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.