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Eric Kavanaugh. All right, ladies and gentlemen, Hello, and welcome back
once again to the only coast to coast radio show all about the information economy.
It's called Inside Analysis or surely Eric Kavanaugh is here. Back from Las
Vegas, Baby, where I ran into my good buddy Eve Mulgers of seven
W Data are official doppelganger in the EU, and we were both at click
World talking about what's going on in the world of data management. Lots of
really fun developments. I have to say Click is doing great. They're the
acquisition trail again. They're about to buy Talent. That deal is still being
approved by regulators, I understand, but it's very close to being approved and
that is huge. I mean Talent. A while ago I checked and they
were the number one and the Gartner quadrant from for Data fabric which surprised me,
but it shouldn't have surprised me because they've been working on that for a
long time, and of course Click as a powerhouse in the analytics space,
and so now you're combining the power of talent with click as an end to
end, full stack business intelligence analytic solution. Writing exactly, it was kind
of surprised to me. My way with click goes way back in click view.
The scene is believing and it was so sharped by already running ten million
records, oh my laptop and even having it really powerful and the speed of
insights what you could do with the analysis is du but the click will gluter
me that click here after eleven acquisitions, can you imagine over the last four
five years, it's really they're really focused on data management and visibilization, a
lot of integration focus as well. So they have more than two hundred and
fifty connectors out of the box. So the connection factory, I think they
call it. That's really amazing. And now where you see in the beginning
it was Clivield, the Swedish company, how it became then it was click
Sense rebranded and more powerful, and now it's Click in the cloud. So
they go cloud first type of strategy. But in all my discussions I had,
I was kind of really surprised and were positively surprised that it's not yeah,
let's go to the cloud and throw your customers in. They were really
careful on taking them step by step by step because most customers they just want
to run and jump to the cloud with all the things and the problems that
they see in trying to go too quickly. So they have really a good
strategy on helping their customers getting into the cloud and making the best out of
the technology. And yeah, I mean that was one of the surprises and
positive surprises to get it with the farming network, how big that has grown
as well. Yeah, and this whole thing about moving to the cloud,
you know a lot of people may not realize that. You know, in
the on prem environment, there are all sorts of things you have to do
to get this stuff to work. You have to get it to work in
certain operating systems, etc. Well, the cloud theoretically should be easier,
but you have to largely refactor what you've built to work in the cloud.
And because of that, typically when a vendor who was on prem goes to
the cloud, a lot of times you won't get all the functionality right away,
you get the basic functionality, and then they'll start adding in little bits
and pieces, because again, you have to redesign it or refactor it to
run in this new cloud environment. Now. You and I talk about things
like cloud native all the time and what that really means, and these are
very interesting, compelling concepts, but that doesn't mean it's easy. It's not
easy to just take your stuff and pour it into the cloud. Now.
There are some major vendors where I've been a little bit surprised that they just
took what they had on prem and they just put it up in the cloud.
Vertica is one, for example. It did not refactor that engine.
I think Terrida it is the same way. And you think about those two,
well, they've been around a long time, so there's a whole lot
of engineering that's been done to the on prem versions to keep them running well,
to optimize the throughput, etc. To go and refactor all that for
the cloud would be very difficult and very time consuming. And I think I
think they just said, you know what, let's just get up in the
cloud and do it that way. But Click is not doing it that way.
To your point, they're still telling their clients yes, we're still going
to support these on prem technologies for a significant period of time. That is
a relief because it doesn't force them to move to the cloud, but does
open the door to get into the cloud, right. Yeah, And like
you say, it's not going to go away anytime soon. And I think
there's still well they're really looking into a hybrid solution as well. Didn't get
that so clear, but it's pretty clear that that on prem and cloud combination
that's that's where they want to go eventually having everything in the cloud. I
think that's due to scalability and everything there. But they have a lot of
tools as well available to do the monitoring, to do transparency and and have
lineage in place. So that helps you with understanding how your architecture is performing
in the cloud. And that was one of the major problems what we saw
in the beginning with with AWUS with Google Plower platform, like you say,
Eric, doing it just as you do it on prem and then kind of
being surprised of the build where cloud is mostly just consume what you need when
you need it and not always old type of mentality, and that's a big
difference. Yeah, and I will say that Josh good a VP of marketing
over there, did a fantastic demo and one of the things that blew my
mind with this automation of creating game to marts and the speed with which you
can start playing around with all sorts of different data sets, because you and
I both know that historically, if you're trying to pull eighteen twenty data sources
into a data warehouse, that's going to take you a few months. It
could take you longer than that, and there's just all this time that spends
trying to set up the ETL windows, trying to set up the data pipelines,
all this stuff. It's very time intensive, and by the time you
get it all done, maybe the reality you're trying to understand is changed.
And so the speed with which you can build models and understand your data is
really, really important, and it seems like they've focused on that quite intently
to generate business value, because if you can't move through the data quickly,
you're going to have a hard time getting insights. You're gonna have a hard
time then justifying why you're paying all this money for these technologies. But the
faster you can move the better, the business is going to be as a
result of it, right, Yeah, exactly, And that's all. This
has been the case with click view. Back in the days you throw simply
said throwing the data. Marketing was always telling you you don't have to model,
but that's a different discussion. You still had to model your data.
But the engine behind it was pretty pretty intelligence to connect, intelligent, to
connect everything together. So even that it is not one hundred percent effect,
you could always already do your analysis. If you see there are issues,
you could easily change a relationship and have the results in there, creating an
aggregated table and so on and so forth. But I saw that especially with
the partner network where they are building for example, MPM German company built a
process mining solution on top of click view, on top off click of the
click technology, and the number of records that they are throwing into the application
that it's just insane at what speed they can do the process mining as such,
and the bits and pieces that we're missing are most just put in place
by the partners like version control deployment. So the partner ecosystem is pretty wide
and very supporting on extending the already big value of what you have with Click,
and I think, yeah, we discussed it. When the acquisition of
Tale goes through, that's going to be an insane offering what they will have
in the future. And I think that their focus is pretty determined on really
doing data management everything around that, and that's the core data management and data
visualization because I head in my discussions as well about streaming data and trying to
understand do you offer something that you can deploy on the edge or whatever for
them. It's crystal clear there are other tools that are better suited to do
these type of streamings or data capture at the edge and then provided to the
systems of Clique. This is very interesting and so what we're talking about is
Click with talent becoming a true end and data management to data visualization and analysis
technology, which is great because then it moves fluidly throughout the whole process the
data does you can get to some answers you can do and I like that
they have these confidence scores. So he was talking about churn in the telco
world and being able to visualize all of your customers. Let's say, are
all of your significant customers and see what their score is for likelihood to churn,
and you can play around with things. So they had a very interesting
example where lowering the price actually lowered the confidence score that they're going to stay,
whereas raising the price increase the confidence score that they're going to stay.
And that's because the theory is, oh, they're not respecting the value of
the service now because it's too inexpensive. And what's cool, though, is
that you can make a change to one particular record. So you make a
particular offer to that person, and then you want to be able to map
that out to other other people, but you want to test it first.
Say Okay, let's see what happens here. I'll change this setting. I'll
give this person a new offer, and let's see where it goes. Well,
then if it works, then you can say, okay, now apply
that to all the cohorts, all the people who look like this person.
And they had a really interesting quote. He's like, on the edge,
you're probably not going to change someone's mind. If it's ninety percent like they're
going to churn, you're not going to change their mind. But in the
middle, that's where you can do some interesting things. Maybe the lower middle
area where it's like, well, you know sixty percent chance they'll churn focus
on them. That's so interesting to me because it allows the business analyst to
play around with ideas, to try things, then to track the results of
that and thus get a better understanding of what's really happening. That's the whole
purpose of analysis is to be able to do stuff just like that, right,
Yeah, a real time a B testing and you called it in marketing.
But the scenarios which already exist for a longer time. But if you
can do that at the median in a real time way, that's that's amazing.
One other thing that we picked up is the I don't know the name
anymore. The key performance indicators are very related. So some indicators that they
will provision as well in one of the future solutions where you can benchmark your
company against these type of indicators and that help you as well, will help
you as well, Yeah, trying to get an understanding of where you should
focus upon with your company in a certain industry. So very interesting solutions that
they will launch click cloud within a few months exactly. And of course the
cloud, I mean everyone's moving to the cloud. And the nice thing about
the cloud is that it is a nice marshaling area for your data, for
your analysis, etc. And the more we connect to that kind of environment
over time, I think, the better it's going to be. But a
lot of people still are on prem and so to your point earlier, Click
said, we're not going to get rid of all that stuff. We're still
going to take care of these on prem environments for a significant period of time.
That's a very responsible way to move forward. And it also takes resources,
right, It takes engineers and resources to make sure all that stuff is
working. You know. Technical debt is what some people call some of that
older stuff, But you have to find a way to deal with it and
slowly usher your clientele into the cloud. That seems to be what they're doing
right. Yeah, And they talk a lot with their customers. Not only
talking, they collaborate with their customers a lot. I've had an opportunity to
talk to a few of them and they were telling me as well that no
matter that they don't have a huge number of licenses, they're not a top
to not share a client for Click, but they're listening to what they have
to say to their concerns and whatever. And they told me it's it's so
amazing. Then finally see these requests of your concerns being handled in the future
products and therefore definitely they put a lot of effort in that collaboration part with
the customers, with the partners and bringing that in the value and really bringing
value for what you really need and not purely from a product perspective. So
it's it's interesting to see how they really understand how they should should bring and
create a product that brings real value. Yeah, and I know you were
talking to Professor Sally Eves at the show and she, of course is a
is a real luminary in our field. And I'm just looking at a tweet
from her where she talks about thirteen acquisitions for Click in thirteen years, five
million lines of code and thirty eight thousand multi sector customers globally, thirty eight
thousand customers. That's a lot of customers. I mean, this is like,
you know, for a business intelligence environment, because they have big companies,
they have mid sized companies, they have some small companies, so they're
very diverse in terms of their reach. And you know, now in the
market, we have interesting things happening with Tableau, and people are talking about
Tableau fading away, which I found kind of remarkable really because if you look
at a big visualist enders you had Spotfire, Tableau, you've got Click.
There aren't too many of them, right, And then the data science world,
there are lots of ways you can visualize data. It's a little bit
tangential, but thirty eight thousand customers, I mean that is a force to
be reckoned with, right. Yeah. I think it comes from back in
the days on how they were trying to get more worldwide exposure and foot on
the ground. They couldn't go in back in the days through procurement and the
front door. So what they did is like the trojan horse, what you
have with with I always call Microsoft Office the project horse. It is installed
in the whole world, and Microsoft shoves in powerbi or shoves in some AI
a type of thing, and Click did it in a different way. So
they started with offering a free version of fremium version of a free version to
the departments, and people were so excited about what you could do and how
colickly and they didn't need really need it for that, and that's how they
end infecting the organizations. Later on they evolved into more enterprise wise, and
they had the servers and a real stable environment as such, and that's how
they grew. And the thing that ties into the thirty eight thousand customers where
you say they have the small companies, the SMBs, but they have a
few large enterprises as well where they can bring their services because it's enterprise worthy
since since a few years already. Yeah, and you made a good point
about Microsoft, and you know that whole environment. What Microsoft, of course
is a powerhouse. They've been very effective pivoting to the cloud. They think
we can thank such and Nadella for that one, because he understood what was
happening and he said, this is the direction we're going. Either go with
us or get off the bus, right, and so they did that.
But you're right with Windows, you know, historically you could just roll in
some other like Powerbi for example. Powerbi kind of came out of nowhere all
the way to the top because it's just there as all of a sudden,
you have it, right, So that's very interesting strategy. Click didn't take
that strategy. They couldn't. They didn't only operating system, but they've done
very well without having that advantage, right. Yeah, But if you compare
Powerbi take took a bit that same route, and you have Excel installed everywhere
in the whole world, and power bi started over powered Query, power view
and a lot of power kind of thing. You retreat add ons that you
installed in Excel and we're able to use the vert back engine, the in
memory engine like plea view. It's and you were able to use those ones,
and later on it has been better integrated and it became the whole observer
SSIS sequel server Analysis Services became the vert back engine, So they kind of
created an hybrid where you were able to do everything in an Excel. If
you needed more power, you could migrate too analysis back in the day,
and then everybody comes up powerbi and I think, well, we're doing that
stuff already for the last win twelve years with the add ins, So that's
kind of how Powerbi grew over the days as well. And click view back
in and well they said thirty year anniversary. Very years. I knew it
was a small Swedish company, but that's that's a long while that they've been
all board and and still are there and still growing. So that's that's massively
one thing that that got me spiked as well, where they said we already
did the thirteen acquisitions. They were able to integrate all the new technology technologies
with under within under a year. So that's really amazing. Talents very likely
will take a little longer as they told me, but for the rest of
of the acquisitions, that merely took a year to integrate it in the click
stone. Yeah, and that is really impressive for folks. Don't talk that
that will be right back. You're listening to Inside Analysis all about click world.
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to Inside Analysis. Here's your host, Eric Kavanaugh. All right, let
us and from it back here at click World of very exciting stuff. Chief
product Officer James fishert with me. We're gonna talk about click cloud, we'll
talk about AI, we'll talk about really what's making things click for their customers
and for even their prospects and to get their brains moving a little bit to
get on the ball. He involved and James, you know, I've known
about click for a very long time, and you folk a really impressive platform.
Now it's not just a tool, right, it's it's genuinely a platform.
And I watched the demo today about click Cloud and I have to say,
I think that is the future. I mean, there's all this on
prem stuff and I was appreciating your commitment to taking care of all that and
not just saying Okay, we're going to the clouds see you guys later.
So it does take work to maintain all that stuff that's on FREM. But
tell me about the cloud, the big vision of the cloud and kind of
where things are going. Yeah. Absolutely. So, you know, we've
made a huge investment into the cloud platform and really building out the capabilities that
we've got, and that started very much from the point of view of analytics
and giving people a platform to ask a question of data and get an answer.
And then as we've been cremented put more and more users in that seem
more and more adoption. Then we've been able to add more and more capability
to that, increasing the breadth and depth that we have, including some of
the data integration capabilities you've seen today, the AIML, the automation stuff,
that's all sort of been brought in around that, and I think the cloud
gives us a huge advantage in as of how we put capability into the hands
of users. I think a lot of people look at the cloud and say,
well, this is a great opportunity to me to save some money and
let the vendor take on the cost of management of the infrastructure, which is
absolutely part of the proposition. But I think for me, the most important
piece is that we can deliver incredibly rapidly in the cloud platform from a product
perspective, and then we can enable consumption for our customers, allow people to
experiment, to try new capabilities out to drive adoption. We can speak to
them into that platform. We can provide training, help and advice and pointers
directly as they're engaging. And I think the final piece there is the cloud
because it is an opportunity to create a closed feedback loop, which is one
of the themes of what I was talking about today. But we can leverage
antlemetry to see how people are using the product, how they're engaging with their
data, and that then gives us an opportunity to find new ways sitting about
new ways to improve things and help customers, as you say on that journey
to the Cloud, and I heard that two thirds of the cloud customers are
doing something with AI LML, which is a fantastic statistic. And you know
the I'm a media guy, and I will say that narrative in the media
around AI always tends to be wrong, is how I would put it right.
I mean, they think AI and mL are going to help tremendously by
tackling tasks at scale that are very tedious that no one really wants to do
anyway, and that they're going to supercharge your efforts to improve processes, to
come up with new ideas, new products, new services. Let the machines
do what they do well, let the people do what they do is create,
design, invent in innoby right. Absolutely, I mean that takes us
back right to the foundation of click right. We've talked a little bit about
the thirtieth Anniverse we did this year, but it all started with some very
smart people working with the customer to help them solve business problems and the whole
idea there was the power of the human and the machine. The power of
the humans ability to ask a question and the machines ability to augment that.
I often have also kind of the Scott and Kirk analogy. Yeah sure,
yeah. So the notion there is that startup would never have got out of
season one if it wasn't for the bravado and good looks of Captain Kirk,
but with the mathematical science approach of of Sculty, and I think that's what
we've been trying to do with our associative engine, our analytics engine, all
the way through to what we're doing with AI and an mL today. Interesting
damnagym of a doctor not a bus boy. I remember those old days.
It's good stuff. Well. And then speaking of the future, right,
I think the future is in the cloud. There are still going to be
very long tails the legacy systems on PREM. I don't think on Prem is
ever going to go away. But in the cloud it's such a beautiful marshaling
area. And to your point, with the telemetry, you can understand better
the use cases, what people are doing where things aren't working so well,
where things are working well, and that way all the optimization on the infrastructure
side happens underneath the covers, and the client doesn't have to worry about that.
They just log in and get a good experience. Right. That's the
idea, isn't it absolutely? I mean, we want to make sure that
we're providing highly scalable, always available, high quality to our customers, and
they were always getting feedback on how we can improve that. Ever, want
to lose sight that every time we deliver something to a customer and they're going
to find something that we didn't know about. They're going to find a new
use case or a new idea or a new application, and that gives us
the opportunity to learn. You talked about AI and m L. Another great
statistic is you know over seventy percent of our enterprise cloud customers are using automations.
Now they're using that to not just orchestrate elements within an analytic data pipeline
in the cloud, but also importantly to extend the reach of those analytics.
And I've spoken a lot about the idea that analytics should not be a destination,
right, not something you have to log into. They come to you
where you are, wherever your job is. And the automation capability and now
just to take that embedded analytics to the next level, not just to inform
around an action, but actually to compel that action, and it's doing something
different. As you know, that is what drives change, is that's what
creates value. Right. Well, you know, I start Josh good demo
today, which I thought was fantastic, and he had this really great quote
where he said, it's very interesting in the middle, right, because in
the edge, you know, well, people might just turn, they're probably
already going to turn. But in the middle you can learn things. And
again it's not that the people are ever going to go away. You're always
going to want people in the loop, very actively in the loop, being
educated by being inspired, by being even a bit instigated by the machines and
by the data. But when you start playing around, if you can capture
all that too, the chant of transformations, what's been done, what the
results were, then you get this virtuous circle that's just going around and around,
right, Yeah, absolutely, I mean it's the ability to spot an
outlier, the ability to always ask any of the next question is again one
of those things as right at the heart of what click has always been about,
right, our free form exploration experience is all based around the fact that
in a sequel structure, which is great for the movement and orchestration of data,
it's a terrible analytics parabigne because you have to pre rould your model,
you have to know what question you wanted to ask, and as you it's
that gray area often in the middle, all completely around the periphery. In
other cases that the most interesting insights lieing. It's the ability to go find
them through a natural exploration that is so important. Well, and so yes,
I completely agree with you, And discovery is my favorite part. I
mean, doing all the stuff in the back end is great and it helps
the business get stuff done, but the discovery is the fun stuff. That's
when you're learning about your business. That's when you're understanding. And what's so
cool about the cloud from my perspective, one of the things at least is
that it is this marshaling area and so when someone finds something and they do
something, you're not going to lose that in an app somewhere on trem that
the person leaves and now it's gone. So like this collective intelligence is building
up over time and if you manage it correctly, then everyone can benefit from
all that and yeah, you can change your mind down the road, but
at least you don't lose all those little insights that got you up the mountain
in the first place. What do you think, joh Well, So,
I love the term collective intelligence. I think, you know, the collective
power of people coming together, particularly diverse thinking, right, I mean,
I think that's an really important component. How do you bring diverse perspective together
to sol business problems? And from an analytic perspective, we can do some
of that as well. We made a big investment in bringing collaboration capabilities into
the cloud. Quite often, collaboration is something that people do at the end
of the process, they get an insight and they's like, well, Eric,
what should we do about this insight? Well, we're able to do
is support you and I working together collaborating around how we find that insight,
validating that that's the right insight with the right level of detail, and as
you say, you don't lose the thread of that conversation so you can pick
it up at a later day. So that collective intelligence a component I think
for me is super super important and it aligns I think to again another principle
that hopefully you saw to day, which is everything we're trying to do is
about supporting users from a sort of a singular data foundation, that singular data
fabric but making sure that what with the analytical maturity, what type of analysis,
descriptive, diagnostic, predictive, prescriptive analytics, right, and they can
do that also irrespective of their level of data literacy. Yes, all about
putting those insights into their hands where it makes sense for the jobs that they're
doing. Out of time that they're doing that job right, These people on
the front lines, they are the ones who need the insights to make decisions
in the moment. Should we keep this customers? Should we not keep this
customers? Should should I invest another hour attacking this person on the phone,
or should they say, you know what, We're very sorry that our services
didn't help you and we wish you all the best. Right not Our customers
are good customers, most of them are, but aren't right Well, I
mean again, another great use case there for author mL right, you know,
suggesting that next action. But also then you start to see the value
of these things come together. You know, the ability to leverage autogomer around
churn analysis, and you know understand how you want to react to customers.
But when you find that insight showed in his demo today and you want to
actually then say, well, you know what, I'm going to put them
on the next plan and if I if I'm modeled that out, their chance
of churning is going to go right down. That's amazing, that's great.
But well, now if you say we'll make that happen, use the automation
to create particket in something like a service now to update the record in a
CRM system to actually make that plan change happen. Yeah, that's the next
level of action. That's brilliant stuff, folks. I don't want you to
look online click q L I K click as any of the company. James
Fisher is the expert podcast bonus segment is coming up next and I just say,
folks, it's closing the circuit. Really, We've talked about this for
twenty five years now. I've been in this business a long time. We
talked about operationalizing all that fun stuff. It's finally happening. That's what we're
learning about at click World podcast. About a second is coming up new dot
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two. Welcome back to Inside Analysis. Here's your host, Eric Kavanaugh.
All right, folks, back here at Click World, Inside Analysis, and
I'm very pleased to be speaking with our next guest, Angelica Cletus. She
has a partner with Click and she's a teacher, and she's written a book
and she does all kinds of things. She's all about data. Her company
is called bit Metric. So Angelica, first of all, what was the
book about. The book is about data literacy in practice, which helps people
to understand how to work with data, how to make data informed decisions.
M that's a good thing to do. Data literacy. Yeah, it's been
around as a practice for a number of years, but it's still not really
taking off. I mean, I don't think enough people appreciate the importance of
understanding this concept of data literacy. What are some of the basic tenets that
you start like our first day class, what do you say that everybody is
actually in fact a little bit data literally, because we make data informed decisions
based on this thing that I have in my hand. When we book a
journey, we go to Airbnb or we go to booking dot com, we
set up parameters, we get some results, and we look at the stars
now many stars. We to reviews, and then we discuss it with the
family and then we make a data informed decisions. And it's maddening that that's
not happening until now. In businesses. Yeah, well, you know,
there's so much data and organizations today, and I think that's part of the
challenge is that it's overwhelming to a lot of people. If you look at
the history of data warehousing, most of that data came out of RP systems.
It was relational data, structured data, transactional data really at scale,
so you could understand how many widgets you're selling and what would happen if we
did this for forecasting and planning. But these days we have all this other
data I mean, we've got observability data from systems because of Pubertennis, We've
got all sorts of data about pieces and it gets to be very unwieldy.
Sensor data IoT, which is all over the place these days, and wonderful
stuff. When you do IoT correctly, you get asset management, you get
predicted maintenance, you get all signs of fun things. But what's your advice
for carving a right size of data to play around with him? To work
with al? First you have to look at your future state of your organization.
Where do you want to go? What's your vision? Where do you
want to end up? So I truly believe in creating a vision, but
also looking at where we are now and then the path towards A to B.
That should be your strategy, and your strategy can help also divine your
data strategy. You can't call it at all anymore because we have too much,
if way too much. We have one point seven megabyte per second per
person on Earth that we are creating every second, so it's crazy. We
can't have it all anymore. Well, and there's also this whole shift in
data processing where first we persist data and now you can stream data on These
are two different architectures and two different paradigms quite frankly, very very different.
You need different tools, different mindset. What are your thoughts on this sort
of rivalry that's coming out around now, persisting data versus streaming data. I
think it's two different worlds, right, So you need to think of the
two different worlds separately. You can't have it all in one. From my
opinion, you should. You should think of a strategy for the one for
the one side and the other side as well, because streaming data is something
totally different, but it can bring so much value to an organization, So
you have to think good of the data strategy, which you have to keep
them separate at the end. That's interesting, So I love your focus on
strategy. I've had this belief for a long time that every company of any
significant size should have an information strategy group, even if it's one person or
three people or something small like that, especially these days, because things are
changing so quickly, the number of ways you can get data, the speed
of which you can get data, how you can analyze it, how you
can operationalize it. What are your thoughts about this core component of an organization's
hierarchy if I look at it, and it's also described in my book We
at my firm bit metric. We work through the concept of four pillars.
The first pillar is think about organizational data literacy. It's about planning, it's
about sponsorship, it's about embracing data as an organization as a whole. The
second element is data management and there it fits right there. You have to
think about your data strategy, about the vision that you want to reach and
the path towards it, but also the governance. What can we do with
a data in the future, because it can bring also new models, new
business models, new innovations. And then of course you have the analytic tools
because you have to work with tools to create beautiful dashboards and insights. And
then the last a pillar is about education. That covers all four right and
from there, and it's not mandatory to begin with organizational I see organizations start
with analytics and it just thinks starting, oh, we need to have a
plan because we are growing, we are going too big. And then suddenly
also data management comes in place or education, so it varies a lot,
but you know, it's it's very important to think of those four concepts and
at the end you can buy it flows together. You know, I'm going
to throw out to you one of my big pet peeves about web data,
and in particular in the sales and marketing world, we have now eight thousand
software as a service applications that you can use to automate either sales or marketing
in all sorts of niches. Influencer marketing for example, email marketing, social
media marketing, all these different ways you can do things. And from my
perspective, the numbers are always dubious. Well when I look at the numbers,
and I'll say, when I buy advertising, which because I'm dabbled in
YouTube and Facebook and Twitter and LinkedIn, and they're very different in terms of
what you get for your money. But you will, for example, on
Twitter, if you give them ten dollars, instead of getting one hundred and
ten impressions, you get a thousand pressions. But what were those impressions?
Like? Is that real? Who are these people? You need the details
and you don't, you don't. You never seem to get all the details,
it seems to me. And they're always changing things. They're like changing
behind the scenes. And if you think about from a core perspective. These
social media companies, what do they want to do? They want to sell
stuff, right, they want to make money. They want to engage and
do content. But it's all to make money at the end of the day.
And so to me, that is this constant pressure that's pulling them in
a direction away from authenticity, away from what we could call integrity in data
and reporting. What do you think about that and my paranoid or do you
share that opinion? No, I shared that opinion. I don't have the
sales example, but for example, in one of my presentations, I think
it's still somewhere online. I was talking about my fitbit. Okay, I
usually now I wear a garment, but I used to have a fitbit,
and the moment I got a Fitbit, I was thinking, I can create
a cool dashboard of my own data, combine it with the fitness and my
fitness spell you know, about my food, my consumption and everything. And
then I wanted to download my data and I didn't get it. If I
won't my full set of data, including my heart rate and everything, I
had to pay again. Yeah, but it's my data, right, Yeah.
And that's the same with with with all the advertisement and everything. Everything
is kept. It's it's, it's it's it's sneaky data. I like this.
I haven't heard that before. I'm going to use that one. Sneaking
data. That is funny. That's a good one. Yeah. Well,
and then so you mentioned data ownership, right, and I'm on Facebook,
I'm on LinkedIn. Well, obviously these engines are using personal data to train
their algorithms, to train their predictive models for what kind of content to serve
you, what they might be able to sell you, all these different things.
And then you look at chat GPT. Well, where the chat GPT
and the other large language models get their training was on web data. They're
just scraped from all over the place until twenty twenty one. Right, Because
chat GPT is cool, right, it's a cool feature. I use it.
I use myself all the time. But the main issue here is when
you use open eye like that, you have to fact check. You have
to check the results that are there. That's right, don't use it just
one and say because we are biased, all we are biased, all we
are We need to not assume that everything is the truth, because then we
get the fake right, then we're coming too the fake area right. And
I truly believe that chat ept is powerful. It can help fact check,
check your text, check everything, and then you can do something. So
to assume that everything is right, right, it's wrong. Yeah, No,
that's very true. I was joking just yesterday that chat GPT on a
webinar. Someone looked me up and posted it in the chat and it was
correct. I was like, wow, I was kind of impressive. And
I did it myself and it told me I'd written three books and I was
like, what are you sure? Are you seeing the future? Because I
haven't written three books. But it conflated me with other people, I think
because it associated the Blur group up with Blur Research, which are different companies,
and it just assumed and that's basically what it's doing. It's assuming things.
Yeah, that's very dangerous. Yeah. So, but I will say
that, you know, some of these folks who are engaging in a sort
of philosophical exploration of chat gbt's views of the world and of itself and of
sentience, I think it's a misuse of the tool. Rightly, it's not
there. It's not supposed to be sentient. It's a predictive engine that's trying
to satisfy your prompt with information from its sources. Yeah, right, So
you do have to know how to use the tools. And that's part of
data literacy too, Right, that's data exactly. That's it's it's not only
about how to use it, also how to question, Yeah, because we
tend to forget to question the things that we see or read. I'm Andy
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