email, You're

(40:52):
allowing them to work where they workand live where they live. You're just
adding this layer of value to it, which is a reconciling layer, a
definitional layer. What do you think, he rit, Yeah, absolutely,
I mean it is. Yeah.The way I think about it is,
you know, the stuff that wedo in addition with the AI sort of
capabilities, it becomes kind of likethe great equalizer of SUTs. So it's

(41:15):
no longer that you have just thedata people who are the experts. And
I mean, of course people areexperts, we're not denying that, but
the availability of that expertise is instantaneousto someone who's you know, not in
that role, and so absolutely it'sit's abstraction layer. But it's more than
that, it's an equalizer for forpeople. Yeah, that's I'll ask you

(41:37):
to comment on that, Aaron.I think, I mean, really it's
kind of a slam duck, right, if you can afford to do this
and you can get it done inyour organization, it's kind of a slam
duck to do something like this.What do you think erin for sure?
And one of the points that youkind of touched on there is the fact
that the ability for a product,you know, for a modern day catalog

(42:00):
of product and not just work withthe data assets, but also through the
distribution channels. I find that particularly, I mean, I just think that's
really cool, the fact that youcan monitor and track how often is somebody
talking about this particular data series,right, who's talking about it? What
reports is it appearing in? Imean, I guess that's the you know,

(42:21):
it's part of this concept that peopleare calling active metadata, this idea
that you know you have a feedbackloop. Now you know what your relevant
data points are, much more thanyou're used to under the old environment.
Yeah, and just then use abilityside of the equation. Who's using this?
What are they talking about? Youthink about bubble what do they called
word bubbles? Where you see whichterms are most popular. These are all

(42:44):
useful constructs to help guide the attentionof the executive or help guide the attention
of the frontline worker. I meanthat's the other thing is customer service.
If someone's on the call, ifyou can, you can patch this into
a customer service center. Think aboutit. In the call. That person
needs to have whatever information is availableand relevant right now. You want it

(43:06):
very quickly. You can't be waiting, especially if the customer is angry calling
up all upset. They came belike, oh my systems are running slow
today, Sorry sir. You knownobody likes to hear that. But these
kinds of information flows can be extremelyvaluable for anyone in the organization, whether
you're on the front lines, whetheryou're middle management, whether you're an executive.
If you give partners access for example. The point is it's it's a

(43:30):
knowledge repository. And I'll go backto that comment I made earlier about the
gentleman from Silicon Foundry that the ahamoment in my head was knowledge management.
Remember that guy is like twenty fiveyears ago, before even business intelligence became
a thing, it was knowledge managementand it kind of went nowhere because there
just wasn't the compute power. Thereweren't the technologies available to really make sense

(43:53):
of all that stuff. And nowthat is all here. That whole paradigm
has shifted to where you can doactual, real tangible knowledge management with you
in your organization. Folks. Thatis not a small thing. We're finally
going to be able to truly leveragethis eighty percent of corporate data which is
unstructured. But podcast bone A segmentis coming up next, folks. You

(44:15):
are listening to Inside Analysis. Okay, folks, time for the podcast boning
A segment here on Inside Analysis talkingto Aaron Wilson of Athena Solutions and Kirt
Basu of Metaphor. Aaron is aconsultant. If you need some consulting help,
give a call curate. He's thevendor if you need a new tool,
and I think everyone does. Yougot to get a data catalog,

(44:37):
folks. This is good stuff calledty writ and his team parent. I'll
throw it over to you. Metadatamanagement. You know, I remember,
gosh, twenty years ago talking toa client about his metadata repository, and
I was such a babe in thewoods. It was like twenty four years
ago in fact, I said,I'm like, you know, this is
I keep talking to these vendors andthey all have their own metadata catalogs.

(44:58):
But wouldn't it be good if therewere like one metadata catalog that we all
kind of adhere to, and youknow, and that's like an ontology,
right, I'm like a little babein the woods out here, and he
like kind of shakes his day's like, well, yeah, that would be
good, I guess. But it'sjust not really the way things work out
there in the real world. Butwe're kind of getting close to that.
But maybe just talk real quick,Eric about metadata management, why it's so

(45:20):
important, and how things are changingnow about how you do that. Sure,
I mean, I think it's noaccident that you know, some of
the bigger companies that are really dataintensive, and I mean, you know,
if you look at metaphors origins.You know, coming out of the
data team at LinkedIn, they wereunder enormous pressure, probably more pressure than

(45:40):
most right to make, to getvaluable insights out of an enormous quantity of
data. And you can't really doit effectively if you don't have any concept
of metadata management. You have tohave commonly agreed upon data definitions, you
have to be able to get yourhands on lineage quickly. So I think

(46:00):
the old way of doing things.You know, you need to bring clarity
to these things, and you don'thave a whole lot of time to do
it. You don't really have alot of time for a governance team to
assemble, for example, you know, a collection of terms or a data
dictionary. It's it's tremendously valuable tobe able to kind of get to the
point when it comes to metadata management. Yeah, good point. I'll throw

(46:22):
it over to you. Here itand maybe talk about how metadata management has
changed, because I mean it usedto be a fairly uh what manual process
and only a couple people touched it. They had to go, like I
said earlier, to go talk tothe business try to figure stuff out.
Now it could be much more dynamic, and it's very useful in terms of

(46:43):
understanding who's doing what, which metadatais being accessed, what's the important metadata
we got to watch out for.All those things are now much more clear
because we can see what's happening righthere. It absolutely so you know,
I'll give you a simple example.When you're searching for, like, what's
the definition of revenue. Let's sayyou plug that into our system. The
answer you're going to get is notjust the actual definition of revenue. Of

(47:07):
course, there's that you know,it might be specified in a glossary somewhere,
or could be in a dictionary,depending on you know, what that
metric is. But we will alsogive you other context We'll give you all
the others. Well, yeah,here's the definition, but by the way,
here are the you know, statedowners of these, or here are
the conversations happening about. So whatyou get is a much much more richer

(47:28):
contextual answer about what it is whateverit is you're asking for. And so
yeah, I mean, it's stilla human doing the interpretation, but now
you've made that human into a basicallyan analyst. Even if they were not,
for example, right like they areable to synthesize much much more interesting
data to get to a conclusion thatthey need to. Yeah, that's that's

(47:49):
a really good way to put it. And you know, it's funny you
would say that because our show iscalled Inside Analysis and we our tagline is
take the inside Track to Insight.But our whole mission was to enable the
audience to be an analyst. Likethat's our whole job in the world is
to share information about tools, technologies, processes, people, to help people

(48:13):
understand how to ask the right question, put it in context, and they
make better decisions about the number onethe tech that they use, for sure,
but the business and how they runthe business. So I think that's
really beautiful that you just said that. You spoke to our whole Reison Detras
as the French would say, sogood good, good work. Final comment,
Aaron, final piece of advice abouthow people should get started or do

(48:36):
something with this, Well, yeah, I mean I would say that,
you know, with companies that arestruggling with these types of issues, you
know, this is an important decisionto make. This is you know what
do I need? And this isthe title of one of our prior presentations.
You know what data catalog do Ineed a lot of considerations. One

(48:58):
of them, though, I wouldsay this, this is that if a
company has a reasonably good handle on, you know, their data challenges.
And what's interesting is, you know, I would say ten fifteen years ago,
most companies were consider themselves, bytheir own admission, very much behind
the curve. But now that we'vegotten you know, companies are further ahead
in terms of data warehousing and integration. And then you still have you often

(49:22):
left with the metadata problem. Youknow, it's kind of what's left.
It's kind of like, well,we have good you know, we we
have good time to market, wehave our data is better organized, but
yet we don't have general agreement.We have problems around definition and lineage and
so forth. These these are thekind of tools that companies may very well

(49:42):
be ready for if they find thatthose are their pain points. And that's
part of what we do with Athenais to come in and do an assessment
and to say like, hey,what are your pain points, and then
we can hopefully line you up interms of vendors with the vendors that go
to those strengths. Good stuff forfolks. Th Thank you so much for
your time today for listening in thanksto Kirrit Basu of Metaphor and Aaron Wilson

(50:04):
of Athena Solutions. We do allthese webinars for the interviewing. Share it
with your colleagues. One nice thingabout Zoom is the URL for the live
show is the same for the archive. Relliant. Someone was thinking, that's
an architectural decision. Someone made itzoom a long time ago and it was
fantastic because WebEx was not like that, it was a different URL. Someone
says, you know what, let'smake it the same URL for usability,

(50:24):
for consumption, all this great stuff. So check out Metaphor Online, Metaphor
Data Data Catalog, and Athena Solutions. And with that we're going to bid
you farewell. Folks. You've beenlistening to Inside Analysis. Get all the
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a full review of the event willtake place, but Florida Congressman Michael Waltz
says that he was told Trump hadthe same type of coverage given to former
presidents Obama and Clinton and that wasn'tenough. Former President Trump has landed in
Milwaukee ahead of the Republican National Convention. This morning. On Truth Social he
said he planned to arrive a coupleof days late, but later announced he
had changed his mind. Trump saidhe cannot allow a shooter or potential assassin

(59:07):
to force a change in his scheduleor anything else. The Daily Show is
canceling tapings in Milwaukee during the conventionfollowing the assassination attempt. In a post
on x, the show cited logisticalissues and the evolving situation in Milwaukee is
the reason the Comedy Central News satireshow sold out multiple nights in the city.
Target shoppers can now leave their checkbooksat home. As of tomorrow,

(59:30):
the big retailer will no longer acceptpersonal checks as payment for purchases. It
says few shoppers still pay by check, and eliminating the payments would speed up
checkout. Target's main rival, Walmart, continues to accept checks. I'm Chris
Karagio, NBC News Radio, NBCNews on CACAA Lomlada, sponsored by Teamsters

(59:50):
Local nineteen thirty two, Protecting theFuture of Working Families Teamsters nineteen thirty two,
Dot Org. M hmmm h.Thank you for tuning in to this
edition of Justice Watch with Attorney zuluWali. I am Attorney zulu Wali with
a Justice Watch