KCAA: Inside Analysis with Eric Kavanagh (Sun, 9 Feb, 2025)

KCAA: Inside Analysis with Eric Kavanagh on Sun, 9 Feb, 2025

Episode Transcript

Speaker 1 (00:03):
You're on board. Caseyaas Inland Express. Caseyaa home, Linda ten
fifty am the station that needs no This here behind.

Speaker 2 (00:17):
The information economy has a ride. The world is teeming
with innovation as new business models reinvent every industry industry.
Inside Analysis is your source of information and insight about
how to make the most of this exciting new Eric
learn more at inside analysis dot.

Speaker 3 (00:35):
Com, Inside Analysis dot com.

Speaker 2 (00:37):
And now here's your host, Eric Kavanaugh.

Speaker 4 (00:44):
All Right, ladies and gentlemen, Hello and welcome back once
again to the only coast to coast radio show that's
all about the information economy. It's time for Inside Analysis,
and your host Eric Kavanaugh is here in folks. I
am super psited to be talking to a couple of
experts and in the hardware, and they also know about
software and data and all kinds of technology. And we're
going to talk about a survey. But I've got two

(01:06):
experts from rack Space. Yes, indeed, rack Space, they've been
a powerhouse in the industry for decades, a very serious player,
and they know a lot about what's going on in
this world. Of course, a lot of AI conversations these days.
But Hybrid, we're going to talk about hybrid and what
that actually means. We're going to talk about these experts
and find out and it was a survey that's been done,

(01:27):
so it leaders increasing investment in multi and hybrid cloud
strategies to future proof their operations. That's what the survey reveals.
So let's go ahead and dive right in. So tank
Shield tell us what what did you find in this survey?
What surprised you, what didn't surprise you? What do you
think is interesting?

Speaker 5 (01:45):
So thanks er, thanks for having me.

Speaker 6 (01:48):
You know, what I found quite interesting, especially in the
in the from a context of analytics and bi perspective
here is that, you know, the proportion of data that's
gone to the cloud has really skyrocketed. You know, if
you look in this survey, it's so that more than
fifty percent of the data is actually now sitting in
the public cloud.

Speaker 5 (02:04):
It is across many.

Speaker 6 (02:05):
Different cloud providers, it's across hybrid providers, and you know,
we're expecting to see seven percent more going up there.
And the other interesting thing that I saw is that,
you know, the adoption of SAS has really skyrocketed. And
what that really means is that now, more so than ever,
we have data sitting obviously across different clouds insiders, but

(02:25):
also within these different SaaS applications that are running on
their own or customers clouds, which means bringing the data together,
whether it's for the purposes of analytics, all the further
purposes of AI has become an increasingly challenging.

Speaker 5 (02:41):
Requirement. Now.

Speaker 4 (02:43):
Yeah, and we've also got been like Karra on the
call here and I'll throw this over to you. I remember,
I've been covering this stuff for a long time. So
DM radio turns seventeen years old next month, so I
would do it this a while. And I thought the
cloud would take off twenty years ago, and it really
took off like ten years ago. So for some reason,
it just took a while. I credit actually Second Adella

(03:06):
of Microsoft for pivoting his whole enterprise to cloud. And
I really think that woke up a lot of people
who are like, geez, I guess we better get on this.
But I remember that in our conversations on these shows,
we went from talking hybrid cloud to multi cloud in
about one week because people figured out it's not just
going to be on prem in one cloud, it's on
prem in multiple clouds, which greatly increases the complexity, right.

Speaker 3 (03:29):
Oh absolutely. In our SAVE survey, what really came out
was the notion that it's turning into a much more
complex world and that it much like when you're preparing
a meal for your family. If you just buy one
thing all the time, that's not going to satisfy things.

Speaker 4 (03:50):
Right, that's funny.

Speaker 3 (03:52):
And so when you think about it, organizations providing services
to their businesses, businesses that have different flavors of things
that need to be done, and those different flavors of
things need to be done, you have multiple options now.
And so those options in terms of things to be
done are what we call those workloads. And so when
we think about workloads, we have to think about what

(04:14):
is a workload and where should it be? Much like
you know, do you need something fast for your family
or do you need a big sit down meal. They're
different scenarios, and each of those different scenarios says there's
different options. And what's happened is cloud everyone got. You know,
hyperclouds are wonderful, However one size, they're not fit all.

(04:36):
You know, if you're looking at workloads that have to
be secure, workloads that that are that are that doesn't
need bursting and things like that. Well, you know apps,
you just need to put them on private cloud. Right.
If you need native services that are in hyper skills,
put in the hyper skill. The point is all this

(04:57):
is saying is that to for IT or organizations to
provide the great services to their businesses in terms of capabilities,
you have to give options. And hybrid is a is
a way to think about It's that one, it is
both public and private, and you've got you've got to
be able to do both or else you're not optimizing

(05:19):
the services you're providing to your businesses. That makes sense.

Speaker 4 (05:22):
Yeah, no, it makes complete sense. And you know, I'll
throw another question over at you and then we'll get
tank back in this. But you know, observability I think
is a huge play in what we're seeing right now
and finops, which of course is observability because like five
six years ago, you know, it was hard to tell
what the stuff really costs. Now we have very granular

(05:42):
reporting on what the cost is and that allows you
as an IT organization to do what you just suggested,
which is optimize we're going to do this workload in
this cloud. We're going to do that workload on prem
and when you have the capacity to do that. If
you've embraced cloud nativity, right, if you're cloud native even
in your on prem environment, if you've done something to

(06:03):
make it look more cloud native, you can really move
those workloads around. And that gives tremendous agility to the organization, right.

Speaker 3 (06:11):
Absolutely, And that's a key driver as technology leaders are
looking for how do I future proof? You know anything
about that, Eric, As you think about your future proofon
you have to think about how do I create the
most flexibility with things that I don't even know are
going to be coming? You know, we don't know what
AI workloads are going to look like, we don't know,

(06:32):
we don't know where data is going to be moved,
we don't know, right, So the question is how do
you then create a way of looking creating a platform
for your company that you're not locked in, right, right,
that you're not locked in, and you create the most
flexibility Because as most folks know or have said, AI

(06:53):
is going to change everything. The footprint of how business
is done is going to change pretty significantly, right right,
And so how so it is incumbent on technology leaders
to you know, I have to drive flexiblit and by
the way, I've got to make sure I do that
in a way that I can. I can not spend

(07:15):
everything in the bank, which which has to do with
the finopps, which has to do with observe a bilak.
We think that you know, if you if you're a
major corporation spending it several million dollars a year, probably
you know there's probably twenty to forty million, twenty to
forty percent of utilization that you could optimize, right, right,
But it's like have you ever read a bill for

(07:36):
your cloud? It's like the phone bill times a million, right,
you have it, right, unless you can actually understand it, right,
you don't know what buttons to change, and and so
you know, like we at a rack space, you know
we we are you know if we are the largest
pin opps service provider in the world.

Speaker 4 (07:56):
Right, that's a great way to look at it. That's
a very interesting perspective because what this is old school,
but what gets measured gets managed. I'll throw that over
to should talk. Right, what gets measured gets managed, and
when you can start to see where all these costs
are coming from and align them with the projects and
understand what is the TCO understand what is the ROI

(08:18):
Are we getting return on this investment. The more you
can do that, the better you can manage your environment,
the more efficient you become, the more money your enterprise
makes on the bottom line, right absolutely.

Speaker 6 (08:28):
And I think what we're seeing now is that you know,
this entire idea about ROI, which in a sense has
been driven by the explosion of AI because now everybody
wants to do AI and wants to what's.

Speaker 5 (08:39):
The benefit of that?

Speaker 6 (08:40):
But that is you know, trickering down all the way
to the bottom of the stack, which is the entire
cloud stack, right Because you know, when we talk about
you know, data pipelines, or we talk about storage, or
we talk about computer associated with you know, doing transformation,
it's very important to understand the unit economics of that.
It's not just you know, how much does my data
warehouse cost to run?

Speaker 5 (09:00):
How much am I spending on GPUs?

Speaker 6 (09:02):
It is what is the unit economics price performance that
I want to optimize for, you know, my inventory optimization
because I'll spend two hundred thousand, But it only makes
sense if I'm actually saving more than two hundred thousand
from the business benefit that's coming from that particular EMIL model.
And you know, one of the other things that that
that sort of listed out in the studies that you
know there's this rise of centralization.

Speaker 5 (09:25):
Or COEs, right, because the way we think.

Speaker 6 (09:28):
About it is that you know, there are a large
number of what i'd refer to as non functional requirements
where you need to have observability, you need to have quality,
you need to have a standardized way of figuring out
as Ben was saying, what kind of workload works best
in what component in the club, right, is while they
give you a lot of flexibility, they also end up

(09:50):
actually giving you hundreds of options to do the same
kind of thing on different components and having guidance around
you know, I'm trying to build an inventory model, I'm
trying to build a chat point, I'm trying to build
a bi report. Means I need to have these standards
regardless of what actual data is going in. There is
something that's going that is becoming quite key to analytics,

(10:11):
but also all workloads in the cloud.

Speaker 4 (10:14):
Yeah, I mean, this is it's fascinating. Go ahead, man.

Speaker 3 (10:16):
Yeah, And and now I'm just sort of going to
jump in here and what we're seeing like and this
is what the survey said is, uh, you know, there
there's a sort of phase of going through technologists Phase zero,
day zero, day one, day two, day zero, strategy. Day
one is sort of transformation. Day two is the run side, right,
And the challenge is when you start thinking about hybrid cloud,
the talent required to go through all those phases and

(10:38):
run things and run things at scale right, and run
things at scale that are secure in some run things
get reliable. There's a big talent gap which came out
in the survey as well. And so I think we're
going to start seeing more models that have to do
with managed services on the run side because of in

(11:00):
there's going to be you know, my crystal ball says,
companies are going to want to use align with folks
who can provide the managed service for the full stack
side of things so that they can concentrate on what
what what makes my beer taste better? Mm hmm right,
that's funny, right, Yeah, you got to worry about things

(11:21):
and your team members have to worry about things that
make your what is the thing that makes my beer
taste better? Versus the things I need to run? And
so I think they're going to be emerging models regarding
the ecosystem of operations, right that that's going to be
really important.

Speaker 4 (11:36):
Well, and you know you mentioned this already, and i'll
throw this to you first, ben AI workloads. There are
all kinds of AI workloads. I mean, people, I think
a lot of folks in the business world are starting
to appreciate that models, deep learning modules can take any
number of shapes. You can have any number of inference layers.
You're going to ingest all these different component parts. You

(11:58):
can stack and layer and design that as you wish,
and there's really no limit to the permutations for how
you do those things. And what I see happening I
think that's very interesting is a company stor are building
up bespoke models for individual clients. I mean, just one
random company I'll throw out there. I'm going to read

(12:18):
safe books AI. And what they do is they build
they take a model off a hugging face, and then
they train your data, your accounting data, your financial data
on your model that is your bespoke model, and then
once it gets to a certain level of proficiency, it
just watches all your finances and says, hey, I think
I found a problem here. Because there is a story

(12:38):
of Macy's where some person hid huge amounts of expenses.
They didn't find out about it until they went to
the quarterly earnings and it was like whoa, No, like
red three alarm fire, that stuff won't go away. My
point is that this is a fantastic use case for AI.
It's not just Jenai. There's a lot of excitement around Jenai.
That is one whole category of very interesting technologies. But

(13:00):
AI models have been around for decades.

Speaker 3 (13:02):
Forever, forever.

Speaker 4 (13:03):
This is extremely efficient at understanding particular business use cases, right.

Speaker 3 (13:08):
Ben, Oh, they absolutely are. And as you said, Eric,
you know, if you look at models, it's you know,
the essence of these models is you you have trained
a model based on the data set right right, and
you can make that day SAT really big, which is
you know, general chat GPT massive, or you can focus
on your specific data set right right. And so I

(13:33):
think you're I like you bringing this out for folks
to understand, right, what is that? What is the thing
you're trying to do right right? That's right because based
on what you're trying to do generates a whole lot option.
The challenges is most folks just go to hey, I
just do chat GPT, right, and so all the different
nuances associated how do you optimize get lost really fast? Right,

(13:56):
And so unless you're actually talking to someone who understands
the nuances but also understands think about this er the model.
If you look at the value chain of like from
data to models to the decision, that model is just
a little bit. It's like five percent of the stack
tech stack related the delivering value, right right, And everyone's
getting really excited about that. But guess what, folks, that

(14:17):
other ninety percent underneath underneath the water line? Did you
That ain't correct?

Speaker 4 (14:22):
Right?

Speaker 3 (14:23):
It doesn't matter, It doesn't matter. Yeah, So that that's
really that That's what you know, when we think about
hybrid clond what came out in the service, you got
to worry about.

Speaker 4 (14:33):
That stuff, right, Well, yeah, go ahead, go ahead to that.

Speaker 6 (14:37):
I just want to remplain that, you know, that's a
very good example of what we mean by workload of
were because if you think about it, the training of
a model or fine tuning of a model, et cetera,
this requires a large amount of data, a large amount
of compute, but for short periods of time. You know,
you may do it daily, weekly, Whatever is the cadence
required for that particular workload, which definitely you know, for
which things like public clouds tend to be much better.

Speaker 5 (14:58):
But the actual usage of.

Speaker 6 (14:59):
That model, of the the influence of that model is
a much more predictable compute activity where the rest of
influence becomes much more important. Uh, And kinds of things
tend to be much better in private cloud or you know,
self hosted kind of environment. So you know, even within
a particular use case, thinking about different stages, what's the

(15:19):
right place for that to go, whether it's public cloud
one or more public clouds or it's private cloud, actually
becomes quite important.

Speaker 5 (15:26):
And that's I think something that is captured in this
as well.

Speaker 4 (15:29):
Yeah, and that is important. And you know, I've said
this for years now and I feel somewhat vindicated a
throat of event. First my mantra was that the rumors
of on Prem's demise have been greatly exaggerated. What do
you think, Oh.

Speaker 3 (15:42):
My god, Oh my god, that is Think of that
like it is an urban myth. It is like the
biggest urban myth that has been perpetuated on us, right right.
This is because the thing on the workload you need, Yeah, yeah,

(16:04):
I mean like if I'm taking if I'm driving my
kids to school every day, right, my Honda Civical work
just fine.

Speaker 4 (16:12):
Right right?

Speaker 3 (16:12):
Or or my Honda because I have a bunch of kids, right,
I don't need my sports car right right. And so
you know, when you start looking at the the economics
of this, it starts looking like, you know, maybe that
truck is a much better value and that's really the

(16:34):
key word, is a much better value for what I'm doing.
So and we are the survey will show the amount
of folks think about repatriation.

Speaker 4 (16:43):
Mm hmm.

Speaker 3 (16:44):
Right. It's kind of like buying, like buying a boat.
You know, you get really excited to day you buy
a boat. Then you realize how much it actually cost, right,
and you're like the upkeep the upkeep right, and you're like,
all right, the best days of a boat or is
and when you buy the boat and when you sell
the boat, right.

Speaker 4 (17:00):
That's pretty funny, especially if our hurricane is coming in
right then you're like, oh no, oh no, this is
a problem that can't get insurance.

Speaker 3 (17:09):
Exactly, which, yeah, which goes back to Eric, like what
are you trying to do? What is the workload that
matters for you? And worder and be clear about the
options because you know what, you don't want to overspend
because with AI, like you know a lot of comps
like I talked about CIOs, I go, hey, I wonder's
AI thing. But you know, I don't have my much.
It didn't go up right, right, customer customers aren't paying anymore,

(17:33):
So who's going to pay for this? Right? So that's
where when you start looking at the delivery costs and
unit economics, Innovation costs money. Right, I've got to figure
out how to optimize my current environment, which means I
actually need to you know, I've got to do spring cleaning.
I've got to do my workload to wear so I
can pay for this stuff.

Speaker 4 (17:53):
Right. Well, I mean this is the you're big of
an excellent point, and we'll dive into this in the
next segment, folks, because spring cleaning, right, just understanding what
you have, where it is, who's doing what, Where is
your data? How much does it costing you to store
this stuff? What is the overall plan? It's very difficult
to know that from a large enterprise perspective. It's very
hard to sort of piece together all the components to

(18:15):
really understand what you're looking at. But that is a
huge issue right now that companies need to do, especially
as this ALI era dawns. But don't touch that toll. Folks,
be right back. You're listening to Inside Analysis.

Speaker 2 (18:26):
Respect, Welcome back to Inside Analysis. Here's your host, Eric Tabanac.

Speaker 4 (18:40):
All right, folks, back here on Inside Analysis talking to
a couple of experts from rack Space. Have a big
IT survey that just came out. Very interesting information. Not
not a whole lot of big surprises for me when
I look at the market and understand what's happening here.
And let's face it, the shiny new object now is
AI and everybody wants it. They want to leverage it,
but you got to be careful. Number one, how much

(19:01):
does it cost?

Speaker 3 (19:03):
Well?

Speaker 4 (19:03):
Really, number one is what do you want to do
with it? Like what is the business case around it?
Number two is what it costs? And then you can
kind of figure all that out. And finnops is a
huge part of that. And Ben, I love how you
said rag Space is the biggest finops provider because that's
what you're that's part of what you're doing is helping
people understand what does this stuff cost? And once you
once you define your business objectives, then you really got

(19:25):
to focus on what's it going to cost us, how
are we going to do it, where's all that going
to come from. That's where a rack space wild comes
into play, right.

Speaker 3 (19:33):
Absolutely, what we so we manage over billion, billion and
a half dollars of cloud spend with our customers. Within
that context of that, what we then do is provide
a service to those customers that that uh, that there's
a finnops service that essentially once a month, we go

(19:55):
over your bill, right, whether it's it's Google, you're w S,
we go over your bill right, and we say, hey,
this is happening, what's going on here? What about this?
You might want to turn this off? You want might
want to move things here because it's like, uh, depending
on the configuration. You know, you know, it is really

(20:16):
easy to buy resources on the cloud.

Speaker 4 (20:19):
That's too easy, right, that's the problem. It's too easy.

Speaker 3 (20:21):
It is so easy, right.

Speaker 4 (20:23):
And like your kid buying squish mellows with your credit card.
I want to think about that.

Speaker 3 (20:29):
They're just pressing the button, right.

Speaker 4 (20:32):
That's right, more mellows and.

Speaker 3 (20:34):
And and it's no fault of anyone's other than you know,
you had you said, we had talked earlier about this
notion of observability. If you don't see it, it's just there,
and all of a sudden your bank acount like draining
like a swamp, right, and you're going, what's going on? Well,
that's where thinnops and and and and you know, finops
is continue to grow, just the notion of measure what

(20:55):
you're using and habit at a granular enough level. And
then either way have work with folks who understand what
that thousand page phone bill looks like, right right, right,
because it is not simple.

Speaker 4 (21:11):
No, Well, and in these days, I will I'm just
guessing here, so maybe it should be down if I'm
wrong about this ped But these days one of the
really cool aspects of these large language models is you
can feed them big, bulky documents and then just ask
all kinds of questions from it. I'm guessing you could
load your document if you're willing to do that, into
a chat GBT and say, what the hell's going on here?

(21:33):
Where am I spending money?

Speaker 3 (21:34):
You know, I'm guessing there's probably a startup in the
world that's that's doing that. There's someone figuring that The
challenge is you can feed it in, right, But remember
AI systems is a living, breathing thing, right right. Every
it is learning every day, there's more data. And so
what a lot of folks don't understand is, guess what,

(21:55):
this is a one shot deal. Once you put it in,
you got to run this thing right right, and you
have to do a feedback loop. You have to train it,
you have to tune it like it doesn't it's not
one and done. And so once again, so when we
when we think about the run side of the thing,
you know, CUP organizations are thinking about what is my

(22:17):
operating model?

Speaker 4 (22:19):
Right right?

Speaker 3 (22:20):
What does it mean to run? What do I need
to measure? What do I what? How do I toune?
How do I make sure I you know, it's not
doing bad things? How do I make sure my users
know how to use prompt?

Speaker 4 (22:29):
Right?

Speaker 3 (22:30):
Right?

Speaker 4 (22:30):
Yeah? There are all these there are all these components
that come into it, and it is a lot to absorb, right,
It's and I'm sure it's very easy to to kind
of get distracted and go down a wormhole and you
always got to pull back, all right, what are we doing?
What are we trying to do?

Speaker 7 (22:44):
You know?

Speaker 4 (22:44):
I remember one of my favorite managers ever was a
guy when I was at the data warehousing instidutent. I'll
throw this over to you, sha tonk uh he he
Peter Quinn was his name. He's the best manager I
ever had. I didn't even know what a good manager
was until I had him. And I was like, well,
like my first meeting with him up in Seattle when
I moved up there. We get to the end of
my first meeting and he says, no, is there anything

(23:05):
I could do for you? And I was like, did
a VP just walk in? Is there? Like? Oh, you're
talking to me? I was like what, Oh, okay. But anyway,
he would say something that I love. He said, let's
achieve what we're trying to achieve, right. I Mean, it's
a bit it's a bit simple in a way, but
the point is it focuses you on accomplishing something and

(23:26):
not getting distracted. And I think that's really important to
Ben's point, right with these AI implementations, to know what
you're trying to do, and to monitor it and to
manage it, and to be open to the fact that
maybe you're off target.

Speaker 6 (23:39):
Right, absolutely, And this is why you know, if you
think about phinnops is one aspect of it about what's
already lived. And you know, I think there's not enough
work that was done originally when we put many other
things into the cloud to actually define that.

Speaker 5 (23:52):
But when we're talking about sort of doing new things.

Speaker 6 (23:54):
Like defining new AI projects, you know, one of the
first things becomes being able to define what is this
answer worth? Right, so if I can get this answer,
you know, classic example, we want to put an hr
chatbot out to our users to be able to look
at how much leave they've got left? Right, how much
is that answer worth? Is it worth two clicks and

(24:14):
three dollars? Is it worth you know, five clicks and
ten cents. That becomes an important part of it because
you know, one of the reasons why a lot of
you know, organizations get stuck at the POC level is
because you can optimize for getting that chatbot to give
you an answer really fast. But if that's driving the
cost from ten cents to fifty cents, maybe that's not

(24:35):
the right thing because that's not what you're trying to do.
What you're trying to do is you know, optimize at
the sort of over on a global level, which is
why it becomes very important to both have that definition
upfront and then have the observability tooling the penops practices
to monitor it on an ongoing basis, right, because that's
not a once and done thing either. You're constantly monitoring,

(24:56):
you know, mL mode. I like to take the example
of mL models because that's more pervasive. You know, we've
got optimization models, got next best offer models, et cetera.
Just like with those models, you're constantly monitoring. You know,
what's what's the actual metric achievement? Sure, same way, is
the cost of running this actually lining up with that?

(25:17):
Because the whole foundational premise of the cloud was that
your cost with your business and do that you actually
have to measure.

Speaker 4 (25:26):
It right and know what you're measuring. Is it customer satisfaction?
Is it uplifts on sales for a particular product. And
I'll throw this over to you all. This really comes
down to being able to manage the data around these services.
Know what it's actually costing you. That's the finop side.
Know what it's actually getting for you, that's the business

(25:47):
management side. Just being able to align the numbers, Like
revenue went up, Okay, that was our objective to get
revenue up, but the cost are up to so oh no,
that's kind of a problem. You have to be able
to adjust all those things and these days you can't
do that at every quarter, right, You got to do
that like on a weekly basis and not a daily basis, right, Ben.

Speaker 3 (26:05):
H Absolutely the speed of sort of fine tuning based
on what's happening. The feedback loop is you know, especially retailers, right,
it's daily, right, it's it's daily. I mean it's in
and so I mean that's why. And I think the
airlines has got this figured out where dynamic ticket pricing, right,

(26:27):
and and sort of like it and concerts, oh my gosh,
and when I and and like and I'm seeing you know,
restaurants trying dynamic pricing but online like it's just crazy right, Like,
but it does say they're the world around optimizing optimizing yield. Right,

(26:49):
whether it's on the revenue side or in the cost
it's all about optimization and data is the key to
that optimization.

Speaker 4 (26:57):
Yeah, no, that's exactly right. And throw a back over
to Tonk. You know you've mentioned a couple aspects from
my life, which is data, data, warehousing, analytics, understanding the
data and really it's it's amazing how far we've come
in the last that's even five years. And there are
companies like Snowflake and Data Breaks that had a lot

(27:18):
to do with this, really optimizing the stack, the technology
stack to be able to deliver analytics. And now it's
I mean it's table stakes. You've got to be able
to run analytics on your data, to know how much
money you're making, what's the margin, where is it coming from?
All that stuff is table stakes now, right.

Speaker 6 (27:36):
Absolutely absolutely stable stakes. And let me throw this out
there right. One of the biggest impact of JENNYI on
data and analytics has been that Jeni has driven people
to feel that they can to have democratization of all
of one technology. Right, So you know, writing Python code
has also become commoditized because I can go use a

(27:57):
large language order to help me write that. Part of
that is, you know, as you get table sticks on
from a platform perspective, which definitely the likes of Snowflake,
Data Bricks and you know all the three hyperscalers with
their technology have done.

Speaker 5 (28:09):
What you've now ended up with is a.

Speaker 6 (28:11):
Lot more business users directly transforming data, trying their hat
at building reports, trying their hat at you know, building
snowflake tables or you know, writing data bricks code because
they can and because they feel empowered because of what's
happened with GENII, and that has sort of you know,
one of the things is in the report is this

(28:32):
that I think more than somewhere around sixty percent of
companies actually have data sitting in more than one cloud
because different teams have gone and built out, you know,
different analytical silos at different places. So what's happening is
sort of how do I actually bring that together? How
do I unify that data and say, you know, while
the sales team might be saying our marketing lead gen

(28:53):
conversion is x percent and the marketing team is saying
five percent, how do I actually get to sort of
the truth? That is becoming sort of more of a problem.
And that's where I think. On the technology and cloud side,
this concept of CCoE has evolved you know a lot,
Whereas on the data side, the concept of having an

(29:14):
operating model for data and analytics and ensuring that we
can give many different business teams support is something that
is now starting coming into the forefront because the technology has,
as you said, actually become quite commoditized in that sense.

Speaker 4 (29:31):
Well, and it's also getting baked into a lot of places.
I mean, fifteen years ago, you would have an analyst
who would review and slice and dice data in a
data warehouse and then write some report manually and go
tell people about it and then they would hopefully change
something they're doing in the business. These days, a lot
of that is inline analytics, right. It's being delivered through
whether it's the ERP or some web system or whatever

(29:54):
it is. You have that data baked in, and that's
where you wanted, I think, is in the operational system,
where people are on the front lines doing things. You
want that AI and that analytic input to make suggestions,
to point things out. Say hey, and I think maybe
i'll throw this over to both of you first, Ratan,
I think much of the benefit of AI will come

(30:15):
in the form of suggestion, in the form of, hey,
we noticed this is happening. You know, maybe you should
do something about it, and then the user will say
yes or no. What do you think, Shatan?

Speaker 6 (30:25):
I'd agree with that, Eric, And what I'd add is,
I think the biggest disruption is actually on the consumption
there or the what I would refer as a bi
lair because you.

Speaker 5 (30:33):
Know, we build reports based on.

Speaker 6 (30:35):
The different kinds of questions that we expect people are
trying to answer. But and then you know, you have
a virtual report page which has you know, a chart
and metric, et cetera, and you go look for the
answer that you're expected.

Speaker 5 (30:46):
Whereas this has now.

Speaker 6 (30:47):
Become a lot more conversational conversations triggered by from the
AI agent side saying that you know, this looks like
a normally is this correct? Or from your site saying
you know, I see, you know what was the sale
for this store last week, because I'm going to have
a conversation with the store manager of that, and then
sort of very incrementally the same way as we would
have talked to the analyst to figure out what's happening right,

(31:08):
able to talk to here and what Then that what
that means is that the focus really becomes on having trust,
right you talk about observability, observatory or the data platform
becomes really important because I need to be sure, I
need to be confident as a business user that these
numbers are correct, they're current, they're complete, they are honest
and vetted. The actual UI of the report ETCA doesn't

(31:29):
matter as much because I, you know, that's become an
older Why would I go do three steps when I
can just you know, now always ask that.

Speaker 4 (31:36):
Yeah, Now, I mean that's an excellent point. I'll throw
it over to Ben. The fact that natural language processing
is now darn near proficients is a really big deal
because executives can sit there and ask all sorts of
questions of these information systems. In natural language, you don't
have to learn SQL. You can just ask a question
to get an answer back. Now to Tong's point, you
want to make sure there's trust. You want to make

(31:58):
sure that it's it is very grounded in reality and
not hallucinating. But still they're they're getting pretty good at that,
and the guardrails are getting pretty strong. What do you think, Ben?

Speaker 3 (32:09):
Oh? Absolutely, you know and and and you know, and
executives are getting trained that you can work in a
in a conversational way.

Speaker 5 (32:19):
Right.

Speaker 3 (32:20):
The expectations level because of consumer products regarding hey, I
could just talk to my phone, I can just talk
to Alexa. We're already getting trained behaviorally to expect that, right,
And so absolutely the key though, and when you start
talking on a business setting, the foundational about what's being
keyed up to you has to be really good, right.

(32:40):
And the other thing what we're seeing is and we
had a customer finished sort of customer in sort of
the payment kind of world, talk about the key now
is creating the customer experience around that data, right, you know?
Is it being et up in line as they're making

(33:03):
a decision? Is it a report? Because the more we
can make these things as part of your workflow, right, right,
the more valuable it's going to be. And so we're
seeing the rise of sort of like AI customer experience design.
So it's seamless, So it becomes just seamless to the user. Right,

(33:25):
It's not like I'm using AI. It's like I'm just
you know, I'm just asking siy I'm asking I'm right.

Speaker 4 (33:32):
That's right. Yeah. Well, and and analytics in general, it's
my experience that when someone gets a taste of that,
they want more. You want how it happens when you
can start to ask questions and understand the underpinnings you
ask the next question and the next question, it really
it takes off. And we'll dive into this in the
next segment. Heary just the second folks, But we're talking

(33:52):
to a couple of experts from rack Space. They're a
big survey that just came out about it leaders increasing
investment in multi and hybrid cloud stress energies to do
what to future proof their operations. Folks will be right back.
You're listening to Inside Analysis.

Speaker 2 (34:06):
Expected Welcome back to Inside Analysis. Here's your host, Eric Tabanaugh.

Speaker 4 (34:19):
All right, folks, back here on Inside Analysis talking to
a couple of experts from rack Space. We've got Schwatanka
Shiel and Ben Bunkera from rack Space and we're talking
all about the survey and maybe should talk. I'll throw
this one over at you first. You know, one of
the things that just jumped out at me from the
survey results was forty percent of respondence ciday a lack
of skilled cloud professionals as being a constraint. I mean

(34:42):
forty percent, two out of five are saying we can't
find the people. And you can train yourself these days.
That's the beautiful thing about the cloud. You go on
Coursera and take classes, you know, all night long. Many
of them are free to understand, and it's important to
know the difference between this cloud and that cloud. The
different services they offer, the cost structures, I mean, all

(35:04):
these details. The devil's in the details, right.

Speaker 6 (35:07):
Yes, And I think that's where some of this gap
comes from because you know, as as as men were
saying early, if you think about this as you know,
your cloud bill as a phone as a phone bill
is because you know, it's not that for example, data
bricks is one line item. Data bricks actually has you know,
fifteen twenty different ways in which they will charge you
depending on what you're trying to do. Same with you know,

(35:28):
and even fundamentally, and I think the cloud providers have
you know, our serial offenders in complexity of services. If
people park on awlus, there'll be like eight different ways
in which you can do so in the in the
on prem world, you would have said, okay, I'm going
to use park. Now let me just find someone who's
really good to' spark. And now you need to figure out, okay,
for this particular streaming workload, which is you know, giving

(35:49):
me ten thousand messages per minute, is it going to
be better to run it on EMR or data bricks,
Park or glue Spark And what are the costs associated
with network with compute, with storage that come into that. So, uh,
you know, I think experience there comes for a lot
because every project tells you what you should not have
done and makes learn better better.

Speaker 5 (36:11):
Yeah, but but I think.

Speaker 6 (36:13):
That that is that is one of the key uh uh,
you know, key differences in how quickly you can get started, right,
And that's what we find that you know, Ben was
talking about finnofs, but you know, I refer to the
fact that from an architecture perspective, you know, because we
focus in we in racs based focusing on doing you
know a lot of this work purely in the clouds,

(36:35):
we have developed sort of this framework to determine Okay,
you're trying to do these seven things, so this is
the architecture is going to have these different cost models
which plugs into from a phinnops perspective, does this total
up to the value that.

Speaker 5 (36:47):
You're going to get? And how are you going to
monitor and measure that?

Speaker 4 (36:51):
Yeah? Well, and you know, Ben, I'll throw this over
to you. I'm just guessing here based upon your experience
and the data that you see and and all the
engineers and the sort of frontline workers at rock Space,
you can offer some real good advice on those kinds
of questions that you talk was talking about should you
run this in asures? Should you run that in aws?

(37:11):
Because having experience in those environments will help you understand
not just the cost, but the workflow, the process, what
are the weaknesses? You know, where does it run into trouble?
All those kinds of details are really important and it's
always best to learn from someone else instead of learning
the hard way, right, Ben.

Speaker 3 (37:28):
Well, you've hit the nail on the head. Eric. We've
been in the hosting and infrastructive business for twenty five years.
There's a lot of gray and lost hair in running
operations for thousands of companies. Yeah, and it's like that
learning curve, Like there's that ten thousand hour rule, Like
once you do something for ten thousand urs, you kind

(37:49):
of know what the things are right. Right, if companies
are just entering this world, how many hours of your
team at we got twenty Okay, you got you gotta
You have to then realize there's a learning curve and
it can cost you a lot of money, and so
partnering with folks who understand because it experienced in multiple environments, right,

(38:15):
anyone can be successful when it's a good.

Speaker 4 (38:17):
Day that's a quote too.

Speaker 3 (38:21):
An when it's a good day, right, it's it's when
stuff hits the fan, that's when things shine, right, And
we've we've been through a lot of stuff hitting the
fan because our customers stuff happens, right, That's where you learn,
not on a good day, on crappy days, when.

Speaker 4 (38:41):
You learn, man, that's brutal. It's painful to think about
your time. Go ahead.

Speaker 6 (38:46):
So I just wanted to add to you know, Ben
was saying that, you know, we all live in an
environment of scarcity, which means that whatever team members actually
our customers have, it doesn't make sense for them to
be learning these or relearning you know what a lot
of us already know because we've done this. We want
their time to be focused on, you know, which is

(39:07):
the right model for this kind of use case. So
what is the use case that you want to drive
that is what's driving the business outcomes, and what you
want to focus our customer's time is on that, so
that we can apply you know, the learnings that we
already have where you don't need to reinvent the wheel
on everything, which is what I refer to as below
the line in.

Speaker 4 (39:26):
This case, right, Yeah, reinventing wheels is expensive. I mean,
you can always build a better mouse trap, but you
really have to watch out. It's the blocking and tackling
when you get right down to it, right Ben. I mean,
the blocking and tackling can take you down. If your
offensive line is not strong, your quarterback's going to get sacked,
you know. I mean there are lots of things. It's
not a glorious job. But the cloud architect is kind

(39:47):
of like that that either offensive line or dance defensive
will be security. Right. So the cloud architects or the
offensive line pushing forward allowing you to run into the
end zone, right Ben.

Speaker 3 (39:57):
Oh, it's you at the end of the day. Like
winning is about execution. Execution is about day to day things, right.
It is about the little things that add up to
the big things.

Speaker 4 (40:10):
Right.

Speaker 3 (40:10):
And so you know, as you know, since I am
Ohio State band and I just saw them winning the
national championship, right, it is the blocking and tackling and
consisting on a day to day basis, and that and
and and so when you think about that, that's where
you start thinking about, how do I have playbooks, how
do I automate things? How do I have observability. How
do I have how do I make sure texted I

(40:34):
focus my people looking at the right things, not all
the all the things. Right, That's that's really hard in
in a world of lots of lots of widgets, I
can look at.

Speaker 4 (40:46):
You know what, that's a really really good point in Schwatan,
I'll throw it over to you and we'll probably go
into the podcast on a segment with this threat as well.
Everything is changing, and so what you have your people
focused on? Think about old it t versus new it
and understanding all it is still around. You still have
these data centers, You still have to manage Linux implementations
and all these different things, upgrades, patches, all that stuff

(41:08):
that's still going on. But now you have this multi
cloud environment where you have all these other things to
worry about. And it depends on the company, depends on
the use case, on the size of the organization, etc.
On your actual team. But that's a big challenge, right
is knowing how to get everyone focused on what they
should be focused on, especially when it changes over time.

(41:29):
What's your advice there, Shwatan.

Speaker 6 (41:31):
Absolutely, Eric, And this is why you see one of
the finding these surveys. So this rise of centralization. And
I'm not about centralization from it doing everything, but id
defining the COEs which are saying these are the basic
standards that you need to follow, because the other thing
that's happened is that there has been a lot of
democratization of people actually building their own ail high reports

(41:54):
or playing with their data sets. So as you give
that responsibility out, you need to with great responsibility comes
you know, with a lot of rights come a lot
of responsibility.

Speaker 5 (42:03):
So the iight, what.

Speaker 6 (42:05):
We're seeing is that there are these ces behoving set
up to set up standards so that people can do themselves,
but are doing that and it's not you know, one
I engineer or one cloud architect is not going to
be able to look at you know, the security aspect
and the finnops aspect as well as sort of the
data aspect and how you go from a cloud native perspective.
And that's really where the rise of the COE and
the rise of sort of managed service providers where you

(42:29):
bring that specialization from a managed perspective has really come
into play.

Speaker 4 (42:34):
Yeah, that's a really really good point. That's why you're
going to see more of these managed services, right because
when someone really understands a particular environment. You want them
focused on that environment. You want them sharing the information
with their colleagues. I mean, this whole knowledge sharing side
of the equation is not insignificance, you know. And it's
like people don't want to sit there and read big,
long manuals about stuff. So it's like you have to

(42:56):
populate it in your center of excellence and have someone
shepherding that and watching over it.

Speaker 3 (43:01):
Right, Ben, Well, absolutely, if everyone's you know, creating their
own sauce because they don't have standards, right, then they're
not worried about blocking and tackling. They're just they're just learning, right,
And so you know, history has said you have to
start with basics. Give people a playbook which is the
coe kind of things and says, do these ten things

(43:23):
right first before you get to the fancy stuff, like
you'll learn how to block, right, like put your arms
up in a certain way, right, get down there, like
learn how to tackle. You don't know those basics. This
other fancy stuff that you're going to get excited by,
it doesn't matter right.

Speaker 4 (43:41):
Well, and you know I'm gonna use this line to
lead off our final segment today, But we had a
fantastic event a couple of weeks ago with the Dakota
Bowl and Red Panda, And we had a lady from
Old Mutual, a bank builder, and she had this awesome mantra.
She said, you need to earn your right to do things,
to build things. So first, as Ben suggested, learn to block. Okay,

(44:04):
now you can block. Learn to tackle, Okay, now you
can tackle. Now let's talk about some strategy. You start
with the basics, you build up from there, and you
earn your right to be able to do the cool
stuff by showing that you've blocked, you've tackled, you've done
your It's like a kid having done his or her chores. Right, Okay,
you did your chores. Now we can go to the
game together. Now we can do some fun things. In

(44:25):
the enterprise, you have to really earn your right to
get there to use the powerful tools because they are
expensive and they can do great things, but you know
you've got to pay for it. Someone's going to pay
the piper at the end of the month, and there's
a lot of bills to be paid. But folks don't
touch out. That will be right back of the podcast.
Bonus segment here on Inside Analysis. All right, folks back

(44:47):
here on inside Analysis with Tank Shield and Ben Blankera
of rack Space, we've talked all about AI, analytics data.
Guess what it's got to be secure? The security man.
I went to this Blunk conference last year and just
looking at some of those screens, what these people do
all day? I'm like, Wow, it takes a special kind

(45:07):
of personality to just sit there and like just go
into the deep dark forest and look for the moles
and the foxes and all the other troublemakers that are
going to cost you some issues here. And guess what
AI helps with all this?

Speaker 3 (45:21):
Right?

Speaker 4 (45:21):
If you get a good model, that's what I'm That's
what really fascinates me. Are these bespoke models that companies
are building. You can just grab something off a hugging
face and just deploy it in your environment, start playing
around with it for security. It's good stuff because these
models can be very good at identifying anomalies. An Anomaly
detection is probably like the number one most important thing

(45:42):
in security. What do you think, Ben.

Speaker 3 (45:44):
Well, absolutely, the you know in our survey, you know
we've got more than half folksing we're using AI to
improve our threat detection, which is all about anomalies and
where things are happening. When you look at how many
endpoints most you know, the endpoints where folks bad folks
do come in. It's millions, right, and so what's happening

(46:07):
in all those endpoints, all those kind of things is
incredibly important and AI can play a big part of it.
AI can also play a big part in sort of
monitoring your internal systems to make sure everything's are up
to or a patch level, but everything is secure. You've
got you've got things closed off that should be closed off.
You're you're looking at internal user behavior regarding like who

(46:30):
should be getting permission all the things AI can do
for hygiene internally as well as then AI for threat detection,
and so it is it is uh because guess what
the bad guys are using AI?

Speaker 4 (46:46):
They are I mean it's crazy. The fishing scams are
getting really good. They're getting so good on the design.
I mean, you know, I know to look for them now.
But it's like, oh, a Verizon bill is one thousand dollars?

Speaker 7 (46:58):
Is it?

Speaker 4 (46:58):
No? It isn't. That's a fishing scalm. You got to
watch out with that stuff. But should talk, I'll throw
it over you. Ben made a great point turn things
off if things aren't being used, If access points are
out there and they're not being used, just knowing what's
not being used and turning it off, that's probably half
the battle right there. What do you think?

Speaker 5 (47:16):
Right?

Speaker 6 (47:16):
And I think AI is helping beyond what Ben said,
there's two other things he is helping with. One is
actually you know, being able to go over the large
amount of meddata that we have to understand who is
using what, what is actually you know, normal and what
isn't normal?

Speaker 8 (47:32):
Right?

Speaker 6 (47:33):
But then the other side of that also is that
it is also another additional surface area that we do
need to secure because you know what llms they come
with their own kind of security they will look at
and you know how prompt injection, how do you actually
ensure that you know, the data that you're training on
is not skewed? How are you ensure that it's not

(47:54):
exposed externally? So you know, I think this is one
of those things where you have to do it, but
it is good. You know, it is going to continue
getting more and more complex as we start to become
more dependent on everything that is digital, right, because I
mean just think about it the proportion of things that
we were doing digitally five years ago versus now. You know,

(48:16):
even if you take the cloud out of it, just
think of your driving licenses and now digital.

Speaker 5 (48:21):
Your tickets are now digital.

Speaker 6 (48:22):
So the surface area of both the data as well
as the interactions have skyrocketed. And I you know, I
think the security side of that is trying very hard
to keep up.

Speaker 5 (48:33):
But it's a nuclear and race.

Speaker 4 (48:34):
Yeah, that's right, and been I'll throw it back over
to you. It is a constantly changing battlefield out there,
and you have to stay on top of things, and
you have to be very agile and willing to admit
when you're wrong about stuff. I think you know, probably
pride leads to as many security breaches as anything else.
What do you think, Oh my.

Speaker 3 (48:52):
Gosh, oh absolutely, No one wants to say I left
the door open, right, no one? No one said, no
one else and so and so. Yeah. I do think
like the rate by which people learn is which implies
that I'm the open with what's working and what hasn't worked,
is going to be a key determinant to how secure

(49:15):
things are because you've got to have closed feedback groups
associated with what happened how do we fix it or
what's working and how do we do more of that? Right,
It's all about learning absolutely well.

Speaker 4 (49:26):
And we'll just kind of close on this because I
think it's very exciting. I'll throw it over to show
Talk and then Ben for final thoughts. It's very exciting
to know that we can use these AI engines, whether
it's old fashioned predictive models for example, or all this
Jenni stuff. Jenni is great for just sort of stochastic
use cases for just exploring ideas and looking for things. Yes,

(49:49):
you have to verify it because they do hallucinate, but
still you need people. I mean, you know I personally
I advocate for an information strategy group in every organization,
like almost like a center of excellence around analytics are
just strategic thinking because things change so much. I mean,
look at these like chatchipt. They're adding in new layers,
they're doing new things all the time. And of course

(50:10):
it's a black box now right, it's not open AI,
it's closed AI. But just having a team focused on
how to use these technologies I think is a huge,
huge win for companies to do what do you think?

Speaker 6 (50:22):
Absolutely and and it's also because as you put things
into production, you have to continuously review how those should
be affected or up updated with that, right, And I'll
give you an example of you know, we internally in
rag space actually built a GENII two around a year
agowards as I searched the you know, information co worker

(50:43):
for enterprises that we use in rackspace, and a year
ago when we built it, you know, a lot of
these abstractions around how you do guardrails, how you do
cost monitoring did not exist, so we wrote that ourselves
and now look, you know eighteen well twelve to fifteen
months later, a lot of these abstractions exist. We need
to put in now is other kinds of guardrails around
sort of learning of different kinds of data and things

(51:05):
like that. So we have to constantly go back and
look at, you know, what should I rip out, what
should I use new? Because I want to get the
benefits of the continuously lowering price of inference and of
the better models which are doing that. So it is
a continuously ongoing thing. And having this sort of operating
model which defines a center of excellence which is tasked

(51:27):
with doing that and then cascading that out or guiding
everybody is really a key critical thing.

Speaker 4 (51:32):
Yeah, great, great point in one minute left. Closing thoughts
from you, Ben, what's your advice on companies who know
they have to do something and are just not sure
where to start. What's your advice?

Speaker 3 (51:42):
Real? Couple of key things. One, look at your workloads
down optimizer workloads so you can free up some money
to pay for these things. Two, as you're looking at AI,
it is an emerging field, but you have to have
an operating model about how you're going to run it.
It is not one and done. It is you just
you just you have just now created a new set

(52:02):
of employees, and just like a new set of employees,
you have to do care and feeding and life cycles.
So that right, that's what I would suggest.

Speaker 4 (52:11):
Excellent, excellent advice. Will look up this survey, folks. New
research by RAX based Technology revisals, hybrid cloud and AI
integration are key drivers for IT innovation in twenty twenty five.
Look them up online. Great talking to you, gentlemen. You've
been listening to Inside Analysis.

Speaker 9 (52:26):
Now you can listen to KCAA radio anytime on your
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Speaker 7 (55:29):
Hi, y'all, merle here. Good news for once. My neighbors
is jealous of me. You want to know why because
my grass is growing and looking green and I can
see on my sofa out in front yard and I
don't even have to overwater it anymore. You know how
I did it. I listened to damn water Boys underwater
Zone every Thursday night on KCIA. Well, I got me

(55:51):
a smart controller and now a water's at night yard
looks darn tooting. No more sleeping around and hooking up
my horse to my neighbors pigott in the middle of
and his dog won't buy me anymore. And you can
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now screaming online.

Speaker 9 (56:09):
It's streaming what it's streaming? You dumm it?

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Well, I don't know much about streaming, but they doing
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Speaker 1 (56:27):
KCAA Radio has openings for one hour talk shows. If
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radio plus the Internet, contact our CEO at two eight
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(57:14):
Just time Kcaaradio dot com into your browser to learn
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Speaker 13 (58:01):
NBC News Radio, I'm Lisa Carton. President Trump says he
expects to find hundreds of billions in wasteful spending at
the Education and Defense departments. Trump made the prediction in
a pre Super Bowl interview with Fox News, during which
he outlined plans for Elon Musk and the Department of
Government Efficiency to expand its probe of fraud, waste, and
abuse in the federal government.

Speaker 3 (58:23):
I'm going to tell him very soon, maybe in twenty
four hours, to.

Speaker 5 (58:27):
Go check the Department of Education.

Speaker 13 (58:29):
He's going to find the same thing, Trump said. The
funding freeze for the US Agency for International Development is
only the beginning. Heavy security is in place for today's
Super Bowl in New Orleans. President Trump is expected to
become the first sitting president to attend the Big game.
Extra protection includes an enhanced security zone near Bourbon Street,
which is where the deadly New Year's Day terrorist attack happened.

(58:51):
Governor Jeff Landry created the zone, allowing law enforcement officers
to search bags of folks entering the area. Kickoff for
the game between the Kansas City each Chiefs and the
Philadelphia Eagles is set for six thirty pm Eastern. Homeland
Security Secretary Christy Nomes says illegal immigrants already at Guantanamo
Bay will remain there until officials can reach agreements to

(59:13):
return them to their home countries. Nomes said this could
mean some may be imprisoned at the US military detention
facility in Cuba for weeks.

Speaker 4 (59:21):
My goal is that people are not in these facilities
for weeks and months, that there's a short term stay,
they're able to go, incarcerate them, take them, follow the process,
and get them back to their country.

Speaker 13 (59:31):
She stressed. Those being housed there currently are accused of
serious violent crimes and have been afforded their right to
do process. Another wave of the flu has hit the nation.
Scott Carr has an update.

Speaker 8 (59:42):
Forty five states now reporting high or very high flu activity.
Health officials say a rapid rise in cases is causing
doctor visits for flu related symptoms to reach their highest
in fifteen years.

Speaker 13 (59:54):
You're listening to the latest on NBC News Radio.

Speaker 7 (01:00:00):
News on KCAA Lomel and sponsored by Teamsters Local nineteen
thirty two. Protecting the Future of Working Families Teamsters nineteen
thirty two, dot org