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.