WEBVTT

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

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

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

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

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Eric Kavanaugh. All right, ladies
and gentlemen, Hello, and welcome back

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once again to the only coast to
coast radio show all about the information economy.

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It's called Inside Analysis or surely Eric
Kavanaugh is here. Back from Las

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Vegas, Baby, where I ran
into my good buddy Eve Mulgers of seven

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W Data are official doppelganger in the
EU, and we were both at click

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World talking about what's going on in
the world of data management. Lots of

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really fun developments. I have to
say Click is doing great. They're the

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acquisition trail again. They're about to
buy Talent. That deal is still being

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approved by regulators, I understand,
but it's very close to being approved and

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that is huge. I mean Talent. A while ago I checked and they

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were the number one and the Gartner
quadrant from for Data fabric which surprised me,

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but it shouldn't have surprised me because
they've been working on that for a

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long time, and of course Click
as a powerhouse in the analytics space,

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and so now you're combining the power
of talent with click as an end to

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end, full stack business intelligence analytic
solution. Writing exactly, it was kind

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of surprised to me. My way
with click goes way back in click view.

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The scene is believing and it was
so sharped by already running ten million

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records, oh my laptop and even
having it really powerful and the speed of

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insights what you could do with the
analysis is du but the click will gluter

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me that click here after eleven acquisitions, can you imagine over the last four

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five years, it's really they're really
focused on data management and visibilization, a

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lot of integration focus as well.
So they have more than two hundred and

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fifty connectors out of the box.
So the connection factory, I think they

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call it. That's really amazing.
And now where you see in the beginning

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it was Clivield, the Swedish company, how it became then it was click

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Sense rebranded and more powerful, and
now it's Click in the cloud. So

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they go cloud first type of strategy. But in all my discussions I had,

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I was kind of really surprised and
were positively surprised that it's not yeah,

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let's go to the cloud and throw
your customers in. They were really

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careful on taking them step by step
by step because most customers they just want

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to run and jump to the cloud
with all the things and the problems that

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they see in trying to go too
quickly. So they have really a good

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strategy on helping their customers getting into
the cloud and making the best out of

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the technology. And yeah, I
mean that was one of the surprises and

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positive surprises to get it with the
farming network, how big that has grown

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as well. Yeah, and this
whole thing about moving to the cloud,

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you know a lot of people may
not realize that. You know, in

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the on prem environment, there are
all sorts of things you have to do

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to get this stuff to work.
You have to get it to work in

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certain operating systems, etc. Well, the cloud theoretically should be easier,

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but you have to largely refactor what
you've built to work in the cloud.

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And because of that, typically when
a vendor who was on prem goes to

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the cloud, a lot of times
you won't get all the functionality right away,

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you get the basic functionality, and
then they'll start adding in little bits

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and pieces, because again, you
have to redesign it or refactor it to

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run in this new cloud environment.
Now. You and I talk about things

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like cloud native all the time and
what that really means, and these are

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very interesting, compelling concepts, but
that doesn't mean it's easy. It's not

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easy to just take your stuff and
pour it into the cloud. Now.

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There are some major vendors where I've
been a little bit surprised that they just

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took what they had on prem and
they just put it up in the cloud.

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Vertica is one, for example.
It did not refactor that engine.

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I think Terrida it is the same
way. And you think about those two,

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well, they've been around a long
time, so there's a whole lot

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of engineering that's been done to the
on prem versions to keep them running well,

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to optimize the throughput, etc.
To go and refactor all that for

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the cloud would be very difficult and
very time consuming. And I think I

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think they just said, you know
what, let's just get up in the

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cloud and do it that way.
But Click is not doing it that way.

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To your point, they're still telling
their clients yes, we're still going

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to support these on prem technologies for
a significant period of time. That is

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a relief because it doesn't force them
to move to the cloud, but does

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open the door to get into the
cloud, right. Yeah, And like

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you say, it's not going to
go away anytime soon. And I think

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there's still well they're really looking into
a hybrid solution as well. Didn't get

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that so clear, but it's pretty
clear that that on prem and cloud combination

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that's that's where they want to go
eventually having everything in the cloud. I

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think that's due to scalability and everything
there. But they have a lot of

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tools as well available to do the
monitoring, to do transparency and and have

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lineage in place. So that helps
you with understanding how your architecture is performing

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in the cloud. And that was
one of the major problems what we saw

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in the beginning with with AWUS with
Google Plower platform, like you say,

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Eric, doing it just as you
do it on prem and then kind of

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being surprised of the build where cloud
is mostly just consume what you need when

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you need it and not always old
type of mentality, and that's a big

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difference. Yeah, and I will
say that Josh good a VP of marketing

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over there, did a fantastic demo
and one of the things that blew my

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mind with this automation of creating game
to marts and the speed with which you

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can start playing around with all sorts
of different data sets, because you and

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I both know that historically, if
you're trying to pull eighteen twenty data sources

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into a data warehouse, that's going
to take you a few months. It

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could take you longer than that,
and there's just all this time that spends

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trying to set up the ETL windows, trying to set up the data pipelines,

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all this stuff. It's very time
intensive, and by the time you

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get it all done, maybe the
reality you're trying to understand is changed.

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And so the speed with which you
can build models and understand your data is

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really, really important, and it
seems like they've focused on that quite intently

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to generate business value, because if
you can't move through the data quickly,

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you're going to have a hard time
getting insights. You're gonna have a hard

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time then justifying why you're paying all
this money for these technologies. But the

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faster you can move the better,
the business is going to be as a

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result of it, right, Yeah, exactly, And that's all. This

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has been the case with click view. Back in the days you throw simply

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said throwing the data. Marketing was
always telling you you don't have to model,

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but that's a different discussion. You
still had to model your data.

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But the engine behind it was pretty
pretty intelligence to connect, intelligent, to

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connect everything together. So even that
it is not one hundred percent effect,

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you could always already do your analysis. If you see there are issues,

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you could easily change a relationship and
have the results in there, creating an

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aggregated table and so on and so
forth. But I saw that especially with

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the partner network where they are building
for example, MPM German company built a

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process mining solution on top of click
view, on top off click of the

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click technology, and the number of
records that they are throwing into the application

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that it's just insane at what speed
they can do the process mining as such,

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and the bits and pieces that we're
missing are most just put in place

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by the partners like version control deployment. So the partner ecosystem is pretty wide

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and very supporting on extending the already
big value of what you have with Click,

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and I think, yeah, we
discussed it. When the acquisition of

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Tale goes through, that's going to
be an insane offering what they will have

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in the future. And I think
that their focus is pretty determined on really

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doing data management everything around that,
and that's the core data management and data

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visualization because I head in my discussions
as well about streaming data and trying to

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understand do you offer something that you
can deploy on the edge or whatever for

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them. It's crystal clear there are
other tools that are better suited to do

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these type of streamings or data capture
at the edge and then provided to the

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systems of Clique. This is very
interesting and so what we're talking about is

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Click with talent becoming a true end
and data management to data visualization and analysis

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technology, which is great because then
it moves fluidly throughout the whole process the

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data does you can get to some
answers you can do and I like that

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they have these confidence scores. So
he was talking about churn in the telco

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world and being able to visualize all
of your customers. Let's say, are

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all of your significant customers and see
what their score is for likelihood to churn,

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and you can play around with things. So they had a very interesting

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example where lowering the price actually lowered
the confidence score that they're going to stay,

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whereas raising the price increase the confidence
score that they're going to stay.

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And that's because the theory is,
oh, they're not respecting the value of

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the service now because it's too inexpensive. And what's cool, though, is

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that you can make a change to
one particular record. So you make a

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particular offer to that person, and
then you want to be able to map

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that out to other other people,
but you want to test it first.

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Say Okay, let's see what happens
here. I'll change this setting. I'll

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give this person a new offer,
and let's see where it goes. Well,

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then if it works, then you
can say, okay, now apply

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that to all the cohorts, all
the people who look like this person.

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And they had a really interesting quote. He's like, on the edge,

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you're probably not going to change someone's
mind. If it's ninety percent like they're

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going to churn, you're not going
to change their mind. But in the

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middle, that's where you can do
some interesting things. Maybe the lower middle

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area where it's like, well,
you know sixty percent chance they'll churn focus

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on them. That's so interesting to
me because it allows the business analyst to

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play around with ideas, to try
things, then to track the results of

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that and thus get a better understanding
of what's really happening. That's the whole

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purpose of analysis is to be able
to do stuff just like that, right,

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Yeah, a real time a B
testing and you called it in marketing.

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But the scenarios which already exist for
a longer time. But if you

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can do that at the median in
a real time way, that's that's amazing.

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One other thing that we picked up
is the I don't know the name

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anymore. The key performance indicators are
very related. So some indicators that they

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will provision as well in one of
the future solutions where you can benchmark your

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company against these type of indicators and
that help you as well, will help

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you as well, Yeah, trying
to get an understanding of where you should

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focus upon with your company in a
certain industry. So very interesting solutions that

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they will launch click cloud within a
few months exactly. And of course the

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cloud, I mean everyone's moving to
the cloud. And the nice thing about

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the cloud is that it is a
nice marshaling area for your data, for

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your analysis, etc. And the
more we connect to that kind of environment

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over time, I think, the
better it's going to be. But a

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lot of people still are on prem
and so to your point earlier, Click

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said, we're not going to get
rid of all that stuff. We're still

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going to take care of these on
prem environments for a significant period of time.

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That's a very responsible way to move
forward. And it also takes resources,

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right, It takes engineers and resources
to make sure all that stuff is

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working. You know. Technical debt
is what some people call some of that

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older stuff, But you have to
find a way to deal with it and

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slowly usher your clientele into the cloud. That seems to be what they're doing

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right. Yeah, And they talk
a lot with their customers. Not only

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talking, they collaborate with their customers
a lot. I've had an opportunity to

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talk to a few of them and
they were telling me as well that no

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matter that they don't have a huge
number of licenses, they're not a top

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to not share a client for Click, but they're listening to what they have

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to say to their concerns and whatever. And they told me it's it's so

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amazing. Then finally see these requests
of your concerns being handled in the future

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products and therefore definitely they put a
lot of effort in that collaboration part with

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the customers, with the partners and
bringing that in the value and really bringing

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value for what you really need and
not purely from a product perspective. So

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it's it's interesting to see how they
really understand how they should should bring and

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create a product that brings real value. Yeah, and I know you were

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talking to Professor Sally Eves at the
show and she, of course is a

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is a real luminary in our field. And I'm just looking at a tweet

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from her where she talks about thirteen
acquisitions for Click in thirteen years, five

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million lines of code and thirty eight
thousand multi sector customers globally, thirty eight

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thousand customers. That's a lot of
customers. I mean, this is like,

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you know, for a business intelligence
environment, because they have big companies,

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they have mid sized companies, they
have some small companies, so they're

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very diverse in terms of their reach. And you know, now in the

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market, we have interesting things happening
with Tableau, and people are talking about

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Tableau fading away, which I found
kind of remarkable really because if you look

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at a big visualist enders you had
Spotfire, Tableau, you've got Click.

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There aren't too many of them,
right, And then the data science world,

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there are lots of ways you can
visualize data. It's a little bit

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tangential, but thirty eight thousand customers, I mean that is a force to

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be reckoned with, right. Yeah. I think it comes from back in

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the days on how they were trying
to get more worldwide exposure and foot on

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the ground. They couldn't go in
back in the days through procurement and the

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front door. So what they did
is like the trojan horse, what you

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have with with I always call Microsoft
Office the project horse. It is installed

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in the whole world, and Microsoft
shoves in powerbi or shoves in some AI

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a type of thing, and Click
did it in a different way. So

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they started with offering a free version
of fremium version of a free version to

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the departments, and people were so
excited about what you could do and how

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colickly and they didn't need really need
it for that, and that's how they

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end infecting the organizations. Later on
they evolved into more enterprise wise, and

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they had the servers and a real
stable environment as such, and that's how

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they grew. And the thing that
ties into the thirty eight thousand customers where

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you say they have the small companies, the SMBs, but they have a

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few large enterprises as well where they
can bring their services because it's enterprise worthy

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since since a few years already.
Yeah, and you made a good point

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about Microsoft, and you know that
whole environment. What Microsoft, of course

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is a powerhouse. They've been very
effective pivoting to the cloud. They think

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we can thank such and Nadella for
that one, because he understood what was

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happening and he said, this is
the direction we're going. Either go with

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us or get off the bus,
right, and so they did that.

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But you're right with Windows, you
know, historically you could just roll in

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some other like Powerbi for example.
Powerbi kind of came out of nowhere all

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the way to the top because it's
just there as all of a sudden,

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you have it, right, So
that's very interesting strategy. Click didn't take

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that strategy. They couldn't. They
didn't only operating system, but they've done

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very well without having that advantage,
right. Yeah, But if you compare

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Powerbi take took a bit that same
route, and you have Excel installed everywhere

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in the whole world, and power
bi started over powered Query, power view

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and a lot of power kind of
thing. You retreat add ons that you

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installed in Excel and we're able to
use the vert back engine, the in

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memory engine like plea view. It's
and you were able to use those ones,

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and later on it has been better
integrated and it became the whole observer

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SSIS sequel server Analysis Services became the
vert back engine, So they kind of

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created an hybrid where you were able
to do everything in an Excel. If

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you needed more power, you could
migrate too analysis back in the day,

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and then everybody comes up powerbi and
I think, well, we're doing that

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stuff already for the last win twelve
years with the add ins, So that's

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kind of how Powerbi grew over the
days as well. And click view back

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in and well they said thirty year
anniversary. Very years. I knew it

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was a small Swedish company, but
that's that's a long while that they've been

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00:17:18.240 --> 00:17:22.599
all board and and still are there
and still growing. So that's that's massively

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00:17:22.160 --> 00:17:26.000
one thing that that got me spiked
as well, where they said we already

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did the thirteen acquisitions. They were
able to integrate all the new technology technologies

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with under within under a year.
So that's really amazing. Talents very likely

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00:17:37.839 --> 00:17:41.559
will take a little longer as they
told me, but for the rest of

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of the acquisitions, that merely took
a year to integrate it in the click

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00:17:45.680 --> 00:17:49.279
stone. Yeah, and that is
really impressive for folks. Don't talk that

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00:17:49.400 --> 00:17:52.960
that will be right back. You're
listening to Inside Analysis all about click world.

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four nine, eight four nine.
That's eight hundred five one. Welcome back

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00:22:04.480 --> 00:22:11.119
to Inside Analysis. Here's your host, Eric Kavanaugh. All right, let

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00:22:11.160 --> 00:22:15.680
us and from it back here at
click World of very exciting stuff. Chief

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00:22:15.680 --> 00:22:18.200
product Officer James fishert with me.
We're gonna talk about click cloud, we'll

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00:22:18.240 --> 00:22:22.519
talk about AI, we'll talk about
really what's making things click for their customers

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and for even their prospects and to
get their brains moving a little bit to

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get on the ball. He involved
and James, you know, I've known

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about click for a very long time, and you folk a really impressive platform.

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Now it's not just a tool,
right, it's it's genuinely a platform.

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And I watched the demo today about
click Cloud and I have to say,

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I think that is the future.
I mean, there's all this on

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prem stuff and I was appreciating your
commitment to taking care of all that and

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not just saying Okay, we're going
to the clouds see you guys later.

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So it does take work to maintain
all that stuff that's on FREM. But

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tell me about the cloud, the
big vision of the cloud and kind of

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where things are going. Yeah.
Absolutely. So, you know, we've

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made a huge investment into the cloud
platform and really building out the capabilities that

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we've got, and that started very
much from the point of view of analytics

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and giving people a platform to ask
a question of data and get an answer.

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And then as we've been cremented put
more and more users in that seem

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more and more adoption. Then we've
been able to add more and more capability

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to that, increasing the breadth and
depth that we have, including some of

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the data integration capabilities you've seen today, the AIML, the automation stuff,

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that's all sort of been brought in
around that, and I think the cloud

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gives us a huge advantage in as
of how we put capability into the hands

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of users. I think a lot
of people look at the cloud and say,

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well, this is a great opportunity
to me to save some money and

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let the vendor take on the cost
of management of the infrastructure, which is

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absolutely part of the proposition. But
I think for me, the most important

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piece is that we can deliver incredibly
rapidly in the cloud platform from a product

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perspective, and then we can enable
consumption for our customers, allow people to

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experiment, to try new capabilities out
to drive adoption. We can speak to

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them into that platform. We can
provide training, help and advice and pointers

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directly as they're engaging. And I
think the final piece there is the cloud

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because it is an opportunity to create
a closed feedback loop, which is one

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of the themes of what I was
talking about today. But we can leverage

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00:24:30.000 --> 00:24:33.680
antlemetry to see how people are using
the product, how they're engaging with their

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data, and that then gives us
an opportunity to find new ways sitting about

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new ways to improve things and help
customers, as you say on that journey

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to the Cloud, and I heard
that two thirds of the cloud customers are

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doing something with AI LML, which
is a fantastic statistic. And you know

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the I'm a media guy, and
I will say that narrative in the media

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around AI always tends to be wrong, is how I would put it right.

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I mean, they think AI and
mL are going to help tremendously by

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tackling tasks at scale that are very
tedious that no one really wants to do

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00:25:07.119 --> 00:25:11.759
anyway, and that they're going to
supercharge your efforts to improve processes, to

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come up with new ideas, new
products, new services. Let the machines

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do what they do well, let
the people do what they do is create,

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design, invent in innoby right.
Absolutely, I mean that takes us

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back right to the foundation of click
right. We've talked a little bit about

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the thirtieth Anniverse we did this year, but it all started with some very

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smart people working with the customer to
help them solve business problems and the whole

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idea there was the power of the
human and the machine. The power of

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the humans ability to ask a question
and the machines ability to augment that.

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I often have also kind of the
Scott and Kirk analogy. Yeah sure,

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yeah. So the notion there is
that startup would never have got out of

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season one if it wasn't for the
bravado and good looks of Captain Kirk,

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but with the mathematical science approach of
of Sculty, and I think that's what

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we've been trying to do with our
associative engine, our analytics engine, all

352
00:26:04.920 --> 00:26:10.359
the way through to what we're doing
with AI and an mL today. Interesting

353
00:26:10.559 --> 00:26:14.799
damnagym of a doctor not a bus
boy. I remember those old days.

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00:26:14.799 --> 00:26:17.400
It's good stuff. Well. And
then speaking of the future, right,

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I think the future is in the
cloud. There are still going to be

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00:26:19.599 --> 00:26:23.000
very long tails the legacy systems on
PREM. I don't think on Prem is

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ever going to go away. But
in the cloud it's such a beautiful marshaling

358
00:26:26.279 --> 00:26:30.119
area. And to your point,
with the telemetry, you can understand better

359
00:26:30.240 --> 00:26:33.359
the use cases, what people are
doing where things aren't working so well,

360
00:26:33.359 --> 00:26:37.799
where things are working well, and
that way all the optimization on the infrastructure

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00:26:37.839 --> 00:26:41.440
side happens underneath the covers, and
the client doesn't have to worry about that.

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They just log in and get a
good experience. Right. That's the

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00:26:45.079 --> 00:26:47.480
idea, isn't it absolutely? I
mean, we want to make sure that

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we're providing highly scalable, always available, high quality to our customers, and

365
00:26:53.839 --> 00:27:00.359
they were always getting feedback on how
we can improve that. Ever, want

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00:27:00.400 --> 00:27:03.960
to lose sight that every time we
deliver something to a customer and they're going

367
00:27:03.960 --> 00:27:06.000
to find something that we didn't know
about. They're going to find a new

368
00:27:06.079 --> 00:27:08.880
use case or a new idea or
a new application, and that gives us

369
00:27:08.880 --> 00:27:15.960
the opportunity to learn. You talked
about AI and m L. Another great

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00:27:15.000 --> 00:27:21.039
statistic is you know over seventy percent
of our enterprise cloud customers are using automations.

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Now they're using that to not just
orchestrate elements within an analytic data pipeline

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00:27:26.319 --> 00:27:32.839
in the cloud, but also importantly
to extend the reach of those analytics.

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And I've spoken a lot about the
idea that analytics should not be a destination,

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00:27:37.559 --> 00:27:40.640
right, not something you have to
log into. They come to you

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where you are, wherever your job
is. And the automation capability and now

376
00:27:45.240 --> 00:27:48.839
just to take that embedded analytics to
the next level, not just to inform

377
00:27:48.880 --> 00:27:53.599
around an action, but actually to
compel that action, and it's doing something

378
00:27:53.640 --> 00:27:56.559
different. As you know, that
is what drives change, is that's what

379
00:27:56.640 --> 00:28:00.799
creates value. Right. Well,
you know, I start Josh good demo

380
00:28:00.880 --> 00:28:03.480
today, which I thought was fantastic, and he had this really great quote

381
00:28:03.480 --> 00:28:06.119
where he said, it's very interesting
in the middle, right, because in

382
00:28:06.240 --> 00:28:07.839
the edge, you know, well, people might just turn, they're probably

383
00:28:07.839 --> 00:28:11.799
already going to turn. But in
the middle you can learn things. And

384
00:28:11.839 --> 00:28:15.359
again it's not that the people are
ever going to go away. You're always

385
00:28:15.359 --> 00:28:18.440
going to want people in the loop, very actively in the loop, being

386
00:28:18.599 --> 00:28:22.359
educated by being inspired, by being
even a bit instigated by the machines and

387
00:28:22.400 --> 00:28:26.400
by the data. But when you
start playing around, if you can capture

388
00:28:26.440 --> 00:28:29.559
all that too, the chant of
transformations, what's been done, what the

389
00:28:29.640 --> 00:28:33.359
results were, then you get this
virtuous circle that's just going around and around,

390
00:28:33.440 --> 00:28:36.640
right, Yeah, absolutely, I
mean it's the ability to spot an

391
00:28:36.640 --> 00:28:41.880
outlier, the ability to always ask
any of the next question is again one

392
00:28:41.880 --> 00:28:44.839
of those things as right at the
heart of what click has always been about,

393
00:28:44.920 --> 00:28:48.599
right, our free form exploration experience
is all based around the fact that

394
00:28:48.680 --> 00:28:52.200
in a sequel structure, which is
great for the movement and orchestration of data,

395
00:28:52.480 --> 00:28:56.119
it's a terrible analytics parabigne because you
have to pre rould your model,

396
00:28:56.160 --> 00:29:00.359
you have to know what question you
wanted to ask, and as you it's

397
00:29:00.400 --> 00:29:03.119
that gray area often in the middle, all completely around the periphery. In

398
00:29:03.160 --> 00:29:07.680
other cases that the most interesting insights
lieing. It's the ability to go find

399
00:29:07.720 --> 00:29:11.839
them through a natural exploration that is
so important. Well, and so yes,

400
00:29:11.880 --> 00:29:15.759
I completely agree with you, And
discovery is my favorite part. I

401
00:29:15.759 --> 00:29:18.400
mean, doing all the stuff in
the back end is great and it helps

402
00:29:18.440 --> 00:29:21.640
the business get stuff done, but
the discovery is the fun stuff. That's

403
00:29:21.640 --> 00:29:23.839
when you're learning about your business.
That's when you're understanding. And what's so

404
00:29:23.880 --> 00:29:27.200
cool about the cloud from my perspective, one of the things at least is

405
00:29:27.200 --> 00:29:30.559
that it is this marshaling area and
so when someone finds something and they do

406
00:29:30.640 --> 00:29:36.039
something, you're not going to lose
that in an app somewhere on trem that

407
00:29:36.119 --> 00:29:40.519
the person leaves and now it's gone. So like this collective intelligence is building

408
00:29:40.599 --> 00:29:45.240
up over time and if you manage
it correctly, then everyone can benefit from

409
00:29:45.240 --> 00:29:47.720
all that and yeah, you can
change your mind down the road, but

410
00:29:47.759 --> 00:29:51.079
at least you don't lose all those
little insights that got you up the mountain

411
00:29:51.039 --> 00:29:52.279
in the first place. What do
you think, joh Well, So,

412
00:29:52.400 --> 00:29:56.799
I love the term collective intelligence.
I think, you know, the collective

413
00:29:56.799 --> 00:30:00.720
power of people coming together, particularly
diverse thinking, right, I mean,

414
00:30:00.960 --> 00:30:06.400
I think that's an really important component. How do you bring diverse perspective together

415
00:30:06.440 --> 00:30:08.960
to sol business problems? And from
an analytic perspective, we can do some

416
00:30:10.039 --> 00:30:15.279
of that as well. We made
a big investment in bringing collaboration capabilities into

417
00:30:15.440 --> 00:30:19.200
the cloud. Quite often, collaboration
is something that people do at the end

418
00:30:19.200 --> 00:30:22.680
of the process, they get an
insight and they's like, well, Eric,

419
00:30:22.680 --> 00:30:25.759
what should we do about this insight? Well, we're able to do

420
00:30:25.839 --> 00:30:30.960
is support you and I working together
collaborating around how we find that insight,

421
00:30:30.279 --> 00:30:34.680
validating that that's the right insight with
the right level of detail, and as

422
00:30:34.680 --> 00:30:37.440
you say, you don't lose the
thread of that conversation so you can pick

423
00:30:37.440 --> 00:30:41.319
it up at a later day.
So that collective intelligence a component I think

424
00:30:41.359 --> 00:30:48.000
for me is super super important and
it aligns I think to again another principle

425
00:30:48.119 --> 00:30:52.119
that hopefully you saw to day,
which is everything we're trying to do is

426
00:30:52.160 --> 00:30:56.000
about supporting users from a sort of
a singular data foundation, that singular data

427
00:30:56.039 --> 00:31:03.279
fabric but making sure that what with
the analytical maturity, what type of analysis,

428
00:31:03.680 --> 00:31:07.359
descriptive, diagnostic, predictive, prescriptive
analytics, right, and they can

429
00:31:07.400 --> 00:31:11.519
do that also irrespective of their level
of data literacy. Yes, all about

430
00:31:11.519 --> 00:31:15.799
putting those insights into their hands where
it makes sense for the jobs that they're

431
00:31:15.799 --> 00:31:18.279
doing. Out of time that they're
doing that job right, These people on

432
00:31:18.319 --> 00:31:21.680
the front lines, they are the
ones who need the insights to make decisions

433
00:31:21.720 --> 00:31:23.200
in the moment. Should we keep
this customers? Should we not keep this

434
00:31:23.240 --> 00:31:26.599
customers? Should should I invest another
hour attacking this person on the phone,

435
00:31:26.839 --> 00:31:30.000
or should they say, you know
what, We're very sorry that our services

436
00:31:30.039 --> 00:31:33.440
didn't help you and we wish you
all the best. Right not Our customers

437
00:31:33.480 --> 00:31:36.000
are good customers, most of them
are, but aren't right Well, I

438
00:31:36.000 --> 00:31:38.440
mean again, another great use case
there for author mL right, you know,

439
00:31:38.519 --> 00:31:42.359
suggesting that next action. But also
then you start to see the value

440
00:31:42.359 --> 00:31:48.640
of these things come together. You
know, the ability to leverage autogomer around

441
00:31:48.720 --> 00:31:52.160
churn analysis, and you know understand
how you want to react to customers.

442
00:31:52.160 --> 00:31:56.839
But when you find that insight showed
in his demo today and you want to

443
00:31:56.839 --> 00:31:57.720
actually then say, well, you
know what, I'm going to put them

444
00:31:57.759 --> 00:32:01.160
on the next plan and if I
if I'm modeled that out, their chance

445
00:32:01.200 --> 00:32:05.200
of churning is going to go right
down. That's amazing, that's great.

446
00:32:05.519 --> 00:32:08.759
But well, now if you say
we'll make that happen, use the automation

447
00:32:08.799 --> 00:32:14.200
to create particket in something like a
service now to update the record in a

448
00:32:14.279 --> 00:32:19.480
CRM system to actually make that plan
change happen. Yeah, that's the next

449
00:32:19.519 --> 00:32:22.480
level of action. That's brilliant stuff, folks. I don't want you to

450
00:32:22.839 --> 00:32:27.000
look online click q L I K
click as any of the company. James

451
00:32:27.000 --> 00:32:30.319
Fisher is the expert podcast bonus segment
is coming up next and I just say,

452
00:32:30.400 --> 00:32:32.640
folks, it's closing the circuit.
Really, We've talked about this for

453
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twenty five years now. I've been
in this business a long time. We

454
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talked about operationalizing all that fun stuff. It's finally happening. That's what we're

455
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learning about at click World podcast.
About a second is coming up new dot

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five one three seven five eight hundred two

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four five one three seven five.
Do you own an annuity, either fixed

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rate, indexed or variable? Are
you paying high fees and getting low returns?

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learn the pitfalls and mistakes off buying
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for Baby Boomers contains the little known
truths about annuities, like how to help

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reduce your fees and increase retirement income. And it's free, that's right free.

500
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As a bonus, we'll also throw
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for calling. We researched over one
thousand annuities and summarized rates and benefits from

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financially strong insurers. You get annuity
dues and don'ts for baby boomers and the

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hundred four nine five three six five two.

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Eight hundred four nine five three six
five two. Eight hundred four nine

506
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five three six five two. That's
eight hundred four nine five thirty six fifty

507
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two. Welcome back to Inside Analysis. Here's your host, Eric Kavanaugh.

508
00:42:42.880 --> 00:42:45.360
All right, folks, back here
at Click World, Inside Analysis, and

509
00:42:45.440 --> 00:42:50.760
I'm very pleased to be speaking with
our next guest, Angelica Cletus. She

510
00:42:50.880 --> 00:42:53.480
has a partner with Click and she's
a teacher, and she's written a book

511
00:42:53.800 --> 00:42:57.719
and she does all kinds of things. She's all about data. Her company

512
00:42:57.760 --> 00:43:00.239
is called bit Metric. So Angelica, first of all, what was the

513
00:43:00.280 --> 00:43:07.320
book about. The book is about
data literacy in practice, which helps people

514
00:43:07.320 --> 00:43:10.079
to understand how to work with data, how to make data informed decisions.

515
00:43:10.320 --> 00:43:14.239
M that's a good thing to do. Data literacy. Yeah, it's been

516
00:43:14.280 --> 00:43:16.679
around as a practice for a number
of years, but it's still not really

517
00:43:16.800 --> 00:43:21.280
taking off. I mean, I
don't think enough people appreciate the importance of

518
00:43:21.519 --> 00:43:24.800
understanding this concept of data literacy.
What are some of the basic tenets that

519
00:43:24.920 --> 00:43:29.880
you start like our first day class, what do you say that everybody is

520
00:43:29.920 --> 00:43:32.960
actually in fact a little bit data
literally, because we make data informed decisions

521
00:43:34.519 --> 00:43:37.480
based on this thing that I have
in my hand. When we book a

522
00:43:37.559 --> 00:43:40.039
journey, we go to Airbnb or
we go to booking dot com, we

523
00:43:40.159 --> 00:43:45.519
set up parameters, we get some
results, and we look at the stars

524
00:43:45.679 --> 00:43:47.639
now many stars. We to reviews, and then we discuss it with the

525
00:43:47.719 --> 00:43:52.599
family and then we make a data
informed decisions. And it's maddening that that's

526
00:43:52.639 --> 00:43:58.440
not happening until now. In businesses. Yeah, well, you know,

527
00:43:58.679 --> 00:44:02.199
there's so much data and organizations today, and I think that's part of the

528
00:44:02.320 --> 00:44:06.639
challenge is that it's overwhelming to a
lot of people. If you look at

529
00:44:06.679 --> 00:44:09.880
the history of data warehousing, most
of that data came out of RP systems.

530
00:44:10.039 --> 00:44:15.760
It was relational data, structured data, transactional data really at scale,

531
00:44:15.800 --> 00:44:19.360
so you could understand how many widgets
you're selling and what would happen if we

532
00:44:19.480 --> 00:44:22.159
did this for forecasting and planning.
But these days we have all this other

533
00:44:22.360 --> 00:44:28.519
data I mean, we've got observability
data from systems because of Pubertennis, We've

534
00:44:28.599 --> 00:44:32.199
got all sorts of data about pieces
and it gets to be very unwieldy.

535
00:44:32.760 --> 00:44:37.000
Sensor data IoT, which is all
over the place these days, and wonderful

536
00:44:37.079 --> 00:44:40.800
stuff. When you do IoT correctly, you get asset management, you get

537
00:44:40.880 --> 00:44:45.360
predicted maintenance, you get all signs
of fun things. But what's your advice

538
00:44:45.519 --> 00:44:50.559
for carving a right size of data
to play around with him? To work

539
00:44:50.599 --> 00:44:52.800
with al? First you have to
look at your future state of your organization.

540
00:44:52.960 --> 00:44:55.199
Where do you want to go?
What's your vision? Where do you

541
00:44:55.239 --> 00:45:00.360
want to end up? So I
truly believe in creating a vision, but

542
00:45:00.480 --> 00:45:05.119
also looking at where we are now
and then the path towards A to B.

543
00:45:05.880 --> 00:45:09.360
That should be your strategy, and
your strategy can help also divine your

544
00:45:09.440 --> 00:45:14.960
data strategy. You can't call it
at all anymore because we have too much,

545
00:45:15.400 --> 00:45:20.199
if way too much. We have
one point seven megabyte per second per

546
00:45:20.760 --> 00:45:24.840
person on Earth that we are creating
every second, so it's crazy. We

547
00:45:24.920 --> 00:45:29.320
can't have it all anymore. Well, and there's also this whole shift in

548
00:45:29.840 --> 00:45:35.800
data processing where first we persist data
and now you can stream data on These

549
00:45:35.800 --> 00:45:39.199
are two different architectures and two different
paradigms quite frankly, very very different.

550
00:45:39.440 --> 00:45:44.760
You need different tools, different mindset. What are your thoughts on this sort

551
00:45:44.800 --> 00:45:49.400
of rivalry that's coming out around now, persisting data versus streaming data. I

552
00:45:50.400 --> 00:45:52.320
think it's two different worlds, right, So you need to think of the

553
00:45:52.400 --> 00:45:58.880
two different worlds separately. You can't
have it all in one. From my

554
00:45:59.000 --> 00:46:01.400
opinion, you should. You should
think of a strategy for the one for

555
00:46:01.559 --> 00:46:06.719
the one side and the other side
as well, because streaming data is something

556
00:46:06.840 --> 00:46:12.719
totally different, but it can bring
so much value to an organization, So

557
00:46:12.920 --> 00:46:15.599
you have to think good of the
data strategy, which you have to keep

558
00:46:15.639 --> 00:46:20.039
them separate at the end. That's
interesting, So I love your focus on

559
00:46:20.159 --> 00:46:22.360
strategy. I've had this belief for
a long time that every company of any

560
00:46:22.440 --> 00:46:28.480
significant size should have an information strategy
group, even if it's one person or

561
00:46:28.519 --> 00:46:31.840
three people or something small like that, especially these days, because things are

562
00:46:31.960 --> 00:46:35.840
changing so quickly, the number of
ways you can get data, the speed

563
00:46:35.880 --> 00:46:37.000
of which you can get data,
how you can analyze it, how you

564
00:46:37.039 --> 00:46:44.280
can operationalize it. What are your
thoughts about this core component of an organization's

565
00:46:44.559 --> 00:46:49.440
hierarchy if I look at it,
and it's also described in my book We

566
00:46:49.960 --> 00:46:53.440
at my firm bit metric. We
work through the concept of four pillars.

567
00:46:54.119 --> 00:46:59.719
The first pillar is think about organizational
data literacy. It's about planning, it's

568
00:46:59.719 --> 00:47:04.719
about sponsorship, it's about embracing data
as an organization as a whole. The

569
00:47:04.840 --> 00:47:08.679
second element is data management and there
it fits right there. You have to

570
00:47:08.760 --> 00:47:13.960
think about your data strategy, about
the vision that you want to reach and

571
00:47:14.039 --> 00:47:17.159
the path towards it, but also
the governance. What can we do with

572
00:47:17.239 --> 00:47:22.000
a data in the future, because
it can bring also new models, new

573
00:47:22.039 --> 00:47:24.800
business models, new innovations. And
then of course you have the analytic tools

574
00:47:24.840 --> 00:47:30.920
because you have to work with tools
to create beautiful dashboards and insights. And

575
00:47:30.000 --> 00:47:36.920
then the last a pillar is about
education. That covers all four right and

576
00:47:37.159 --> 00:47:42.360
from there, and it's not mandatory
to begin with organizational I see organizations start

577
00:47:42.400 --> 00:47:45.440
with analytics and it just thinks starting, oh, we need to have a

578
00:47:45.480 --> 00:47:47.559
plan because we are growing, we
are going too big. And then suddenly

579
00:47:47.599 --> 00:47:52.679
also data management comes in place or
education, so it varies a lot,

580
00:47:52.920 --> 00:47:55.480
but you know, it's it's very
important to think of those four concepts and

581
00:47:55.800 --> 00:48:00.880
at the end you can buy it
flows together. You know, I'm going

582
00:48:00.920 --> 00:48:04.639
to throw out to you one of
my big pet peeves about web data,

583
00:48:04.719 --> 00:48:08.079
and in particular in the sales and
marketing world, we have now eight thousand

584
00:48:08.480 --> 00:48:14.360
software as a service applications that you
can use to automate either sales or marketing

585
00:48:14.880 --> 00:48:17.920
in all sorts of niches. Influencer
marketing for example, email marketing, social

586
00:48:19.000 --> 00:48:22.079
media marketing, all these different ways
you can do things. And from my

587
00:48:22.239 --> 00:48:27.760
perspective, the numbers are always dubious. Well when I look at the numbers,

588
00:48:27.840 --> 00:48:30.960
and I'll say, when I buy
advertising, which because I'm dabbled in

589
00:48:30.199 --> 00:48:36.440
YouTube and Facebook and Twitter and LinkedIn, and they're very different in terms of

590
00:48:36.519 --> 00:48:38.000
what you get for your money.
But you will, for example, on

591
00:48:38.079 --> 00:48:42.480
Twitter, if you give them ten
dollars, instead of getting one hundred and

592
00:48:42.519 --> 00:48:45.679
ten impressions, you get a thousand
pressions. But what were those impressions?

593
00:48:45.760 --> 00:48:50.239
Like? Is that real? Who
are these people? You need the details

594
00:48:50.280 --> 00:48:52.480
and you don't, you don't.
You never seem to get all the details,

595
00:48:52.519 --> 00:48:55.719
it seems to me. And they're
always changing things. They're like changing

596
00:48:55.840 --> 00:49:00.199
behind the scenes. And if you
think about from a core perspective. These

597
00:49:00.239 --> 00:49:02.559
social media companies, what do they
want to do? They want to sell

598
00:49:02.639 --> 00:49:06.400
stuff, right, they want to
make money. They want to engage and

599
00:49:06.559 --> 00:49:08.119
do content. But it's all to
make money at the end of the day.

600
00:49:08.639 --> 00:49:14.400
And so to me, that is
this constant pressure that's pulling them in

601
00:49:14.519 --> 00:49:20.719
a direction away from authenticity, away
from what we could call integrity in data

602
00:49:20.800 --> 00:49:23.639
and reporting. What do you think
about that and my paranoid or do you

603
00:49:23.760 --> 00:49:28.320
share that opinion? No, I
shared that opinion. I don't have the

604
00:49:28.400 --> 00:49:31.760
sales example, but for example,
in one of my presentations, I think

605
00:49:31.760 --> 00:49:37.920
it's still somewhere online. I was
talking about my fitbit. Okay, I

606
00:49:37.480 --> 00:49:40.239
usually now I wear a garment,
but I used to have a fitbit,

607
00:49:40.360 --> 00:49:45.400
and the moment I got a Fitbit, I was thinking, I can create

608
00:49:45.440 --> 00:49:50.559
a cool dashboard of my own data, combine it with the fitness and my

609
00:49:50.639 --> 00:49:53.039
fitness spell you know, about my
food, my consumption and everything. And

610
00:49:53.159 --> 00:49:59.639
then I wanted to download my data
and I didn't get it. If I

611
00:49:59.719 --> 00:50:02.199
won't my full set of data,
including my heart rate and everything, I

612
00:50:02.320 --> 00:50:07.840
had to pay again. Yeah,
but it's my data, right, Yeah.

613
00:50:08.639 --> 00:50:13.760
And that's the same with with with
all the advertisement and everything. Everything

614
00:50:13.960 --> 00:50:19.239
is kept. It's it's, it's
it's it's sneaky data. I like this.

615
00:50:19.440 --> 00:50:22.280
I haven't heard that before. I'm
going to use that one. Sneaking

616
00:50:22.519 --> 00:50:24.320
data. That is funny. That's
a good one. Yeah. Well,

617
00:50:24.400 --> 00:50:30.400
and then so you mentioned data ownership, right, and I'm on Facebook,

618
00:50:30.440 --> 00:50:35.280
I'm on LinkedIn. Well, obviously
these engines are using personal data to train

619
00:50:35.360 --> 00:50:38.280
their algorithms, to train their predictive
models for what kind of content to serve

620
00:50:38.360 --> 00:50:42.119
you, what they might be able
to sell you, all these different things.

621
00:50:42.519 --> 00:50:45.400
And then you look at chat GPT. Well, where the chat GPT

622
00:50:45.559 --> 00:50:50.000
and the other large language models get
their training was on web data. They're

623
00:50:50.039 --> 00:50:52.280
just scraped from all over the place
until twenty twenty one. Right, Because

624
00:50:52.760 --> 00:50:57.440
chat GPT is cool, right,
it's a cool feature. I use it.

625
00:50:57.559 --> 00:51:00.360
I use myself all the time.
But the main issue here is when

626
00:51:00.400 --> 00:51:06.440
you use open eye like that,
you have to fact check. You have

627
00:51:06.599 --> 00:51:09.960
to check the results that are there. That's right, don't use it just

628
00:51:10.119 --> 00:51:14.760
one and say because we are biased, all we are biased, all we

629
00:51:14.880 --> 00:51:19.800
are We need to not assume that
everything is the truth, because then we

630
00:51:19.880 --> 00:51:22.840
get the fake right, then we're
coming too the fake area right. And

631
00:51:22.920 --> 00:51:27.639
I truly believe that chat ept is
powerful. It can help fact check,

632
00:51:28.119 --> 00:51:30.480
check your text, check everything,
and then you can do something. So

633
00:51:30.639 --> 00:51:35.559
to assume that everything is right,
right, it's wrong. Yeah, No,

634
00:51:35.679 --> 00:51:39.360
that's very true. I was joking
just yesterday that chat GPT on a

635
00:51:39.400 --> 00:51:44.280
webinar. Someone looked me up and
posted it in the chat and it was

636
00:51:44.360 --> 00:51:45.880
correct. I was like, wow, I was kind of impressive. And

637
00:51:46.000 --> 00:51:49.480
I did it myself and it told
me I'd written three books and I was

638
00:51:49.519 --> 00:51:52.199
like, what are you sure?
Are you seeing the future? Because I

639
00:51:52.320 --> 00:51:57.440
haven't written three books. But it
conflated me with other people, I think

640
00:51:57.480 --> 00:52:01.239
because it associated the Blur group up
with Blur Research, which are different companies,

641
00:52:01.280 --> 00:52:05.760
and it just assumed and that's basically
what it's doing. It's assuming things.

642
00:52:06.039 --> 00:52:08.440
Yeah, that's very dangerous. Yeah. So, but I will say

643
00:52:08.519 --> 00:52:12.719
that, you know, some of
these folks who are engaging in a sort

644
00:52:12.760 --> 00:52:17.000
of philosophical exploration of chat gbt's views
of the world and of itself and of

645
00:52:17.159 --> 00:52:21.760
sentience, I think it's a misuse
of the tool. Rightly, it's not

646
00:52:21.880 --> 00:52:25.639
there. It's not supposed to be
sentient. It's a predictive engine that's trying

647
00:52:25.719 --> 00:52:30.320
to satisfy your prompt with information from
its sources. Yeah, right, So

648
00:52:30.440 --> 00:52:32.880
you do have to know how to
use the tools. And that's part of

649
00:52:32.960 --> 00:52:37.920
data literacy too, Right, that's
data exactly. That's it's it's not only

650
00:52:37.000 --> 00:52:42.079
about how to use it, also
how to question, Yeah, because we

651
00:52:42.480 --> 00:52:46.679
tend to forget to question the things
that we see or read. I'm Andy

652
00:52:46.760 --> 00:52:51.719
Solomon. Now that it's easier to
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more for gamers to look forward to
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in any other station in California,
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on the stations that leave no listener
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NBC Saturday Night at the Movies presents Peter

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at one oh six point five FMK
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NBC News Radio, I'm Chris Garraggio, the head of Homeland Security, says

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our hemisphere is experiencing its greatest migration
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day one delivered a solution. He
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Congress would act on swiftly. They
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DHS Secretary Alejandro Myorcus said that the