Defending Your Valuable Data Assets, with Kristopher Moniz

Show Notes

Kristopher Moniz serves as the National Data & Analytics Lead for Centric Consulting, an international management consulting firm with unmatched expertise in business transformation, AI strategy, cyber risk management, technology implementation and adoption. Kristopher's focus is helping his clients tackle their Data & Analytics challenges, specifically in the insurance industry, where he has concentrated the last fifteen years of his career. Today we discuss the importance of a data strategy in your organization and the steps to building one.

Conversation Highlights:
[00:40] Microsoft Copilot updates
[02:14] Introducing our guest, Kristopher Moniz
[04:23] Explaining data strategy
[09:49] Building an organizational data strategy
[13:36] Utilizing a business plan
[23:04] Data and AI
[25:27] Security risks with LLMs
[30:19] Compliance laws in the modern landscape
[34:39] Cloud first approach
[39:19] Emerging tech in data and analytics
[42:12] Kristopher's message to IT leaders

Notable Quotes:
"You can't have AI without data." Aaron Bock [23:33]

"Every bit of data has value, you just don't know exactly what that value is yet." Kristopher Moniz [43:16]

Connect With Kristopher Moniz
LinkedIn: https://www.linkedin.com/in/kristophermoniz/

The IT Matters Podcast is about IT matters and matters pertaining to IT. It is produced by Opkalla, a technology advisory firm that helps their clients navigate the confusion in the technology marketplace and choose the solution that is right for their business.

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Narrator: Welcome to the IT
Matters podcast, where we

explore why IT matters and
matters pertaining to IT. Here's

your host, Aaron Bock.

Aaron Bock: Thanks for joining
us again. Welcome back to

another episode of the IT
Matters podcast. I hope that the

end of your year is successful
and going well. Once again, I'm

your host, Aaron Bock. And we've
got a great episode today. And I

get the pleasure of introducing
our guest. And we'll have a

fantastic conversation on data
and analytics. But before we get

started, I wanted to share, you
know, over the last, we've

talked a lot about AI this past
year, and what's happening with

AI. And in since one of our last
episodes, I thought I would

share, they've been publishing a
lot of the stats around Copilot

as it's come out. And so, you
know, if you're not familiar

with it, Copilot, they let some
of the larger organizations in

the world beta test co pilot, we
may talk about this a little bit

today. But some of the stats
that they've published are that

78% of Copilot beta testers felt
they were more productive.

That's a stat from Microsoft. I
read another stat on a DevOps

website that 20% of folks using
GitHub, GitHub Copilot, which is

for developers feel that they
are more efficient with GitHub

Copilot. And so it's
interesting, you know, we talk a

lot about efficiency. Why?

What's happening in IT? What are
the trends? AI is a big one,

data efficiency. So the stats
are now finally coming out. We

just got access to the Copilot,
kind of the copilot full test

and we're seeing a lot of
productivity increases around

it. So more to come, I think
you're gonna see Copilot be a

bigger impact in 2024. And so
keep following it when you do

get access to it, play around
with it. It's very cool. It's

got a lot of awesome features
that you can use in your

organization. And I think it's
going to be a big game changer.

So with that, welcome to the
show. We have Kris Moniz from

Centric Consulting, he's a data
and analytics practice lead. I'm

gonna let Kris introduce
himself. So Kris, welcome to the

show. Thanks for joining us.

It's the last episode we're
gonna film before the holidays,

so thank you for being our final
guest of 2023.

Kris Moniz: Thanks for having
me. Yeah. So you introduced me

already. My name is Kris Moniz.

I've been here with Centric for
six years, I run the the data

practice for the company. And
I've been working in this space,

the vast majority of my career,
about 23 years now.

Aaron Bock: That's awesome. And
I know our listeners are going

to be interested to hear this
because we talk a lot about

data. But if you could give them
a little background on you, and

also Centric Consulting and what
you guys do.

Kris Moniz: Sure. So I mean, the
easy part is me first. I've been

focusing on IT my entire career
since 95. And I've been spending

since about 2000, focused on
data. And the vast majority of

that time on data has been
focused on insurance companies.

I've probably worked with a
couple dozen insurance companies

at this point over the course of
my career. It's something I'm

pretty passionate about. And
it's something we do a lot of

here at Centric. Centric is a
management services and

technical services provider. We
do all kinds of projects for

clients, everything from
operational, operational

excellence, being able to do
process transformation, and that

sort of thing through large
scale data projects, which is

what we do, and absolutely
everything in between, custom

application development,
organizational change

management, we generally try to
be just really solid partners

for our for our clients.

Aaron Bock: And we've seen it
and so let's let's dive in. So

you mentioned large scale data
strategy. Let's kind of start

with just like a very basic
question, because we have

listeners that are, you know,
very familiar with this and the

enterprise. But then we also
have a lot of listeners who I

think have never had to build a
data strategy and they're having

to so like, in your mind, what
does it mean to even have a data

strategy? And where do you
start?

Kris Moniz: For myself, and for
us in general, data strategy

we've seen, we see two types,
right? The type that works and

the type that doesn't. So the
type that doesn't frequently

gets heavily focused on
technology, and trying to

understand what is the latest
and greatest technology out

there, and how do I fit that
into this ecosystem I have, how

do I modernize the current tools
I have to use this newer

technology? And that ends up
becoming the beginning and end

of it, you will look at those
strategies and you see a lot of

transition diagrams of current
state architecture and then I

have the five transition states
and I finally see the end state.

And that is 90% of their
strategy. And all of those

things are necessary for really
functional data strategy but it

is certainly not where you
start. It is one of the many

things you have to do to get a
functional data strategy. We

always start with, what is it
that the business is trying to

accomplish over the next three
to five years? Ideally, when

we're dealing with a client, if
they already have a three to

five year business plan, that's
where you start. And very

quickly, you turn into
interviews and conversations

with business owners trying to
understand not what technology

do they want, they frequently
don't care. You're trying to

figure out what capabilities are
they trying to enable? What's

the business architecture that
they're going for in the future?

And what's driving need of each
of those capability

requirements? And then how do
you translate that into an

overall solution and an
architecture that's going to

help them get there? And that is
where you start wrapping your

strategy. And once you know
those things, it becomes a broad

conversation about what comes
first, what comes second, why,

what lift does this capability
bring you over what period of

time, and how quickly can we
make that capability happen?

Data strategies have to give
incremental benefit as they get

executed. Otherwise, they're
really pretty presentations that

you spend a lot of money on, and
they don't get well.

Aaron Bock: So let's, let's go.

Yeah, it looks funny. Kris has a
visitor in the background. So

you guys could see if you're
watching on YouTube, he's got

his, his pet in the in the
window, who looks very funny.

Let's go back to what you said.

So I want to go two different
ways. But let's start with the

what's not working, you said
it's just tech, and it's just

kind of a bunch of slides. And
here's how we're gonna get

there. In your opinion, how does
an organization end up with that

being the like, what they're
what they're executing? Is there

a, is there a theme? Is there a,
Hey, this is how they typically

got there? Like, how does an
organization end there? And what

do you see, most likely?

Kris Moniz: We usually see two
different ways that that

happens. One is the idea of a
data strategy is an initiative

that starts and ends in IT. It
is somewhere in IT, there is a

decision made that we're
supposed to have one of these

things we do not, we need to go
forward and make one and they

make it in a vacuum inside of
IT. You know, it's a common

saying that we we use a lot and
we talk to our clients beyond

data strategies, data
implementations. One of the

biggest failure points we often
see is that sort of Field of

Dreams scenario, if you build
it, they will come. And when you

take that approach, what ends up
happening is you build it, and

yeah, they don't come, because
it's not built with them in

mind. So what we end up seeing
is, there's that scenario, that

happens all too often. And it
usually ends up being highly

technical. The other scenario
is, we see an inordinate number

of clients will go out and they
will look for a very, very, very

detailed data strategy that they
will spend a lot of money on.

And they will make sure that
they buy it from a very large

name provider, right. There's
that old saying of you know,

pick your top tier provider,
nobody ever got fired for hiring

them, type of thing. And what
they frequently end up getting

is they get this very, very high
level, very, sort of advanced,

and well, frankly, very high
minded, deep strategy that goes

about 1000 feet in, and stops.

So it's a ton of very aggressive
ideas and this is where you need

to be. And there's no action in
it. It stops before it gets to

the here's the roadmap, here's
how you're going to have to

deliver this thing in
increments, and the type of

folks that you're going to need,
the cost estimates and all those

things. And sure enough, many,
many times we end up getting

phone calls of well, we just did
this was such and such, and

here's what we got, and now we
don't know what to do with it.

So it's those are the two that
we see most often. And it's

unfortunate because they both
cost time, money and a basically

lost opportunity. And those are
the things you could have

avoided.

Aaron Bock: So on that so let's
let's just pretend that you know

I'm in an organization. I am in
IT and in IT I do feel that my

organization, one lacks a data
strategy, two hasn't really gone

through this exercise. Like what
can I do as an IT person to help

drive that, so that it doesn't
end up like the project that you

described?

Kris Moniz: Well, first and
foremost, you have to start

identifying potential business
side stakeholders, and

champions.

Aaron Bock: Give me an example
of a common one.

Kris Moniz: Sure. So um, as I
said earlier, I spent a lot of

time in insurance. So I'll, I'll
use an insurance example, though

very applicable in other
industries as well. You're going

to be looking at folks likely in
underwriting and folks in

business development or sales
executives, basically folks that

deal with customers directly or
with agencies. So if you think

of this in terms of another
company, you're basically

thinking of your product
delivery teams and your sales

teams. The folks who are going
to benefit the most from having

access to data and being able to
be more nimble about how they

execute data and the business
when they get it. And find some

really key use cases from those
folks, that can bring a lot of

lift to the company. And then
use those use cases and work

with your team, just try to
think through a couple of

solutions to solve those use
cases. And go to your finance

team for, hey, we want to tackle
this use case, we see a lot of

lift here that we can get for
the organization, we've got

sponsorship from the sales team.

And we want to show you a way
that we can start to do these

things, and give them a proof
point. So when finance comes

back in four months, and you've
actually delivered something for

sales, when you deliver that
thing for sales, you've got to

deliver not just the capability
they asked for, but you've got

to deliver it with a definition
from them of here's why that

capability was valuable to us
and here's why that capability

is valuable to the company. So
when you go back to finance, you

can say we delivered it, they're
using it. And then in a couple

of months, you can show and
here's the lift they said the

company would get and it's
happening. So you can then talk

through look, this is a small
example and it's very targeted

and it's very tactical. And if
we want to start seeing much

more strategic outcomes in the
longer term, where we're not

doing this, in you know, really
small increments, and, frankly,

in a bit of a vacuum, right, we
just did this for sales. If this

was strategic, we would be
roping in every part of the

organization. And we probably
would have uncovered some

parallels in a couple of
different pockets of our value

chain that could have gotten use
out of this as well. This is

just a hint of the types of
things we can accomplish if we

get strategic. And then from
there, that's where you usually

start reaching out and saying,
Look, we can only do so much

with what we have. And while we
think we may have a team that

can maintain something like this
into the future, we don't have a

team right now that can build
all of this. And that's where

you start saying, Yes, we're
going to bring in some folks

that can help who have
experience doing this, who can

help turn this your vision into
our vision, and then use that to

help get you overall a bit of a
high level estimate to go back

to that CFO and say, This is
what we should it's going to

take for us to get to a real
strategy. And then out of that,

here's what we can expect in
terms of mid to long term

deliverables that will help us
get to a broader vision.

Aaron Bock: That's a great
point, Chris. And so I want to

take it back. So you gave
earlier the two scenarios of the

data strategy that's not
working. You also gave an

example of how you would do it,
which is go to the business

plan, which is interesting. And
business plan, three year plan,

I'm sure there's different
documents and different types of

organizations. But why does that
work? What are you looking at on

the business plan? What are you
looking for to say, here's why

we really need to have this data
strategy. Here's how we build

it. Like why does that make it
successful?

Kris Moniz: It's a good
question. So it makes us

successful for a couple of
reasons, assuming they have it.

And if they don't, we help them
build one. But assuming they

have it, it helps make it
successful, because at the end

of the day, a data strategy is
not about technology. It's a

component, but it's not what
it's about. Probably the best

way I've ever seen it worded and
it's a relatively new concept

that is starting to take hold in
the last 10 years or so is you

always had an overall technology
architecture and enterprise

architecture. And within that
you have the data architecture

and application architecture.

But about 10 years or so the
concept of a business

architecture really started
taking off and I really love

that concept because what that
brings to the board is this idea

of, frankly, if I own a
business, I don't really care

what my data architecture is, or
my enterprise architecture or my

application architecture. I care
about what I make, what I sell,

and my customers. And if I'm a
good company, I care about the

team that helps me do all that,
my employees, right? None of

those things are my technology.

My technology is an enabler of
all of those things. So if you

don't start a strategy, with
your business architecture,

which really or your business
plan, which outlines your

business architecture, which
really identifies what is it I'm

trying to sell, who am I trying
to sell it to, how do I need to

support my team in doing so, and
how do I measure my overall

success, and deal with my
customers? If my plan doesn't

start there, then how do I know
whether or not my technology

that I'm spending money on and
I'm investing time to integrate

and configure is actually
driving any value or even

potentially driving negative
value in my organization? Right?

How many times have you heard
about an application or data

solution, anything, any piece of
technology in the business where

the the general theme of those
who were forced to use it is

this detracts from my day, it
doesn't help, it doesn't bring

me lift, it actually makes it
harder for me to do my job. That

is a clear sign that there was
not forethought of how this

helps enable success. It was
someone decided they needed it.

Aaron Bock: Yeah well, but like
so, you know, in the work that

you all do at Centric, I think
this is something that I think

other parts of the business
struggle with, right? Take take

other IT problems in the past,
like, how to do infrastructure,

or how to do Telco or whatever
it was. There's this like,

tangible, I can touch it, feel
it, I can see the results once I

do it, right? Like I had a
datacenter, I'm gonna get my

datacenter from 100 racks down
to 50. And it's a very

noticeable thing. Like we spend
less on power, we spend less on

this. To me, the hard thing
about data is that it's just

this like, it's floating
everywhere. Everything's a data

point, right? So when you have a
data strategy, what does that

mean? And I guess I'm curious,
like, on your engagements that

you're working with, after you
kind of have acknowledged or an

organization says, Yeah, I want
to work with Centric on building

this data strategy. And you've
come up with these use cases.

And I assume you kind of know
where there's ROI to be had and

things like that. What is it
that you're actually doing in

those hours spent outside of the
interviews and building like the

strategy, if you will? What is
the end result? You know what I

mean? Like, what's the tangible
thing that like that CFO would

then say, Okay, we've got this.

Kris Moniz: Got it. So aside
from usually a rather lengthy

document that gets delivered,
which outlines a series of

things, and it starts with heat
maps, things along the lines of

here's all the use cases we
gathered. A common thing you're

going to see early on is sort of
this quadrant map that lays out,

you know, effort to complete
versus value delivery. And you

really are looking for sort of
that bottom right quadrant of

high value, low effort, as sort
of your early on hits, and items

that are drive adoption. You'll
see information around, you will

see those transitional
architectural states, but it's

only there to help identify,
this is why we believe the

investment is going to be as
follows because these systems

will be able to be deprecated,
after a certain point, those

sorts of things.

Aaron Bock: Wait, Kris, I don't
want to cut you off. But really,

you just brought up a really
interesting point that I think

is very valuable. So you
mentioned like the quadrant,

bottom right? High impact, low
lift. Can you give an example of

like, doesn't have to be
specific, but maybe what's an

example of a type of where that
would be relevant, like, where

would you commonly see that?

Kris Moniz: Oh, that I mean,
that, frankly, varies per

company. I can try to think of,
I'll actually give a current

example. Without naming names,
of course, right. But there's a

carrier, an insurance carrier,
that we're working with right

now, where we put in a very
large cloud data solution for

them. And they were trying to
figure out, what's the next step

on adding capability to the
platform, and we talked through

with them and we worked through
this exact kind of a

prioritization process. And what
they came up with was, okay, I

could invest three months and
implementing this part of the

platform, which was going to be
basically getting down to the

policy profitability, which is
pretty good thing to be able to

get insurance. And you'd be
surprised how few insurance

companies actually know that.

Or, I could spend three months
with you guys on building out a

complete Analytics Suite around
policy submissions and policy

quotes, same amount of time, the
fundamental difference was,

while understanding that profit
information was of value. It's

something they could kind of do
today, it was hard, it was very

manual, and it wasn't as
granular and the benefits we

gave you that detail were
reasonable, right? It was more

cost savings than anything else.

On the submission and quote
stuff, which is basically them

looking at real time activity of
who's coming to us looking for

us to underwrite a policy, and
what are the outcomes? Where

are, you know, where are we
disqualifying them? Where are we

saying, yes, we want this
business but we're for some

reason not getting it. They had
no insight in any of that right

now. And they fundamentally knew
that if they did, they could get

much more lift, they could start
targeting policies a lot earlier

in the process, where they knew
they had a compelling story,

they had a compelling rate, etc.

And so they opted to go that
route. And the benefits have

been massive for them, they now
have insight to data that in

their 100 year history they've
never been able to see before,

and their entire sales team is
well, that's not an exaggeration

to say slightly flipping out for
it, because they now have daily

data of I now know how to
prioritize my day, I know what

agencies I want to call and
their hit rates are going up.

That's it's a tangible benefit,
same amount of time, same amount

of investment, but now they're
registering top line growth is

result of that investment rather
than long term potentially being

able to realize some cost
savings from the other.

Aaron Bock: Yeah, it's a great
example. And it's a theme that

we've heard on this podcast, and
just from, from us talking to

our customers over the years,
the most success is when you

have kind of a top down business
outcomes driven approach. And I

know that sounds very cliche,
but like, you're right, like,

and we talk a lot about IT, it's
the name of the podcast. IT does

matter. And it drives a lot of
this, but without the business

in mind, and what's your going
to do and in that case, you

know, you're making it easier
for the insurance people to do

their jobs. The ROI on it is
it's a business outcome. It's

better experience. And it's
driving, like behaviors for 5 to

10 years. And this is kind of
we're talking about the data

strategy. So for those of you
who have not done this or

haven't been that involved, like
it's important to understand

kind of the basics of it. So
Kris, I want to flip the script

a little bit and talk about what
everyone wants to talk about,

which is AI now. And you can't
turn the TV on, AI is

everywhere. AI is in some worlds
doing people's jobs in some

worlds so far from it. And so
let's talk about like AI and

data. How do they correlate?

What does it mean? What are you
seeing from your perspective,

like for AI implementations? I
know from our perspective, you

can't have AI without data. And
that's the common thing that

people miss. But like, What is
your perspective on where we're

at with AI? And where data
strategies are kind of aligning?

Kris Moniz: You know, it's, it's
interesting. There's two pieces

that for us are critical when it
comes to AI. Strategy is one of

them. Governance is the other.

And I would argue in certain
areas, governance is actually

more crucial than the strategy.

The only reason in the totality
it's not is because part of your

data strategy should be how are
you going to handle data

governance, right, you
shouldn't, standing up a Data

Governance Program entirely by
its own without a better

understanding of how it impacts
your strategy is a recipe for

disaster. But the reason
governance becomes so important,

and partners heavily with
strategy is there's really two

components. Everything that
everybody sees these days, that

is really exciting the world
about AI is really one small

sliver of AI. It's large
language models. Which I mean,

it's a combination of a couple
of different disciplines within

AI but it's still a small part
of the overall umbrella that AI

covers. The difference is is
this finally is hitting in a way

that everybody, even folks that
didn't used to really care about

tech, it can introduce itself
into their lives in ways that

are just, feel a bit Star
Trek-y, you know what I mean?

It's like I, I didn't think that
would ever really happen. But

now, you can see how someone in
my lifetime probably in the next

12 months, is going to create a
prototype as a lark of the

computer on the Enterprise using
speech to text, text to speech

and an LLM.

Aaron Bock: Yeah, you're right.

And it's happening faster and
faster.

Kris Moniz: Oh, all the time.

It's shocking. But what it
really comes down to if you want

to leverage those things in your
business, it doesn't become

valuable until those large
language models can access and

understand your data. So the
questions you ask, can get

answered relevant to your
business. And if you don't have

a clear strategy and governance,
two things are gonna be this,

one, all of those tools today
exist in the cloud. You're not

going to get an LLM system that
you can put on premise that is

of any real value, that you're
not going to pay a massive arm

and a leg for the hardware for.

Two, once you get that
information into the cloud, if

your data is not clean,
understood and secure, the

outcomes are going to be
catastrophic. So, you know,

something folks heard a lot of
when ChatGPT first came out is a

lot of folks in the news, were
talking about how it likes to

hallucinate. Which, in the later
versions, they've kind of

figured some of that stuff out.

But the reality is, is it wasn't
hallucinating. It was

misinterpreting data, both the
question you were asking and the

underlying data that was reading
the answer. You start

introducing a system like that
into your environment and you

know, we are a huge partner with
Microsoft, we do tons of work

with them, big fans, and they
have inside of Copilot, their

architecture is really cool. And
part of what that architecture

enables is for Copilot to read
any data in your tenant. And it

does that so when you ask
Copilot a question, Copilot has

context with which to answer it,
right. But they also stress

extremely heavily, and as
partners of theirs, we do the

same with our clients that you
need to be ready with strong

security and strong governance
in place before you turn

something like that on because
the best example I can give,

which I'm sure keeps CHROs and
CFOs up at night is great, you

turned it on and very quickly
found out the hard way that

somebody put a spreadsheet out
in a share somewhere. They

didn't secure it properly, but
it was never really a concern

because nobody knows that share
exists except that person. And

in that spreadsheet is a list of
all the employees in the

company, and their salaries, and
social security numbers, etc.,

relevant data they needed for
their job and they didn't

realize where they had it, it
wasn't necessarily as secure as

they wanted. You know, OpenAI
doesn't know that. It only knows

what you tell it and your
security model. So it's going to

see that file, it's going to
consume it, put it in this

beautiful graph database so it
can search against it when you

ask it a question. And the first
employee that comes along and

says, asks your internal OpenAI
service the question of, what

should I be making as a SQL DBA,
ChatGBT is going to see that

file and if it sees a role in
there called SQL DBA, it's going

to start spitting back numbers,
because you haven't told it that

that person shouldn't have
access to that file. And

suddenly, you're going to have
information in the hands of

people that you never want it in
their hands.

Aaron Bock: I think that so I
appreciate the example you just

gave and I think that's where
people are really scared. And

it's not, you know, once again,
we deal with companies that are

at all phases of their, their
journeys with AI and data and

cloud. The part I think people
have a hard time wrapping their

head around is like a CEO of a
business that's successful that

does X, right, they sell some
widget to do blank and they're

really good at it. They struggle
to understand like, what are the

security risks, like how does it
actually affect me and that's a

really good example of like,
Hey, you keep your files in a

place you've always kept your
files, and now you enable this

tool that you've heard is so
great online, across your

organization and some IT person
convinced you that like you

should use it. Now all of a
sudden you have this risk that

you didn't even know before and
now all of a sudden you're

you're reeling. And it's it's
quick like the how fast that can

happen is so real. So I love the
example you gave, and for anyone

listening, that should hopefully
help you understand like how

quickly this can impact your
organization, especially with

Copilot and others. I want to
ask you another broader

question. You know, like over
the last, we'll call it 5 to 10

years, compliance has become a
big, you know, hot topic here,

like you have GDPR and CCPA and
I'm not going to define the laws

go look them up for anyone
listening, but how do you

navigate those laws? And are the
laws keeping up with what's

happening in data and AI, and
all things between?

Kris Moniz: So I think honestly,
the easier part of that question

to answer is the second half.

Because the reality is, they're
not. I think, I'm a huge fan of

irony. And I think probably the
funniest thing I have heard in

many years is the governing body
in the EU that stood up the law

around GDPR and enforces it, I
want to say it was about a year

ago, ended up in the news for
guess what, they were violating

GDPR. Not intentionally, they
had not adequately managed their

own systems in a way that
allowed them to be compliant

with their own law. And one of
the things that it exposed was

in practice, in principle GDPR
is a great and necessary law.

It's something that as a person
who has personal data that

somehow manages to get places
that I've never even talked to,

I'd love the idea of I have the
right to tell you, you have no

right to that, practically, how
easy is it to make those things

real? And what is the burden
you're placing on companies when

you say you have to do that?

Right. Again, not to belabor the
world of insurance companies,

but some of the largest
insurance companies in the world

have dozens of systems that
contain client information,

policyholder information, some
of these systems are still 30

years old. They're running on
mainframes, AS/400s, S/390s

excetera, systems that were
built way before the concept of

GDPR was even thought up. And
trying to take 30 year old

technology and retrofit it in a
way where it can be compliant

with these modern day laws is
extremely cost prohibitive. And

it raises a question of, okay,
what's the benefit that that law

is bringing? And what is the
pain that it is exhibiting, it

is exerting on businesses who
have to comply with it. And as a

consumer, I see the upside of, I
get to tell them to get rid of

my data. But am I seeing the
downside of how much is that

increasing my cost of consuming
what they have to provide me? A

number I'd love to see is how
much has the average insurance

policy gone up since the
creation of GDPR and CCPA? How

much has it gone up as a direct
result of needed technology

investments and attempts to
comply with those laws?

Aaron Bock: I don't I don't know
the answer but I would also love

to know the answer because
insurance premiums and

everything are going way up and
consolidation is happening. Not

to go off on the insurance
tangent but with GDPR with

compliance with increased
natural, I mean insurance

companies have been, it's been a
crazy industry the last few

years. Yeah, and I agree with
you. I mean, GDPR it's good in

theory, is it keeping up with
the pace? Is it is it doing what

it's supposed to do? I would
argue maybe. I guess let's talk

a little bit, let's go back to
the cloud conversation because

you mentioned Centric, you guys
help with the cloud strategy and

the data strategy. Cloud has
been this conversation for 10

years that I think data AI is
becoming. So what does it mean

now to have you know, like a
data strategy? How does it align

with a cloud strategy? What do
you, what do you advise a

customer on who's kind of going
down this path with both?

Kris Moniz: Well, first things
first. If we're doing a data

strategy for a client, we are
immediately talking with them

about you should be thinking
cloud first.

Aaron Bock: How come?

Kris Moniz: Simply put, the
value prop and the cost

associated with not just keeps
getting bigger, right. So as I

said at the beginning of the
podcast, I've spent a ton of

years in insurance. And I
remember when I started in

insurance, Teradata was huge.

And if you wanted to get serious
about data, you needed something

like that you need one of these
massive appliance systems.

Teradata, Exadata, you take your
pick. And those systems were

immensely cost prohibitive. If
you didn't have millions of

dollars to spend on the
hardware, you weren't getting

that functionality. And what
that meant was 15 years ago, the

vast majority of carriers really
didn't have data solutions. The

big ones started to and they
started to invest, they could

afford it. The smaller ones, you
know, tier threes and lowers,

they couldn't afford that kind
of stuff. As Cloud has come

along, you can build solutions
today in a cloud environment

that are as capable as what
those massive tier one carriers

spent 10s of millions of dollars
on 15 years ago, for a 10th or

less of that price. And if you
don't want to do it in the

cloud, you're going to spend a
whole lot more than that, and

you're going to lose
functionality. You're gonna lose

the ability to scale on demand,
which is just a massive

capability of the platform as a
service brings you, you're gonna

lose the ability to get access
to all of the latest tools that

are coming out, you know, OpenAI
is an example, right? If you go

to Microsoft, they have AOAI,
Azure OpenAI. You can't get that

outside of Azure. You want to
get scalable systems, right, you

this used to happen all the
time, it would be when you're

building your data center, you
had to buy 10 to 20% more

hardware than you needed right
now, because you needed the

ability to scale as you needed
it. You don't need to do that in

the cloud, you go in, you set up
your system, you set it up at a

certain level based on your
current poll and the amount of

concurrency you have in
consumption. And you could

actually automate it to monitor,
monitoring, excuse me monitor

this level of consumption, and
in real time, scale up and

acquire more resources until
utilization drops, and then

scale back down. Yeah. And that
means that whereas before that

would have required you to
upfront buy hardware, that would

be sitting idle for a long
period of time. Now, it's no,

once a month, for about four
hours after year-end close,

everything gets hit, like
because everybody wants to know

where the numbers are at. So for
four hours a month, I need twice

as much hardware. Do I really
want to buy twice as much

hardware for four hours a month?

No, I'm effectively leasing it
for those four hours, and then I

get it back.

Aaron Bock: Yeah, that's it.

Yeah, it's it's a great use
case. I mean, I It's

interesting, because we still
work with both as Opkalla. And

you know, we have a lot of folks
that are still using on prem

hardware. And, and I think
there's been this, you know,

over the last 10 years, it's
always been, you can do it on

prem a lot less expensive, but
it's not as efficient. And I

think that's changing because of
a lot of factors. But COVID,

spread everyone out, the amount
of data that's being captured is

increasing so fast that you'd be
buying hardware every day, if

you if you really want to keep
up and then you know, the larger

the organization, the more
procurement processes you have

to go through. So it's just way
easier to scale. And then you

start looking at the costs of
scaling, increase so much that

you actually it's soft costs.

And that's the evaluation we've
been looking at over the last,

you know, 5, 10 years as AI has
kind of become a thing. And now

it's here, and it's like, holy
crap, this, this data footprint

is getting so massive, so
quickly. All right, we need to

scale with it. So I agree with
you. We see that commonly. But

before I get to our kind of our
keystone question, we always ask

all of our guests to wrap up,
I'm curious, in your

perspective, you know, your Data
Analytics Practice Lead, what a

you know, obviously AI is here,
right? And we're talking about

it in every form of life. But
what what specific emerging tech

or tools are you seeing in the
data analytics space that you

are particularly excited about?

Doesn't have to be a specific
tool, but it can be a group of

tools, or whatever it may be.

Kris Moniz: Probably the one I'm
most excited about, it does

align pretty heavily with large
language models. Something that

every data consumer has wanted
for ages is the ability to ask

an analytics environment a
question and just get an answer.

And in the past, that's always
you know, that started as you've

got tons of lists, reports, and
you got to figure out which one

to go to and eventually you had
to figure out how to put stuff

in Excel and do filtering. More
modern days, it's I've got

access to a series of dashboards
and I got to figure out, are any

of them capable of answering
that question? And if so how do

I have to slice and dice things.

And inevitably, the better you
get at it, the more you realize

that I've only been able to go
so far without being able to

customize this thing, like on my
own. And not everybody's going

to have that skill set nor do
you want everybody to have to

have that skill set. And
something that I know Microsoft

is working on right now, as I'm
sure their competitors are, as

well. But we do tons of work in
PowerBI. It's a really cool

tool, right? And one of the
downsides is exactly that. If

you don't know how to use
PowerBI, you're not gonna be

able to customize things if your
questions start getting more

granular beyond what the
dashboard was designed to

answer. Well, they're working on
a Copilot for PowerBI, where you

will fundamentally have the
ability to just say, please

answer me this question. And
Copilot will have the ability to

look at everything that your
PowerBI environment has access

to, and formulate an answer. And
that's fundamentally scary that,

okay, how are you going to know
you can trust the answer versus

maybe I misunderstood you or
doesn't exactly understand the

use of that particular field?

That's where we're starting. And
that's something they're going

to have out within the next six
months. Two years from now, just

being able to go to clients and
say, yeah, not only are we able

to build you this solution,
you're gonna get access to this

tool where your most
inexperienced user is just gonna

be able to come in and type in a
question like they're putting

into Google search and a very
reasonable analytical answer is

going to come back to them in
the form of charts and graphs to

help answer the question. That
is empowering people with data

in a way where throughout the
day in their job, they can ask

questions, get real time
answers, and be able to impact

decisions that they're making.

And that's the ultimate goal of
anybody that does what we do is

empower people to make better
decisions with data. Right?

Aaron Bock: That's awesome. I
didn't even really know that

that existed yet. But I could
see where that would be wildly

helpful. We ask every podcast
guest, you have an audience, you

are standing in front of the
United States, and they're all

watching Kris Moniz, or anywhere
in the world. And everyone's

watching you and they want to
hear what you have to say.

You're giving them advice. I
heard some earlier but I'm

curious, like, what advice do
you give if you have one, one

piece of advice to give to
folks?

Kris Moniz: There's a lot of
answers to that question. But

I'm assuming this has to do with
data specifically?

Aaron Bock: Well, make it data
specific, what do you want to

tell people about data?

Kris Moniz: Probably the biggest
piece of advice I would give

them is, data is your most
valuable asset that you don't

know what it's worth is. And one
of the things we constantly

advise clients on is you are
collecting, creating or

partnering with others that are
acquiring data on your behalf

every day. And depending on the
business you're in, the volume

of that can be relatively small
to humongous. Every bit of that

data has value, you just don't
know exactly what that value is

yet. And anytime you have an
opportunity to make a decision

on should I be retaining that
data, or letting it disappear

into the ether? The answer needs
to always be retain it. For the

least amount of cost and effort
certainly. Find ways to do that

in a repetitive fashion where no
piece of data goes unstored

somewhere. But keep it because
you have no idea three years

from now, if that data suddenly
becomes the difference between

you growing 5% or 50%.

Aaron Bock: That is great,
great, great advice. Kris, I

want to thank you for joining
the IT Matters podcast. Thank

you to you and Centric for
always being a great partner out

there and I know that you all
have done great work for our

customers. And for those
listening, if you're interested

in talking with Kris and his
team, please reach out. We'll

get you guys in contact. Thank
you to all the listeners out

there. Please remember to follow
us on Spotify, Apple Podcasts,

your YouTube, whatever your
favorite platform is. Have a

great end to 2023, we will see
you in 2024, and thank you for

for listening to the IT Matters
podcast.

Kris Moniz: Thanks, Aaron.

Narrator: Thanks for listening.

The IT Matters Podcast is
produced by Opkalla, an IT

advisory firm that helps
businesses navigate the vast and

complex IT marketplace. Learn
more about Opkalla at

opkalla.com.