I mean of Biden's announcement may be intentional. Democrats up here were privately urging
the President and his team to bow out of the race before they were forced
to essentially call on him to exit publicly. According to Serkin, the timing
of the announcement may be making it difficult for Democrats to coordinate and present a
unified front. Biden's announcement comes as an increasing number of Democrats have been calling
for his withdrawal due to concerns about his advanced age, But top Republicans want
President Biden to step down now before the end of the term. House Speaker
Mike Johnson posted, if Joe Biden is not fit to run for president,
he's not fit to serve as president. This is an NBC News special report
NBC News on CACAA Lomelada, sponsored by Teamsters Local nineteen thirty two, Protecting
the Future of Working Families Teamsters nineteen thirty two, dot org. The information
economy has a rived. The world is teeming with innovation as new business models
reinvent every industry industry. Inside Analysis is your source of information and insight about
how to make the most of this exciting new eric. Learn more at Inside
Analysis dot Cossideanalysis dot com and now here's your host, through Eric Kavanaugh,
and all right, ladies and gentlemen, Hello and welcome back once again to
the only coast to coast radio show in the US. Today that's all about
the information economy. It's time for Inside Analysis. You're truly Eric Kavanaugh here
with an all star cast, some good buddies on our call. Today.
We'll be talking with Aaron Wilson of Athena Solutions. They are a strategic consultancy
systems integrator. They do a lot of data governance, a lot of data
warehousing, master data management, although these days they say mastering data because MDM
apparently is a bad word now, so they say mastering data. Everyone understands
that. We'll also be hearing from Jim Smith of Click in Fact the webinar
we just did a moment ago which you can hop online to Inside Analysis dot
com to find It was all about the associative engine and Click, which is
very interesting stuff. We're going to talk about that and what it means and
why it's special. The title for our show is Inside by Association, Exploration
without Constraints. And then, last, but not least, our good buddy
David Lintikam is online. He's been doing a lot of where this guy's been
around, I mean he needs to work for NASA. He's doing agentic AI
work these days. That refers to AI agents. We may even talk about
that on our next show, or we're going to be featuring kindy a very
interesting large language model tooled. Of course, these large language models CHATGBT,
Gemini, Claude. They've taken the market by storm, but they have certain
use cases. There are times when you want to use those to do interesting
things like code, like code generation or text generation, but they're not really
analytic engines in the traditional sense. So we're going to talk about what that
all means and basically help you figure out what does your business need, well,
what kind of solution will make sense for you. So I'll just throw
out a few comments. We did talk about the associative engine and click can.
I remember getting a first briefing on that, gosh, probably fifteen years
ago by good buddy mind Donald Farmer. And you want to talk about when
something does clicks? Man, I watched it. I was like, wow,
that's cool. So what it does and this goes all the way back
to the genesis of the tool to the kernel is it will automatically show you
visually relationships between entities, between concepts like products that you sell and customers you
sell them to, for example, pretty important information. You're not really going
to see that as readily if you're looking at ros and columns. I mean,
you can look and excel and build different graphics and different things to visualize
the data. But how nice is it to just see the relationships right out
of the box. It's very very important stuff because the whole process of discovery
requires thinking through and understanding what you're looking at. You know, in a
previous show we did in this series with a lask and also from Athena Solutions,
had this great quote where she said, in order for data to be
an asset, it must be understood. I was like, good point.
If you don't understand the data, it's not an asset, and in fact,
it might be a liability if you don't understand what that data is telling
you. This gets back to a concept called data literacy, which is very
much in conversation these days for good reason, because now we have all this
data. You know, twenty years ago, really only the fortune two thousand
companies could afford to build an enterprise data warehouse. It took six months,
It costs ten or twenty or thirty million dollars. It was a huge effort.
It took a lot of time. You want to talk about time to
value, We're talking years to get to value. Well, that's just not
even acceptable anymore. You cannot. No one is going to approve a three
year project in data that will only provide value a year or two later.
There's no chance of that happening. And luckily it doesn't have to happen.
The new technologies these days and the old technologies that are modernized, they allow
you to get value very very quickly from your data, and that's what you
need. It's a very fast moving environment these days. Just think about the
Internet and all the stuff that you could buy on the internet. Think about
all the data that's out there. Think about data science. There's this whole
data science industry. And we've talked on this show before. I've marveled in
fact that the data science teams in large organizations often don't interact with the data
warehousing teams, which in my opinion makes exactly use zero sense. You want
these folks talking to each other, but it's very common that they don't.
So on this show we're going to try to hash through some of these issues
and really explain why this associative engine is so important for analysis, because,
again, anytime you're doing what's called decision support, that's one of the older
terms, you're trying to get information that helps you make better decisions about where
to spend your money, where to spend your time, how to hire,
whom to hire, where that person will fit in an organization. All these
questions can be aided and should be aided with data and analysis. And the
data, again from an analytical perspective, has no value until you've analyzed it
right. And just very quickly, last comment, One very cool thing about
all these large language models hit in the markets today is that most of the
analytics world deals with structured data, so data that's in relational databases, data
that's in tables, rolls, and columns. Being able to analyze that and
get some value. One cool thing about large language models, we'll pick this
up on our final show in a couple of weeks on the twenty ninth,
is that it analyzes unstructured data too, text, documents, word, documents,
PowerPoint presentations, some really really cool stuff in there to get context to
help understand the numbers. And so with that, let's bring in our first
guest, Aaron Wilson of Athena Solutions. Welcome back to the show. You're
in the industry with us, and you've done some pretty serious work in financial
services. You got to get those numbers right. What do you think about
the importance of an associative engine for being able to analyze data? Yeah,
I mean, I think it can't be overstated. And I think one of
the the the interesting thing is, and there are people at Click who I'm
sure would say this, is that it's not it's not that complicated a concept,
but many people don't really understand it. And I think it goes to
Jim's point about how usually the emphasis is on you know, okay, how
many cool visuals can we can we generate from this thing, or you know,
oh wow, there's the pop that comes out of like, you know,
a visual that maybe nobody's ever seen before, and which is all that's
great, but it's very important in this transition in terms of, you know,
making it not just a visualization tool, making it a tool for analysis
because the associative engine is key to that. It's you know, query based
tools are very good, just like I said, if you know where you're
going, but to be able to explore the I think the associative engine is
I'm I mean, it's huge because it shows you all the data in front
of you. It shows you contextual data, things you might not be thinking
about it you didn't sit out to look for in the first place. Yeah,
you know. And in fact, before I throw it over to Jim,
I'll just throw it back to you for a comment on this. Aaron
Jim had a great quote in the webinar we just did where he said,
this tool will give you answers to questions you ask and answers to questions you
didn't ask, right, which is great because it helps shape the contours of
your understanding and that's important stuff. It's just like being able to visually assess
a situation. Like if you're a security guard at a rock concert or something.
You want to have a good view so you can see everything that's happening.
Yeah, you may want to keep an eye on this person or that
person, but you also want to have the ability to see the whole room
and I think that's what this does, is it gives you the ability to
see not just one answer to a question you're asking, but the broader context.
And then when you kind of move around and select and decel, whether
it's products or services, or regions or individuals or financial amounts or whatever it
is, everything changes in that discovery process. It's a learning experience. I
mean, you've got to try hard to not learn something by engaging in that
process right completely. And I think you know the idea really, you know,
SEQL queries aren't necessarily designed for this kind of analysis. You're you're continually
either drilling down or moving back up the ladder. But the idea of being
able to have all the data front in front of you and explore, I
mean, I can tell you that users really like this because you know,
for a user, once they get their hands on the data and they get
their hands on the visualization tools, the next thing they want to do is
explore it. They want to do analysis. And in that sense, you
know, if visualization maybe part one over the last few years has been democratizing
these graphical capabilities along with that access to the data. In a sense,
what Click's able to do is democratize democratize analysis, which is extremely powerful.
Yeah, that's a good point. We have a couple of good comments from
our audience members too. I'll bring them in probably later on in this segment,
but let me throw it over to Jim Smith from Click. I mean
you've talked about how this goes back to the kernel. This was an idea
someone how long time ago, and I'm always fascinated by the kernel of a
technology, right because you have some idea, you're trying to address a particular
issue, and I mean, I have to say, I think that this
associative engine is central to the capacity of really exploring data very quickly and getting
that value, that time to value way way down. What do you think,
Jim? Yeah, First of all, Eric, thanks for having me
on. Absolutely. I mean two things about this associative engine that really brings
valued organizations right away. The first is the fact that you know when you're
looking at your visualizations, when that data is in memory and you start to
slice and dice it. As an end user, what you don't want to
do is you don't want to see a bunch of progress indicators saying oh,
you just asked for this very complex piece of information, Why don't you just
wait five minutes as I go to these different sources to get that information.
So that's the first thing that kind of click helps with is kind of that
immediate access to the information you ask about. And then the second thing,
and we kind of saw this in the webinar if you were able to attend
that is this whole concept of green, white, and gray. That's kind
of the color scheme that click uses when a business user is starting to filter
the information. Well, that clearly allows a user to get smacked in the
face with what they're asking for and what they want to see and what they
don't want to see. And I've done a lot of demos of the technology
over the years, and it's amazing. When you show an organization this technology
with their data, people are always on the edge of the seat, you
know, when they come in and they're sitting back, and then you can
kind of go and start clicking on their information and they see those gray values,
which I refer to as kind of the golden nuggets. Those are the
things you didn't ask for. People are the users of the data just get
very excited. Sometimes it's happy excitement, sometimes it's not so happy. Excitement
because they're seeing things they don't want to see, but they're always able to
see them above and beyond what they would have in other solutions. And the
key is the context, right context, And this is actually one of the
real challenges with artificial intelligence is what context does it have? What is the
context window people talk about. That's something that has to do with both time
and dimensions. And if you play with these large language models, you'll know
if you get a very very complicated task, it'll sit there and think for
a while, and then sometimes they'll go, oh, I'm a language model.
I can't figure that out. That's where it just defaults. Now.
Sometimes that's a guardrail. Sometimes it was just too complex and it doesn't want
it to do that. Yeah, although I heard something very strange that will
just pick up later maybe. But some guy told me that if you tell
chat GBT that gem and I can do something, it'll work harder. That's
true. That's crazy, But kind of back to you, Jim, the
context is so important. In that webinar we did earlier, you were showing
an example of snack items that a company is selling, and you can see
one of the dimensions that's visible is what kinds of customers get that, like
grocery stores and schools and other things. And there was a gray area at
the bottom, which was hotels for example. So the beauty from an analytical
perspective is that right away the user sees, wait a minute, we're not
selling these to hotels. How come? And that's a question you asd that's
that's a golden nugget, right, yeah, yeah, and we see that
all the time. The example I always I get excited about is when we
go in and you start showing this to let's say a sales organization. You're
showing it to sales reps or sales managers. And you know a lot of
times when you're doing using that kind of scenario, you always say, oh,
you know, the company wants to see the top reps or the top
products, so they use a tool like click to show that information. And
with click, what always comes out is, oh, well, here are
the reps that aren't selling. Here are the products we're not selling at all,
and we're not selling them into this region. They never thought about that
information as far as you know, kind of the context they were looking at.
They were looking for top reps and top products and what they're getting now
is reps who aren't selling products, that aren't selling, regions that aren't selling,
which they win that see with other solutions, and that actually changes the
query or the question that they started to ask initially, which always gets them
excited. Yeah, that's an excellent point, and I'll bring in David Linham
here to comment on this. You know, David, we're kind of talking
about the null set right where there's nothing, and you don't typically search for
that. I mean, I've heard lots and lots of analysts over the years
say, look, if you really don't want to explore your data, look
for the zero values, look for the null values where is there nothing?
Because that could be interesting, But that's not terribly intuitive, right, you
want to look, as Jim was suggesting, oh, who are the top
selling salespeople. That's good information, but it's also very good to know that
we're not selling any of these products or in any of these regions, and
these guys aren't doing anything like wait a minute, let's call a meeting.
Right, What do you think, David think's absolutely right? I mean,
you know, give you an example. You know, had a ceramics manufacturer
that was concerned about the quality of the ceramics going down at certain times,
and they couldn't figure it out. They looked at the quality of the suppliers,
they look at the quality of the goods that went into it. Ultimately,
and you know, at the end of the day, it was related
to environmental factors whin the factory. When the humidity was up to a certain
amount of a certain amount, that's when the error started occur and they lost
lots of money in making these happen. So looking at ultimately how information relates
to other information without an intuitive understanding, and how they relate, in other
words, making these are previously unrelated things that come together and then they make
sense. And sometimes that's going to be the ability to look at null sets
and other operations within certain data sets and how they relate to certain systems.
When I see data missing, that's data onto itself. That doesn't mean that
the data is missing. That this means that's a data point that I need
to explore why is the data missing, and ultimately what it means that the
data is missing. And so in this case, they had a certain margin
of errors and a certain defect rate that went up, and they were looking
for correlations between it and looking at things that were unrelated, and ultimately they
left to a huge amount of value. They saved huge amounts of money.
In some instances they save the whole product line. That's amazing. And what's
so interesting is it's atmospheric orright, Probably no one going in thought for a
second, hey, maybe it's the humidity until you started looking at the data
and you're like, oh, wait a minute. Every time this happens,
the humidity goes up. So that's a classic aha moment that can change a
whole business and save, as you suggest, the whole product line. It's
something I'm always fascinated by these revelations that are outside the sphere of what you
were considering, right, And I think that's one of the problems with structured
data is that we're trying to force the world into the structured model where it
doesn't always fit perfectly. Right, David, Right, and absolutely. And
also it comes down to the people are trying to look for AI to save
them in this area, and it won't. Unless the AAI system is going
to be trained in the information, it can't make the correlation. So everything
dependent. All an AI system does is a mirror of the data that's used
to train it. And so if we're not training it with all of the
correlated data points and the ability to kind of look at all these unrelated systems
because they're not trained, because the people who train the data don't know that
they're related, then you can't really kind of uncover the value and that data.
So and you know, looking at the webin we just went through,
and that was kind of an AHA moment in me that the ability to leverage
data in new dynamic ways is the ability to find value and information that previously
wasn't there. And that's really what understanding data analytics is all about and how
it brings value back to the business. And I wish more businesses would see
this and kind of understand where the value is. Yeah, that's just an
excellent point. And seeing is believing, And one of the promotions I sent
out for this event was seeing is knowing. When you can see something,
the visual metaphor is a very powerful one because you can see disparities, you
can see connections, and when you can start to play with that, especially
moving things around, like I'm a huge fan of slider bars. If you
slide over time back and forth, where does something happen. Well, it's
kind of important to know where something happens, but folks don't touch up.
De will be right back. You're listening to Inside Analysis. Expect you welcome
back to Inside Analysis. Here's your host, me, Eric Tavanaugh, to
show. Okay, folks back here on Inside Analysis talking all things associative analytics.
We've got Aaron Wilson with us from Athena Solutions, as well as Jim
Smith of Click and our good buddy David Linthikam, an industry analyst formerly of
Deloitte. Now he's on his own doing all kinds of interesting things. And
this guy has got to answers for questions I haven't even asked yet, so
we'll try to get to those at some point in the show. But Erin,
I'm going to throw it back over to you to comment on to me.
And we've talked about this for many years. That DM radio or other
show is in year seventeen, so we've been going a long long time.
And I'm always amazed by the importance of the fluidity of your experience with data.
In other words, you can't just click something and run a reportant come
back on Monday to see what I mean. You can do that, but
it's not a whole lot of value in that. What you really want is
this experience where you can play around with things, select, deselect, change,
maneuver, bring in different dimensions, and that experience, especially if it's
in memory and that's the way it's designed and click, that fluidity is crucial
to sort of match the analytical process of the brain. What do you think
erin Yeah, I'd say that's definitely true. I mean, one of the
things that you know at Athena that is kind of near and dear to our
hearts. I mean, we have a product that you I know you've heard
about called the data analysis Sandbox, right, So this idea of exploratory analysis,
and what our product does is basically brings in a semantic layer over top
of all different types of data, different sources, different formats, and allows
you to essentially do an exploratory process kind of in the same way as click
the click associated engine does. But I definitely think that there's a demand out
there for it. I think that you know, it's really again, I
think that once you give people the power to get their hands on data and
to produce visualizations is short hop from there to they really want to explore it
and they want to produce real analysis. It's like a video game. I
mean, there's this whole concept of gamifying things to make it fun, to
make it interesting. And when you can do that rapid fire analysis, whether
it's with slider bars or selecting and de selecting entities and characteristics to look for
whatever the case may be, as long as it's fast, as long as
it's real snappy, that's gamification, right, Aaron, what do you think
A bit? I mean, and that's a really interesting analogy. Of course,
I'm of a generation where the analogy isn't lost on me, but it's
mostly what I've seen from my kids. But people do like people do like
working that way. I think that you know, the idea of just having
the ability to go somewhere just at your fingertips, you know, it's it's
extremely compelling, it's powerful, you know, and if you can increase engagement
amongst your users, I mean, that's powerful in and of itself. I
think that's a great point. I'll throw it over to Jim to comment on
that. Getting the user to use the data, I mean, if you
don't use the data, that data is not being used and it's not generating
value, and it might just be a liability. What do you think,
Jim, Yeah, I definitely agree with that. And that's one of the
things. I know, what we've been talking about is kind of that that
analytics type user who goes in there and maybe sees what someone has created for
them and then wants to start changing and slicing and dicing. But a click,
you mean there's a whole set of users that don't do that. I
mean you still have users who want to come in in the morning and get
an email with a PDF attachment with a bunch of rows and columns, and
you've got executives who don't really do any slicing and dicing. They just want
to get their dashboard and they don't want to have to go to a separate
tool. They want their dashboard to be in the application of choice that they
want. So I think one of the things that we always talk about at
Click is visualizations are great, but you got to get the right data to
the right person in the right right and it's not always taking advantage of let's
say that associative engine. But I take that back, it's always taking advantage
of the associated of associative engine, but it's not necessarily always interacting, you
know, slicing and dicing. Sometimes it's just, Hey, I need my
information when I need to make a business decision, and I need a tool
that can provide me that data in the format that I want at the time
that I want. And again, I think that's something that at CLIP we
do a really good job at providing those different avenues to get the data to
the user. Yeah, and you know, Aaron brought up one of the
magic words in his commentary a moment ago. I'll throw it over to you
because I think in this environment, with the associative engine and just what you've
described, you enable analysis of semantics, right, semantics are very important.
And you know, for example, you could see, as a user,
wait a second, why is this area down here? Gray? I know
we sell this product. Maybe the semantic engine was wrong, maybe it wasn't
coded properly, or maybe when the data was imported there was a column missing
for example. I mean, we're starting to see that very adeptly addressed by
observability tools that show when something doesn't happen, because yeah, hitherto it's like
you would load it and you just presume it's in there. Is it?
I don't know, let's take a look, but you don't know it's not
there until you see that gray and you're like, wait a minute, why
is that gray? It gets back to this golden nugget thing which I just
love. It's the null set, like why is this a zero that shouldn't
be a zero? I know that it should be X y Z number.
But that's what helps you get to the answer, but helps you figure out
what's wrong in the system, right Jim. Yeah, And that's one of
the things that I would never sell click Sense, which is the tool from
click as a data quality tool. There are data quality tools out there well.
One of the things that you always run into with this associative engine.
When we do let's say a proof of concept or just get some sample data
from a customer. You can load that in and sometimes you get gray values
because they're gray values you didn't sell a product in a particular region. But
a lot of times you'll start to get gray values because of data quality issues.
Now again we don't fix it. We kind of highlight it for you
and kind of smacking in the face. As I mentioned before, with it
so that you can go back and say, all right, you know what,
I've got two hundred and fifty thousand dollars of missing revenue. It's not
missing revenue, it's missing data that sales transaction is not tied to the proper
customer ID. And I see that easily and click. So I think you're
right there. The semantics of the missing data sometimes is because the data isn't
there, and sometimes it's because the data is wrong, and that's what an
associative engine can help you see. Yeah, I'll throw it over to David
linthencom. Figuring out what's wrong. That's pretty important because making decisions based on
bad data is a very, very bad idea. You can think that this
group of salespeople is doing a fantastic job. In fact they're not, and
you give them all raise, and then you wind up throwing good money after
bad as they say, so, understanding what's incorrect is a huge part of
this equation, right David, Yeah, it's everything. And most enterprises out
there aren't utilizing their data in the correct way where they're able to find insights
into what is incorrect. They can't see what's wrong with their business based on
the way that they're currently tracking information, so everything's transaction oriented. Everything is
basically entering things into an inventory database and a sales database, things like that.
They don't see the force through the trees and understanding their data. And
of course we went through the whole data warehousing stuff. We're supposed to have
analytics to get us into there so we could see the force through the trees.
And now we're moving into AI. And you look at the utilization of
data by most enterprises out there, they really couldn't tell you where the single
source of truth is. They couldn't tell you where their business is rising and
failing. They couldn't tell you where where a certain product lines are becoming weakening
into the marketplace until another six months of data transactions, and so they're missing
a huge piece. And I think that a lot of those businesses are just
going to fall by the wayside because they can't see where they're steering the ship
and they're gonna en up running into icebergs. Yeah, no, that's an
excellent point, and Aaron, I'll bring you back in. Our good friend
Kate Stratchnia from Dedicated had a great post on LinkedIn the other day. She
said, some companies love a single source of truth so much that they have
many of them, which I threw back my favorite quote about standards is the
good thing about standards is there are so many of them, right, but
we do need to watch out for these things. It kind of gets back
to semantics too, right, understanding, But the whole point is that data
literacy itself is an ongoing process, and especially for some midsized or large organization,
there's gonna be a lot of stuff that you don't know and you don't
understand about how the business operates. Maybe it's in operations are manufacturing, like
the example that David gave. There's a lot to be learned out there,
and you want to be learning it, so you have to be using the
information and then collaborating with people too. That's another big part of the equation,
right, is don't just use it for your own personal consumption, but
use it to start conversations, to ask people about things, to get that
collaboration going, because that fuels analysis and it also enables data governance. Right.
What do you think, Eric, I definitely think so. I mean,
it definitely ties in with the you know, the other part of this
series, the series that we were doing with you about data catalog, where
the idea of how data cavalog can be so important to governance, the idea
of, you know, first of all, the more people can get their
hands on the data and work with it, the more they can point things
out, the more they can find, like you said, problems with the
data, maybe problems with the semantics, and you know, offer their expertise
and maybe fixed problems. Click helps in that regard, in the sense that
the more people get their hands on the data, the more people get involved,
they get engaged, and you have the potential to improve governance. Really
engagement, I mean, you jumped on that a minute ago, Jim.
I'll throw it back over to you. Engagement collaboration, you know, and
I've seen this myself. When someone becomes engaged, it's a very powerful thing
and they don't want to let it go. I mean, it's like I've
been doing tracked email marketing now for oh my goodness, twenty four and a
half years or so, because I have a friend who built a solution in
nineteen ninety nine, so I was using it back then. Once you can
see who opens and who clicks, you can't go back into the darkness.
I mean, you can't go back into just the spray and pray nonsense.
And it's like it's an iterative process. You get closer and closer to the
signal, and that's what you want, right as signal. You don't want
noise, you want signal. And the more people you get to collaborate,
that's engagement and that gets you somewhere. I guarantee if people aren't engage,
these good things are happening. What do you think, Kim, Yeah.
Absolutely. One of the ways that we've always gone to market at Click is
this whole concept of land and expand and really really what that was all about
is Click going into a particular business division and showing the power of the solution
and getting users in that particular business division very excited about it. And guess
what, when a set of business users is excited about things, they'll talk
and they'll talk to you know, their friends of the company, and they'll
show kind of the reports that they're using. And then sure enough, another
business division says, hey, I want that. I want to be able
to do those things that this first business division has been able to do.
And I think that just kind of makes Click or any other bi tools.
Again, it's not just for click spread like wildfire in an organization. When
users can start to consume data in a way that makes sense for them,
everybody wants to do that. Yeah, that's right, And it's like the
snowball going downhill. It gets bigger and bigger, it gets better, you
get more attention focused on it. I'm actually looking our live studio audience has
lots of good comments and quotes, so I'll share those in the break and
we'll tackle them in the final couple segments of the show. But you know,
David, I'll bring you back in. You know else is very interesting
here is that like just like a data catalog. We're doing a separate series
on that, but obviously it's related. The analysis of data is a galvanizing
agent. And when you can look at this stuff and then have meaningful conversations
with people, that's very compelling because you're not just asking what's going on.
You can see the data, you can see the relationships, and you can
call someone and say, hey, Bob, I it's realized we're not selling
any snacks to hotels. Do you know who's responsible for that? Like,
oh, let me check, Well that person actually left the company last year
and we haven't fulfilled that position. There you go, that's the kind of
thing you're looking for when you look at this data, right David, Yeah,
absolutely, I mean you got to even like the readiness for AI systems
is your ability to understand the use of data, and I think as if
you can't do that, you don't have these insights, these current even the
rudimentary insights, then you have no hopes of leveraging AI to any kind of
value purpose. And these are very expensive systems to implement, So people seem
to be trying to jump directly from kind of core understanding data semantics and core
understanding data analytics into the ability to leverage AI systems to kind of amplify that.
And my thing the readiness there. If you don't have an understanding what
your data is, what it means, and also the power that it's able
to be leveraging your way to derive meaning and derive insights in the data,
you have no possibility of moving into AI. And kind of that's a core
metric to success. Yeah, and this is something that I've been on a
soapbox talking about it. We have a couple good questions from our audience.
For example, one gentleman is asking, do analysts expect that data analysis tools
like Gemini Advanced and Chat GPT plus one accelerate data use? Well, David
is making a good point there. You need to make sure your data house
is in order and you can actually use these tools. I mean, I've
already seen amazing use cases where you give a chat GPT or a Gemini a
significant amount of data and ask it to summarize. The summarized function is fantastic,
by the way, it's really interesting stuff. But you can ask it
questions. And I think that we're going to head down a road where this
nexus of GENI and traditional structured analysis will be very, very compelling and very
powerful. Not quite there yet, but to David's point, you've got to
get your house in order. And maybe we've got a minute left in here
in this segment, Aaron Wilson, I'll throw it over to you. The
key ingredient there to fuse these worlds is data governance, right, and data
quality and understanding your data before you go point in years, before you go
training a model, for example, on all this stuff that you found.
First, you want to sort through that stuff and make sure it's good,
real good. Thirty seconds go ahead, erin no question there. I mean,
you know, all kinds of things can happen when you put AI on
data that isn't ready for it. I mean even something I think a common
mistake that people do is is there's old data floating around, right, you
know, there's data that's that may have been relevant at one time but no
longer is. And if you train a model on, you know, using
calculations and formulas that have been discarded, it's not going to be very helpful
to you. Yeah, that's exactly right. Well, folks, don't touch
up that. We'll be right back. You're listening to Inside Analysis. Welcome
back to Inside Analysis. Here's your host, Eric Tabanac. All right,
folks, back here on Inside Analysis, talking to several experts. A wonderful
show today, Aaron Wilson of Athena Solutions, Jim Smith of Click and David
Lentkam our industry analyst of Today and Aaron, you a question for Jim,
so take it away. Yeah. Well, one of the things we haven't
talked about in terms of, you know, the advantages of the associative engine,
but I know It's something that Click has well has pointed out in some
of their videos that I've seen, for example, is the advantages that you
have when you're trying to bring in different data sources, maybe a data source
that comes from outside of you know, maybe you have a data warehouse,
we have certain tables and so forth, and then somebody says, well,
you know, what would happen if we connected in this whole other you know
column, or this whole other data set and look at those relationships. The
associative engine can really help you with that, can it? It absolutely can.
So one of the things about the associative Engine and what it's been able
to do since it was rolled out at Click back in the nineties is there
is a component of kind of data integration built into Click sense. Now again,
we have a data integration offering that says, hey, if you've got
one hundred of data sources and you want to get your information into a data
warehouse to be used by a business intelligence tool or machine learning tool, can
absolutely do that. But what's great about Click is if you're a smaller organization
and you just need to get lots of sources put together, you can go
into Click and do it directly into that in that solution. So you could
say, hey, I've got a table in Oracle on prem, I've got
a Snowflake table, and I've got someone's personal Excel spreadsheet, and I need
to bring those things together. Not only can you make those connections within click
Click's got kind of this capability that we'll look at kind of column definitions,
profile the data and say, yeah, even though this is Excel and this
is Oracle, we can put those together based on what we see in the
data, and we'll do that for you. And then you know, you've
got users who are able to put together their own reports. Even though it
hasn't built out the you know, the defined data warehouse, they're still allowing
users to kind of do that self service against lots of different sources. Does
that answer your question? Aron? That pretty much hits it right on the
head. Yes, that's where I was going. Cool. Well, and
this is such a good point because and I'll throw this over to David to
comment on when you're people still build data warehouses, right, It's not going
away. They're going to be data warehouses. I'm pretty sure forever and ever.
Llms are not going to supplant the data warehouse against the different tools text
generation. It's great for summarizing things. It's great for getting a consensus about
what has been published on a topic. That's really what these lllms are,
their text generative consensus engines, and it's good for a big part of the
process. But the understanding the data and the relationships it's a separate deal and
it's very very important. And David, when they were talking about this,
I'm thinking to myself, that is really valuable to in the process of deciding
what's going to go into your warehouse. Explore that data with click, explore
the relationships between things, because that's going to help you figure out what is
the optimal set of data that we're going to use to put into this warehouse
to fulfill business needs. It's a very important part of the process. What
do you think data It's everything, And I think that ultimately that's what's missing
in terms of the data analytics as to the data, what the data means
and what it can be used for, and the kinds of insights that we're
looking to get out of the information. So your ability to understand the usage
of data and your ability to find value within the data before you start building
these things, before you build these analytic tools and data warehouses and things like
that is something that many enterprises are missing. I think ninety percent of the
enterprises out there are grossly under utilizing their data and the other ten percent are
almost are minorly under utilizing their data. So everybody's under utilizing their data.
And ultimately, the companies that are able to utilize their data for strategic purposes
are able to gain insights, They're going to provide innovative differentiators for them to
accelerate themselves in the marketplace. I truly think in ten years we're going to
see lots of businesses that have gone under and lots of businesses that have succeeded
into in a meteoric success, and look at the differentiators there. How do
they do it. They're able to labage data the age labor data in the
context of analytics and the context of AI, and something that provides them of
the core force multiplier for their ability to take the business. So it's everything.
Yeah, Jim, go ahead. Yeah. The one thing I wanted
to add the David's comment, and we see it a lot at click,
is you know, there are some organizations we go out to and they say,
okay, yeah, we don't want to look at a data analytics tool
yet we've got this twelve to eighteen month project to build the data warehouse,
and then we'll come back and look at a business intelligence tool. And I
think what we tell customers, I think it's similar to what David said,
is use a tool upfront that is easy to get started with, so that
you know what data your users actually want to consume in your analytics environment.
And if you can do that quickly and easily, you can go back and
make that some of the requirements for your data warehouse projects. So in a
sense, it's kind of and I don't mean to use an old term there,
but rapid application development. You know, start with kind of some of
the reports, see if they're successful, work that into your data warehouse projects,
so that when your data warehouse project's done, you're actually giving information that
users want to see as opposed to, you know, try to do that
all up front in twelve months and then realize you failed and have to go
back and change that data warehouse. Right, that's exactly right. And just
to put some meat in the bones here, I'll throw this one over to
Aaron. We've got about four minutes left in this segment. If you want
to know where the value accrues, where is generated, Look at companies that
come out with new promotions like three will sell three for a discount of thirty
percent, for example, or buy one now, get one free. Most
of those deals are data driven. Someone has crunched the numbers and figured out,
aha, if we sell this many at this price point, we'll get
this many new customers. And then they test that stuff. I mean they
make sure that working. So you get your idea, you put into the
market, then you test it, and I mean those cycle times are coming
down and down and down. To Jim's point about wraplet application development, new
data products are what people are creating. Think insurance companies. I mean,
you've done a lot of work in financial services understanding that you can test these
theories out, see how they work, then double down, triple down,
offer it in more regions for example. That's where you actually see the innovations
occurring because of the data, right, Aaron, go ahead, Yeah,
I definitely think so. And I mean that gets to the point of exploratory
analysis, right, because you know you've you've mentioned really sometimes you're testing a
specific you know, idea specific hypothesis. You've mentioned a couple of use cases
there. But you also have situations where you don't know ahead of time,
you know, I mean the in the promotions case, right, you may
have a whole lot of factor that you could throw into the promotion that could
make a big difference in terms of, you know, sales. But it
could be sitting in front of you. But it could be sitting in the
data. And that's the thing where the associated engine might help you to bring
in that you know, that data point that comes you know, you know
you weren't looking for it, but here it is. Yeah, And it's
all part of this process. It's part of discovery. Never stops. Discovery
is ongoing because market conditions change, you get new products, new services,
You're having to adjust pricing, I mean, pricing is one of these things
that is really under scrutiny right now. I've been paying close attention to inflationary
forces like most Americans are. And I saw staff the other day that just
jumped off the screen at me, which is that juices like orange juice and
drinks are up forty percent forty percent. And what that tells me is people
running grocery stores have figured out, hey, we can inch that stuff up
because everyone wants their orange juice, right. It's also why they put it
way in the back of the store, so you got to go through everything
to get to the orange juice. But Jim, what do you think The
one thing that you made me think about we're talking about that Eric, was
just the fact that, you know, when you're using a data analytics tool,
it's great to actually see historical performance. You know, you can kind
of see all the visuals to say how we've done. What we're seeing a
lot of people are moving towards is basically the machine learning capabilities within our product
that says, hey, not only do we need to know what happened in
the past, we need to know what values are the biggest influencers. And
it'd be nice to be able to take those influencers and kind of propagate out
what's going to happen in the future. And I don't want to just call
it forecasting, because forecasting is hey, I've got a line I'm going to
draw through the data points. Where it becomes really helpful is when you can
kind of look at that historical information and then say these are the influencers,
and let me change those influencers to see what might happen in the future.
And I think with the inflation example you had, that's what we're seeing a
lot of people wanting to do with the data analytics tool. Yeah, that's
right, to understand what are the vectors impacting our business right now. And
again, the fluidity of that experience is just crucial because as soon as it
stops, as soon as you have to go to it to get some other
query or to get access to some data set or whatever the case may be,
that analytical process is dead. Like it's just over. Maybe you wrote
it down, maybe you'll think about it next week when you come back in
the office. But the point is that you want it to always enable that
fluidity of interaction with data and understanding of the data, and that's going to
get you somewhere. Well, folks, we got one more segment coming up,
and we have some fantastic questions from the audience today. I'll give our
guests a bit of a teaser so they're ready. Some folks are asking about
Apache, Iceberg and Delta Lake and Hoodie and does Click support all these things?
I mean, this is this whole movement now, a patche Iceberg in
particular, which really took this the market by storm and generated tremendous traction.
Everyone agreed upon it as a standard. And then Data Bricks announced that they're
buying Tabular, the company that sits on top of it, during the Snowflake
con So yeah, I don't think that was a coincidence. We'll be right
back. You're listening to Inside Analysis, all right, folks, Tom for
the podcast bonus segment here and a fantastic inside analysis. We've been talking to
Aaron Wilson of Athena Solutions, Jim Smith of Click, and David lnthiccom Ore
Entry analyst of the day. We had some fantastic questions today, folks.
We'll be sure to pass these along to our presenters if we did not get
around to your question. But there are questions about AI, and there are
questions also about these new table formats like Iceberg, Apache Iceberg, who do
he is another one? And Delta Lake that's the data break specific one.
And then of course Data Bricks bought Tabular, which sits on top of Iceberg.
So first I'll throw it over to you, Jim, How does that
fit into the clickworld. Yeah, I mean a click we've got. You
know, the important thing about an analytics tool or a data integration tool,
and Click has both of those is being able to connect to any source and
pretty much deal with any target. And because of that, there are hundreds
of data sources that we can support, both on the analytics side and the
integration side. I don't know that I can address those in particular, but
I can tell you that we work on adding data sources on a monthly basis.
So we're I believe, rolling out fifteen more data connectors on our data
integration than data analytics side within the month. So we are really good partners
with Hyperscaler databases, Snowflake data bricks. So as those vendors start providing different
ways to get access to information, I think you'll find Click following along with
that support. Yeah, and David, I'll bring you in. This open
table format stuff is very interesting because what we were talking about earlier context and
being able to leverage new data sources, new types of data. For example,
I mean, one of the challenges is that from an analytical perspective,
SQL queries it's a structured query language. It doesn't deal so well with unstructured
data or some other unwieldy sources like j ON in different formats like that.
But now in these open table formats, you're going to be able to bring
in lots of external data that's going to improve context and really help kind of
see the big picture. Of course, you have to know how to do
it. But what are your thoughts on all that data. It's extremely valuable,
I mean data unto itself without the context of where it exists and what
it means to other data. I mean, like we went in to the
previous example, in other words, the data of erroneous the errors that occur
in production, and how it means in the context of other things, other
environmental factors that have to come into play. And so your ability to find
problems, your ability to understand what the data actually means to the business other
than the data as it exists fundamentally into itself. Everybody likes to do the
analysis to what sales data means to sales data, that's meaningless to me.
What does sales data mean to demographics? What does sales data mean to the
environment, What does sales data mean into social media means and are we able
to make the correlations which allows us to adjust the business to actually get the
growth that we're looking looking for. And that's core to what businesses need data
to do. Yeah, that's brilliant, Aaron. I'll throw it over to
you for some final thoughts. I mean, it's getting very exciting in the
data world. It's a lot less expensive to play around with the stuff that
it used to be. There are many more data sources, it's a lot
more fluid. I mean, really, it's it's kind of a golden age
for data. What do you think erin it is? I mean, we're
it's an interesting time for so many reasons. I mean the question about open
table format, I mean it shows you that, you know, we're kind
of looking at integration challenges all up and down the spectrum. I mean,
there's still a lot of legacy on TREMD that you know, as an implementing
station analyst, you have to be aware of that, but you also have
to be aware of these new formats as well. But it is a very
exciting time for both AI and analysis. And I think what we've kind of
shown here in this show is an analysis you know, having a human being
and actually get curious about the data and go down that exploitatory path. You're
not going to be able. You're not going to do effective AI unless you've
got people doing that. Yeah, that's a really good point. And I
will use the last minute or so here to tease our next show and our
past show is also online. You can hop online to Insideanalysis dot com to
see past events that we've done there, radio shows, you can watch the
podcast, you can listen to it. We have several new stations that have
picked us up recently in Saint Louis, in Sarasota and Tampa and in Iowa.
I got a bunch of new stations out there carrying us, so big
shout out to our friends out there. And if you want to be in
the show, send me an email info at inside Analysis dot com. But
our first show is about data fabric, this one is about visualization and click.
Next show is going to be about AI. And to points made by
each one of these guests, if you want to leverage the power of AI,
you need to make sure that your data house is in order and you
want to and click is great for this just for the reasons we talked about
for knowing the associations between things, for being able to assess which data fits
with which other data set, understanding covariance, which is the key to analytics,
and to understand any relationships between things. These are all important, they're
all part and parcel. But you do want to take your time right.
Don't rush into this stuff. Definitely don't rush into using AI or jenai.
And frankly, one of the best bits of advice I've heard yet about JENI
is use it internally first. Be careful about using it externally. There are
some great use cases around customer support, for example, but just wait,
be careful, make sure that it's really, really good. You'll hear all
about RAG models, retrieval augmented generation very important. I think that most enterprise
data companies are going to have to figure out where they fit in the RAG
model. It's going to be huge. I mean, your RAG model is
almost like your operating system for GENAI. It's very important to understand what your
anchors of truth are going to be, what your embeddings will be, how
you use these tools. But as long as it's for decision support, as
long as it's inward focused and inward in nature. You're going to be pretty
safe because and the last thing I mentioned. A great friend of mine pointed
this out the other day. Michael Barras will be on a show sometime soon
around data governance through data catalogs. He said, Remember, you don't have
to be one hundred percent accurate. The systems we have today are not one
hundred percent accurate, but you can find the mistakes. Now you want to
be at least eighty to ninety percent accurate, but you can find the mistakes
and then go back and address that. And I've got to tell you,
man, addressing root causes is very, very important. It's going to do
a lot of benefit to your organization if you understand where the data quality problems
are, how did they get there, how did they get there in the
first place, what's the data onboarding process? All this stuff can be addressed
in data governance programs, and that's really important before you use AI. With
that, we're going to bid you farewell, folks, Thanks so much for
your time and attention. Send me an email info at inside analysis dot com.
We'll talk to you next time. By bye. Wishing for a little
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I'm Chris Gragio. President Biden is dropping out of the twenty twenty four
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