WEBVTT

1
00:00:00.040 --> 00:00:05.200
I mean of Biden's announcement may be
intentional. Democrats up here were privately urging

2
00:00:05.240 --> 00:00:09.359
the President and his team to bow
out of the race before they were forced

3
00:00:09.599 --> 00:00:13.839
to essentially call on him to exit
publicly. According to Serkin, the timing

4
00:00:13.880 --> 00:00:18.559
of the announcement may be making it
difficult for Democrats to coordinate and present a

5
00:00:18.679 --> 00:00:23.640
unified front. Biden's announcement comes as
an increasing number of Democrats have been calling

6
00:00:23.679 --> 00:00:28.800
for his withdrawal due to concerns about
his advanced age, But top Republicans want

7
00:00:28.839 --> 00:00:32.399
President Biden to step down now before
the end of the term. House Speaker

8
00:00:32.439 --> 00:00:35.759
Mike Johnson posted, if Joe Biden
is not fit to run for president,

9
00:00:35.840 --> 00:00:40.479
he's not fit to serve as president. This is an NBC News special report

10
00:00:42.640 --> 00:00:48.479
NBC News on CACAA Lomelada, sponsored
by Teamsters Local nineteen thirty two, Protecting

11
00:00:48.479 --> 00:00:58.399
the Future of Working Families Teamsters nineteen
thirty two, dot org. The information

12
00:00:58.520 --> 00:01:03.320
economy has a rived. The world
is teeming with innovation as new business models

13
00:01:03.320 --> 00:01:08.840
reinvent every industry industry. Inside Analysis
is your source of information and insight about

14
00:01:08.879 --> 00:01:14.000
how to make the most of this
exciting new eric. Learn more at Inside

15
00:01:14.040 --> 00:01:19.760
Analysis dot Cossideanalysis dot com and now
here's your host, through Eric Kavanaugh,

16
00:01:19.879 --> 00:01:26.719
and all right, ladies and gentlemen, Hello and welcome back once again to

17
00:01:26.760 --> 00:01:30.799
the only coast to coast radio show
in the US. Today that's all about

18
00:01:30.799 --> 00:01:34.200
the information economy. It's time for
Inside Analysis. You're truly Eric Kavanaugh here

19
00:01:34.359 --> 00:01:38.280
with an all star cast, some
good buddies on our call. Today.

20
00:01:38.319 --> 00:01:42.319
We'll be talking with Aaron Wilson of
Athena Solutions. They are a strategic consultancy

21
00:01:42.599 --> 00:01:46.879
systems integrator. They do a lot
of data governance, a lot of data

22
00:01:47.120 --> 00:01:51.840
warehousing, master data management, although
these days they say mastering data because MDM

23
00:01:51.840 --> 00:01:55.359
apparently is a bad word now,
so they say mastering data. Everyone understands

24
00:01:55.400 --> 00:01:59.959
that. We'll also be hearing from
Jim Smith of Click in Fact the webinar

25
00:02:00.040 --> 00:02:02.200
we just did a moment ago which
you can hop online to Inside Analysis dot

26
00:02:02.239 --> 00:02:07.480
com to find It was all about
the associative engine and Click, which is

27
00:02:07.640 --> 00:02:09.039
very interesting stuff. We're going to
talk about that and what it means and

28
00:02:09.080 --> 00:02:14.080
why it's special. The title for
our show is Inside by Association, Exploration

29
00:02:14.199 --> 00:02:16.360
without Constraints. And then, last, but not least, our good buddy

30
00:02:16.439 --> 00:02:20.759
David Lintikam is online. He's been
doing a lot of where this guy's been

31
00:02:20.800 --> 00:02:23.599
around, I mean he needs to
work for NASA. He's doing agentic AI

32
00:02:23.719 --> 00:02:28.360
work these days. That refers to
AI agents. We may even talk about

33
00:02:28.360 --> 00:02:30.680
that on our next show, or
we're going to be featuring kindy a very

34
00:02:30.680 --> 00:02:35.879
interesting large language model tooled. Of
course, these large language models CHATGBT,

35
00:02:36.439 --> 00:02:40.319
Gemini, Claude. They've taken the
market by storm, but they have certain

36
00:02:40.479 --> 00:02:45.120
use cases. There are times when
you want to use those to do interesting

37
00:02:45.240 --> 00:02:49.800
things like code, like code generation
or text generation, but they're not really

38
00:02:49.879 --> 00:02:53.319
analytic engines in the traditional sense.
So we're going to talk about what that

39
00:02:53.400 --> 00:02:57.360
all means and basically help you figure
out what does your business need, well,

40
00:02:57.360 --> 00:03:00.479
what kind of solution will make sense
for you. So I'll just throw

41
00:03:00.479 --> 00:03:05.159
out a few comments. We did
talk about the associative engine and click can.

42
00:03:05.240 --> 00:03:07.280
I remember getting a first briefing on
that, gosh, probably fifteen years

43
00:03:07.280 --> 00:03:10.759
ago by good buddy mind Donald Farmer. And you want to talk about when

44
00:03:10.800 --> 00:03:13.919
something does clicks? Man, I
watched it. I was like, wow,

45
00:03:14.080 --> 00:03:16.039
that's cool. So what it does
and this goes all the way back

46
00:03:16.080 --> 00:03:21.319
to the genesis of the tool to
the kernel is it will automatically show you

47
00:03:21.520 --> 00:03:27.960
visually relationships between entities, between concepts
like products that you sell and customers you

48
00:03:28.000 --> 00:03:31.159
sell them to, for example,
pretty important information. You're not really going

49
00:03:31.240 --> 00:03:35.599
to see that as readily if you're
looking at ros and columns. I mean,

50
00:03:35.639 --> 00:03:39.520
you can look and excel and build
different graphics and different things to visualize

51
00:03:39.560 --> 00:03:44.840
the data. But how nice is
it to just see the relationships right out

52
00:03:44.879 --> 00:03:49.599
of the box. It's very very
important stuff because the whole process of discovery

53
00:03:49.919 --> 00:03:54.120
requires thinking through and understanding what you're
looking at. You know, in a

54
00:03:54.159 --> 00:03:58.840
previous show we did in this series
with a lask and also from Athena Solutions,

55
00:03:59.159 --> 00:04:01.199
had this great quote where she said, in order for data to be

56
00:04:01.240 --> 00:04:06.759
an asset, it must be understood. I was like, good point.

57
00:04:08.039 --> 00:04:11.960
If you don't understand the data,
it's not an asset, and in fact,

58
00:04:11.960 --> 00:04:15.439
it might be a liability if you
don't understand what that data is telling

59
00:04:15.479 --> 00:04:18.639
you. This gets back to a
concept called data literacy, which is very

60
00:04:18.759 --> 00:04:24.040
much in conversation these days for good
reason, because now we have all this

61
00:04:24.240 --> 00:04:28.000
data. You know, twenty years
ago, really only the fortune two thousand

62
00:04:28.079 --> 00:04:32.040
companies could afford to build an enterprise
data warehouse. It took six months,

63
00:04:32.079 --> 00:04:35.959
It costs ten or twenty or thirty
million dollars. It was a huge effort.

64
00:04:36.000 --> 00:04:39.360
It took a lot of time.
You want to talk about time to

65
00:04:39.480 --> 00:04:43.120
value, We're talking years to get
to value. Well, that's just not

66
00:04:43.279 --> 00:04:46.920
even acceptable anymore. You cannot.
No one is going to approve a three

67
00:04:46.000 --> 00:04:50.079
year project in data that will only
provide value a year or two later.

68
00:04:50.199 --> 00:04:54.519
There's no chance of that happening.
And luckily it doesn't have to happen.

69
00:04:54.560 --> 00:04:59.399
The new technologies these days and the
old technologies that are modernized, they allow

70
00:04:59.439 --> 00:05:01.240
you to get value very very quickly
from your data, and that's what you

71
00:05:01.319 --> 00:05:06.000
need. It's a very fast moving
environment these days. Just think about the

72
00:05:06.000 --> 00:05:09.319
Internet and all the stuff that you
could buy on the internet. Think about

73
00:05:09.319 --> 00:05:13.439
all the data that's out there.
Think about data science. There's this whole

74
00:05:13.519 --> 00:05:17.480
data science industry. And we've talked
on this show before. I've marveled in

75
00:05:17.480 --> 00:05:23.920
fact that the data science teams in
large organizations often don't interact with the data

76
00:05:23.959 --> 00:05:28.319
warehousing teams, which in my opinion
makes exactly use zero sense. You want

77
00:05:28.319 --> 00:05:30.360
these folks talking to each other,
but it's very common that they don't.

78
00:05:30.879 --> 00:05:34.199
So on this show we're going to
try to hash through some of these issues

79
00:05:34.399 --> 00:05:40.720
and really explain why this associative engine
is so important for analysis, because,

80
00:05:40.759 --> 00:05:44.920
again, anytime you're doing what's called
decision support, that's one of the older

81
00:05:45.040 --> 00:05:49.040
terms, you're trying to get information
that helps you make better decisions about where

82
00:05:49.079 --> 00:05:53.519
to spend your money, where to
spend your time, how to hire,

83
00:05:53.639 --> 00:05:57.040
whom to hire, where that person
will fit in an organization. All these

84
00:05:57.120 --> 00:06:00.199
questions can be aided and should be
aided with data and analysis. And the

85
00:06:00.319 --> 00:06:04.879
data, again from an analytical perspective, has no value until you've analyzed it

86
00:06:05.519 --> 00:06:10.120
right. And just very quickly,
last comment, One very cool thing about

87
00:06:10.120 --> 00:06:13.720
all these large language models hit in
the markets today is that most of the

88
00:06:13.759 --> 00:06:17.360
analytics world deals with structured data,
so data that's in relational databases, data

89
00:06:17.360 --> 00:06:20.879
that's in tables, rolls, and
columns. Being able to analyze that and

90
00:06:20.920 --> 00:06:25.279
get some value. One cool thing
about large language models, we'll pick this

91
00:06:25.399 --> 00:06:28.160
up on our final show in a
couple of weeks on the twenty ninth,

92
00:06:28.639 --> 00:06:33.199
is that it analyzes unstructured data too, text, documents, word, documents,

93
00:06:33.199 --> 00:06:38.600
PowerPoint presentations, some really really cool
stuff in there to get context to

94
00:06:38.600 --> 00:06:42.560
help understand the numbers. And so
with that, let's bring in our first

95
00:06:42.800 --> 00:06:46.839
guest, Aaron Wilson of Athena Solutions. Welcome back to the show. You're

96
00:06:46.920 --> 00:06:51.199
in the industry with us, and
you've done some pretty serious work in financial

97
00:06:51.279 --> 00:06:55.439
services. You got to get those
numbers right. What do you think about

98
00:06:55.439 --> 00:07:01.079
the importance of an associative engine for
being able to analyze data? Yeah,

99
00:07:01.240 --> 00:07:03.959
I mean, I think it can't
be overstated. And I think one of

100
00:07:04.000 --> 00:07:09.920
the the the interesting thing is,
and there are people at Click who I'm

101
00:07:09.920 --> 00:07:15.639
sure would say this, is that
it's not it's not that complicated a concept,

102
00:07:15.680 --> 00:07:19.839
but many people don't really understand it. And I think it goes to

103
00:07:19.920 --> 00:07:25.040
Jim's point about how usually the emphasis
is on you know, okay, how

104
00:07:25.560 --> 00:07:28.800
many cool visuals can we can we
generate from this thing, or you know,

105
00:07:28.879 --> 00:07:30.000
oh wow, there's the pop that
comes out of like, you know,

106
00:07:30.000 --> 00:07:33.120
a visual that maybe nobody's ever seen
before, and which is all that's

107
00:07:33.759 --> 00:07:39.560
great, but it's very important in
this transition in terms of, you know,

108
00:07:39.959 --> 00:07:44.439
making it not just a visualization tool, making it a tool for analysis

109
00:07:44.800 --> 00:07:51.199
because the associative engine is key to
that. It's you know, query based

110
00:07:51.199 --> 00:07:55.720
tools are very good, just like
I said, if you know where you're

111
00:07:55.759 --> 00:07:59.360
going, but to be able to
explore the I think the associative engine is

112
00:07:59.800 --> 00:08:01.959
I'm I mean, it's huge because
it shows you all the data in front

113
00:08:01.959 --> 00:08:05.800
of you. It shows you contextual
data, things you might not be thinking

114
00:08:05.879 --> 00:08:09.519
about it you didn't sit out to
look for in the first place. Yeah,

115
00:08:09.600 --> 00:08:11.959
you know. And in fact,
before I throw it over to Jim,

116
00:08:11.160 --> 00:08:13.600
I'll just throw it back to you
for a comment on this. Aaron

117
00:08:15.279 --> 00:08:16.920
Jim had a great quote in the
webinar we just did where he said,

118
00:08:18.800 --> 00:08:22.480
this tool will give you answers to
questions you ask and answers to questions you

119
00:08:22.519 --> 00:08:28.680
didn't ask, right, which is
great because it helps shape the contours of

120
00:08:28.759 --> 00:08:35.039
your understanding and that's important stuff.
It's just like being able to visually assess

121
00:08:35.080 --> 00:08:37.519
a situation. Like if you're a
security guard at a rock concert or something.

122
00:08:37.840 --> 00:08:41.360
You want to have a good view
so you can see everything that's happening.

123
00:08:41.519 --> 00:08:43.759
Yeah, you may want to keep
an eye on this person or that

124
00:08:43.799 --> 00:08:48.159
person, but you also want to
have the ability to see the whole room

125
00:08:48.559 --> 00:08:50.600
and I think that's what this does, is it gives you the ability to

126
00:08:50.639 --> 00:08:56.679
see not just one answer to a
question you're asking, but the broader context.

127
00:08:56.120 --> 00:09:00.879
And then when you kind of move
around and select and decel, whether

128
00:09:00.919 --> 00:09:07.399
it's products or services, or regions
or individuals or financial amounts or whatever it

129
00:09:07.480 --> 00:09:13.440
is, everything changes in that discovery
process. It's a learning experience. I

130
00:09:13.440 --> 00:09:16.919
mean, you've got to try hard
to not learn something by engaging in that

131
00:09:16.960 --> 00:09:24.840
process right completely. And I think
you know the idea really, you know,

132
00:09:24.879 --> 00:09:28.879
SEQL queries aren't necessarily designed for this
kind of analysis. You're you're continually

133
00:09:28.879 --> 00:09:33.840
either drilling down or moving back up
the ladder. But the idea of being

134
00:09:33.840 --> 00:09:37.519
able to have all the data front
in front of you and explore, I

135
00:09:37.519 --> 00:09:41.480
mean, I can tell you that
users really like this because you know,

136
00:09:41.600 --> 00:09:43.840
for a user, once they get
their hands on the data and they get

137
00:09:43.840 --> 00:09:46.759
their hands on the visualization tools,
the next thing they want to do is

138
00:09:48.279 --> 00:09:52.759
explore it. They want to do
analysis. And in that sense, you

139
00:09:52.759 --> 00:09:56.840
know, if visualization maybe part one
over the last few years has been democratizing

140
00:09:56.879 --> 00:10:01.320
these graphical capabilities along with that access
to the data. In a sense,

141
00:10:01.399 --> 00:10:07.600
what Click's able to do is democratize
democratize analysis, which is extremely powerful.

142
00:10:09.399 --> 00:10:11.679
Yeah, that's a good point.
We have a couple of good comments from

143
00:10:11.679 --> 00:10:13.919
our audience members too. I'll bring
them in probably later on in this segment,

144
00:10:13.960 --> 00:10:18.000
but let me throw it over to
Jim Smith from Click. I mean

145
00:10:18.039 --> 00:10:20.360
you've talked about how this goes back
to the kernel. This was an idea

146
00:10:20.399 --> 00:10:24.799
someone how long time ago, and
I'm always fascinated by the kernel of a

147
00:10:24.879 --> 00:10:28.120
technology, right because you have some
idea, you're trying to address a particular

148
00:10:28.200 --> 00:10:31.759
issue, and I mean, I
have to say, I think that this

149
00:10:31.840 --> 00:10:39.279
associative engine is central to the capacity
of really exploring data very quickly and getting

150
00:10:39.320 --> 00:10:41.519
that value, that time to value
way way down. What do you think,

151
00:10:41.600 --> 00:10:45.440
Jim? Yeah, First of all, Eric, thanks for having me

152
00:10:45.480 --> 00:10:50.159
on. Absolutely. I mean two
things about this associative engine that really brings

153
00:10:50.240 --> 00:10:54.840
valued organizations right away. The first
is the fact that you know when you're

154
00:10:56.000 --> 00:11:00.440
looking at your visualizations, when that
data is in memory and you start to

155
00:11:00.480 --> 00:11:03.039
slice and dice it. As an
end user, what you don't want to

156
00:11:03.039 --> 00:11:05.399
do is you don't want to see
a bunch of progress indicators saying oh,

157
00:11:05.600 --> 00:11:09.840
you just asked for this very complex
piece of information, Why don't you just

158
00:11:09.919 --> 00:11:15.720
wait five minutes as I go to
these different sources to get that information.

159
00:11:16.080 --> 00:11:18.159
So that's the first thing that kind
of click helps with is kind of that

160
00:11:18.240 --> 00:11:22.279
immediate access to the information you ask
about. And then the second thing,

161
00:11:22.480 --> 00:11:26.399
and we kind of saw this in
the webinar if you were able to attend

162
00:11:26.399 --> 00:11:30.480
that is this whole concept of green, white, and gray. That's kind

163
00:11:30.480 --> 00:11:35.480
of the color scheme that click uses
when a business user is starting to filter

164
00:11:35.600 --> 00:11:39.799
the information. Well, that clearly
allows a user to get smacked in the

165
00:11:39.879 --> 00:11:45.600
face with what they're asking for and
what they want to see and what they

166
00:11:45.639 --> 00:11:48.480
don't want to see. And I've
done a lot of demos of the technology

167
00:11:48.519 --> 00:11:56.519
over the years, and it's amazing. When you show an organization this technology

168
00:11:56.519 --> 00:12:00.240
with their data, people are always
on the edge of the seat, you

169
00:12:00.279 --> 00:12:01.840
know, when they come in and
they're sitting back, and then you can

170
00:12:01.919 --> 00:12:07.080
kind of go and start clicking on
their information and they see those gray values,

171
00:12:07.120 --> 00:12:09.120
which I refer to as kind of
the golden nuggets. Those are the

172
00:12:09.159 --> 00:12:15.919
things you didn't ask for. People
are the users of the data just get

173
00:12:16.559 --> 00:12:20.679
very excited. Sometimes it's happy excitement, sometimes it's not so happy. Excitement

174
00:12:20.720 --> 00:12:24.879
because they're seeing things they don't want
to see, but they're always able to

175
00:12:24.919 --> 00:12:28.519
see them above and beyond what they
would have in other solutions. And the

176
00:12:28.600 --> 00:12:33.960
key is the context, right context, And this is actually one of the

177
00:12:33.960 --> 00:12:39.200
real challenges with artificial intelligence is what
context does it have? What is the

178
00:12:39.240 --> 00:12:43.279
context window people talk about. That's
something that has to do with both time

179
00:12:43.840 --> 00:12:48.039
and dimensions. And if you play
with these large language models, you'll know

180
00:12:48.080 --> 00:12:52.039
if you get a very very complicated
task, it'll sit there and think for

181
00:12:52.080 --> 00:12:54.399
a while, and then sometimes they'll
go, oh, I'm a language model.

182
00:12:54.399 --> 00:12:56.080
I can't figure that out. That's
where it just defaults. Now.

183
00:12:56.120 --> 00:13:00.799
Sometimes that's a guardrail. Sometimes it
was just too complex and it doesn't want

184
00:13:00.840 --> 00:13:03.240
it to do that. Yeah,
although I heard something very strange that will

185
00:13:03.240 --> 00:13:07.600
just pick up later maybe. But
some guy told me that if you tell

186
00:13:07.000 --> 00:13:15.000
chat GBT that gem and I can
do something, it'll work harder. That's

187
00:13:15.039 --> 00:13:18.159
true. That's crazy, But kind
of back to you, Jim, the

188
00:13:18.320 --> 00:13:22.600
context is so important. In that
webinar we did earlier, you were showing

189
00:13:22.679 --> 00:13:28.080
an example of snack items that a
company is selling, and you can see

190
00:13:28.080 --> 00:13:31.600
one of the dimensions that's visible is
what kinds of customers get that, like

191
00:13:31.679 --> 00:13:35.519
grocery stores and schools and other things. And there was a gray area at

192
00:13:35.559 --> 00:13:39.879
the bottom, which was hotels for
example. So the beauty from an analytical

193
00:13:39.879 --> 00:13:43.159
perspective is that right away the user
sees, wait a minute, we're not

194
00:13:43.200 --> 00:13:48.000
selling these to hotels. How come? And that's a question you asd that's

195
00:13:48.120 --> 00:13:50.320
that's a golden nugget, right,
yeah, yeah, and we see that

196
00:13:50.440 --> 00:13:56.440
all the time. The example I
always I get excited about is when we

197
00:13:56.519 --> 00:14:00.200
go in and you start showing this
to let's say a sales organization. You're

198
00:14:00.200 --> 00:14:03.320
showing it to sales reps or sales
managers. And you know a lot of

199
00:14:03.360 --> 00:14:07.720
times when you're doing using that kind
of scenario, you always say, oh,

200
00:14:09.200 --> 00:14:11.440
you know, the company wants to
see the top reps or the top

201
00:14:11.480 --> 00:14:16.000
products, so they use a tool
like click to show that information. And

202
00:14:16.080 --> 00:14:20.879
with click, what always comes out
is, oh, well, here are

203
00:14:20.919 --> 00:14:24.919
the reps that aren't selling. Here
are the products we're not selling at all,

204
00:14:26.320 --> 00:14:30.000
and we're not selling them into this
region. They never thought about that

205
00:14:30.080 --> 00:14:33.080
information as far as you know,
kind of the context they were looking at.

206
00:14:33.320 --> 00:14:37.240
They were looking for top reps and
top products and what they're getting now

207
00:14:37.399 --> 00:14:41.440
is reps who aren't selling products,
that aren't selling, regions that aren't selling,

208
00:14:41.600 --> 00:14:46.919
which they win that see with other
solutions, and that actually changes the

209
00:14:46.039 --> 00:14:50.720
query or the question that they started
to ask initially, which always gets them

210
00:14:50.720 --> 00:14:54.879
excited. Yeah, that's an excellent
point, and I'll bring in David Linham

211
00:14:54.919 --> 00:14:58.480
here to comment on this. You
know, David, we're kind of talking

212
00:14:58.519 --> 00:15:01.720
about the null set right where there's
nothing, and you don't typically search for

213
00:15:01.759 --> 00:15:05.559
that. I mean, I've heard
lots and lots of analysts over the years

214
00:15:05.559 --> 00:15:07.600
say, look, if you really
don't want to explore your data, look

215
00:15:07.600 --> 00:15:11.000
for the zero values, look for
the null values where is there nothing?

216
00:15:11.240 --> 00:15:15.639
Because that could be interesting, But
that's not terribly intuitive, right, you

217
00:15:15.639 --> 00:15:16.960
want to look, as Jim was
suggesting, oh, who are the top

218
00:15:18.000 --> 00:15:20.919
selling salespeople. That's good information,
but it's also very good to know that

219
00:15:20.960 --> 00:15:24.559
we're not selling any of these products
or in any of these regions, and

220
00:15:24.600 --> 00:15:26.720
these guys aren't doing anything like wait
a minute, let's call a meeting.

221
00:15:26.799 --> 00:15:30.399
Right, What do you think,
David think's absolutely right? I mean,

222
00:15:31.120 --> 00:15:33.840
you know, give you an example. You know, had a ceramics manufacturer

223
00:15:33.879 --> 00:15:37.679
that was concerned about the quality of
the ceramics going down at certain times,

224
00:15:37.720 --> 00:15:43.120
and they couldn't figure it out.
They looked at the quality of the suppliers,

225
00:15:43.120 --> 00:15:46.440
they look at the quality of the
goods that went into it. Ultimately,

226
00:15:46.120 --> 00:15:50.320
and you know, at the end
of the day, it was related

227
00:15:50.360 --> 00:15:54.240
to environmental factors whin the factory.
When the humidity was up to a certain

228
00:15:54.240 --> 00:15:56.600
amount of a certain amount, that's
when the error started occur and they lost

229
00:15:56.639 --> 00:16:03.519
lots of money in making these happen. So looking at ultimately how information relates

230
00:16:03.519 --> 00:16:07.159
to other information without an intuitive understanding, and how they relate, in other

231
00:16:07.200 --> 00:16:11.600
words, making these are previously unrelated
things that come together and then they make

232
00:16:11.679 --> 00:16:14.799
sense. And sometimes that's going to
be the ability to look at null sets

233
00:16:14.840 --> 00:16:21.879
and other operations within certain data sets
and how they relate to certain systems.

234
00:16:21.879 --> 00:16:25.799
When I see data missing, that's
data onto itself. That doesn't mean that

235
00:16:25.840 --> 00:16:29.000
the data is missing. That this
means that's a data point that I need

236
00:16:29.039 --> 00:16:32.519
to explore why is the data missing, and ultimately what it means that the

237
00:16:32.600 --> 00:16:36.679
data is missing. And so in
this case, they had a certain margin

238
00:16:36.720 --> 00:16:40.320
of errors and a certain defect rate
that went up, and they were looking

239
00:16:40.320 --> 00:16:44.480
for correlations between it and looking at
things that were unrelated, and ultimately they

240
00:16:44.639 --> 00:16:47.720
left to a huge amount of value. They saved huge amounts of money.

241
00:16:47.720 --> 00:16:52.360
In some instances they save the whole
product line. That's amazing. And what's

242
00:16:52.360 --> 00:16:56.399
so interesting is it's atmospheric orright,
Probably no one going in thought for a

243
00:16:56.440 --> 00:17:00.840
second, hey, maybe it's the
humidity until you started looking at the data

244
00:17:00.879 --> 00:17:03.759
and you're like, oh, wait
a minute. Every time this happens,

245
00:17:03.840 --> 00:17:07.000
the humidity goes up. So that's
a classic aha moment that can change a

246
00:17:07.039 --> 00:17:11.920
whole business and save, as you
suggest, the whole product line. It's

247
00:17:11.960 --> 00:17:17.200
something I'm always fascinated by these revelations
that are outside the sphere of what you

248
00:17:17.279 --> 00:17:19.319
were considering, right, And I
think that's one of the problems with structured

249
00:17:19.400 --> 00:17:23.759
data is that we're trying to force
the world into the structured model where it

250
00:17:23.759 --> 00:17:27.079
doesn't always fit perfectly. Right,
David, Right, and absolutely. And

251
00:17:27.119 --> 00:17:32.240
also it comes down to the people
are trying to look for AI to save

252
00:17:32.319 --> 00:17:34.200
them in this area, and it
won't. Unless the AAI system is going

253
00:17:34.240 --> 00:17:38.079
to be trained in the information,
it can't make the correlation. So everything

254
00:17:38.079 --> 00:17:41.319
dependent. All an AI system does
is a mirror of the data that's used

255
00:17:41.319 --> 00:17:45.039
to train it. And so if
we're not training it with all of the

256
00:17:45.079 --> 00:17:49.240
correlated data points and the ability to
kind of look at all these unrelated systems

257
00:17:49.240 --> 00:17:52.519
because they're not trained, because the
people who train the data don't know that

258
00:17:52.559 --> 00:17:56.519
they're related, then you can't really
kind of uncover the value and that data.

259
00:17:57.000 --> 00:18:00.920
So and you know, looking at
the webin we just went through,

260
00:18:00.960 --> 00:18:03.880
and that was kind of an AHA
moment in me that the ability to leverage

261
00:18:03.920 --> 00:18:07.680
data in new dynamic ways is the
ability to find value and information that previously

262
00:18:07.799 --> 00:18:12.960
wasn't there. And that's really what
understanding data analytics is all about and how

263
00:18:12.960 --> 00:18:17.960
it brings value back to the business. And I wish more businesses would see

264
00:18:17.960 --> 00:18:21.839
this and kind of understand where the
value is. Yeah, that's just an

265
00:18:21.839 --> 00:18:25.400
excellent point. And seeing is believing, And one of the promotions I sent

266
00:18:25.720 --> 00:18:30.279
out for this event was seeing is
knowing. When you can see something,

267
00:18:30.559 --> 00:18:34.000
the visual metaphor is a very powerful
one because you can see disparities, you

268
00:18:34.039 --> 00:18:37.759
can see connections, and when you
can start to play with that, especially

269
00:18:38.079 --> 00:18:41.160
moving things around, like I'm a
huge fan of slider bars. If you

270
00:18:41.200 --> 00:18:45.079
slide over time back and forth,
where does something happen. Well, it's

271
00:18:45.200 --> 00:18:48.920
kind of important to know where something
happens, but folks don't touch up.

272
00:18:48.920 --> 00:18:59.079
De will be right back. You're
listening to Inside Analysis. Expect you welcome

273
00:18:59.119 --> 00:19:04.039
back to Inside Analysis. Here's your
host, me, Eric Tavanaugh, to

274
00:19:04.200 --> 00:19:11.200
show. Okay, folks back here
on Inside Analysis talking all things associative analytics.

275
00:19:11.240 --> 00:19:14.759
We've got Aaron Wilson with us from
Athena Solutions, as well as Jim

276
00:19:14.799 --> 00:19:18.720
Smith of Click and our good buddy
David Linthikam, an industry analyst formerly of

277
00:19:18.799 --> 00:19:21.799
Deloitte. Now he's on his own
doing all kinds of interesting things. And

278
00:19:21.839 --> 00:19:23.599
this guy has got to answers for
questions I haven't even asked yet, so

279
00:19:23.640 --> 00:19:26.640
we'll try to get to those at
some point in the show. But Erin,

280
00:19:26.680 --> 00:19:30.759
I'm going to throw it back over
to you to comment on to me.

281
00:19:30.960 --> 00:19:33.880
And we've talked about this for many
years. That DM radio or other

282
00:19:33.920 --> 00:19:37.079
show is in year seventeen, so
we've been going a long long time.

283
00:19:37.200 --> 00:19:42.519
And I'm always amazed by the importance
of the fluidity of your experience with data.

284
00:19:42.640 --> 00:19:45.480
In other words, you can't just
click something and run a reportant come

285
00:19:45.519 --> 00:19:48.960
back on Monday to see what I
mean. You can do that, but

286
00:19:49.039 --> 00:19:52.799
it's not a whole lot of value
in that. What you really want is

287
00:19:52.839 --> 00:19:56.920
this experience where you can play around
with things, select, deselect, change,

288
00:19:56.000 --> 00:20:00.640
maneuver, bring in different dimensions,
and that experience, especially if it's

289
00:20:00.640 --> 00:20:06.200
in memory and that's the way it's
designed and click, that fluidity is crucial

290
00:20:06.359 --> 00:20:10.559
to sort of match the analytical process
of the brain. What do you think

291
00:20:10.680 --> 00:20:15.039
erin Yeah, I'd say that's definitely
true. I mean, one of the

292
00:20:15.079 --> 00:20:18.519
things that you know at Athena that
is kind of near and dear to our

293
00:20:18.559 --> 00:20:22.279
hearts. I mean, we have
a product that you I know you've heard

294
00:20:22.279 --> 00:20:26.160
about called the data analysis Sandbox,
right, So this idea of exploratory analysis,

295
00:20:26.200 --> 00:20:30.680
and what our product does is basically
brings in a semantic layer over top

296
00:20:30.720 --> 00:20:36.519
of all different types of data,
different sources, different formats, and allows

297
00:20:36.559 --> 00:20:41.039
you to essentially do an exploratory process
kind of in the same way as click

298
00:20:41.240 --> 00:20:45.720
the click associated engine does. But
I definitely think that there's a demand out

299
00:20:45.759 --> 00:20:49.160
there for it. I think that
you know, it's really again, I

300
00:20:49.200 --> 00:20:52.960
think that once you give people the
power to get their hands on data and

301
00:20:53.079 --> 00:20:57.279
to produce visualizations is short hop from
there to they really want to explore it

302
00:20:57.279 --> 00:21:02.519
and they want to produce real analysis. It's like a video game. I

303
00:21:02.519 --> 00:21:06.480
mean, there's this whole concept of
gamifying things to make it fun, to

304
00:21:06.480 --> 00:21:11.079
make it interesting. And when you
can do that rapid fire analysis, whether

305
00:21:11.079 --> 00:21:15.240
it's with slider bars or selecting and
de selecting entities and characteristics to look for

306
00:21:15.559 --> 00:21:18.480
whatever the case may be, as
long as it's fast, as long as

307
00:21:18.480 --> 00:21:22.960
it's real snappy, that's gamification,
right, Aaron, what do you think

308
00:21:22.920 --> 00:21:26.880
A bit? I mean, and
that's a really interesting analogy. Of course,

309
00:21:27.119 --> 00:21:32.119
I'm of a generation where the analogy
isn't lost on me, but it's

310
00:21:32.119 --> 00:21:37.640
mostly what I've seen from my kids. But people do like people do like

311
00:21:37.680 --> 00:21:41.319
working that way. I think that
you know, the idea of just having

312
00:21:41.400 --> 00:21:45.960
the ability to go somewhere just at
your fingertips, you know, it's it's

313
00:21:47.039 --> 00:21:52.519
extremely compelling, it's powerful, you
know, and if you can increase engagement

314
00:21:52.039 --> 00:21:56.880
amongst your users, I mean,
that's powerful in and of itself. I

315
00:21:56.920 --> 00:22:00.119
think that's a great point. I'll
throw it over to Jim to comment on

316
00:22:00.200 --> 00:22:04.039
that. Getting the user to use
the data, I mean, if you

317
00:22:04.039 --> 00:22:07.599
don't use the data, that data
is not being used and it's not generating

318
00:22:07.680 --> 00:22:10.480
value, and it might just be
a liability. What do you think,

319
00:22:10.559 --> 00:22:12.880
Jim, Yeah, I definitely agree
with that. And that's one of the

320
00:22:12.880 --> 00:22:15.799
things. I know, what we've
been talking about is kind of that that

321
00:22:15.960 --> 00:22:22.160
analytics type user who goes in there
and maybe sees what someone has created for

322
00:22:22.200 --> 00:22:26.000
them and then wants to start changing
and slicing and dicing. But a click,

323
00:22:26.160 --> 00:22:30.480
you mean there's a whole set of
users that don't do that. I

324
00:22:30.480 --> 00:22:33.480
mean you still have users who want
to come in in the morning and get

325
00:22:33.480 --> 00:22:38.119
an email with a PDF attachment with
a bunch of rows and columns, and

326
00:22:38.160 --> 00:22:42.359
you've got executives who don't really do
any slicing and dicing. They just want

327
00:22:42.400 --> 00:22:45.720
to get their dashboard and they don't
want to have to go to a separate

328
00:22:45.720 --> 00:22:51.359
tool. They want their dashboard to
be in the application of choice that they

329
00:22:51.400 --> 00:22:53.519
want. So I think one of
the things that we always talk about at

330
00:22:53.559 --> 00:22:57.400
Click is visualizations are great, but
you got to get the right data to

331
00:22:57.440 --> 00:23:03.519
the right person in the right right
and it's not always taking advantage of let's

332
00:23:03.519 --> 00:23:07.000
say that associative engine. But I
take that back, it's always taking advantage

333
00:23:07.039 --> 00:23:11.759
of the associated of associative engine,
but it's not necessarily always interacting, you

334
00:23:11.839 --> 00:23:15.480
know, slicing and dicing. Sometimes
it's just, Hey, I need my

335
00:23:15.680 --> 00:23:19.799
information when I need to make a
business decision, and I need a tool

336
00:23:19.839 --> 00:23:25.240
that can provide me that data in
the format that I want at the time

337
00:23:25.279 --> 00:23:27.440
that I want. And again,
I think that's something that at CLIP we

338
00:23:27.480 --> 00:23:32.440
do a really good job at providing
those different avenues to get the data to

339
00:23:32.480 --> 00:23:34.519
the user. Yeah, and you
know, Aaron brought up one of the

340
00:23:34.519 --> 00:23:37.440
magic words in his commentary a moment
ago. I'll throw it over to you

341
00:23:37.799 --> 00:23:41.440
because I think in this environment,
with the associative engine and just what you've

342
00:23:41.440 --> 00:23:48.880
described, you enable analysis of semantics, right, semantics are very important.

343
00:23:48.079 --> 00:23:52.200
And you know, for example,
you could see, as a user,

344
00:23:52.480 --> 00:23:55.519
wait a second, why is this
area down here? Gray? I know

345
00:23:55.599 --> 00:23:59.519
we sell this product. Maybe the
semantic engine was wrong, maybe it wasn't

346
00:23:59.559 --> 00:24:03.480
coded properly, or maybe when the
data was imported there was a column missing

347
00:24:03.559 --> 00:24:08.559
for example. I mean, we're
starting to see that very adeptly addressed by

348
00:24:08.599 --> 00:24:14.759
observability tools that show when something doesn't
happen, because yeah, hitherto it's like

349
00:24:14.799 --> 00:24:15.880
you would load it and you just
presume it's in there. Is it?

350
00:24:15.920 --> 00:24:18.680
I don't know, let's take a
look, but you don't know it's not

351
00:24:18.799 --> 00:24:22.039
there until you see that gray and
you're like, wait a minute, why

352
00:24:22.119 --> 00:24:26.400
is that gray? It gets back
to this golden nugget thing which I just

353
00:24:26.480 --> 00:24:30.079
love. It's the null set,
like why is this a zero that shouldn't

354
00:24:30.079 --> 00:24:33.200
be a zero? I know that
it should be X y Z number.

355
00:24:33.720 --> 00:24:37.799
But that's what helps you get to
the answer, but helps you figure out

356
00:24:37.880 --> 00:24:41.480
what's wrong in the system, right
Jim. Yeah, And that's one of

357
00:24:41.559 --> 00:24:45.319
the things that I would never sell
click Sense, which is the tool from

358
00:24:45.359 --> 00:24:49.960
click as a data quality tool.
There are data quality tools out there well.

359
00:24:49.960 --> 00:24:53.599
One of the things that you always
run into with this associative engine.

360
00:24:53.799 --> 00:24:57.200
When we do let's say a proof
of concept or just get some sample data

361
00:24:57.200 --> 00:25:02.799
from a customer. You can load
that in and sometimes you get gray values

362
00:25:03.119 --> 00:25:07.640
because they're gray values you didn't sell
a product in a particular region. But

363
00:25:07.720 --> 00:25:11.000
a lot of times you'll start to
get gray values because of data quality issues.

364
00:25:11.559 --> 00:25:15.880
Now again we don't fix it.
We kind of highlight it for you

365
00:25:15.920 --> 00:25:18.480
and kind of smacking in the face. As I mentioned before, with it

366
00:25:18.799 --> 00:25:21.640
so that you can go back and
say, all right, you know what,

367
00:25:22.079 --> 00:25:26.119
I've got two hundred and fifty thousand
dollars of missing revenue. It's not

368
00:25:26.200 --> 00:25:30.680
missing revenue, it's missing data that
sales transaction is not tied to the proper

369
00:25:30.720 --> 00:25:36.519
customer ID. And I see that
easily and click. So I think you're

370
00:25:36.599 --> 00:25:42.759
right there. The semantics of the
missing data sometimes is because the data isn't

371
00:25:42.759 --> 00:25:47.279
there, and sometimes it's because the
data is wrong, and that's what an

372
00:25:47.319 --> 00:25:49.880
associative engine can help you see.
Yeah, I'll throw it over to David

373
00:25:49.920 --> 00:25:56.119
linthencom. Figuring out what's wrong.
That's pretty important because making decisions based on

374
00:25:56.240 --> 00:26:00.599
bad data is a very, very
bad idea. You can think that this

375
00:26:00.680 --> 00:26:03.640
group of salespeople is doing a fantastic
job. In fact they're not, and

376
00:26:03.640 --> 00:26:07.400
you give them all raise, and
then you wind up throwing good money after

377
00:26:07.440 --> 00:26:11.400
bad as they say, so,
understanding what's incorrect is a huge part of

378
00:26:11.400 --> 00:26:15.839
this equation, right David, Yeah, it's everything. And most enterprises out

379
00:26:15.839 --> 00:26:19.039
there aren't utilizing their data in the
correct way where they're able to find insights

380
00:26:19.039 --> 00:26:25.759
into what is incorrect. They can't
see what's wrong with their business based on

381
00:26:25.799 --> 00:26:30.599
the way that they're currently tracking information, so everything's transaction oriented. Everything is

382
00:26:30.920 --> 00:26:34.920
basically entering things into an inventory database
and a sales database, things like that.

383
00:26:36.319 --> 00:26:38.359
They don't see the force through the
trees and understanding their data. And

384
00:26:38.440 --> 00:26:41.519
of course we went through the whole
data warehousing stuff. We're supposed to have

385
00:26:41.680 --> 00:26:45.200
analytics to get us into there so
we could see the force through the trees.

386
00:26:45.720 --> 00:26:48.839
And now we're moving into AI.
And you look at the utilization of

387
00:26:48.960 --> 00:26:52.400
data by most enterprises out there,
they really couldn't tell you where the single

388
00:26:52.400 --> 00:26:56.640
source of truth is. They couldn't
tell you where their business is rising and

389
00:26:56.640 --> 00:27:02.359
failing. They couldn't tell you where
where a certain product lines are becoming weakening

390
00:27:02.440 --> 00:27:07.599
into the marketplace until another six months
of data transactions, and so they're missing

391
00:27:07.680 --> 00:27:10.680
a huge piece. And I think
that a lot of those businesses are just

392
00:27:10.680 --> 00:27:14.119
going to fall by the wayside because
they can't see where they're steering the ship

393
00:27:14.400 --> 00:27:18.319
and they're gonna en up running into
icebergs. Yeah, no, that's an

394
00:27:18.319 --> 00:27:21.960
excellent point, and Aaron, I'll
bring you back in. Our good friend

395
00:27:22.160 --> 00:27:26.519
Kate Stratchnia from Dedicated had a great
post on LinkedIn the other day. She

396
00:27:26.640 --> 00:27:30.680
said, some companies love a single
source of truth so much that they have

397
00:27:30.759 --> 00:27:36.480
many of them, which I threw
back my favorite quote about standards is the

398
00:27:36.519 --> 00:27:38.599
good thing about standards is there are
so many of them, right, but

399
00:27:40.160 --> 00:27:41.519
we do need to watch out for
these things. It kind of gets back

400
00:27:41.559 --> 00:27:45.279
to semantics too, right, understanding, But the whole point is that data

401
00:27:45.400 --> 00:27:52.119
literacy itself is an ongoing process,
and especially for some midsized or large organization,

402
00:27:52.400 --> 00:27:53.319
there's gonna be a lot of stuff
that you don't know and you don't

403
00:27:53.359 --> 00:27:57.799
understand about how the business operates.
Maybe it's in operations are manufacturing, like

404
00:27:57.920 --> 00:28:02.960
the example that David gave. There's
a lot to be learned out there,

405
00:28:03.000 --> 00:28:04.720
and you want to be learning it, so you have to be using the

406
00:28:04.759 --> 00:28:08.599
information and then collaborating with people too. That's another big part of the equation,

407
00:28:08.759 --> 00:28:12.920
right, is don't just use it
for your own personal consumption, but

408
00:28:14.079 --> 00:28:18.000
use it to start conversations, to
ask people about things, to get that

409
00:28:18.079 --> 00:28:22.039
collaboration going, because that fuels analysis
and it also enables data governance. Right.

410
00:28:22.119 --> 00:28:26.200
What do you think, Eric,
I definitely think so. I mean,

411
00:28:26.240 --> 00:28:29.759
it definitely ties in with the you
know, the other part of this

412
00:28:29.839 --> 00:28:33.920
series, the series that we were
doing with you about data catalog, where

413
00:28:33.079 --> 00:28:38.680
the idea of how data cavalog can
be so important to governance, the idea

414
00:28:40.039 --> 00:28:42.240
of, you know, first of
all, the more people can get their

415
00:28:42.240 --> 00:28:45.559
hands on the data and work with
it, the more they can point things

416
00:28:45.599 --> 00:28:48.960
out, the more they can find, like you said, problems with the

417
00:28:49.039 --> 00:28:56.880
data, maybe problems with the semantics, and you know, offer their expertise

418
00:28:57.119 --> 00:29:03.920
and maybe fixed problems. Click helps
in that regard, in the sense that

419
00:29:03.279 --> 00:29:07.240
the more people get their hands on
the data, the more people get involved,

420
00:29:07.240 --> 00:29:10.559
they get engaged, and you have
the potential to improve governance. Really

421
00:29:11.000 --> 00:29:14.440
engagement, I mean, you jumped
on that a minute ago, Jim.

422
00:29:14.480 --> 00:29:18.480
I'll throw it back over to you. Engagement collaboration, you know, and

423
00:29:18.799 --> 00:29:22.400
I've seen this myself. When someone
becomes engaged, it's a very powerful thing

424
00:29:22.640 --> 00:29:26.000
and they don't want to let it
go. I mean, it's like I've

425
00:29:26.000 --> 00:29:30.839
been doing tracked email marketing now for
oh my goodness, twenty four and a

426
00:29:30.880 --> 00:29:33.839
half years or so, because I
have a friend who built a solution in

427
00:29:33.920 --> 00:29:37.440
nineteen ninety nine, so I was
using it back then. Once you can

428
00:29:37.480 --> 00:29:41.039
see who opens and who clicks,
you can't go back into the darkness.

429
00:29:41.119 --> 00:29:45.160
I mean, you can't go back
into just the spray and pray nonsense.

430
00:29:45.559 --> 00:29:48.839
And it's like it's an iterative process. You get closer and closer to the

431
00:29:48.920 --> 00:29:52.240
signal, and that's what you want, right as signal. You don't want

432
00:29:52.279 --> 00:29:56.119
noise, you want signal. And
the more people you get to collaborate,

433
00:29:56.440 --> 00:30:00.000
that's engagement and that gets you somewhere. I guarantee if people aren't engage,

434
00:30:00.000 --> 00:30:02.480
these good things are happening. What
do you think, Kim, Yeah.

435
00:30:02.519 --> 00:30:06.920
Absolutely. One of the ways that
we've always gone to market at Click is

436
00:30:06.960 --> 00:30:11.880
this whole concept of land and expand
and really really what that was all about

437
00:30:11.039 --> 00:30:15.839
is Click going into a particular business
division and showing the power of the solution

438
00:30:17.039 --> 00:30:22.000
and getting users in that particular business
division very excited about it. And guess

439
00:30:22.000 --> 00:30:26.599
what, when a set of business
users is excited about things, they'll talk

440
00:30:26.920 --> 00:30:30.839
and they'll talk to you know,
their friends of the company, and they'll

441
00:30:30.880 --> 00:30:33.960
show kind of the reports that they're
using. And then sure enough, another

442
00:30:34.000 --> 00:30:37.599
business division says, hey, I
want that. I want to be able

443
00:30:37.599 --> 00:30:41.920
to do those things that this first
business division has been able to do.

444
00:30:42.519 --> 00:30:47.839
And I think that just kind of
makes Click or any other bi tools.

445
00:30:47.960 --> 00:30:52.640
Again, it's not just for click
spread like wildfire in an organization. When

446
00:30:52.759 --> 00:30:56.000
users can start to consume data in
a way that makes sense for them,

447
00:30:56.319 --> 00:31:00.759
everybody wants to do that. Yeah, that's right, And it's like the

448
00:31:00.799 --> 00:31:03.960
snowball going downhill. It gets bigger
and bigger, it gets better, you

449
00:31:03.000 --> 00:31:07.920
get more attention focused on it.
I'm actually looking our live studio audience has

450
00:31:07.000 --> 00:31:11.319
lots of good comments and quotes,
so I'll share those in the break and

451
00:31:11.319 --> 00:31:15.319
we'll tackle them in the final couple
segments of the show. But you know,

452
00:31:15.400 --> 00:31:18.000
David, I'll bring you back in. You know else is very interesting

453
00:31:18.039 --> 00:31:22.559
here is that like just like a
data catalog. We're doing a separate series

454
00:31:22.559 --> 00:31:29.079
on that, but obviously it's related. The analysis of data is a galvanizing

455
00:31:29.160 --> 00:31:33.400
agent. And when you can look
at this stuff and then have meaningful conversations

456
00:31:33.400 --> 00:31:37.480
with people, that's very compelling because
you're not just asking what's going on.

457
00:31:37.599 --> 00:31:40.680
You can see the data, you
can see the relationships, and you can

458
00:31:40.720 --> 00:31:42.119
call someone and say, hey,
Bob, I it's realized we're not selling

459
00:31:42.119 --> 00:31:47.039
any snacks to hotels. Do you
know who's responsible for that? Like,

460
00:31:47.039 --> 00:31:49.400
oh, let me check, Well
that person actually left the company last year

461
00:31:49.720 --> 00:31:55.319
and we haven't fulfilled that position.
There you go, that's the kind of

462
00:31:55.359 --> 00:31:57.319
thing you're looking for when you look
at this data, right David, Yeah,

463
00:31:57.359 --> 00:32:00.839
absolutely, I mean you got to
even like the readiness for AI systems

464
00:32:01.000 --> 00:32:05.279
is your ability to understand the use
of data, and I think as if

465
00:32:05.319 --> 00:32:07.440
you can't do that, you don't
have these insights, these current even the

466
00:32:07.480 --> 00:32:13.200
rudimentary insights, then you have no
hopes of leveraging AI to any kind of

467
00:32:13.279 --> 00:32:16.319
value purpose. And these are very
expensive systems to implement, So people seem

468
00:32:16.359 --> 00:32:22.640
to be trying to jump directly from
kind of core understanding data semantics and core

469
00:32:22.759 --> 00:32:28.680
understanding data analytics into the ability to
leverage AI systems to kind of amplify that.

470
00:32:28.799 --> 00:32:32.079
And my thing the readiness there.
If you don't have an understanding what

471
00:32:32.200 --> 00:32:36.240
your data is, what it means, and also the power that it's able

472
00:32:36.279 --> 00:32:40.359
to be leveraging your way to derive
meaning and derive insights in the data,

473
00:32:40.440 --> 00:32:44.839
you have no possibility of moving into
AI. And kind of that's a core

474
00:32:44.920 --> 00:32:47.960
metric to success. Yeah, and
this is something that I've been on a

475
00:32:49.000 --> 00:32:51.880
soapbox talking about it. We have
a couple good questions from our audience.

476
00:32:52.359 --> 00:32:55.839
For example, one gentleman is asking, do analysts expect that data analysis tools

477
00:32:55.920 --> 00:33:02.319
like Gemini Advanced and Chat GPT plus
one accelerate data use? Well, David

478
00:33:02.359 --> 00:33:06.680
is making a good point there.
You need to make sure your data house

479
00:33:06.799 --> 00:33:08.759
is in order and you can actually
use these tools. I mean, I've

480
00:33:09.039 --> 00:33:15.680
already seen amazing use cases where you
give a chat GPT or a Gemini a

481
00:33:15.720 --> 00:33:20.279
significant amount of data and ask it
to summarize. The summarized function is fantastic,

482
00:33:20.319 --> 00:33:22.319
by the way, it's really interesting
stuff. But you can ask it

483
00:33:22.440 --> 00:33:27.279
questions. And I think that we're
going to head down a road where this

484
00:33:27.440 --> 00:33:32.279
nexus of GENI and traditional structured analysis
will be very, very compelling and very

485
00:33:32.319 --> 00:33:37.200
powerful. Not quite there yet,
but to David's point, you've got to

486
00:33:37.240 --> 00:33:38.400
get your house in order. And
maybe we've got a minute left in here

487
00:33:38.400 --> 00:33:42.160
in this segment, Aaron Wilson,
I'll throw it over to you. The

488
00:33:42.240 --> 00:33:46.279
key ingredient there to fuse these worlds
is data governance, right, and data

489
00:33:46.359 --> 00:33:51.599
quality and understanding your data before you
go point in years, before you go

490
00:33:51.640 --> 00:33:54.039
training a model, for example,
on all this stuff that you found.

491
00:33:54.319 --> 00:33:57.400
First, you want to sort through
that stuff and make sure it's good,

492
00:33:57.400 --> 00:34:00.400
real good. Thirty seconds go ahead, erin no question there. I mean,

493
00:34:00.759 --> 00:34:05.359
you know, all kinds of things
can happen when you put AI on

494
00:34:06.079 --> 00:34:09.079
data that isn't ready for it.
I mean even something I think a common

495
00:34:09.239 --> 00:34:14.480
mistake that people do is is there's
old data floating around, right, you

496
00:34:14.519 --> 00:34:17.239
know, there's data that's that may
have been relevant at one time but no

497
00:34:17.280 --> 00:34:22.519
longer is. And if you train
a model on, you know, using

498
00:34:22.719 --> 00:34:28.400
calculations and formulas that have been discarded, it's not going to be very helpful

499
00:34:28.400 --> 00:34:30.079
to you. Yeah, that's exactly
right. Well, folks, don't touch

500
00:34:30.159 --> 00:34:40.760
up that. We'll be right back. You're listening to Inside Analysis. Welcome

501
00:34:40.800 --> 00:34:49.239
back to Inside Analysis. Here's your
host, Eric Tabanac. All right,

502
00:34:49.280 --> 00:34:52.960
folks, back here on Inside Analysis, talking to several experts. A wonderful

503
00:34:52.000 --> 00:34:57.039
show today, Aaron Wilson of Athena
Solutions, Jim Smith of Click and David

504
00:34:57.119 --> 00:35:00.000
Lentkam our industry analyst of Today and
Aaron, you a question for Jim,

505
00:35:00.079 --> 00:35:05.679
so take it away. Yeah.
Well, one of the things we haven't

506
00:35:05.679 --> 00:35:08.199
talked about in terms of, you
know, the advantages of the associative engine,

507
00:35:08.800 --> 00:35:15.000
but I know It's something that Click
has well has pointed out in some

508
00:35:15.079 --> 00:35:17.679
of their videos that I've seen,
for example, is the advantages that you

509
00:35:17.719 --> 00:35:22.280
have when you're trying to bring in
different data sources, maybe a data source

510
00:35:22.320 --> 00:35:25.079
that comes from outside of you know, maybe you have a data warehouse,

511
00:35:25.159 --> 00:35:30.239
we have certain tables and so forth, and then somebody says, well,

512
00:35:30.280 --> 00:35:32.599
you know, what would happen if
we connected in this whole other you know

513
00:35:34.199 --> 00:35:37.480
column, or this whole other data
set and look at those relationships. The

514
00:35:37.480 --> 00:35:40.199
associative engine can really help you with
that, can it? It absolutely can.

515
00:35:40.280 --> 00:35:45.000
So one of the things about the
associative Engine and what it's been able

516
00:35:45.000 --> 00:35:49.079
to do since it was rolled out
at Click back in the nineties is there

517
00:35:49.119 --> 00:35:52.880
is a component of kind of data
integration built into Click sense. Now again,

518
00:35:53.800 --> 00:35:58.039
we have a data integration offering that
says, hey, if you've got

519
00:35:58.280 --> 00:36:00.480
one hundred of data sources and you
want to get your information into a data

520
00:36:00.519 --> 00:36:06.320
warehouse to be used by a business
intelligence tool or machine learning tool, can

521
00:36:06.360 --> 00:36:10.199
absolutely do that. But what's great
about Click is if you're a smaller organization

522
00:36:10.880 --> 00:36:15.440
and you just need to get lots
of sources put together, you can go

523
00:36:15.519 --> 00:36:20.960
into Click and do it directly into
that in that solution. So you could

524
00:36:21.000 --> 00:36:23.280
say, hey, I've got a
table in Oracle on prem, I've got

525
00:36:23.280 --> 00:36:28.360
a Snowflake table, and I've got
someone's personal Excel spreadsheet, and I need

526
00:36:28.400 --> 00:36:31.960
to bring those things together. Not
only can you make those connections within click

527
00:36:32.519 --> 00:36:38.320
Click's got kind of this capability that
we'll look at kind of column definitions,

528
00:36:38.519 --> 00:36:43.280
profile the data and say, yeah, even though this is Excel and this

529
00:36:43.320 --> 00:36:46.239
is Oracle, we can put those
together based on what we see in the

530
00:36:46.320 --> 00:36:49.920
data, and we'll do that for
you. And then you know, you've

531
00:36:49.920 --> 00:36:53.920
got users who are able to put
together their own reports. Even though it

532
00:36:54.000 --> 00:37:00.920
hasn't built out the you know,
the defined data warehouse, they're still allowing

533
00:37:00.000 --> 00:37:05.119
users to kind of do that self
service against lots of different sources. Does

534
00:37:05.159 --> 00:37:08.000
that answer your question? Aron?
That pretty much hits it right on the

535
00:37:08.000 --> 00:37:12.079
head. Yes, that's where I
was going. Cool. Well, and

536
00:37:12.119 --> 00:37:15.039
this is such a good point because
and I'll throw this over to David to

537
00:37:15.079 --> 00:37:19.400
comment on when you're people still build
data warehouses, right, It's not going

538
00:37:19.440 --> 00:37:22.079
away. They're going to be data
warehouses. I'm pretty sure forever and ever.

539
00:37:22.280 --> 00:37:25.760
Llms are not going to supplant the
data warehouse against the different tools text

540
00:37:25.840 --> 00:37:31.599
generation. It's great for summarizing things. It's great for getting a consensus about

541
00:37:31.639 --> 00:37:35.679
what has been published on a topic. That's really what these lllms are,

542
00:37:35.719 --> 00:37:39.840
their text generative consensus engines, and
it's good for a big part of the

543
00:37:39.880 --> 00:37:44.960
process. But the understanding the data
and the relationships it's a separate deal and

544
00:37:45.000 --> 00:37:46.840
it's very very important. And David, when they were talking about this,

545
00:37:46.880 --> 00:37:52.239
I'm thinking to myself, that is
really valuable to in the process of deciding

546
00:37:52.280 --> 00:37:57.400
what's going to go into your warehouse. Explore that data with click, explore

547
00:37:57.400 --> 00:38:00.519
the relationships between things, because that's
going to help you figure out what is

548
00:38:00.599 --> 00:38:06.760
the optimal set of data that we're
going to use to put into this warehouse

549
00:38:06.800 --> 00:38:08.920
to fulfill business needs. It's a
very important part of the process. What

550
00:38:08.960 --> 00:38:13.840
do you think data It's everything,
And I think that ultimately that's what's missing

551
00:38:13.880 --> 00:38:16.960
in terms of the data analytics as
to the data, what the data means

552
00:38:17.000 --> 00:38:20.719
and what it can be used for, and the kinds of insights that we're

553
00:38:20.760 --> 00:38:24.480
looking to get out of the information. So your ability to understand the usage

554
00:38:24.519 --> 00:38:30.159
of data and your ability to find
value within the data before you start building

555
00:38:30.199 --> 00:38:32.639
these things, before you build these
analytic tools and data warehouses and things like

556
00:38:32.679 --> 00:38:37.320
that is something that many enterprises are
missing. I think ninety percent of the

557
00:38:37.440 --> 00:38:43.719
enterprises out there are grossly under utilizing
their data and the other ten percent are

558
00:38:43.760 --> 00:38:47.920
almost are minorly under utilizing their data. So everybody's under utilizing their data.

559
00:38:49.480 --> 00:38:52.320
And ultimately, the companies that are
able to utilize their data for strategic purposes

560
00:38:52.360 --> 00:38:57.480
are able to gain insights, They're
going to provide innovative differentiators for them to

561
00:38:57.559 --> 00:39:00.760
accelerate themselves in the marketplace. I
truly think in ten years we're going to

562
00:39:00.800 --> 00:39:05.320
see lots of businesses that have gone
under and lots of businesses that have succeeded

563
00:39:05.440 --> 00:39:08.280
into in a meteoric success, and
look at the differentiators there. How do

564
00:39:08.360 --> 00:39:12.760
they do it. They're able to
labage data the age labor data in the

565
00:39:12.800 --> 00:39:17.000
context of analytics and the context of
AI, and something that provides them of

566
00:39:17.039 --> 00:39:21.920
the core force multiplier for their ability
to take the business. So it's everything.

567
00:39:22.360 --> 00:39:23.559
Yeah, Jim, go ahead.
Yeah. The one thing I wanted

568
00:39:23.559 --> 00:39:27.440
to add the David's comment, and
we see it a lot at click,

569
00:39:27.559 --> 00:39:30.840
is you know, there are some
organizations we go out to and they say,

570
00:39:30.840 --> 00:39:32.320
okay, yeah, we don't want
to look at a data analytics tool

571
00:39:32.400 --> 00:39:37.719
yet we've got this twelve to eighteen
month project to build the data warehouse,

572
00:39:37.760 --> 00:39:42.000
and then we'll come back and look
at a business intelligence tool. And I

573
00:39:42.000 --> 00:39:45.559
think what we tell customers, I
think it's similar to what David said,

574
00:39:45.599 --> 00:39:49.719
is use a tool upfront that is
easy to get started with, so that

575
00:39:49.800 --> 00:39:54.440
you know what data your users actually
want to consume in your analytics environment.

576
00:39:55.000 --> 00:39:59.119
And if you can do that quickly
and easily, you can go back and

577
00:39:59.159 --> 00:40:02.559
make that some of the requirements for
your data warehouse projects. So in a

578
00:40:02.599 --> 00:40:06.639
sense, it's kind of and I
don't mean to use an old term there,

579
00:40:06.639 --> 00:40:09.360
but rapid application development. You know, start with kind of some of

580
00:40:09.360 --> 00:40:14.519
the reports, see if they're successful, work that into your data warehouse projects,

581
00:40:14.519 --> 00:40:17.280
so that when your data warehouse project's
done, you're actually giving information that

582
00:40:17.400 --> 00:40:22.199
users want to see as opposed to, you know, try to do that

583
00:40:22.239 --> 00:40:24.119
all up front in twelve months and
then realize you failed and have to go

584
00:40:24.159 --> 00:40:29.000
back and change that data warehouse.
Right, that's exactly right. And just

585
00:40:29.000 --> 00:40:30.280
to put some meat in the bones
here, I'll throw this one over to

586
00:40:30.280 --> 00:40:35.119
Aaron. We've got about four minutes
left in this segment. If you want

587
00:40:35.119 --> 00:40:39.280
to know where the value accrues,
where is generated, Look at companies that

588
00:40:39.360 --> 00:40:45.079
come out with new promotions like three
will sell three for a discount of thirty

589
00:40:45.119 --> 00:40:47.719
percent, for example, or buy
one now, get one free. Most

590
00:40:47.760 --> 00:40:52.280
of those deals are data driven.
Someone has crunched the numbers and figured out,

591
00:40:52.280 --> 00:40:55.719
aha, if we sell this many
at this price point, we'll get

592
00:40:55.760 --> 00:40:59.400
this many new customers. And then
they test that stuff. I mean they

593
00:40:59.400 --> 00:41:01.280
make sure that working. So you
get your idea, you put into the

594
00:41:01.320 --> 00:41:05.440
market, then you test it,
and I mean those cycle times are coming

595
00:41:05.480 --> 00:41:08.840
down and down and down. To
Jim's point about wraplet application development, new

596
00:41:08.920 --> 00:41:13.880
data products are what people are creating. Think insurance companies. I mean,

597
00:41:13.880 --> 00:41:17.480
you've done a lot of work in
financial services understanding that you can test these

598
00:41:17.519 --> 00:41:21.679
theories out, see how they work, then double down, triple down,

599
00:41:21.760 --> 00:41:24.519
offer it in more regions for example. That's where you actually see the innovations

600
00:41:24.519 --> 00:41:28.920
occurring because of the data, right, Aaron, go ahead, Yeah,

601
00:41:28.960 --> 00:41:32.000
I definitely think so. And I
mean that gets to the point of exploratory

602
00:41:32.000 --> 00:41:37.840
analysis, right, because you know
you've you've mentioned really sometimes you're testing a

603
00:41:37.880 --> 00:41:45.239
specific you know, idea specific hypothesis. You've mentioned a couple of use cases

604
00:41:45.239 --> 00:41:49.440
there. But you also have situations
where you don't know ahead of time,

605
00:41:49.599 --> 00:41:52.800
you know, I mean the in
the promotions case, right, you may

606
00:41:52.840 --> 00:41:55.280
have a whole lot of factor that
you could throw into the promotion that could

607
00:41:55.320 --> 00:42:00.199
make a big difference in terms of, you know, sales. But it

608
00:42:00.239 --> 00:42:01.199
could be sitting in front of you. But it could be sitting in the

609
00:42:01.280 --> 00:42:05.960
data. And that's the thing where
the associated engine might help you to bring

610
00:42:06.000 --> 00:42:08.480
in that you know, that data
point that comes you know, you know

611
00:42:08.559 --> 00:42:12.719
you weren't looking for it, but
here it is. Yeah, And it's

612
00:42:12.760 --> 00:42:15.760
all part of this process. It's
part of discovery. Never stops. Discovery

613
00:42:15.800 --> 00:42:21.360
is ongoing because market conditions change,
you get new products, new services,

614
00:42:21.880 --> 00:42:24.480
You're having to adjust pricing, I
mean, pricing is one of these things

615
00:42:24.800 --> 00:42:30.760
that is really under scrutiny right now. I've been paying close attention to inflationary

616
00:42:30.199 --> 00:42:34.800
forces like most Americans are. And
I saw staff the other day that just

617
00:42:34.920 --> 00:42:38.039
jumped off the screen at me,
which is that juices like orange juice and

618
00:42:38.119 --> 00:42:44.000
drinks are up forty percent forty percent. And what that tells me is people

619
00:42:44.079 --> 00:42:45.599
running grocery stores have figured out,
hey, we can inch that stuff up

620
00:42:45.639 --> 00:42:49.639
because everyone wants their orange juice,
right. It's also why they put it

621
00:42:49.679 --> 00:42:51.920
way in the back of the store, so you got to go through everything

622
00:42:51.960 --> 00:42:57.039
to get to the orange juice.
But Jim, what do you think The

623
00:42:57.039 --> 00:43:00.559
one thing that you made me think
about we're talking about that Eric, was

624
00:43:00.000 --> 00:43:04.800
just the fact that, you know, when you're using a data analytics tool,

625
00:43:05.480 --> 00:43:08.519
it's great to actually see historical performance. You know, you can kind

626
00:43:08.559 --> 00:43:13.280
of see all the visuals to say
how we've done. What we're seeing a

627
00:43:13.280 --> 00:43:19.280
lot of people are moving towards is
basically the machine learning capabilities within our product

628
00:43:19.320 --> 00:43:22.360
that says, hey, not only
do we need to know what happened in

629
00:43:22.440 --> 00:43:27.280
the past, we need to know
what values are the biggest influencers. And

630
00:43:27.320 --> 00:43:30.320
it'd be nice to be able to
take those influencers and kind of propagate out

631
00:43:30.320 --> 00:43:32.360
what's going to happen in the future. And I don't want to just call

632
00:43:32.400 --> 00:43:37.480
it forecasting, because forecasting is hey, I've got a line I'm going to

633
00:43:37.519 --> 00:43:40.159
draw through the data points. Where
it becomes really helpful is when you can

634
00:43:40.239 --> 00:43:45.760
kind of look at that historical information
and then say these are the influencers,

635
00:43:45.920 --> 00:43:50.000
and let me change those influencers to
see what might happen in the future.

636
00:43:50.000 --> 00:43:52.480
And I think with the inflation example
you had, that's what we're seeing a

637
00:43:52.519 --> 00:43:57.559
lot of people wanting to do with
the data analytics tool. Yeah, that's

638
00:43:57.639 --> 00:44:01.159
right, to understand what are the
vectors impacting our business right now. And

639
00:44:01.199 --> 00:44:07.039
again, the fluidity of that experience
is just crucial because as soon as it

640
00:44:07.119 --> 00:44:09.400
stops, as soon as you have
to go to it to get some other

641
00:44:09.760 --> 00:44:13.639
query or to get access to some
data set or whatever the case may be,

642
00:44:14.639 --> 00:44:19.159
that analytical process is dead. Like
it's just over. Maybe you wrote

643
00:44:19.199 --> 00:44:21.719
it down, maybe you'll think about
it next week when you come back in

644
00:44:21.719 --> 00:44:24.639
the office. But the point is
that you want it to always enable that

645
00:44:24.760 --> 00:44:30.559
fluidity of interaction with data and understanding
of the data, and that's going to

646
00:44:30.599 --> 00:44:32.239
get you somewhere. Well, folks, we got one more segment coming up,

647
00:44:32.280 --> 00:44:36.599
and we have some fantastic questions from
the audience today. I'll give our

648
00:44:36.639 --> 00:44:38.960
guests a bit of a teaser so
they're ready. Some folks are asking about

649
00:44:39.159 --> 00:44:44.760
Apache, Iceberg and Delta Lake and
Hoodie and does Click support all these things?

650
00:44:44.760 --> 00:44:46.559
I mean, this is this whole
movement now, a patche Iceberg in

651
00:44:46.599 --> 00:44:52.639
particular, which really took this the
market by storm and generated tremendous traction.

652
00:44:52.760 --> 00:44:55.400
Everyone agreed upon it as a standard. And then Data Bricks announced that they're

653
00:44:55.440 --> 00:44:59.719
buying Tabular, the company that sits
on top of it, during the Snowflake

654
00:44:59.719 --> 00:45:02.000
con So yeah, I don't think
that was a coincidence. We'll be right

655
00:45:02.039 --> 00:45:08.360
back. You're listening to Inside Analysis, all right, folks, Tom for

656
00:45:08.400 --> 00:45:12.719
the podcast bonus segment here and a
fantastic inside analysis. We've been talking to

657
00:45:12.719 --> 00:45:15.679
Aaron Wilson of Athena Solutions, Jim
Smith of Click, and David lnthiccom Ore

658
00:45:16.119 --> 00:45:20.800
Entry analyst of the day. We
had some fantastic questions today, folks.

659
00:45:20.800 --> 00:45:23.000
We'll be sure to pass these along
to our presenters if we did not get

660
00:45:23.039 --> 00:45:25.960
around to your question. But there
are questions about AI, and there are

661
00:45:27.000 --> 00:45:32.800
questions also about these new table formats
like Iceberg, Apache Iceberg, who do

662
00:45:32.800 --> 00:45:37.000
he is another one? And Delta
Lake that's the data break specific one.

663
00:45:37.000 --> 00:45:40.039
And then of course Data Bricks bought
Tabular, which sits on top of Iceberg.

664
00:45:40.679 --> 00:45:44.239
So first I'll throw it over to
you, Jim, How does that

665
00:45:44.280 --> 00:45:47.239
fit into the clickworld. Yeah,
I mean a click we've got. You

666
00:45:47.280 --> 00:45:52.400
know, the important thing about an
analytics tool or a data integration tool,

667
00:45:52.440 --> 00:45:55.159
and Click has both of those is
being able to connect to any source and

668
00:45:55.199 --> 00:46:00.400
pretty much deal with any target.
And because of that, there are hundreds

669
00:46:00.400 --> 00:46:02.679
of data sources that we can support, both on the analytics side and the

670
00:46:02.679 --> 00:46:08.320
integration side. I don't know that
I can address those in particular, but

671
00:46:08.519 --> 00:46:14.400
I can tell you that we work
on adding data sources on a monthly basis.

672
00:46:14.440 --> 00:46:17.039
So we're I believe, rolling out
fifteen more data connectors on our data

673
00:46:17.039 --> 00:46:22.400
integration than data analytics side within the
month. So we are really good partners

674
00:46:22.480 --> 00:46:29.960
with Hyperscaler databases, Snowflake data bricks. So as those vendors start providing different

675
00:46:29.960 --> 00:46:34.039
ways to get access to information,
I think you'll find Click following along with

676
00:46:34.079 --> 00:46:37.440
that support. Yeah, and David, I'll bring you in. This open

677
00:46:37.480 --> 00:46:43.000
table format stuff is very interesting because
what we were talking about earlier context and

678
00:46:43.119 --> 00:46:46.480
being able to leverage new data sources, new types of data. For example,

679
00:46:46.519 --> 00:46:50.880
I mean, one of the challenges
is that from an analytical perspective,

680
00:46:51.800 --> 00:46:55.920
SQL queries it's a structured query language. It doesn't deal so well with unstructured

681
00:46:57.000 --> 00:47:00.960
data or some other unwieldy sources like
j ON in different formats like that.

682
00:47:01.440 --> 00:47:05.960
But now in these open table formats, you're going to be able to bring

683
00:47:06.000 --> 00:47:09.119
in lots of external data that's going
to improve context and really help kind of

684
00:47:09.119 --> 00:47:10.920
see the big picture. Of course, you have to know how to do

685
00:47:10.960 --> 00:47:15.119
it. But what are your thoughts
on all that data. It's extremely valuable,

686
00:47:15.159 --> 00:47:17.960
I mean data unto itself without the
context of where it exists and what

687
00:47:19.000 --> 00:47:21.840
it means to other data. I
mean, like we went in to the

688
00:47:21.840 --> 00:47:25.719
previous example, in other words,
the data of erroneous the errors that occur

689
00:47:25.800 --> 00:47:29.840
in production, and how it means
in the context of other things, other

690
00:47:29.960 --> 00:47:32.960
environmental factors that have to come into
play. And so your ability to find

691
00:47:34.000 --> 00:47:38.039
problems, your ability to understand what
the data actually means to the business other

692
00:47:38.119 --> 00:47:43.039
than the data as it exists fundamentally
into itself. Everybody likes to do the

693
00:47:43.079 --> 00:47:46.039
analysis to what sales data means to
sales data, that's meaningless to me.

694
00:47:46.440 --> 00:47:50.880
What does sales data mean to demographics? What does sales data mean to the

695
00:47:50.960 --> 00:47:54.280
environment, What does sales data mean
into social media means and are we able

696
00:47:54.280 --> 00:47:59.280
to make the correlations which allows us
to adjust the business to actually get the

697
00:47:59.320 --> 00:48:01.360
growth that we're looking looking for.
And that's core to what businesses need data

698
00:48:01.400 --> 00:48:05.519
to do. Yeah, that's brilliant, Aaron. I'll throw it over to

699
00:48:05.559 --> 00:48:08.239
you for some final thoughts. I
mean, it's getting very exciting in the

700
00:48:08.280 --> 00:48:12.719
data world. It's a lot less
expensive to play around with the stuff that

701
00:48:12.760 --> 00:48:15.760
it used to be. There are
many more data sources, it's a lot

702
00:48:15.760 --> 00:48:17.960
more fluid. I mean, really, it's it's kind of a golden age

703
00:48:17.960 --> 00:48:22.360
for data. What do you think
erin it is? I mean, we're

704
00:48:22.679 --> 00:48:28.480
it's an interesting time for so many
reasons. I mean the question about open

705
00:48:28.519 --> 00:48:32.400
table format, I mean it shows
you that, you know, we're kind

706
00:48:32.440 --> 00:48:36.360
of looking at integration challenges all up
and down the spectrum. I mean,

707
00:48:36.360 --> 00:48:38.440
there's still a lot of legacy on
TREMD that you know, as an implementing

708
00:48:38.639 --> 00:48:40.760
station analyst, you have to be
aware of that, but you also have

709
00:48:40.800 --> 00:48:45.320
to be aware of these new formats
as well. But it is a very

710
00:48:45.360 --> 00:48:50.400
exciting time for both AI and analysis. And I think what we've kind of

711
00:48:50.400 --> 00:48:52.920
shown here in this show is an
analysis you know, having a human being

712
00:48:53.000 --> 00:48:59.719
and actually get curious about the data
and go down that exploitatory path. You're

713
00:48:59.719 --> 00:49:02.159
not going to be able. You're
not going to do effective AI unless you've

714
00:49:02.159 --> 00:49:07.119
got people doing that. Yeah,
that's a really good point. And I

715
00:49:07.159 --> 00:49:09.039
will use the last minute or so
here to tease our next show and our

716
00:49:09.159 --> 00:49:13.960
past show is also online. You
can hop online to Insideanalysis dot com to

717
00:49:14.000 --> 00:49:16.440
see past events that we've done there, radio shows, you can watch the

718
00:49:16.480 --> 00:49:20.360
podcast, you can listen to it. We have several new stations that have

719
00:49:20.360 --> 00:49:23.599
picked us up recently in Saint Louis, in Sarasota and Tampa and in Iowa.

720
00:49:23.639 --> 00:49:25.840
I got a bunch of new stations
out there carrying us, so big

721
00:49:27.079 --> 00:49:28.920
shout out to our friends out there. And if you want to be in

722
00:49:28.920 --> 00:49:31.880
the show, send me an email
info at inside Analysis dot com. But

723
00:49:31.920 --> 00:49:36.079
our first show is about data fabric, this one is about visualization and click.

724
00:49:36.360 --> 00:49:38.159
Next show is going to be about
AI. And to points made by

725
00:49:38.199 --> 00:49:42.440
each one of these guests, if
you want to leverage the power of AI,

726
00:49:42.960 --> 00:49:45.079
you need to make sure that your
data house is in order and you

727
00:49:45.119 --> 00:49:49.360
want to and click is great for
this just for the reasons we talked about

728
00:49:49.400 --> 00:49:53.719
for knowing the associations between things,
for being able to assess which data fits

729
00:49:53.760 --> 00:49:59.239
with which other data set, understanding
covariance, which is the key to analytics,

730
00:49:59.239 --> 00:50:02.480
and to understand any relationships between things. These are all important, they're

731
00:50:02.480 --> 00:50:06.519
all part and parcel. But you
do want to take your time right.

732
00:50:06.559 --> 00:50:09.880
Don't rush into this stuff. Definitely
don't rush into using AI or jenai.

733
00:50:10.239 --> 00:50:15.000
And frankly, one of the best
bits of advice I've heard yet about JENI

734
00:50:15.199 --> 00:50:20.639
is use it internally first. Be
careful about using it externally. There are

735
00:50:20.719 --> 00:50:23.480
some great use cases around customer support, for example, but just wait,

736
00:50:23.800 --> 00:50:28.280
be careful, make sure that it's
really, really good. You'll hear all

737
00:50:28.320 --> 00:50:34.559
about RAG models, retrieval augmented generation
very important. I think that most enterprise

738
00:50:34.719 --> 00:50:37.079
data companies are going to have to
figure out where they fit in the RAG

739
00:50:37.119 --> 00:50:40.599
model. It's going to be huge. I mean, your RAG model is

740
00:50:40.599 --> 00:50:45.679
almost like your operating system for GENAI. It's very important to understand what your

741
00:50:45.719 --> 00:50:49.639
anchors of truth are going to be, what your embeddings will be, how

742
00:50:49.679 --> 00:50:52.199
you use these tools. But as
long as it's for decision support, as

743
00:50:52.239 --> 00:50:58.039
long as it's inward focused and inward
in nature. You're going to be pretty

744
00:50:58.079 --> 00:51:00.920
safe because and the last thing I
mentioned. A great friend of mine pointed

745
00:51:00.960 --> 00:51:05.119
this out the other day. Michael
Barras will be on a show sometime soon

746
00:51:05.159 --> 00:51:07.519
around data governance through data catalogs.
He said, Remember, you don't have

747
00:51:07.599 --> 00:51:10.639
to be one hundred percent accurate.
The systems we have today are not one

748
00:51:10.719 --> 00:51:14.599
hundred percent accurate, but you can
find the mistakes. Now you want to

749
00:51:14.599 --> 00:51:17.119
be at least eighty to ninety percent
accurate, but you can find the mistakes

750
00:51:17.119 --> 00:51:21.039
and then go back and address that. And I've got to tell you,

751
00:51:21.079 --> 00:51:23.639
man, addressing root causes is very, very important. It's going to do

752
00:51:23.679 --> 00:51:28.559
a lot of benefit to your organization
if you understand where the data quality problems

753
00:51:28.559 --> 00:51:30.320
are, how did they get there, how did they get there in the

754
00:51:30.360 --> 00:51:35.159
first place, what's the data onboarding
process? All this stuff can be addressed

755
00:51:35.400 --> 00:51:39.039
in data governance programs, and that's
really important before you use AI. With

756
00:51:39.119 --> 00:51:42.119
that, we're going to bid you
farewell, folks, Thanks so much for

757
00:51:42.159 --> 00:51:45.320
your time and attention. Send me
an email info at inside analysis dot com.

758
00:51:45.320 --> 00:51:46.920
We'll talk to you next time.
By bye. Wishing for a little

759
00:51:46.960 --> 00:51:54.559
bit more information, you'll get it. All here on KCAA Radio. Redland's

760
00:51:54.639 --> 00:52:00.599
Ranch Market is a unique, full
service international or grocery store that specializes authentic

761
00:52:00.599 --> 00:52:05.159
food items from Mexico, India,
and from many Mediterranean and Asian countries,

762
00:52:05.280 --> 00:52:08.360
including popular items from the US.
They offer fresh baked items from their in

763
00:52:08.480 --> 00:52:14.880
house bakery, housemade tortillas from their
Tortillirea, a delicious array of prepared Mexican

764
00:52:14.920 --> 00:52:17.800
foods, a terrific fresh food and
juice bar, and a large selection of

765
00:52:17.840 --> 00:52:22.440
meats, seafoods and deli sandwiches,
salads and hellal meats. Their produce department

766
00:52:22.519 --> 00:52:27.840
is stock full with fresh, local
and hard to find international fruits and vegetables

767
00:52:27.920 --> 00:52:31.239
that you cannot find anywhere else.
Don't forget to step into the massive beer

768
00:52:31.360 --> 00:52:37.599
cave and experience the largest selection of
domestic, artisan and imported beers in the

769
00:52:37.679 --> 00:52:40.679
IE. They can also cater your
next event with one of the delicious takeout

770
00:52:40.760 --> 00:52:45.960
catering trades of food. Visit them
at Redlands ranch Market dot com. That's

771
00:52:45.119 --> 00:52:51.519
Redlands ranch Market dot com. Redlands
Ranch Market a unique and fun shopping destination

772
00:52:52.199 --> 00:52:57.840
to Heblotea Club's original pure powdy Arco
super Tea helps build red corpuscles in the

773
00:52:57.840 --> 00:53:01.280
blood which carry oxygen into our organ
and cells. Our organs them cells need

774
00:53:01.320 --> 00:53:07.199
oxygen to regenerate themselves. The immune
system needs oxygen to develop, and cancer

775
00:53:07.360 --> 00:53:10.280
dies in oxygen. So the T
is great for healthy people because it helps

776
00:53:10.280 --> 00:53:15.079
build the immune system, and it
can truly be miraculous for someone fighting a

777
00:53:15.119 --> 00:53:19.840
potentially life threatening disease due to an
infection, diabetes, or cancer. The

778
00:53:19.880 --> 00:53:22.880
T is also organic and naturally caffeine
free. A one pound package of T

779
00:53:23.079 --> 00:53:27.639
is forty nine ninety five, which
includes shipping. To order, please visit

780
00:53:27.679 --> 00:53:30.760
to Hebot club dot com. To
hebo is spelled T like tom, a

781
00:53:31.239 --> 00:53:36.320
h ee b like boy. Oh, then continue with the word T and

782
00:53:36.400 --> 00:53:39.760
then the word club. The complete
website is to Hebot club dot com or

783
00:53:39.800 --> 00:53:45.000
call us at eight one eight sixty
one zero eight zero eight eight Monday through

784
00:53:45.039 --> 00:53:49.119
Saturday nine am to five pm,
California time. That's eight one eight sixty

785
00:53:49.159 --> 00:53:53.880
one zero eight zero eight eight to
Ebot club dot com. With sixty years

786
00:53:53.920 --> 00:54:00.320
of fascinating facts, This is the
man from Yesterday and back in time.

787
00:54:00.360 --> 00:54:06.199
We go to this time in nineteen
seventy seven on NBC's Midnight Special this week.

788
00:54:06.280 --> 00:54:08.840
Here in nineteen seventy seven, it's
Casey and the Sunshine Band as they

789
00:54:08.880 --> 00:54:22.320
host with Cat Stevens and the Bay
City Roller. And from this time in

790
00:54:22.400 --> 00:54:29.559
nineteen sixty five, Nancy Sinatra and
Tommy Sands divorce. This happens just as

791
00:54:29.719 --> 00:54:35.760
Nancy Sinatra's recording career takes off.
These boots on a fuck and that's just

792
00:54:35.920 --> 00:54:46.360
what they'll do. One of these
Daisy Bootstock gonna walk all over. And

793
00:54:46.440 --> 00:54:52.000
from this time in nineteen ninety nine, it's the second Coming of Woodstock.

794
00:54:52.039 --> 00:54:55.039
Performing at Woodstock ninety nine. On
the second day, the Dave Matthews Band,

795
00:54:55.119 --> 00:55:07.280
Counting Crows, Alanis Morissett and Metallica
lost. You soon told you all,

796
00:55:07.760 --> 00:55:19.119
You'll come crash with more at man
from yesterday dot Com. Hey,

797
00:55:19.159 --> 00:55:22.079
Southern California, you know what's going
on. It's my new hitch show,

798
00:55:22.159 --> 00:55:27.119
The Scene with Dorian and I'm your
host, Dorian Taylor. Join me every

799
00:55:27.159 --> 00:55:30.840
week as I chat with my celebrity
friends from TV, movies, music,

800
00:55:30.920 --> 00:55:35.159
theater, sports, and everything in
between. We explore the lives and careers

801
00:55:35.159 --> 00:55:38.360
of some of the entertainment industries the
biggest names, with some fun surprises too.

802
00:55:38.800 --> 00:55:44.199
Tune into the scene with Dorian Tuesdays, it's seven am on KCAA Radio

803
00:55:44.400 --> 00:55:49.239
ten fifty am. I always hear
from our clients who hired another firm that

804
00:55:49.239 --> 00:55:52.119
they wish they'd hire DNA Financial First. Don't have regrets about your IRS tax

805
00:55:52.159 --> 00:55:54.920
case. Just hire the best in
the first place. One owed one hundred

806
00:55:54.960 --> 00:55:59.440
fifty thousand to the IRS and it's
spent thousands on another firm. We stopped

807
00:55:59.480 --> 00:56:01.800
the lovey's, negotiated a payment plan
and had their penalties forgiven. And while

808
00:56:01.840 --> 00:56:06.440
every case is different, we guarantee
that we'll find your perfect resolution and get

809
00:56:06.440 --> 00:56:08.360
it done right. For a free
consultation, call us at eight six six

810
00:56:08.400 --> 00:56:13.079
two zero one zero one five six. That's eight sixty six two zero one

811
00:56:13.199 --> 00:56:15.840
zero one five six. Then you
can say DNA did right buy me?

812
00:56:16.480 --> 00:56:22.639
TV journalist Jane Veles Mitchell here excited
to tell you about my show Unchained TV.

813
00:56:22.000 --> 00:56:25.679
Animals people Planet. Most of us
say we love animals. When we

814
00:56:25.800 --> 00:56:30.920
truly respect them, we benefit with
a happier outlook, better health, even

815
00:56:31.039 --> 00:56:37.760
nicer weather. I'll explain when you
join me Mondays at one pm on KCAA

816
00:56:37.920 --> 00:56:40.719
ten fifty am one oh six point
five FM. The stations that leave no

817
00:56:40.840 --> 00:56:47.920
listener behind and check out UNCHAINEDTV dot
com. KCAA Radio has openings for one

818
00:56:47.960 --> 00:56:52.159
hour talk shows. If you want
to host a radio show, now is

819
00:56:52.239 --> 00:56:55.679
the time. Make KCAA your flagship
station. Our rates are affordable and our

820
00:56:55.719 --> 00:57:00.519
services are second to none. We
broadcast to a population of five million people

821
00:57:00.639 --> 00:57:05.800
plus. We stream and podcast on
all major online audio and video systems.

822
00:57:05.880 --> 00:57:10.000
If you've been thinking about broadcasting a
weekly radio program on real radio plus the

823
00:57:10.079 --> 00:57:15.239
internet, contact our CEO at two
eight one five nine nine ninety eight hundred.

824
00:57:15.360 --> 00:57:19.960
Two eight one five nine nine ninety
eight hundred. You can skype your

825
00:57:19.960 --> 00:57:23.039
show from your home to our Redlands, California studio, where our live producers

826
00:57:23.039 --> 00:57:28.920
and engineers are ready to work with
you personally. A radio program on KCAA

827
00:57:29.119 --> 00:57:32.960
is the perfect work from home avocation
in these stressful times. Just time KCAA

828
00:57:34.119 --> 00:57:37.920
radio dot Com into your browser to
learn more about hosting a show on the

829
00:57:37.039 --> 00:57:40.920
best station in the nation, or
call our CEO for details. Two eight

830
00:57:40.960 --> 00:57:46.199
one five nine nine ninety eight hundred. Listening to KCAA lo Melinda at one

831
00:57:46.199 --> 00:57:53.760
O six point five FM K two
ninety three cf Brito Valley NBC News Radio.

832
00:57:53.840 --> 00:57:58.320
I'm Chris Gragio. President Biden is
dropping out of the twenty twenty four

833
00:57:58.360 --> 00:58:02.199
presidential race after inten pressure from his
own party. Democratic leaders for weeks have

834
00:58:02.280 --> 00:58:07.880
expressed concerns about the eighty one year
old's mental fitness and his path to victory

835
00:58:07.920 --> 00:58:12.519
over Donald Trump. Democratic National Committee
chair Jamie Harrison gave his reaction to the

836
00:58:12.559 --> 00:58:19.400
news. I am emotional about the
President's decision because this President, Joe Biden,

837
00:58:19.559 --> 00:58:22.480
has been a transformational president. He's
been a great leader. He's a

838
00:58:22.519 --> 00:58:27.280
good man, a decent man.
In a letter posted on x Biden said

839
00:58:27.280 --> 00:58:30.039
it was time for him to step
down and focus on the rest of his

840
00:58:30.119 --> 00:58:35.360
presidency. Biden also announced he's endorsing
his VP, Kamal Harris, who said

841
00:58:35.400 --> 00:58:38.280
she's honored by that endorsement and said
she'll do everything in her power to unite

842
00:58:38.280 --> 00:58:43.719
the Democratic Party to defeat Donald Trump, but she isn't automatically the official Democratic

843
00:58:43.719 --> 00:58:46.400
nominee. That'll be decided over the
next few weeks. The Clintons are endorsing

844
00:58:46.480 --> 00:58:51.079
Vice President Harris. In a post, the couple said they'll do whatever they

845
00:58:51.119 --> 00:58:53.920
can to support her. The Clintons
went on to thank President Biden for his

846
00:58:54.000 --> 00:58:58.960
service. Recent polls are showing a
close race between Donald Trump and Vice President

847
00:58:59.000 --> 00:59:02.519
Harris should she be the nominee.
A CNN poll of poll's average show Trump

848
00:59:02.519 --> 00:59:07.320
has forty eight percent support, Harris
holds forty seven percent. Third party or

849
00:59:07.320 --> 00:59:12.280
independent candidates were not included in the
polling average. This is the first time

850
00:59:12.320 --> 00:59:15.239
a sitting president hasn't sought re election
in more than half a century. Democrat

851
00:59:15.320 --> 00:59:20.119
Lyndon B. Johnson dropped out in
nineteen sixty eight, but his circumstances were

852
00:59:20.199 --> 00:59:23.119
much different. He was in a
tight primary and the DNC was months away.

853
00:59:23.239 --> 00:59:28.280
Biden drops out with just four weeks
to go until the DNC. This

854
00:59:28.360 --> 00:59:31.400
week kicks off the release of a
new postage stamp. Tomorrow, the United

855
00:59:31.400 --> 00:59:36.800
States Postal Service is honoring longtime Jeopardy
host Alex Trebek on what would have been

856
00:59:36.840 --> 00:59:39.400
his eighty fourth birthday, the daytime
Emmy Award winner died in twenty twenty from

857
00:59:39.480 --> 00:59:44.639
pancreatic cancer. The new first Class
Forever stamps design is in the form of

858
00:59:44.679 --> 00:59:51.559
a Jeopardy style question. I'm Chris
Caragio, NBC News Radio, NBC News

859
00:59:51.599 --> 00:59:57.239
on CACAA Lowel sponsored by Teamsters Local
nineteen thirty two. Protecting the future of

860
00:59:57.320 --> 01:00:07.760
working families, Teamsters nineteen thirty two. ALCOH thank you for tuning in for

861
01:00:07.800 --> 01:00:08.320
this addition of