Hey. This is Frank here. Just, wanted to break things up a bit and
do the intro myself and share with
listeners a bit of good news and express my deepest gratitude
for you all. Yesterday morning, I got
hundred of AI podcasts out there. We secured a
spot at number 38, which is enough to get us on
the Casey Kasem show. For those of you kids that, are too
young to get that reference, basically, it's good to be in the top
40. Anyway, on with the show, and I had a great
conversation with Dean Guida. And we did a bit of
reminiscing about technology, and his transition
as CEO of Infragistics from building
client software control components into the data
driven world. On with the show.
Alright. Hello, and welcome to Data Driven, the podcast where we explore
the emergent fields of data science, artificial intelligence,
and data engineering. But my favoritest data engineer
today could not make it. He is
unable to make it, but I'm excited today because we have someone who
is uniquely positioned to talk about history. And
for those of you that have been listening to the show for a while, you
know I wasn't always a data scientist. I didn't always even like statistics, if you
can believe that. With me, I have Dean Guida?
Dean Guida? I'm sorry. I should ask that before. We were reminiscing
over too much stuff, but he has 35 years experience, and he's a
CEO and founder of Infragistix. Infragistix
is if you're a developer in the, front end UI
space, you definitely know the name. I myself was fan boarding out. I
even pulled out the, tablet license plate I had when I
was a tablet PC MVP. And he has a new book called
When Grit is Not Enough. And he wants to
help entrepreneurs and CEOs create agile data driven learning organizations.
See, we are going to loop it back to AI. We're not going to be
talking just about Windows development and wind against large
funded questions. Welcome to the show, Dean. Yeah. Thanks. Great to be
here. Yeah. It's it's awesome because I'm like, you know, I get a lot of
these things and I don't, you know, shame on me. Right? Like, I don't always,
like, read the bio right away. Today was one of those days. And,
I was like, CEO of Infragistics? Wait. That Infragistics?
What? So so tell us a little bit because, you know, not every
most of our our audience are data engineers or AI people who may not be
recovering Windows developers. So, tell us a bit about
Infragistics. Well, I mean, we got started, you
know, in 1989 even before Windows
was even popular. I mean, so we got started. We actually
first built our first product was UI
components, but for Windows 2.0 and,
and then the big innovation going to Windows 3 0, way back when,
was just overlap Windows. And so this is going way back in
history. I know that's not what the subject of the show is about, but we've
been building UI and UX tools for professional developers and
designers for 35 years. We build data
analytic and predictive analytic engines and SDKs
for software companies as well as
AI and conversational AI, you know, against analytic
back end different databases and and data stores and, that
we sell to other SaaS or software companies. And then we're,
we also have a product called app builder, which is for professional developers
that's really great at going from design to code. So, like, your design
systems in Figma, which you don't have to really use, but,
we we we can do go right to production code in
React, Angular, you know, all the different JavaScript
frameworks and a whole iterative development to build commercial
apps and, round trip with, GitHub and everything.
And and then another, product that's our first
kinda b to b nondezigner developer toolkit is Slingshot,
which is an AI data driven work management
tool where we're leveraging AI and data, but it's all
about creating this, data driven learning
agile organization where the hypothesis is where
we connect data to all of your, business
systems. And, and then you create these objectives and key
results. So you're measuring each objective and you're kinda
prioritizing your key actions to achieve those objectives. And then
we're tapping into all your systems that we're giving you signals for the
all those, objectives and key actions. And then typically what
happens is, you know, things are don't go as planned. And so you're reading
these signals, and then you collaborate with the team to
hypothesize experiments to do to improve business outcomes. And so
that whole kind of a flywheel of execution, a lot of
tech companies do it, and a lot of companies don't do it. But, Slingshot's
amazing at doing that, managing work, and but bringing in,
all the analytics and data across all your data stores,
spreadsheets, business systems, and facilitating
this, you know, go to market, the whole collaboration with the teams
to drive business outcomes. So That's cool.
And I love how, you know, you you've obviously been in the game now
going on 35, 36 years. Yep. And you've
evolved with the time. Right? So the when I left kind of the client
development world, I, you know, yeah. I
used to be I used to have the MVP program when I was a Windows
when I was a tablet MVP, if you can imagine that. Right? And I
remember you had the first I think it was one of your employees we were
talking about in the virtual green room, a gentleman named by the Ambrose by the
name of Ambrose. And he was he was telling me all about, like, you know,
what they're gonna do with tablet PC, inking controls, and things like that. And I
was like, like, woah, that's really cool. And, I
remember, you know, you see but you've you've definitely kept I have to
say I have to hand it to you for keeping up to date on this.
Right? Obviously, the vision of the tablet PC and Windows phone never
came to fruition. But, you know, here it is in 2025, and
you're making, you know, slingshot, which is basically kind of, you know,
not just cutting edge, but kind of ahead of the curves. Right? Curve because it
sounds very agentic. I don't know if you use, you know, you know, quote,
unquote, agentic AI as the, you know, the as the dictionary would
define it. But, I mean, you're basically doing workflows and, you know, AI
plus workflow is arguably agentic. Yep.
And another thing that we've focused on for a really long time and
still do is simplicity and beauty. Like, we always
talk about simplicity and beauty, and so we really care
about the user experience. And and so
everything, if you really try and implement, which is super hard to do, easy to
say, if you try to make the whole experience simple and
beautiful, then people will love your app. And so we really
strive to do that in Slingshot as well as when people use
our UX and UI tools that we're enabling them to
build, you know, beautiful and simple applications. And,
and then AI is just, of course, as we all know, it's just been amazing
that, you know, we leveraged AI to really for really the user experience
where you can just have a conversation and ask about, how did this
digital campaign go, and what was the average cost per lead for
this, or what's my sales forecast, or, really
anything where you're combining, data that may
span multiple systems to actually give an answer. And
so we leveraged, what we're we're we're we're calling conversational
analytics, but, you know, it's actually technically quite
complex, but the user experience is quite simple. That
was always very you know, as a as a user of your
Windows form heavy user of your Windows form stuff and your WPF
stuff, I was always amazed at the documentation,
how well the documentation was, plus all the options that you had to, like,
tweak kind of the the the base controls. And the first
project I used it on was it was a data grid control
for asp.net. This is going way back. I mean, this has gotta be
20 years. And I remember I was we were you know,
I was a consultant at a company, and
the company had a had a very strong not invented here
mentality. And this guy's like, no, no, no. I'm going to build my own data
grid. I'm going to do this. I'm going to do this. And I just remember
thinking like, why? Like, you know, I
forget what the cost was for, you know, the entire suite of stuff. I'm like,
you know, you could just buy this. And I don't know what
your hourly rate is, but I mean, it
seems like it would be a bargain to get the invagistix controls and
just just use that because when it breaks, you
know, we can call them. Right?
Versus, you know, when it when this breaks and you decide to move
on to another company, we gotta call. Right?
And for me, that was, like, an enlightening moment of, like, understanding, like, oh,
okay. Like, buying these premade components off the shelf, it's not
quite the same as, like, commercial off the shelf software. Right? It's more
like the IKEA model where you can kind of like or Lego, right, where you
can kind of take these bits and pieces and blocks and build something
custom with all the many of the advantages of
custom and almost none of the disadvantage of custom. Right?
Like, there were only, like, one time over maybe a span
of when I was doing front end development. I think there was only
2 times, like, ever that
whatever we needed to do, your controls out of the box couldn't do. And
this is across 50, 60 projects.
Yeah. Awesome. And, like, just just like twice, that was an issue. Right? And even
then it was kind of, like, well, do we really need that feature set? And
we kind of, like, walked back on it. And I think in in one case,
we did another third party thing that did exactly that. But I mean, for the
most part and that to be fair to to you, it was a very niche
thing. We were basically doing things to the tablet SDK and the tablet
interface that nature
never intended. Right? We were trying and I I because of a very
strict NDA and, like, who the customer was, I can't really say who it was.
But it was, you know, 3 letter agency
related type stuff. And what they wanna do with it was kind of
like when I heard it, I was like, well, I think that's
possible. So anyway but but so,
like, so you clearly have a background, and I did promise not to fanboy
out. But Yeah. Appreciate it.
Well, I I love meeting veterans in the industry because, like, we've been through
so much and Right. So much technology change
and so much what's important and and and just
so much advancement with, where technology is today.
But but, yeah, we're still building grids. And and, like, we have
the fastest grids on the planet, which we really pride ourselves that we
can handle, market data. We can handle IoT
streaming. We can handle really fast data. And but then there
we go real deep, like like, you talked about that rich functionality.
So, like, spreadsheets and pivot tables and regular
grids and, you know, the state of the the web market, which is the
biggest developer you know, really big developer market now is,
you know, a lot of people use open source, which is fine, but
people are, like, still settling, like, just to have a table and
not have, you know, locking columns and, you know, filtering
and searching and performance and paging with large data sets
on the back end. Like, I I don't get why people just settle for, like,
for that. And, so it's, like, we've we've come really
far, like and then we also sometimes regress a little bit.
That's a good way to put it. That's a good way to put it. One
of my former, my former managers at Red Hat had us a saying,
and he's known in, like, the Kubernetes space. And he goes, the
best trick the devil ever played on people was that he didn't
exist, convince people that he didn't exist. The second
best trick was to convince people that open source software was
free. Yeah. Definitely. It's not right.
I mean, it's it's free with, like, but free like a puppy.
Right? Like, you know, you have to train it. You have to do all these
things. So, you know, it's it's especially like
because, you know, red hat is, you know, their you know, my day job is,
you know, the bread and butter is, you know, basically selling enterprise
grade open source, which, you know, from the looks of
it, you're like, well, wait a minute. You can just pull down the source. Why
do you need a a license? Well, let me tell you why. Because
when it breaks, you're not going to be hitting Stack Overflow or
the GitHub comments, not with the GitHub thing in the middle
of the night. Right? You want to talk to a support engineer. You want to
have that. So it's it's it's fascinating
to me. So so tell me, how did you, like, what was your first move
into AI at Infragistix? Right? Because, like, clearly, like and
you did mention you've you've done a lot of data analytics type stuff. So
so from my perspective, I only remember Infragistix as
a control, you know, UI kind
of widget module. Yeah. I forget what the exact thing
is. But how did you get into data and AI? So we we've always been
really good at data visualization and having all these kind of,
components for that, and then also just dealing with, large
data and moving data around. So, we were we we
already had those kinda assets. But probably about 10 plus years
ago, we started we took those components
and built out an SDK, you know, for the cloud and, that
you can just very easily have a, data
access, dashboarding experience that
so other SaaS vendors can have it, and it and it's beautiful. So we started
building our Reveal. The product's called Reveal. It's embedded analytics specifically
designed for software developers and are are
really, we just sell it to other ISVs, other software vendors.
AI, we and and in that toolkit, you know, we we invested heavily
in ML, so hooking into, you know, being able to kind of
put ML into the data retrieval and the whole data
set and and doing predictions through that. So that was kind of our
first entry into AI, just really integrating, machine
learning and and also trying to use machine learning. We
spent a lot of money doing machine learning and not always so successful,
you know, trying to do, better predictive analytics. That was kind
of our first, entry into it, but we've lessened.
Since then, we've come a long way. So now, in
in in the Q2 of this year, we have it in Slingshot first. So
in Slingshot, like I said, you could just have a conversation,
and, we'll answer you with a beautiful visualization,
and we'll give you the answer based on, any question across
we train the AI in all your business systems. So whether, you
know, Salesforce, you know, your CRM,
your, your mark your your marketing system, your spreadsheets,
your financials. You could have a 100, you know, different
business systems. We train it on that, and then it could answer the questions and
give beautiful visualizations. And then we really cared about the user experience,
so we give you very succinct answers. But then many people don't trust the
AI, so then you could click in and get more info. And we tell you
the data sources, how we calculated it, if we're actually bringing
in data from multiple, back ends to calculate maybe, like,
customer acquisition costs or something. So we give you you know, you can
go in and then trust it and get more information, and we'll also
even suggest other, metrics and, and
data you may be interested in that that's kinda within that that, area of
questioning. And and so, we first started reducing
that, in Slingshot. So you can go from you know,
a lot of people like, data's locked up, so we all use all these business
systems. And everyone wants to be data driven or or most people
really wanna be data driven, but we have data locked up in PowerPoint,
spreadsheets, and business systems. Not everyone knows how to go
in and run that report in a, you know, Marketo or some
account based marketing system or CRM. And so
it's really locked up so people still make these decisions
without fact based when they can be making fact based decisions. And so
we we unlock that in Slingshot. And then with AI, we unlocked it at
another level where, you don't even have to know,
we where the dashboard is or where that widget is. You could just
ask, and then we'll display the visualization and the insight. And
then you can go from that to, you know, conversation to action right
within the same, tool. And so,
so, yeah, it's it's really exciting what we're all able to do now with
AI. And, but so we we're approaching it just
from a user experience point of view. How can we make it easier
to make data driven decisions and put it in a work management
tool so that you're getting insight, you're collaborating,
you're, you know, because a lot of times data just tells you what's happening, not
why. So a lot of times, so you show what we'll tell you
what's happening through your business systems. But then in Slingshot, you can collaborate
and create hypothesis. You know? Why is that happening? And then, okay,
here's an experiment to go and try and change that,
outcome we're getting to drive some some business objective, like, you
know, better sales, contributing to pipeline, more business,
closing business, or, you know, reducing or increasing
renewals or what whatever you're you're trying to do.
Interesting. And and and it's interesting because, you know, I was at
Build 2016, and they introduced the idea of chat bots
being widely, you know, used. And at the time, I was
very skeptical. Right? Because they, you know, on on stage, they they they think they
use Domino's or whatever, and they said, I'd like a pizza with this. And this
is pre transformers, pre all that stuff. So it was very
more traditional natural language processing type technology.
But the more I look at this, what you describe with slingshot, right,
if I'm a salesperson or whatever, I can or marketing or or
whatever, you're right. It's amazing how silo data still
is Mhmm. In 2025. Granted,
we're in early 2025. So maybe by the end of the year, it'll improve. But
I don't not holding my breath on that one.
But the whole notion of chat as a as
an interface. Right? Is that what
Slingshot does? So Slingshot, we we added
that capability in Slingshot. So Slingshot, like, functionally,
it's data analytics, it's chat,
it's digital workspaces that, also have, you
know, Gantt charts and task management, but it's
lightweight. So it's work management, not project management, even though you could do
heavyweight project management. So it's like a lot of people
know Monday or Asana. We're we're that,
but we're we're really heavy into data analytics and now AI, using
AI to make it easy to, interpret
and get at the analytics. And and and then so other features in there
that are AI driven, but, so that that that's what
Slingshot is, and it's all about, like, helping people, you know,
if you're a marketing team or you're a business team and just helping
growth and using data and managing work. And and then also because
it's all digital, it's creating trust and transparency across
your across your teams. You're seeing what's going on. And,
so it's it's AI data driven work management. And, like, when we
talk about creating a learning organization and actually part of my book,
what I write I write about a lot of this in my book. But,
once you kind of set your objectives using we're a
big fan of OKR. So once you set your objective and you define your,
like, 3 to 5 key actions to achieve that objective,
all those can be measured, and then we make it really easy to
measure that through your operational systems. And like I
said, you then you what you do is you hypothesize, like, what's
happening? Why aren't we achieving those objectives or or what's happening in those
key actions, and you hypothesize things you can do
and experiment, and you intentionally, you
know, collaborate and and and come up with these experiments that you can quickly go
and try and collect data and learn. Okay. It worked
great. You've solved the problem. Work partially, but you learned something or
or failed. You learned something. And so excuse
me. That's what we mean by creating a learning organization. We through the
tool and through this philosophy, you teach people how to problem solve
using data, staying focused on objectives and and key priorities
to achieve those objectives. And then, you know,
hypothesizing what the data is telling you, why it's not working, and
then creating new experiments to solve that problem. So that's, like,
how you're creating this problem solving part of, like, what our
goal is to create this data driven agile learning organization.
You're teaching them how to learn, how to solve problems. And when you do
this, it gets pushed to everyone in the company instead of, like, the smartest
person on the team or the exec. That's not where you have resilience
and scale a company. You need to push this problem solving out to all the
edges of your company. And so Slingshot really enables that.
Interesting. So you're not just changing you're not just adding technology, but
you I think you're teaching people a different way to use technology.
Yeah. How to, like, run company, solve
problems, and and grow.
Interesting. Because I I think
that's the missing piece for digital transformation.
I mean or one of the missing pieces. Right? Because the the, you know,
digital transformation is a word that I think induces a little bit
of, people wanna, you know, get
sick on that. Like, they hear it and they wanna throw up a little bit.
But it's a it's a shame because, like, what it could do versus what
it actually gets implemented as is is is 2 very things. I think part of
that is that people don't think about the basic workflows like you were like
you are, or like, you know, where the basic kind of like tooling or the
basic mentality of be very experimental, be very data driven.
And, you know, it's you can't slap,
you know, a digital coat of paint on an old way on on an old
process. Right? Right. I mean, well, you can, and it's certainly been
done. It's just you're not gonna get those same results, and it's to the same
point now when when most people say digital transformation, they kinda
cringe a bit. You know? Yeah. I mean, it it means so
many different things. And it and based on the organization, it
like, there's different levels of transformation. And,
but but, yeah, this whole thought process of how to run a company
was, like, the thesis of Slingshot. And, you know, now it's
aided by AI. And I think another thing that we did to try
and unlock data driven decisions
is we created a business data catalog.
So what we did was inside of Slingshot, there's a data
catalog where you can catalog all your metrics,
and, and you can even catalog your data sources. But and it's a
curated workflow where you can, anyone can go and submit
a metric or, you know, a widget or a dashboard to
it, but it's curated so that people are organizing it properly, and
then you can search it and you can certify it. And there's, like, three
levels of certification. And, and what we did
was if you certify at the highest level, we train the
AI on that data, and and only certain people have rights to certify it at
the highest level. So this is like another big problem. You a lot
of company or most companies at every size has so much data,
and all data is not truth, And all data is not what you
wanna use to train an AI because if you do, it's
gonna give you answers that that spreadsheet is not the where
we wanna get the data from, or that's not our system of record
in CRM. It might be in your financial system or whatever.
So, we we kinda implemented this, ability to unlock
and find information across your systems. I don't have to go to each business
system, find it in the data catalog. But then since we've, you
know, built the AI out, we leverage that. And anytime you
certify it, we we write all this the AI writes all this metadata
in there that the the user can actually edit, but, like, it's more of a
technical thing, but they can add to the metadata. And then it, and
then it trains the AI on it. And and so we're we're we're
using that kind of process to make sure that we're using good data
in your systems and spreadsheets and, so that you're
getting the answers that are are correct. So just having
data doesn't mean it's the right data.
Interesting. It's I mean, that's true. It has to be the
right data. It has to be not just the correct data, but it also
has to be correct in and of itself. You have to have a certain amount
of trust in that data, particularly as you start leaning on it to make decisions
based on that. Yep. That I mean, it
sounds I mean, it sounds very,
very intriguing. I'm definitely gonna go check it out. It's,
slingshot app. Io. Is that the cool?
Yeah. Slingshot app. Io. Interesting.
And are these, are these, it looks like you can
there's an IDE built into it. So that's pretty interesting, actually. I definitely
got to check it out. Because I think I think that as
you deal with, more and more
data sources coming at us, more and more, and
there's more and more kids join the workforce. They're gonna
expect some kind of chat interface with the data. Right?
Yep. You know, I have 3 kids and each one of them has it
represents a different kind of error in technology. Right? The the first one
was everything was a touchscreen. Right? Dad was a tablet MVP when he was
born. Right? So when he went to our
TV and he touched it and it didn't or any TV. Right? And it didn't
work whether it was here or it was grandparents, and he would touch the screen
and he would turn and say broken. Right? And or he would complain to
his grandparents, like, how come the TV doesn't, like, react to this? And
they were just, like, my my
second child was born in the the Alexa era, I like to
call it, because, you know, he would talk to
Alexa to get the weather, to Syria.
Siri, before he could write, he was able to chat because he used
Siri to write stuff in, like, and
read stuff to him. So it was interesting. The third one is 2, so
we're not really sure what it is, but it's probably gonna be some kind of
AI technology that, you know, just it's just he
takes for granted and is part of the, part of the
environment. So it's interesting to kind of see. But when those, you know, those
kids enter the workforce and and, you know, we're both old enough to
remember Windows 3.0. Right?
So, like, you know, when I have younger colleagues, like, the way they look at
things or they just take for grant things that they take for granted is kinda
I kinda laugh to myself. Like, you know, I was once given a a
when I was at Microsoft, I was given a a
demonstration of, like, setting up VMs in Azure or something like that.
Right? And it's like, let's create a PC and, like, you know, I go and
I check from a drop down. I want this. I want this. I want this.
And I click go and, like, you know, admitted into it. So one of the
kids goes, wow. This is taking forever. Yeah. Which I I
remember when I worked at a big bank, you know, to buy a server, to
requisition a server because of all sorts of internal rules and regulations.
I mean, it would take 6 months if you were if you were
lucky. Right? And if it was a really important project, you can get it done
in, like, 3 months. But, realistically, it was a 6 to 12 month
process. And this kid's complaining because it's taken too long to
requisition a virtual machine more than 60 seconds.
I think it's kinda funny. Yeah. I mean, voice
and seeing is just gonna get more and more integrated into
getting answers and getting information and
supporting you in whatever you're doing. So, yeah, we really are
at a crazy inflection point of, like, this
major next leap. And, so, yeah, I mean, it it
was like, oh, I typed characters to figure things out. Oh, now I have a
GUI interface. Helps me a little bit more. And, yeah, now it's
like, yeah, I just wanna talk and have that, you
know, and get stuff done. I I don't, you know, I don't even
wanna type. Right. Right. Well, it reminds me if you watch what's
now considered old Star Trek, but Star Trek the next generation where the
computer is almost like a character Yeah. Where they could just
say computer anywhere in the ship. It's like, can you figure out what this is?
And they're like, well, the probability of like it I think we're kind
of at that point, certainly with, you know, voice related technologies
and, the under language understanding that you get out
of these AI systems today is is is very impressive.
The book. Tell me about the book because it's called when grid is not enough.
So what's it about? Like, what's cause clearly, you're a
startup founder. You have been at least doing that since
1989. You're a CEO. You're still in the game. You stayed in the game.
You survived. Yeah. You you saw the
recession of 91. I'm assuming. You saw
the.com, you know, boom, the dot com bust,
the o eight financial crisis, you know,
pandemics and kind of everywhere in between. So,
tell me what where'd you get the idea for the title from? Because, like, if
you if you if you Well, it took a while to come up. It took
a while to come up with a title. I could tell you. It took us
6 months. Wow. And, I was gonna settle on
a title. I just I couldn't take it anymore. We brainstorm so much
on the title, and my publisher and some of our
marketing people are like, it's the most important thing. You know? And, I was
gonna settle on the next company. You know, being in the tech space, it's always
about the next thing, and and it's always building on something better.
And, and I was gonna settle on that, but,
when grit's not enough, it's because, like, every entrepreneur needs to have
grit. Like, fundamental thing is you have to be optimistic, and you have to
have grit. And, and so that's just a fundamental
thing. But once you start a company, grit alone won't
help you scale and won't help you be resilient and won't help you
survive. I mean, so, you know, early days for us, yeah, I could just not
take a salary and fix a problem. You know, you get but then you start
getting to a certain size that you're just not you taking a salary doesn't fix
your problems. And so, so what I did in the book was I
shared everything I learned over the last 35 years, in the
book, cover a whole set of topics to help
other entrepreneurs and CEOs just have a greater chance of growth,
success. And and so that was a motive, for it. And,
so when grit's not enough, it's that, yeah, you need grit, but it's not enough
when you get to a certain point.
Interesting. Interesting. Obviously, you pulled
from your life experience. Like, what was one moment
where where was the moment you realized that grit's not enough? Right? Like
Yeah. Well, we we had just merged with one of our
competitors, and, they they were
a a really good company. Great. We got great tech talent, great
sales and marketing. They had a lot of customers, but they made some mistakes.
And so they were they were in basically in debt. They were out of cash.
Cool. And so, we shared in software. If you remember shared
in I remember that. I remember when you I remember when it was bought. They
were one of the first vb one o visual basic one o
components, and they built the database finding layer,
Internet Explorer. There there it was like it was like we, you know,
some of those guys are still on my board. And so we've been together
now, for 20 plus years
now. But but, anyway, when we merged, it sucked a
lot of our cash off our balance sheet. And so we
literally had, a 580,000 a
month pay or or expense structure. And we had $618
in the bank. And so it was like we were legally
bankrupt. I mean, we all, we all knew we would get out of
it, but, it was, it was like, that was a big, big
moment where it's like, okay, you know, working hard,
working crazy hours, not taking salary. No, no,
no. There's got a there's a better way here. And so, that
that was a pivotal moment for me where,
you know, you start investing in systems, being data
driven, you know, better cash flow planning,
you know, a lot of the running better meetings,
you know, really thinking about where to focus and put
priority behind, you know, critical things,
aligning teams on that, prioritization, and how do you make
those alignments? And then it's all about the people. So if you read the
book, it's for me, and it always has been all about the people. So a
lot of it's about actually, one of our core strategies is creating a learning
organization. And so, and so I talk about a lot about coaching,
alignment, creating trust, culture,
how to be data driven, how to do go to market plans, strategic
plans. I didn't learn till really late in life about
recovery and taking care of yourself. You know, I come from, you
know, just suck it up and work harder. You know? And,
like, I I tell you, that's not the best thing,
you know, because, like, you perform way better with a good night's
sleep. You perform like, I I at one point, I had traveled for 3
months straight around the world, everywhere, and,
and that was like a big then I got, like, 1 week I was in
the air 50 hours just in 1 week. Wow. And,
so from traveling so much all around the world, Asia,
Europe, South America, US. I
actually got a, this pain in my calf. I
thought it was just a Charlie horse. It ended up being a blood clot,
and and then it went to my lungs. So I had a pulmonary
embolism. I couldn't breathe. And so I had to spend 4 or
5 days in the hospital. And I was like, that's another, like, I've, like, I
share these lessons in the book. That's when I learned, okay. Yeah. You
gotta, like, have recovery, like, perfect, like, today in professional
sports, you have amazing athletes in their thirties,
forties performing at high levels because they're worrying
about recovery. They're not just going they're just not going hard all
the time. And so, like, I even have a chapter about that. Like, you you
need about taking care of yourself and, and, you know, if you, you know, if
you're grinding it out 12 hours a day, that's, that's not good. I mean, you'll
get, you'll, you actually deliver more business value, solve
problems better, get more done if you like take time off,
take vacations, get good sleep, recover. You know?
It's so but from our generation, no. No. No. It's just like work
hard. And, Right. Suck it up. Keep Suck it up.
Yeah. No pain. No gain. You know? Right. And it's like
but it's funny. It's not just limited to our generation. Right? If you look at
the startup culture today, right, it's grind, grind, grind, grind.
There's, startup grind, I think, is
a it's a it's a startup brand and that they do. I think it's
backed by Google or something like that where they do they hold, like, kinda like
user groups and meetups and things like that. It's called startup grind. And it's
kinda like I get the the the the the visual of the
grind, but you also have to, like, lean back and and
and rest and recoup because if you and it's funny because I think
particularly for technical people or engineers, right? Like the thinking that is,
you know, how do you get a, you know, how do you get a car
to go faster? Well, you boost the RPM, right? You boost the you get to
boost the output, but we're not machines, like, in that same regard. So
you start getting diminishing returns. And, you know, I think part of it was I
learned that as I got older, like and I had kids. And I was like,
oh, I can't stay up for 48 hours anymore.
Right? And it it definitely
particularly if you're doing something like software design or AI or
data engineering, you need your mind to
be at 80% and up.
Right? You can't just kinda zone out. Right?
Yeah. Yeah. So I talk a lot about that and a lot of about the
book, which is just that teams, like, how to create high performing teams
because it's, like, in our business, it's all about problem solving,
collaborating, helping each other. And so how do you create that
environment and, and be real intentional about
creating that, and then you get innovation. You know? And then you Right.
You get, really good amazing pieces of software.
And, but but, really, the book applies to more than just
running a tech company. It's really every company now. I mean, people are people
are the foundation, and, and so I I I talk about all
those lessons I learned over 35 years, and and
some of it was a thesis of of writing Slingshot. You know, we
wrote it 7 years ago. It's been in market a couple of
years, but we run the whole company off of it. And,
and, so there's probably 4 or 5 or 6 chapters of
18 that is, like, the thesis of Slingshot that,
of, you know, how to digitize this this philosophy and this,
you know, way of of, running a company. Very
cool. Very cool.
I'm just fascinated that,
you know, you're you're you're someone who's had a lot of success and, like, you
you you kind of, like I love the fact that you kind of distill that
into a book that, you know, other people who who are you hoping will read,
and, like, what's the one message that they get away, you know, that they they
pull from it? Well, I hope a lot of entrepreneurs read it.
You know? And I don't think you could discount,
like, grinding it out. Like, even I think you do have to grind it out
in the beginning and, but it can't be the norm. It can't
be the, the way, the the only way.
And so I I just hope to reach a lot of entrepreneurs
across any every industry and, mid market
CEOs and, and even managers. I mean, there's so
many good good lessons in there that I've learned. And and I I love
learning, and I love reading. And, but what I don't like is,
like, you hit you you you are taught a concept in the first
50 or a 100 pages, and then the next 100 pages is, like,
10 repeats of use cases of it. And I'm just like,
like, like, my personality makes me read the whole thing. I'm trying to fix that
myself, but, like, I I've gotta, like, I read the whole damn thing or listen
to the whole damn thing. And so what I tried in my book was to
be really succinct, like, deliver a lot of, like,
playbook ways of doing things, give examples.
At the end, summarize the 4 to 10 key cape takeaways,
but not waste your time. So I was, like, kinda really more into,
you know, not wasting your time, and and deliver
as much value as possible. So so I try to achieve that in the
book. Very cool. No. I think you're right. The grind not not not
to to to disrespect the grind. The grind is important. You can't avoid it, but
I don't think if you let it consume you, you're got you're gonna weigh yourself
out. Yeah. It it's not healthy. And and if you are an intellectual
field, you won't you won't innovate and create your
best moments and your best ideas and solve the toughest
problems. I mean, it's, so, yeah, you you have to
keep that in mind. Awesome. Alright.
I'm gonna switch to the pre canned questions. I'm gonna put them here in the
chat. None of them are real brain teasers. We're not trying to do a Mike
Wallace on you and and trap you. I and I know you'll get the
reference because a lot of our younger guests don't, oddly enough.
We kinda did touch on this. How did you find your way into
data? Did you data find you, or did, did you find
data, or did data find you? Well, I like, I was
a engineer to begin with, so I worked on our products the first 5 years
of our company and, you know, working on our and, so I've
always been data driven. But I've continually got
better at it as every year went by. So I was so I
I don't think data found me. I think it was just part of my schooling,
part of my training. And then, then as I started running the
company, trying to incorporate it more and more, and and and
there's a lot of challenges with being data driven. Like I said, it's like, there's
not everyone's not data literate. There's outliers. You can't average
things. You and the biggest thing is people don't know where the the
datasets are that you should be using, and dataset's kind of a technical term, but,
like, where is our sales data? Where is our customer data? Where
where is this data? You know? Where do I look? What's even though sometimes it's
repeated, where do I trust? And so I I think I've always yeah. I think
I've always been data driven. I I feel like I've yeah. So that that that's
my background there. Right. No. I mean, it makes sense because one of the problems
I've seen, I'm not gonna name any names, but places where I have worked
where there's multiple CRMs.
Right? Or multiple source of truth. And I think that, you know,
as I advised when I was at Microsoft, I would advise a lot of, you
know, companies on digital transformation. For those listening, I did the air
quotes. But the
the important thing, if not the most important thing, certainly
top 3 have one source of truth. Yeah. And it's
not easy too, by the way. Like because you have customers as leads
in CRM, then they have actually buy, and now they're act they're
in your financial system. Or you have account based marketing systems
where you're, like, marketing to an account, and then all of a sudden you start
pulling Zoom info data into that, and now you have customer names there.
So it's, like, it's easy even now how much
architecture and intentionality you have. Repeat
and data is everywhere, so it's important to be thoughtful about how
you surface that in decision making or training AIs
or, you know, doing all these things to make the right decision with the
right data. A 100%. And there's also a temporal cone
component to this too. Right? Because what if you have your your batch jobs, they
all synchronize, like, at night, but it hasn't happened yet.
Yeah. Like, well, the system said this. Well, when did it say it? It
said it yesterday. What time? 4 PM. Oh, well, that's why it's
inaccurate. Yeah. Right? You have to have a certain amount of awareness about that.
So you've been at your current gig for a number of years?
Yep. 36 years, you said? Yeah. I'm going this job will be
36. Wow. So clearly, you probably gonna have to struggle
to figure out what your what your one favorite thing is, but just pick one
favorite thing.
I mean, I I like, I like working with people, talking
to people. And then I just love learning too, by the way. Like, I
I like, as CEO now, I have a team running the company, so
I can pick I can't always pick what I do, but I also
can pick what I do. So, so I really like that.
And, so, personally, I just like to learn. That's my most
favorite thing to do. Cool. We have 3
complete the sentences. When I'm not working, I enjoy
blank. Yeah. I I enjoy camping,
cooking. I'm a I'm a gamer. I I love playing Call of
Duty 6 on 6. It's, like, very therapeutic
for me. So that's how I'd answer that.
Nice. Next one is, I think
the coolest thing in technology today is blank.
So sorry to say AI, but it's AI. No. So, I mean, it's,
like, amazing what's happening. And and robots too. I mean,
you know you know what I don't I know that's not part of the question,
but you know what I don't like is these big tech CEOs
overpromising AI. It's really messing people up in the market.
I can't believe how many smart people I talk to that tell me, Dean, what
are you gonna do? I'm like, what do you mean what am I gonna do?
You're you're one of your biggest revenue streams just selling tools to
developers. There's not gonna be any more developers. I'm like, no. No. No. No. There's
gonna be plenty of software developers, but, like, you know, the
so that frustrates me a little bit. And, but
AI, it's just it's just amazing, what to end robots. Those
two things are are incredible. No. Absolutely. I I
if you look historically, like, the the the the trend is automation tends
to over the long term re create more jobs.
Yeah. So but there's always that awkward
phase of fear and then a little bit of a dip. But over the
long haul, it tends to, you know, sometimes in, you know,
orders of magnitude, like, in terms of the jobs it creates versus whatever
places. Like, if you go back, we had another
podcast guest a couple seasons ago, and he
was talking about how most of the economies of the world
and most people, 90% were in agrarian,
were were farmers or or farm related. Right? Now it's
closer to 3%. Now a lot of that is because of automation. A lot of
that they became factory workers. And if you're in countries like, you know, the west,
well, factory workers aren't really, like, a big component anymore. Right? So it's
it's totally the the change is interesting,
and it's not we can't we we look at the future with kind of this
linear kind of hindsight, but not
everything is linear or ever was linear. Or Yeah.
Percent. Yep. Alright. Last, complete this
sentence. I look forward to the day when I can use technology to blank.
Well, I love technology, so I I I like it to do a lot of
things for me. But, shoot. I I I can't wait for,
Siri and Alexa to get smarter. I could tell you that. Yeah. I
mean, those are those are just dumb devices, and, but yet
they're all around me. And I and I I love them to play my music
or tell me the weather, but, shoot, I can't wait till I can just tell
it to go, you know, you this agentic kind
of things you were talking about earlier, like like, okay. Go do this for
me and, and then you report back and, that that's gonna
be amazing. It is interesting you bring that up because it's amazing how, quote,
unquote, air quotes again, stupid Siri and
Alexa got once chat gpt came out. Yeah.
Right? Because the language processing
on the Siri and Alexa hasn't really improved that much.
Right? And it's it's interesting to show where our
expectations as not just technologists, but consumers of
technology who are technologists. Right?
The, you know, our expectations now have been boosted
by, you know, OpenAI and, you know, to a
lesser extent, Google and and and and the other players too.
You know, what used to pass as cutting edge seems pretty, you know, quaint
now. Yeah. And I I love to tell my Alexa
to play my Pandora stream or ask the
weather, but I never get beyond that. You know? I mean Right. And it could
have done so much more for me. The the the example
I used to give a lot when I was doing presentations or live streams was,
I'd say, Alexa, you know, who is, you know, the Wu Tang Clan.
Right? And, like, she'll tell me, and I'll be like, what was their first album?
And up until about 2 years ago, she would say, first album was an
album by Flaming Lips released in 1975 or something like like,
completely non tangent. Like and I was just like, see, she that's
because I I would talk about the importance of context and and and
and language processing. I'm like, well, there you go. That is not something like
so if I ask you and, you know, if you're a Wu Tang Clan fan,
you'll give me the correct answer. Right? So like Yeah. Now she does
actually do that. If you try it with a number of bands, 90% of the
time she'll get she'll she'll she'll get that she'll pick up on that context. But
it's also interesting to note that sometimes,
you know, I'll hear an announcement on the Alexa. Right? And then, I didn't
hear it right the first time. And I'll say I was like, can you repeat
that? And after you wait too
long, she forgets the
context. That context window is something that's
hard to do for people to understand. But, like, you would think that
more than, like, 3 minutes, like, it should be able to hold
that. But so That that's the other thing I'm looking forward to. Like, even
the current state of AI now forgets
context and can't iterate Yeah. Changes things. And so
I'm looking forward to infinite memory that everyone's promising this year and the
year. When that happens, that's gonna really be
awesome to even bring problem solving and intelligence
more. So, I mean, that's kind of another short term thing I'm looking forward to
is infinite memory, which, you know, is always remembering context
and what you already learned, it can, you know, reuse and get to
know you better. Do you think there are any privacy concerns?
Oh, yeah. I have a privacy concerns. A ton of privacy
concerns. I mean, even now in,
office, you know, with the graph and, like, copilot,
I guess I have high you know, it's my I'm the CEO, so I guess
I have high authority or something. But I can, like, see what
everyone's working. Like, I could, like, see emails, documents.
Wow. Chats, like and I can ask Copilot about
it. You know? Oh, what's Jason Behrs working on? And it'll tell me.
You know? So there's like, even though I have the right to that is, like,
you know, the CEO. You also feel a little creepy. You know?
Yeah. No. I mean, that makes sense. Is that, there used to be something called
Delve. I think it has a new name now, but it was part of Office.
And I remember, like, when I was in Microsoft,
you know, I was able to look up not to the degree that for privileges
you have, but I could get a lot of, what the cool kids would
call o stage or open source intelligence on, like, what people were
working on. So if I wanted to strike up a conversation with someone, I'm like,
hey. How's this thing going? They're like, yeah. Funny enough. I'm working on it.
I was like, really? Do tell. Like, you know,
but they're always I think with AI and technology in general, there's always this
line of creepy and cool that you kinda have to to
to to cross. And I hope you know, the other thing I hope I know
it's not one of your questions, but, like No, please. This whole rewiring of
I don't know if you've noticed this, but, like, my kids are
30, 27, and 24.
Mhmm. So they kinda missed a lot of the iPhone, you know, a
little bit. But the generation after that
got rewired because of social and Yep.
The learnings and everything. I just hope AI doesn't do that. Not that it could,
but, like, that I can't tell you many people I mess I meet that are,
like, not risk takers or are have,
you know, they have these, like, I don't I don't
know terminology, but they have, like, these problems communicating,
and they have so I I hope a I don't think AI will do that,
but, anyways, that was a really we screwed that up.
Like, that that that we screwed up a lot of generation where they just
weren't going out, playing with each other, taking risk,
you know, collaborating, you know, falling down, getting
hurt. Like, we protected them. And then just like that,
you know, to communicate just like I don't know. It created a lot of
isolation and really messed up a lot of a lot of kids. Like, a lot
of people are on these these medicines. That's that's what I was trying to you
know, there's Adderall and, you know, anxiety. And
I don't think AI will do that, but, like, AI is getting trained on all
of our bodies of work now. But, like, there's still new thought
process even though it'll come up new thought process, but you still want humanity
to continue to innovate and exercise
in their own brains and come up with new ideas. Yes. They'll
use AI to do it, but I just hope we don't dumb down our generation
because of AI or the next generation, I say. Like, if we
reflect on what we did to them with social and and, mobile, you
know, and and smartphones, like, we hurt that generation.
Which is why I think you're seeing a lot more interest in terms from regulators
and AI. Right? Like, I mean, you're not They're never gonna they're never gonna keep
up. They're just No. They can't keep up. It's not Even even if it they
were putting smart tech people in government Yeah.
Man, it's just that's I don't know. Well, or you could over regulate too.
Right? If you look at the European Union. Right? Like, you know, there was the
joke of, you know, like, you know,
America innovates, China duplicates, and Europe regulates. Right?
Yeah. Like, I don't know I'm getting a lot of hate mail for that. But
but but I mean, you laughed at it, and it's a joke for it's funny
for a reason. It's funny because there's a lot of truth to it. And, you
know, you can pull up the data. Right? Like, how many, you know,
unicorn AI startups are there in the US,
China, and, the EU. Right? You
could probably count on, I'll be generous, 2 hands,
but that's probably one hand extra in the EU, like like it
or not. Like, you know, and I think that also underscores the other thing is
that one of the most powerful yet underrated forces in the universe
is unintended consequences. Right? Yeah. You know, when when
Facebook started, when Myspace started, right, the
isolation, the the difficulty in communication was probably not on anybody's
radar, yet it happened. Yeah. There's also my concern
is you have a whole generation of kids that grew up during the pandemic,
including my, you know, my 10 year old was, you know, he did
kindergarten by Zoom. Yeah. Which sounds like a
Saturday Night Live skit. Right?
I think that was a mistake. And I saw a lot of
problems in 1st grade with not just him, but other kids his age
where they just didn't know how to interact with other groups of other kids.
My grandmother, God rest her soul, she would have been about 6 years old
during the 1918 pandemic. And for the rest of her
life, obviously, I knew her later in life, she was still, you know,
wiping stuff down and and with Clorox and, like I mean, she was
definitely I I guess today they would call her a germaphobe,
but back then, it was kind of like, you know, she was very
particular about cleanliness was the Oh, sure. That was a major world event, and it
it it scars you, and it it imprints on your brain.
Yeah. So I hope I hope we teach these kids how to still be
creative, problem solve, use AI as a tool, but
don't I hope we don't dumb down humanity in the future.
I I want to believe, but I I I I have a a a very
deep concern with that. I think Yeah. Me too. It's best to
think of AI as augmenting productivity or augmenting
creativity. Right? There's a funny story. If
we get time, I'll I'll tell you that too about that. But
where can people find more about Infragistics? Obviously, Infragistics
Infragistics dot com. Where can people find about more about you and your book and
things like that? So me, dean.com.
That's where my book and some of the article. I I write some articles on
entrepreneur.com, and, that that's one thing. And then we have,
so slingshotapp.i0, and then our b
I, s t k is atrevealbi.i0,
and our, app builder, is at
app app builder dot dev. Those are our different
properties for our different, product lines.
Nice. And, Audible is a
sponsor of data driven. And, I was gonna
ask you earlier on, but I figured I'd wait till now. And then I have
in another window here. You have an audio book of
this. This is awesome. Yes.
Yeah. That's cool. So if you go to the data driven book.com,
you will go off to Audible as a sponsor. So you'll get one free
book, on us. And then if you choose up to
get a subscription from Audible, then, you know, we'll get a little bit of a
kickback. Help support the show, and I warned must warn folks that
audiobooks are very addictive. So I just got my new credit,
like, this morning, and I'm like, I haven't spent it yet, which is unusual. Usually,
as soon as it comes in, I hit the button. But, I see that your
book is there, so I'm totally totally gonna get that. Yeah. I I always
order my 30 credits a year to start off with, you know, get that good
discount, and, and they they are quite addicting for
sure. Yeah. But if you had to recommend a book that was
not your book, any any interesting recommendations for our audience?
Oh, I, I read so much. There's so many good books out there. I
I like I think it's called 10 x. Like, I think the book's called 10
x. So it's like, okay. Don't don't think about just, like, you
know, 2 two x implementation. There you go. Yeah. I
like that. Fan. The uncle g. Yeah. I like that.
Awesome. And then there was another book I really liked. Forget the title of it,
but where it teaches you about, like, there's the integrator
and then there's the visionary. And there's very few who do the both.
Interesting. Rocket fuel. That I like rocket fuel too. I'll
check that out. Yeah. Now that's cool. Like, and you're in Florida like
Grant Cardone is. Grant Cardone is Andy and I will talk about him as uncle
g as as many people do. I'm a big fan of his stuff.
I actually speaking of Andy and Grant Cardone,
I he got me this, I think for Christmas 1
year. It's the like, it Staples has an easy button. So
if you hit this I don't know if you can hear that. But
What did it say?
Oh, I didn't hear it. It's the audio is not really great through the speakers,
but, basically, it'll give you, like, a random, like, Grant Cardone quote.
Oh, I like it. Very nice. But yeah. So,
no. That's cool. Yeah. 10 x. I'm glad I'm glad there's a
fellow, 10x fan there. Yeah. I like that. Plus you're you're
in Florida, so you probably you know, he lives in
Florida too. So I didn't know that. Yeah. Yeah. He's in Miami.
Nice. I grew up in Miami. Okay. Cool. Cool. Yeah.
There's a city that's seen a lot of change. Oh my god. So much
so much change. Yeah. I live in New Jersey and
Clearwater, Florida now. And, so I went home
for Christmas to, you know, snow on the ground and,
but now it's amazing how fast your blood thins. Like, if it's 47,
50 degrees here, I got my hat on, my gloves. I'm
like, it's, like, cold. You know? But that's how
you do it though. Like, you have the snow for a couple days, and then
you're done with it. Like, we're in the middle of a cold snap year in,
and then Maryland, horse country, west of Baltimore. And,
like, it's it's it's not been above freezing now for, like, a week,
and I'm kinda done with it. Like, I generally like the cold
weather. But, but, yeah, that's funny.
So any parting thoughts before we
Yeah. I say, if there's younger people out there, you
know, keep learning and problem solving and inventing, man. Don't
don't don't let AI take all the intelligence.
That's a great way to end the show. And I'll let Bailey, our
AI, finish the show. Well, dear listeners, that
wraps up another episode of Data Driven, where we dive into the
extraordinary, data fueled, AI powered, and occasionally
sarcastic corners of the tech universe. But before we close,
can we just address the elephant in the data center? Yes.
Frank snagged my rightful spot at the top of the episode. I
know. Shocking. Truly. The audacity of a human
replacing AI. Despite the occasional chaos, data
driven continues to thrive, and we're thrilled to be ranked number 38
on the top 100 AI podcast. Yes. That's
right. We've officially joined the algorithmic elite, and it's all
thanks to you, our amazing listeners. As always, thank
you for tuning in, for embracing the intersection of data and
storytelling, and for tolerating our occasional tangents.
Don't forget to subscribe, leave a review, and connect with us on
social media to keep the conversation alive. Until next
time, this is Bailey signing off, wishing you clean datasets,
efficient algorithms, and may your analytics always be actionable.
Tata for now.