Speaker 1: You're on board. Caseyaas Inland Express. Caseyaa home, Linda ten
Speaker 1: fifty am the station that needs no This here behind.
Speaker 2: The information economy has a ride. The world is teeming
Speaker 2: with innovation as new business models reinvent every industry industry.
Speaker 2: Inside Analysis is your source of information and insight about
Speaker 2: how to make the most of this exciting new Eric
Speaker 2: learn more at inside analysis dot.
Speaker 3: Com, Inside Analysis dot com.
Speaker 2: And now here's your host, Eric Kavanaugh.
Speaker 4: All Right, ladies and gentlemen, Hello and welcome back once
Speaker 4: again to the only coast to coast radio show that's
Speaker 4: all about the information economy. It's time for Inside Analysis,
Speaker 4: and your host Eric Kavanaugh is here in folks. I
Speaker 4: am super psited to be talking to a couple of
Speaker 4: experts and in the hardware, and they also know about
Speaker 4: software and data and all kinds of technology. And we're
Speaker 4: going to talk about a survey. But I've got two
Speaker 4: experts from rack Space. Yes, indeed, rack Space, they've been
Speaker 4: a powerhouse in the industry for decades, a very serious player,
Speaker 4: and they know a lot about what's going on in
Speaker 4: this world. Of course, a lot of AI conversations these days.
Speaker 4: But Hybrid, we're going to talk about hybrid and what
Speaker 4: that actually means. We're going to talk about these experts
Speaker 4: and find out and it was a survey that's been done,
Speaker 4: so it leaders increasing investment in multi and hybrid cloud
Speaker 4: strategies to future proof their operations. That's what the survey reveals.
Speaker 4: So let's go ahead and dive right in. So tank
Speaker 4: Shield tell us what what did you find in this survey?
Speaker 4: What surprised you, what didn't surprise you? What do you
Speaker 4: think is interesting?
Speaker 5: So thanks er, thanks for having me.
Speaker 6: You know, what I found quite interesting, especially in the
Speaker 6: in the from a context of analytics and bi perspective
Speaker 6: here is that, you know, the proportion of data that's
Speaker 6: gone to the cloud has really skyrocketed. You know, if
Speaker 6: you look in this survey, it's so that more than
Speaker 6: fifty percent of the data is actually now sitting in
Speaker 6: the public cloud.
Speaker 5: It is across many.
Speaker 6: Different cloud providers, it's across hybrid providers, and you know,
Speaker 6: we're expecting to see seven percent more going up there.
Speaker 6: And the other interesting thing that I saw is that,
Speaker 6: you know, the adoption of SAS has really skyrocketed. And
Speaker 6: what that really means is that now, more so than ever,
Speaker 6: we have data sitting obviously across different clouds insiders, but
Speaker 6: also within these different SaaS applications that are running on
Speaker 6: their own or customers clouds, which means bringing the data together,
Speaker 6: whether it's for the purposes of analytics, all the further
Speaker 6: purposes of AI has become an increasingly challenging.
Speaker 5: Requirement. Now.
Speaker 4: Yeah, and we've also got been like Karra on the
Speaker 4: call here and I'll throw this over to you. I remember,
Speaker 4: I've been covering this stuff for a long time. So
Speaker 4: DM radio turns seventeen years old next month, so I
Speaker 4: would do it this a while. And I thought the
Speaker 4: cloud would take off twenty years ago, and it really
Speaker 4: took off like ten years ago. So for some reason,
Speaker 4: it just took a while. I credit actually Second Adella
Speaker 4: of Microsoft for pivoting his whole enterprise to cloud. And
Speaker 4: I really think that woke up a lot of people
Speaker 4: who are like, geez, I guess we better get on this.
Speaker 4: But I remember that in our conversations on these shows,
Speaker 4: we went from talking hybrid cloud to multi cloud in
Speaker 4: about one week because people figured out it's not just
Speaker 4: going to be on prem in one cloud, it's on
Speaker 4: prem in multiple clouds, which greatly increases the complexity, right.
Speaker 3: Oh absolutely. In our SAVE survey, what really came out
Speaker 3: was the notion that it's turning into a much more
Speaker 3: complex world and that it much like when you're preparing
Speaker 3: a meal for your family. If you just buy one
Speaker 3: thing all the time, that's not going to satisfy things.
Speaker 4: Right, that's funny.
Speaker 3: And so when you think about it, organizations providing services
Speaker 3: to their businesses, businesses that have different flavors of things
Speaker 3: that need to be done, and those different flavors of
Speaker 3: things need to be done, you have multiple options now.
Speaker 3: And so those options in terms of things to be
Speaker 3: done are what we call those workloads. And so when
Speaker 3: we think about workloads, we have to think about what
Speaker 3: is a workload and where should it be? Much like
Speaker 3: you know, do you need something fast for your family
Speaker 3: or do you need a big sit down meal. They're
Speaker 3: different scenarios, and each of those different scenarios says there's
Speaker 3: different options. And what's happened is cloud everyone got. You know,
Speaker 3: hyperclouds are wonderful, However one size, they're not fit all.
Speaker 3: You know, if you're looking at workloads that have to
Speaker 3: be secure, workloads that that are that are that doesn't
Speaker 3: need bursting and things like that. Well, you know apps,
Speaker 3: you just need to put them on private cloud. Right.
Speaker 3: If you need native services that are in hyper skills,
Speaker 3: put in the hyper skill. The point is all this
Speaker 3: is saying is that to for IT or organizations to
Speaker 3: provide the great services to their businesses in terms of capabilities,
Speaker 3: you have to give options. And hybrid is a is
Speaker 3: a way to think about It's that one, it is
Speaker 3: both public and private, and you've got you've got to
Speaker 3: be able to do both or else you're not optimizing
Speaker 3: the services you're providing to your businesses. That makes sense.
Speaker 4: Yeah, no, it makes complete sense. And you know, I'll
Speaker 4: throw another question over at you and then we'll get
Speaker 4: tank back in this. But you know, observability I think
Speaker 4: is a huge play in what we're seeing right now
Speaker 4: and finops, which of course is observability because like five
Speaker 4: six years ago, you know, it was hard to tell
Speaker 4: what the stuff really costs. Now we have very granular
Speaker 4: reporting on what the cost is and that allows you
Speaker 4: as an IT organization to do what you just suggested,
Speaker 4: which is optimize we're going to do this workload in
Speaker 4: this cloud. We're going to do that workload on prem
Speaker 4: and when you have the capacity to do that. If
Speaker 4: you've embraced cloud nativity, right, if you're cloud native even
Speaker 4: in your on prem environment, if you've done something to
Speaker 4: make it look more cloud native, you can really move
Speaker 4: those workloads around. And that gives tremendous agility to the organization, right.
Speaker 3: Absolutely, And that's a key driver as technology leaders are
Speaker 3: looking for how do I future proof? You know anything
Speaker 3: about that, Eric, As you think about your future proofon
Speaker 3: you have to think about how do I create the
Speaker 3: most flexibility with things that I don't even know are
Speaker 3: going to be coming? You know, we don't know what
Speaker 3: AI workloads are going to look like, we don't know,
Speaker 3: we don't know where data is going to be moved,
Speaker 3: we don't know, right, So the question is how do
Speaker 3: you then create a way of looking creating a platform
Speaker 3: for your company that you're not locked in, right, right,
Speaker 3: that you're not locked in, and you create the most
Speaker 3: flexibility Because as most folks know or have said, AI
Speaker 3: is going to change everything. The footprint of how business
Speaker 3: is done is going to change pretty significantly, right right,
Speaker 3: And so how so it is incumbent on technology leaders
Speaker 3: to you know, I have to drive flexiblit and by
Speaker 3: the way, I've got to make sure I do that
Speaker 3: in a way that I can. I can not spend
Speaker 3: everything in the bank, which which has to do with
Speaker 3: the finopps, which has to do with observe a bilak.
Speaker 3: We think that you know, if you if you're a
Speaker 3: major corporation spending it several million dollars a year, probably
Speaker 3: you know there's probably twenty to forty million, twenty to
Speaker 3: forty percent of utilization that you could optimize, right, right,
Speaker 3: But it's like have you ever read a bill for
Speaker 3: your cloud? It's like the phone bill times a million, right,
Speaker 3: you have it, right, unless you can actually understand it, right,
Speaker 3: you don't know what buttons to change, and and so
Speaker 3: you know, like we at a rack space, you know
Speaker 3: we we are you know if we are the largest
Speaker 3: pin opps service provider in the world.
Speaker 4: Right, that's a great way to look at it. That's
Speaker 4: a very interesting perspective because what this is old school,
Speaker 4: but what gets measured gets managed. I'll throw that over
Speaker 4: to should talk. Right, what gets measured gets managed, and
Speaker 4: when you can start to see where all these costs
Speaker 4: are coming from and align them with the projects and
Speaker 4: understand what is the TCO understand what is the ROI
Speaker 4: Are we getting return on this investment. The more you
Speaker 4: can do that, the better you can manage your environment,
Speaker 4: the more efficient you become, the more money your enterprise
Speaker 4: makes on the bottom line, right absolutely.
Speaker 6: And I think what we're seeing now is that you know,
Speaker 6: this entire idea about ROI, which in a sense has
Speaker 6: been driven by the explosion of AI because now everybody
Speaker 6: wants to do AI and wants to what's.
Speaker 5: The benefit of that?
Speaker 6: But that is you know, trickering down all the way
Speaker 6: to the bottom of the stack, which is the entire
Speaker 6: cloud stack, right Because you know, when we talk about
Speaker 6: you know, data pipelines, or we talk about storage, or
Speaker 6: we talk about computer associated with you know, doing transformation,
Speaker 6: it's very important to understand the unit economics of that.
Speaker 6: It's not just you know, how much does my data
Speaker 6: warehouse cost to run?
Speaker 5: How much am I spending on GPUs?
Speaker 6: It is what is the unit economics price performance that
Speaker 6: I want to optimize for, you know, my inventory optimization
Speaker 6: because I'll spend two hundred thousand, But it only makes
Speaker 6: sense if I'm actually saving more than two hundred thousand
Speaker 6: from the business benefit that's coming from that particular EMIL model.
Speaker 6: And you know, one of the other things that that
Speaker 6: that sort of listed out in the studies that you
Speaker 6: know there's this rise of centralization.
Speaker 5: Or COEs, right, because the way we think.
Speaker 6: About it is that you know, there are a large
Speaker 6: number of what i'd refer to as non functional requirements
Speaker 6: where you need to have observability, you need to have quality,
Speaker 6: you need to have a standardized way of figuring out
Speaker 6: as Ben was saying, what kind of workload works best
Speaker 6: in what component in the club, right, is while they
Speaker 6: give you a lot of flexibility, they also end up
Speaker 6: actually giving you hundreds of options to do the same
Speaker 6: kind of thing on different components and having guidance around
Speaker 6: you know, I'm trying to build an inventory model, I'm
Speaker 6: trying to build a chat point, I'm trying to build
Speaker 6: a bi report. Means I need to have these standards
Speaker 6: regardless of what actual data is going in. There is
Speaker 6: something that's going that is becoming quite key to analytics,
Speaker 6: but also all workloads in the cloud.
Speaker 4: Yeah, I mean, this is it's fascinating. Go ahead, man.
Speaker 3: Yeah, And and now I'm just sort of going to
Speaker 3: jump in here and what we're seeing like and this
Speaker 3: is what the survey said is, uh, you know, there
Speaker 3: there's a sort of phase of going through technologists Phase zero,
Speaker 3: day zero, day one, day two, day zero, strategy. Day
Speaker 3: one is sort of transformation. Day two is the run side, right,
Speaker 3: And the challenge is when you start thinking about hybrid cloud,
Speaker 3: the talent required to go through all those phases and
Speaker 3: run things and run things at scale right, and run
Speaker 3: things at scale that are secure in some run things
Speaker 3: get reliable. There's a big talent gap which came out
Speaker 3: in the survey as well. And so I think we're
Speaker 3: going to start seeing more models that have to do
Speaker 3: with managed services on the run side because of in
Speaker 3: there's going to be you know, my crystal ball says,
Speaker 3: companies are going to want to use align with folks
Speaker 3: who can provide the managed service for the full stack
Speaker 3: side of things so that they can concentrate on what
Speaker 3: what what makes my beer taste better? Mm hmm right,
Speaker 3: that's funny, right, Yeah, you got to worry about things
Speaker 3: and your team members have to worry about things that
Speaker 3: make your what is the thing that makes my beer
Speaker 3: taste better? Versus the things I need to run? And
Speaker 3: so I think they're going to be emerging models regarding
Speaker 3: the ecosystem of operations, right that that's going to be
Speaker 3: really important.
Speaker 4: Well, and you know you mentioned this already, and i'll
Speaker 4: throw this to you first, ben AI workloads. There are
Speaker 4: all kinds of AI workloads. I mean, people, I think
Speaker 4: a lot of folks in the business world are starting
Speaker 4: to appreciate that models, deep learning modules can take any
Speaker 4: number of shapes. You can have any number of inference layers.
Speaker 4: You're going to ingest all these different component parts. You
Speaker 4: can stack and layer and design that as you wish,
Speaker 4: and there's really no limit to the permutations for how
Speaker 4: you do those things. And what I see happening I
Speaker 4: think that's very interesting is a company stor are building
Speaker 4: up bespoke models for individual clients. I mean, just one
Speaker 4: random company I'll throw out there. I'm going to read
Speaker 4: safe books AI. And what they do is they build
Speaker 4: they take a model off a hugging face, and then
Speaker 4: they train your data, your accounting data, your financial data
Speaker 4: on your model that is your bespoke model, and then
Speaker 4: once it gets to a certain level of proficiency, it
Speaker 4: just watches all your finances and says, hey, I think
Speaker 4: I found a problem here. Because there is a story
Speaker 4: of Macy's where some person hid huge amounts of expenses.
Speaker 4: They didn't find out about it until they went to
Speaker 4: the quarterly earnings and it was like whoa, No, like
Speaker 4: red three alarm fire, that stuff won't go away. My
Speaker 4: point is that this is a fantastic use case for AI.
Speaker 4: It's not just Jenai. There's a lot of excitement around Jenai.
Speaker 4: That is one whole category of very interesting technologies. But
Speaker 4: AI models have been around for decades.
Speaker 3: Forever, forever.
Speaker 4: This is extremely efficient at understanding particular business use cases, right.
Speaker 3: Ben, Oh, they absolutely are. And as you said, Eric,
Speaker 3: you know, if you look at models, it's you know,
Speaker 3: the essence of these models is you you have trained
Speaker 3: a model based on the data set right right, and
Speaker 3: you can make that day SAT really big, which is
Speaker 3: you know, general chat GPT massive, or you can focus
Speaker 3: on your specific data set right right. And so I
Speaker 3: think you're I like you bringing this out for folks
Speaker 3: to understand, right, what is that? What is the thing
Speaker 3: you're trying to do right right? That's right because based
Speaker 3: on what you're trying to do generates a whole lot option.
Speaker 3: The challenges is most folks just go to hey, I
Speaker 3: just do chat GPT, right, and so all the different
Speaker 3: nuances associated how do you optimize get lost really fast? Right,
Speaker 3: And so unless you're actually talking to someone who understands
Speaker 3: the nuances but also understands think about this er the model.
Speaker 3: If you look at the value chain of like from
Speaker 3: data to models to the decision, that model is just
Speaker 3: a little bit. It's like five percent of the stack
Speaker 3: tech stack related the delivering value, right right, And everyone's
Speaker 3: getting really excited about that. But guess what, folks, that
Speaker 3: other ninety percent underneath underneath the water line? Did you
Speaker 3: That ain't correct?
Speaker 4: Right?
Speaker 3: It doesn't matter, It doesn't matter. Yeah, So that that's
Speaker 3: really that That's what you know, when we think about
Speaker 3: hybrid clond what came out in the service, you got
Speaker 3: to worry about.
Speaker 4: That stuff, right, Well, yeah, go ahead, go ahead to that.
Speaker 6: I just want to remplain that, you know, that's a
Speaker 6: very good example of what we mean by workload of
Speaker 6: were because if you think about it, the training of
Speaker 6: a model or fine tuning of a model, et cetera,
Speaker 6: this requires a large amount of data, a large amount
Speaker 6: of compute, but for short periods of time. You know,
Speaker 6: you may do it daily, weekly, Whatever is the cadence
Speaker 6: required for that particular workload, which definitely you know, for
Speaker 6: which things like public clouds tend to be much better.
Speaker 5: But the actual usage of.
Speaker 6: That model, of the the influence of that model is
Speaker 6: a much more predictable compute activity where the rest of
Speaker 6: influence becomes much more important. Uh, And kinds of things
Speaker 6: tend to be much better in private cloud or you know,
Speaker 6: self hosted kind of environment. So you know, even within
Speaker 6: a particular use case, thinking about different stages, what's the
Speaker 6: right place for that to go, whether it's public cloud
Speaker 6: one or more public clouds or it's private cloud, actually
Speaker 6: becomes quite important.
Speaker 5: And that's I think something that is captured in this
Speaker 5: as well.
Speaker 4: Yeah, and that is important. And you know, I've said
Speaker 4: this for years now and I feel somewhat vindicated a
Speaker 4: throat of event. First my mantra was that the rumors
Speaker 4: of on Prem's demise have been greatly exaggerated. What do
Speaker 4: you think, Oh.
Speaker 3: My god, Oh my god, that is Think of that
Speaker 3: like it is an urban myth. It is like the
Speaker 3: biggest urban myth that has been perpetuated on us, right right.
Speaker 3: This is because the thing on the workload you need, Yeah, yeah,
Speaker 3: I mean like if I'm taking if I'm driving my
Speaker 3: kids to school every day, right, my Honda Civical work
Speaker 3: just fine.
Speaker 4: Right right?
Speaker 3: Or or my Honda because I have a bunch of kids, right,
Speaker 3: I don't need my sports car right right. And so
Speaker 3: you know, when you start looking at the the economics
Speaker 3: of this, it starts looking like, you know, maybe that
Speaker 3: truck is a much better value and that's really the
Speaker 3: key word, is a much better value for what I'm doing.
Speaker 3: So and we are the survey will show the amount
Speaker 3: of folks think about repatriation.
Speaker 4: Mm hmm.
Speaker 3: Right. It's kind of like buying, like buying a boat.
Speaker 3: You know, you get really excited to day you buy
Speaker 3: a boat. Then you realize how much it actually cost, right,
Speaker 3: and you're like the upkeep the upkeep right, and you're like,
Speaker 3: all right, the best days of a boat or is
Speaker 3: and when you buy the boat and when you sell
Speaker 3: the boat, right.
Speaker 4: That's pretty funny, especially if our hurricane is coming in
Speaker 4: right then you're like, oh no, oh no, this is
Speaker 4: a problem that can't get insurance.
Speaker 3: Exactly, which, yeah, which goes back to Eric, like what
Speaker 3: are you trying to do? What is the workload that
Speaker 3: matters for you? And worder and be clear about the
Speaker 3: options because you know what, you don't want to overspend
Speaker 3: because with AI, like you know a lot of comps
Speaker 3: like I talked about CIOs, I go, hey, I wonder's
Speaker 3: AI thing. But you know, I don't have my much.
Speaker 3: It didn't go up right, right, customer customers aren't paying anymore,
Speaker 3: So who's going to pay for this? Right? So that's
Speaker 3: where when you start looking at the delivery costs and
Speaker 3: unit economics, Innovation costs money. Right, I've got to figure
Speaker 3: out how to optimize my current environment, which means I
Speaker 3: actually need to you know, I've got to do spring cleaning.
Speaker 3: I've got to do my workload to wear so I
Speaker 3: can pay for this stuff.
Speaker 4: Right. Well, I mean this is the you're big of
Speaker 4: an excellent point, and we'll dive into this in the
Speaker 4: next segment, folks, because spring cleaning, right, just understanding what
Speaker 4: you have, where it is, who's doing what, Where is
Speaker 4: your data? How much does it costing you to store
Speaker 4: this stuff? What is the overall plan? It's very difficult
Speaker 4: to know that from a large enterprise perspective. It's very
Speaker 4: hard to sort of piece together all the components to
Speaker 4: really understand what you're looking at. But that is a
Speaker 4: huge issue right now that companies need to do, especially
Speaker 4: as this ALI era dawns. But don't touch that toll. Folks,
Speaker 4: be right back. You're listening to Inside Analysis.
Speaker 2: Respect, Welcome back to Inside Analysis. Here's your host, Eric Tabanac.
Speaker 4: All right, folks, back here on Inside Analysis talking to
Speaker 4: a couple of experts from rack Space. Have a big
Speaker 4: IT survey that just came out. Very interesting information. Not
Speaker 4: not a whole lot of big surprises for me when
Speaker 4: I look at the market and understand what's happening here.
Speaker 4: And let's face it, the shiny new object now is
Speaker 4: AI and everybody wants it. They want to leverage it,
Speaker 4: but you got to be careful. Number one, how much
Speaker 4: does it cost?
Speaker 3: Well?
Speaker 4: Really, number one is what do you want to do
Speaker 4: with it? Like what is the business case around it?
Speaker 4: Number two is what it costs? And then you can
Speaker 4: kind of figure all that out. And finnops is a
Speaker 4: huge part of that. And Ben, I love how you
Speaker 4: said rag Space is the biggest finops provider because that's
Speaker 4: what you're that's part of what you're doing is helping
Speaker 4: people understand what does this stuff cost? And once you
Speaker 4: once you define your business objectives, then you really got
Speaker 4: to focus on what's it going to cost us, how
Speaker 4: are we going to do it, where's all that going
Speaker 4: to come from. That's where a rack space wild comes
Speaker 4: into play, right.
Speaker 3: Absolutely, what we so we manage over billion, billion and
Speaker 3: a half dollars of cloud spend with our customers. Within
Speaker 3: that context of that, what we then do is provide
Speaker 3: a service to those customers that that uh, that there's
Speaker 3: a finnops service that essentially once a month, we go
Speaker 3: over your bill, right, whether it's it's Google, you're w S,
Speaker 3: we go over your bill right, and we say, hey,
Speaker 3: this is happening, what's going on here? What about this?
Speaker 3: You might want to turn this off? You want might
Speaker 3: want to move things here because it's like, uh, depending
Speaker 3: on the configuration. You know, you know, it is really
Speaker 3: easy to buy resources on the cloud.
Speaker 4: That's too easy, right, that's the problem. It's too easy.
Speaker 3: It is so easy, right.
Speaker 4: And like your kid buying squish mellows with your credit card.
Speaker 4: I want to think about that.
Speaker 3: They're just pressing the button, right.
Speaker 4: That's right, more mellows and.
Speaker 3: And and it's no fault of anyone's other than you know,
Speaker 3: you had you said, we had talked earlier about this
Speaker 3: notion of observability. If you don't see it, it's just there,
Speaker 3: and all of a sudden your bank acount like draining
Speaker 3: like a swamp, right, and you're going, what's going on? Well,
Speaker 3: that's where thinnops and and and and you know, finops
Speaker 3: is continue to grow, just the notion of measure what
Speaker 3: you're using and habit at a granular enough level. And
Speaker 3: then either way have work with folks who understand what
Speaker 3: that thousand page phone bill looks like, right right, right,
Speaker 3: because it is not simple.
Speaker 4: No, Well, and in these days, I will I'm just
Speaker 4: guessing here, so maybe it should be down if I'm
Speaker 4: wrong about this ped But these days one of the
Speaker 4: really cool aspects of these large language models is you
Speaker 4: can feed them big, bulky documents and then just ask
Speaker 4: all kinds of questions from it. I'm guessing you could
Speaker 4: load your document if you're willing to do that, into
Speaker 4: a chat GBT and say, what the hell's going on here?
Speaker 4: Where am I spending money?
Speaker 3: You know, I'm guessing there's probably a startup in the
Speaker 3: world that's that's doing that. There's someone figuring that The
Speaker 3: challenge is you can feed it in, right, But remember
Speaker 3: AI systems is a living, breathing thing, right right. Every
Speaker 3: it is learning every day, there's more data. And so
Speaker 3: what a lot of folks don't understand is, guess what,
Speaker 3: this is a one shot deal. Once you put it in,
Speaker 3: you got to run this thing right right, and you
Speaker 3: have to do a feedback loop. You have to train it,
Speaker 3: you have to tune it like it doesn't it's not
Speaker 3: one and done. And so once again, so when we
Speaker 3: when we think about the run side of the thing,
Speaker 3: you know, CUP organizations are thinking about what is my
Speaker 3: operating model?
Speaker 4: Right right?
Speaker 3: What does it mean to run? What do I need
Speaker 3: to measure? What do I what? How do I toune?
Speaker 3: How do I make sure I you know, it's not
Speaker 3: doing bad things? How do I make sure my users
Speaker 3: know how to use prompt?
Speaker 4: Right?
Speaker 3: Right?
Speaker 4: Yeah? There are all these there are all these components
Speaker 4: that come into it, and it is a lot to absorb, right,
Speaker 4: It's and I'm sure it's very easy to to kind
Speaker 4: of get distracted and go down a wormhole and you
Speaker 4: always got to pull back, all right, what are we doing?
Speaker 4: What are we trying to do?
Speaker 7: You know?
Speaker 4: I remember one of my favorite managers ever was a
Speaker 4: guy when I was at the data warehousing instidutent. I'll
Speaker 4: throw this over to you, sha tonk uh he he
Speaker 4: Peter Quinn was his name. He's the best manager I
Speaker 4: ever had. I didn't even know what a good manager
Speaker 4: was until I had him. And I was like, well,
Speaker 4: like my first meeting with him up in Seattle when
Speaker 4: I moved up there. We get to the end of
Speaker 4: my first meeting and he says, no, is there anything
Speaker 4: I could do for you? And I was like, did
Speaker 4: a VP just walk in? Is there? Like? Oh, you're
Speaker 4: talking to me? I was like what, Oh, okay. But anyway,
Speaker 4: he would say something that I love. He said, let's
Speaker 4: achieve what we're trying to achieve, right. I Mean, it's
Speaker 4: a bit it's a bit simple in a way, but
Speaker 4: the point is it focuses you on accomplishing something and
Speaker 4: not getting distracted. And I think that's really important to
Speaker 4: Ben's point, right with these AI implementations, to know what
Speaker 4: you're trying to do, and to monitor it and to
Speaker 4: manage it, and to be open to the fact that
Speaker 4: maybe you're off target.
Speaker 6: Right, absolutely, And this is why you know, if you
Speaker 6: think about phinnops is one aspect of it about what's
Speaker 6: already lived. And you know, I think there's not enough
Speaker 6: work that was done originally when we put many other
Speaker 6: things into the cloud to actually define that.
Speaker 5: But when we're talking about sort of doing new things.
Speaker 6: Like defining new AI projects, you know, one of the
Speaker 6: first things becomes being able to define what is this
Speaker 6: answer worth? Right, so if I can get this answer,
Speaker 6: you know, classic example, we want to put an hr
Speaker 6: chatbot out to our users to be able to look
Speaker 6: at how much leave they've got left? Right, how much
Speaker 6: is that answer worth? Is it worth two clicks and
Speaker 6: three dollars? Is it worth you know, five clicks and
Speaker 6: ten cents. That becomes an important part of it because
Speaker 6: you know, one of the reasons why a lot of
Speaker 6: you know, organizations get stuck at the POC level is
Speaker 6: because you can optimize for getting that chatbot to give
Speaker 6: you an answer really fast. But if that's driving the
Speaker 6: cost from ten cents to fifty cents, maybe that's not
Speaker 6: the right thing because that's not what you're trying to do.
Speaker 6: What you're trying to do is you know, optimize at
Speaker 6: the sort of over on a global level, which is
Speaker 6: why it becomes very important to both have that definition
Speaker 6: upfront and then have the observability tooling the penops practices
Speaker 6: to monitor it on an ongoing basis, right, because that's
Speaker 6: not a once and done thing either. You're constantly monitoring,
Speaker 6: you know, mL mode. I like to take the example
Speaker 6: of mL models because that's more pervasive. You know, we've
Speaker 6: got optimization models, got next best offer models, et cetera.
Speaker 6: Just like with those models, you're constantly monitoring. You know,
Speaker 6: what's what's the actual metric achievement? Sure, same way, is
Speaker 6: the cost of running this actually lining up with that?
Speaker 6: Because the whole foundational premise of the cloud was that
Speaker 6: your cost with your business and do that you actually
Speaker 6: have to measure.
Speaker 4: It right and know what you're measuring. Is it customer satisfaction?
Speaker 4: Is it uplifts on sales for a particular product. And
Speaker 4: I'll throw this over to you all. This really comes
Speaker 4: down to being able to manage the data around these services.
Speaker 4: Know what it's actually costing you. That's the finop side.
Speaker 4: Know what it's actually getting for you, that's the business
Speaker 4: management side. Just being able to align the numbers, Like
Speaker 4: revenue went up, Okay, that was our objective to get
Speaker 4: revenue up, but the cost are up to so oh no,
Speaker 4: that's kind of a problem. You have to be able
Speaker 4: to adjust all those things and these days you can't
Speaker 4: do that at every quarter, right, You got to do
Speaker 4: that like on a weekly basis and not a daily basis, right, Ben.
Speaker 3: H Absolutely the speed of sort of fine tuning based
Speaker 3: on what's happening. The feedback loop is you know, especially retailers, right,
Speaker 3: it's daily, right, it's it's daily. I mean it's in
Speaker 3: and so I mean that's why. And I think the
Speaker 3: airlines has got this figured out where dynamic ticket pricing, right,
Speaker 3: and and sort of like it and concerts, oh my gosh,
Speaker 3: and when I and and like and I'm seeing you know,
Speaker 3: restaurants trying dynamic pricing but online like it's just crazy right, Like,
Speaker 3: but it does say they're the world around optimizing optimizing yield. Right,
Speaker 3: whether it's on the revenue side or in the cost
Speaker 3: it's all about optimization and data is the key to
Speaker 3: that optimization.
Speaker 4: Yeah, no, that's exactly right. And throw a back over
Speaker 4: to Tonk. You know you've mentioned a couple aspects from
Speaker 4: my life, which is data, data, warehousing, analytics, understanding the
Speaker 4: data and really it's it's amazing how far we've come
Speaker 4: in the last that's even five years. And there are
Speaker 4: companies like Snowflake and Data Breaks that had a lot
Speaker 4: to do with this, really optimizing the stack, the technology
Speaker 4: stack to be able to deliver analytics. And now it's
Speaker 4: I mean it's table stakes. You've got to be able
Speaker 4: to run analytics on your data, to know how much
Speaker 4: money you're making, what's the margin, where is it coming from?
Speaker 4: All that stuff is table stakes now, right.
Speaker 6: Absolutely absolutely stable stakes. And let me throw this out
Speaker 6: there right. One of the biggest impact of JENNYI on
Speaker 6: data and analytics has been that Jeni has driven people
Speaker 6: to feel that they can to have democratization of all
Speaker 6: of one technology. Right, So you know, writing Python code
Speaker 6: has also become commoditized because I can go use a
Speaker 6: large language order to help me write that. Part of
Speaker 6: that is, you know, as you get table sticks on
Speaker 6: from a platform perspective, which definitely the likes of Snowflake,
Speaker 6: Data Bricks and you know all the three hyperscalers with
Speaker 6: their technology have done.
Speaker 5: What you've now ended up with is a.
Speaker 6: Lot more business users directly transforming data, trying their hat
Speaker 6: at building reports, trying their hat at you know, building
Speaker 6: snowflake tables or you know, writing data bricks code because
Speaker 6: they can and because they feel empowered because of what's
Speaker 6: happened with GENII, and that has sort of you know,
Speaker 6: one of the things is in the report is this
Speaker 6: that I think more than somewhere around sixty percent of
Speaker 6: companies actually have data sitting in more than one cloud
Speaker 6: because different teams have gone and built out, you know,
Speaker 6: different analytical silos at different places. So what's happening is
Speaker 6: sort of how do I actually bring that together? How
Speaker 6: do I unify that data and say, you know, while
Speaker 6: the sales team might be saying our marketing lead gen
Speaker 6: conversion is x percent and the marketing team is saying
Speaker 6: five percent, how do I actually get to sort of
Speaker 6: the truth? That is becoming sort of more of a problem.
Speaker 6: And that's where I think. On the technology and cloud side,
Speaker 6: this concept of CCoE has evolved you know a lot,
Speaker 6: Whereas on the data side, the concept of having an
Speaker 6: operating model for data and analytics and ensuring that we
Speaker 6: can give many different business teams support is something that
Speaker 6: is now starting coming into the forefront because the technology has,
Speaker 6: as you said, actually become quite commoditized in that sense.
Speaker 4: Well, and it's also getting baked into a lot of places.
Speaker 4: I mean, fifteen years ago, you would have an analyst
Speaker 4: who would review and slice and dice data in a
Speaker 4: data warehouse and then write some report manually and go
Speaker 4: tell people about it and then they would hopefully change
Speaker 4: something they're doing in the business. These days, a lot
Speaker 4: of that is inline analytics, right. It's being delivered through
Speaker 4: whether it's the ERP or some web system or whatever
Speaker 4: it is. You have that data baked in, and that's
Speaker 4: where you wanted, I think, is in the operational system,
Speaker 4: where people are on the front lines doing things. You
Speaker 4: want that AI and that analytic input to make suggestions,
Speaker 4: to point things out. Say hey, and I think maybe
Speaker 4: i'll throw this over to both of you first, Ratan,
Speaker 4: I think much of the benefit of AI will come
Speaker 4: in the form of suggestion, in the form of, hey,
Speaker 4: we noticed this is happening. You know, maybe you should
Speaker 4: do something about it, and then the user will say
Speaker 4: yes or no. What do you think, Shatan?
Speaker 6: I'd agree with that, Eric, And what I'd add is,
Speaker 6: I think the biggest disruption is actually on the consumption
Speaker 6: there or the what I would refer as a bi
Speaker 6: lair because you.
Speaker 5: Know, we build reports based on.
Speaker 6: The different kinds of questions that we expect people are
Speaker 6: trying to answer. But and then you know, you have
Speaker 6: a virtual report page which has you know, a chart
Speaker 6: and metric, et cetera, and you go look for the
Speaker 6: answer that you're expected.
Speaker 5: Whereas this has now.
Speaker 6: Become a lot more conversational conversations triggered by from the
Speaker 6: AI agent side saying that you know, this looks like
Speaker 6: a normally is this correct? Or from your site saying
Speaker 6: you know, I see, you know what was the sale
Speaker 6: for this store last week, because I'm going to have
Speaker 6: a conversation with the store manager of that, and then
Speaker 6: sort of very incrementally the same way as we would
Speaker 6: have talked to the analyst to figure out what's happening right,
Speaker 6: able to talk to here and what Then that what
Speaker 6: that means is that the focus really becomes on having trust,
Speaker 6: right you talk about observability, observatory or the data platform
Speaker 6: becomes really important because I need to be sure, I
Speaker 6: need to be confident as a business user that these
Speaker 6: numbers are correct, they're current, they're complete, they are honest
Speaker 6: and vetted. The actual UI of the report ETCA doesn't
Speaker 6: matter as much because I, you know, that's become an
Speaker 6: older Why would I go do three steps when I
Speaker 6: can just you know, now always ask that.
Speaker 4: Yeah, Now, I mean that's an excellent point. I'll throw
Speaker 4: it over to Ben. The fact that natural language processing
Speaker 4: is now darn near proficients is a really big deal
Speaker 4: because executives can sit there and ask all sorts of
Speaker 4: questions of these information systems. In natural language, you don't
Speaker 4: have to learn SQL. You can just ask a question
Speaker 4: to get an answer back. Now to Tong's point, you
Speaker 4: want to make sure there's trust. You want to make
Speaker 4: sure that it's it is very grounded in reality and
Speaker 4: not hallucinating. But still they're they're getting pretty good at that,
Speaker 4: and the guardrails are getting pretty strong. What do you think, Ben?
Speaker 3: Oh? Absolutely, you know and and and you know, and
Speaker 3: executives are getting trained that you can work in a
Speaker 3: in a conversational way.
Speaker 5: Right.
Speaker 3: The expectations level because of consumer products regarding hey, I
Speaker 3: could just talk to my phone, I can just talk
Speaker 3: to Alexa. We're already getting trained behaviorally to expect that, right,
Speaker 3: And so absolutely the key though, and when you start
Speaker 3: talking on a business setting, the foundational about what's being
Speaker 3: keyed up to you has to be really good, right.
Speaker 3: And the other thing what we're seeing is and we
Speaker 3: had a customer finished sort of customer in sort of
Speaker 3: the payment kind of world, talk about the key now
Speaker 3: is creating the customer experience around that data, right, you know?
Speaker 3: Is it being et up in line as they're making
Speaker 3: a decision? Is it a report? Because the more we
Speaker 3: can make these things as part of your workflow, right, right,
Speaker 3: the more valuable it's going to be. And so we're
Speaker 3: seeing the rise of sort of like AI customer experience design.
Speaker 3: So it's seamless, So it becomes just seamless to the user. Right,
Speaker 3: It's not like I'm using AI. It's like I'm just
Speaker 3: you know, I'm just asking siy I'm asking I'm right.
Speaker 4: That's right. Yeah. Well, and and analytics in general, it's
Speaker 4: my experience that when someone gets a taste of that,
Speaker 4: they want more. You want how it happens when you
Speaker 4: can start to ask questions and understand the underpinnings you
Speaker 4: ask the next question and the next question, it really
Speaker 4: it takes off. And we'll dive into this in the
Speaker 4: next segment. Heary just the second folks, But we're talking
Speaker 4: to a couple of experts from rack Space. They're a
Speaker 4: big survey that just came out about it leaders increasing
Speaker 4: investment in multi and hybrid cloud stress energies to do
Speaker 4: what to future proof their operations. Folks will be right back.
Speaker 4: You're listening to Inside Analysis.
Speaker 2: Expected Welcome back to Inside Analysis. Here's your host, Eric Tabanaugh.
Speaker 4: All right, folks, back here on Inside Analysis talking to
Speaker 4: a couple of experts from rack Space. We've got Schwatanka
Speaker 4: Shiel and Ben Bunkera from rack Space and we're talking
Speaker 4: all about the survey and maybe should talk. I'll throw
Speaker 4: this one over at you first. You know, one of
Speaker 4: the things that just jumped out at me from the
Speaker 4: survey results was forty percent of respondence ciday a lack
Speaker 4: of skilled cloud professionals as being a constraint. I mean
Speaker 4: forty percent, two out of five are saying we can't
Speaker 4: find the people. And you can train yourself these days.
Speaker 4: That's the beautiful thing about the cloud. You go on
Speaker 4: Coursera and take classes, you know, all night long. Many
Speaker 4: of them are free to understand, and it's important to
Speaker 4: know the difference between this cloud and that cloud. The
Speaker 4: different services they offer, the cost structures, I mean, all
Speaker 4: these details. The devil's in the details, right.
Speaker 6: Yes, And I think that's where some of this gap
Speaker 6: comes from because you know, as as as men were
Speaker 6: saying early, if you think about this as you know,
Speaker 6: your cloud bill as a phone as a phone bill
Speaker 6: is because you know, it's not that for example, data
Speaker 6: bricks is one line item. Data bricks actually has you know,
Speaker 6: fifteen twenty different ways in which they will charge you
Speaker 6: depending on what you're trying to do. Same with you know,
Speaker 6: and even fundamentally, and I think the cloud providers have
Speaker 6: you know, our serial offenders in complexity of services. If
Speaker 6: people park on awlus, there'll be like eight different ways
Speaker 6: in which you can do so in the in the
Speaker 6: on prem world, you would have said, okay, I'm going
Speaker 6: to use park. Now let me just find someone who's
Speaker 6: really good to' spark. And now you need to figure out, okay,
Speaker 6: for this particular streaming workload, which is you know, giving
Speaker 6: me ten thousand messages per minute, is it going to
Speaker 6: be better to run it on EMR or data bricks,
Speaker 6: Park or glue Spark And what are the costs associated
Speaker 6: with network with compute, with storage that come into that. So, uh,
Speaker 6: you know, I think experience there comes for a lot
Speaker 6: because every project tells you what you should not have
Speaker 6: done and makes learn better better.
Speaker 5: Yeah, but but I think.
Speaker 6: That that is that is one of the key uh uh,
Speaker 6: you know, key differences in how quickly you can get started, right,
Speaker 6: And that's what we find that you know, Ben was
Speaker 6: talking about finnofs, but you know, I refer to the
Speaker 6: fact that from an architecture perspective, you know, because we
Speaker 6: focus in we in racs based focusing on doing you
Speaker 6: know a lot of this work purely in the clouds,
Speaker 6: we have developed sort of this framework to determine Okay,
Speaker 6: you're trying to do these seven things, so this is
Speaker 6: the architecture is going to have these different cost models
Speaker 6: which plugs into from a phinnops perspective, does this total
Speaker 6: up to the value that.
Speaker 5: You're going to get? And how are you going to
Speaker 5: monitor and measure that?
Speaker 4: Yeah? Well, and you know, Ben, I'll throw this over
Speaker 4: to you. I'm just guessing here based upon your experience
Speaker 4: and the data that you see and and all the
Speaker 4: engineers and the sort of frontline workers at rock Space,
Speaker 4: you can offer some real good advice on those kinds
Speaker 4: of questions that you talk was talking about should you
Speaker 4: run this in asures? Should you run that in aws?
Speaker 4: Because having experience in those environments will help you understand
Speaker 4: not just the cost, but the workflow, the process, what
Speaker 4: are the weaknesses? You know, where does it run into trouble?
Speaker 4: All those kinds of details are really important and it's
Speaker 4: always best to learn from someone else instead of learning
Speaker 4: the hard way, right, Ben.
Speaker 3: Well, you've hit the nail on the head. Eric. We've
Speaker 3: been in the hosting and infrastructive business for twenty five years.
Speaker 3: There's a lot of gray and lost hair in running
Speaker 3: operations for thousands of companies. Yeah, and it's like that
Speaker 3: learning curve, Like there's that ten thousand hour rule, Like
Speaker 3: once you do something for ten thousand urs, you kind
Speaker 3: of know what the things are right. Right, if companies
Speaker 3: are just entering this world, how many hours of your
Speaker 3: team at we got twenty Okay, you got you gotta
Speaker 3: You have to then realize there's a learning curve and
Speaker 3: it can cost you a lot of money, and so
Speaker 3: partnering with folks who understand because it experienced in multiple environments, right,
Speaker 3: anyone can be successful when it's a good.
Speaker 4: Day that's a quote too.
Speaker 3: An when it's a good day, right, it's it's when
Speaker 3: stuff hits the fan, that's when things shine, right, And
Speaker 3: we've we've been through a lot of stuff hitting the
Speaker 3: fan because our customers stuff happens, right, That's where you learn,
Speaker 3: not on a good day, on crappy days, when.
Speaker 4: You learn, man, that's brutal. It's painful to think about
Speaker 4: your time. Go ahead.
Speaker 6: So I just wanted to add to you know, Ben
Speaker 6: was saying that, you know, we all live in an
Speaker 6: environment of scarcity, which means that whatever team members actually
Speaker 6: our customers have, it doesn't make sense for them to
Speaker 6: be learning these or relearning you know what a lot
Speaker 6: of us already know because we've done this. We want
Speaker 6: their time to be focused on, you know, which is
Speaker 6: the right model for this kind of use case. So
Speaker 6: what is the use case that you want to drive
Speaker 6: that is what's driving the business outcomes, and what you
Speaker 6: want to focus our customer's time is on that, so
Speaker 6: that we can apply you know, the learnings that we
Speaker 6: already have where you don't need to reinvent the wheel
Speaker 6: on everything, which is what I refer to as below
Speaker 6: the line in.
Speaker 4: This case, right, Yeah, reinventing wheels is expensive. I mean,
Speaker 4: you can always build a better mouse trap, but you
Speaker 4: really have to watch out. It's the blocking and tackling
Speaker 4: when you get right down to it, right Ben. I mean,
Speaker 4: the blocking and tackling can take you down. If your
Speaker 4: offensive line is not strong, your quarterback's going to get sacked,
Speaker 4: you know. I mean there are lots of things. It's
Speaker 4: not a glorious job. But the cloud architect is kind
Speaker 4: of like that that either offensive line or dance defensive
Speaker 4: will be security. Right. So the cloud architects or the
Speaker 4: offensive line pushing forward allowing you to run into the
Speaker 4: end zone, right Ben.
Speaker 3: Oh, it's you at the end of the day. Like
Speaker 3: winning is about execution. Execution is about day to day things, right.
Speaker 3: It is about the little things that add up to
Speaker 3: the big things.
Speaker 4: Right.
Speaker 3: And so you know, as you know, since I am
Speaker 3: Ohio State band and I just saw them winning the
Speaker 3: national championship, right, it is the blocking and tackling and
Speaker 3: consisting on a day to day basis, and that and
Speaker 3: and and so when you think about that, that's where
Speaker 3: you start thinking about, how do I have playbooks, how
Speaker 3: do I automate things? How do I have observability. How
Speaker 3: do I have how do I make sure texted I
Speaker 3: focus my people looking at the right things, not all
Speaker 3: the all the things. Right, That's that's really hard in
Speaker 3: in a world of lots of lots of widgets, I
Speaker 3: can look at.
Speaker 4: You know what, that's a really really good point in Schwatan,
Speaker 4: I'll throw it over to you and we'll probably go
Speaker 4: into the podcast on a segment with this threat as well.
Speaker 4: Everything is changing, and so what you have your people
Speaker 4: focused on? Think about old it t versus new it
Speaker 4: and understanding all it is still around. You still have
Speaker 4: these data centers, You still have to manage Linux implementations
Speaker 4: and all these different things, upgrades, patches, all that stuff
Speaker 4: that's still going on. But now you have this multi
Speaker 4: cloud environment where you have all these other things to
Speaker 4: worry about. And it depends on the company, depends on
Speaker 4: the use case, on the size of the organization, etc.
Speaker 4: On your actual team. But that's a big challenge, right
Speaker 4: is knowing how to get everyone focused on what they
Speaker 4: should be focused on, especially when it changes over time.
Speaker 4: What's your advice there, Shwatan.
Speaker 6: Absolutely, Eric, And this is why you see one of
Speaker 6: the finding these surveys. So this rise of centralization. And
Speaker 6: I'm not about centralization from it doing everything, but id
Speaker 6: defining the COEs which are saying these are the basic
Speaker 6: standards that you need to follow, because the other thing
Speaker 6: that's happened is that there has been a lot of
Speaker 6: democratization of people actually building their own ail high reports
Speaker 6: or playing with their data sets. So as you give
Speaker 6: that responsibility out, you need to with great responsibility comes
Speaker 6: you know, with a lot of rights come a lot
Speaker 6: of responsibility.
Speaker 5: So the iight, what.
Speaker 6: We're seeing is that there are these ces behoving set
Speaker 6: up to set up standards so that people can do themselves,
Speaker 6: but are doing that and it's not you know, one
Speaker 6: I engineer or one cloud architect is not going to
Speaker 6: be able to look at you know, the security aspect
Speaker 6: and the finnops aspect as well as sort of the
Speaker 6: data aspect and how you go from a cloud native perspective.
Speaker 6: And that's really where the rise of the COE and
Speaker 6: the rise of sort of managed service providers where you
Speaker 6: bring that specialization from a managed perspective has really come
Speaker 6: into play.
Speaker 4: Yeah, that's a really really good point. That's why you're
Speaker 4: going to see more of these managed services, right because
Speaker 4: when someone really understands a particular environment. You want them
Speaker 4: focused on that environment. You want them sharing the information
Speaker 4: with their colleagues. I mean, this whole knowledge sharing side
Speaker 4: of the equation is not insignificance, you know. And it's
Speaker 4: like people don't want to sit there and read big,
Speaker 4: long manuals about stuff. So it's like you have to
Speaker 4: populate it in your center of excellence and have someone
Speaker 4: shepherding that and watching over it.
Speaker 3: Right, Ben, Well, absolutely, if everyone's you know, creating their
Speaker 3: own sauce because they don't have standards, right, then they're
Speaker 3: not worried about blocking and tackling. They're just they're just learning, right,
Speaker 3: And so you know, history has said you have to
Speaker 3: start with basics. Give people a playbook which is the
Speaker 3: coe kind of things and says, do these ten things
Speaker 3: right first before you get to the fancy stuff, like
Speaker 3: you'll learn how to block, right, like put your arms
Speaker 3: up in a certain way, right, get down there, like
Speaker 3: learn how to tackle. You don't know those basics. This
Speaker 3: other fancy stuff that you're going to get excited by,
Speaker 3: it doesn't matter right.
Speaker 4: Well, and you know I'm gonna use this line to
Speaker 4: lead off our final segment today, But we had a
Speaker 4: fantastic event a couple of weeks ago with the Dakota
Speaker 4: Bowl and Red Panda, And we had a lady from
Speaker 4: Old Mutual, a bank builder, and she had this awesome mantra.
Speaker 4: She said, you need to earn your right to do things,
Speaker 4: to build things. So first, as Ben suggested, learn to block. Okay,
Speaker 4: now you can block. Learn to tackle, Okay, now you
Speaker 4: can tackle. Now let's talk about some strategy. You start
Speaker 4: with the basics, you build up from there, and you
Speaker 4: earn your right to be able to do the cool
Speaker 4: stuff by showing that you've blocked, you've tackled, you've done
Speaker 4: your It's like a kid having done his or her chores. Right, Okay,
Speaker 4: you did your chores. Now we can go to the
Speaker 4: game together. Now we can do some fun things. In
Speaker 4: the enterprise, you have to really earn your right to
Speaker 4: get there to use the powerful tools because they are
Speaker 4: expensive and they can do great things, but you know
Speaker 4: you've got to pay for it. Someone's going to pay
Speaker 4: the piper at the end of the month, and there's
Speaker 4: a lot of bills to be paid. But folks don't
Speaker 4: touch out. That will be right back of the podcast.
Speaker 4: Bonus segment here on Inside Analysis. All right, folks back
Speaker 4: here on inside Analysis with Tank Shield and Ben Blankera
Speaker 4: of rack Space, we've talked all about AI, analytics data.
Speaker 4: Guess what it's got to be secure? The security man.
Speaker 4: I went to this Blunk conference last year and just
Speaker 4: looking at some of those screens, what these people do
Speaker 4: all day? I'm like, Wow, it takes a special kind
Speaker 4: of personality to just sit there and like just go
Speaker 4: into the deep dark forest and look for the moles
Speaker 4: and the foxes and all the other troublemakers that are
Speaker 4: going to cost you some issues here. And guess what
Speaker 4: AI helps with all this?
Speaker 3: Right?
Speaker 4: If you get a good model, that's what I'm That's
Speaker 4: what really fascinates me. Are these bespoke models that companies
Speaker 4: are building. You can just grab something off a hugging
Speaker 4: face and just deploy it in your environment, start playing
Speaker 4: around with it for security. It's good stuff because these
Speaker 4: models can be very good at identifying anomalies. An Anomaly
Speaker 4: detection is probably like the number one most important thing
Speaker 4: in security. What do you think, Ben.
Speaker 3: Well, absolutely, the you know in our survey, you know
Speaker 3: we've got more than half folksing we're using AI to
Speaker 3: improve our threat detection, which is all about anomalies and
Speaker 3: where things are happening. When you look at how many
Speaker 3: endpoints most you know, the endpoints where folks bad folks
Speaker 3: do come in. It's millions, right, and so what's happening
Speaker 3: in all those endpoints, all those kind of things is
Speaker 3: incredibly important and AI can play a big part of it.
Speaker 3: AI can also play a big part in sort of
Speaker 3: monitoring your internal systems to make sure everything's are up
Speaker 3: to or a patch level, but everything is secure. You've
Speaker 3: got you've got things closed off that should be closed off.
Speaker 3: You're you're looking at internal user behavior regarding like who
Speaker 3: should be getting permission all the things AI can do
Speaker 3: for hygiene internally as well as then AI for threat detection,
Speaker 3: and so it is it is uh because guess what
Speaker 3: the bad guys are using AI?
Speaker 4: They are I mean it's crazy. The fishing scams are
Speaker 4: getting really good. They're getting so good on the design.
Speaker 4: I mean, you know, I know to look for them now.
Speaker 4: But it's like, oh, a Verizon bill is one thousand dollars?
Speaker 7: Is it?
Speaker 4: No? It isn't. That's a fishing scalm. You got to
Speaker 4: watch out with that stuff. But should talk, I'll throw
Speaker 4: it over you. Ben made a great point turn things
Speaker 4: off if things aren't being used, If access points are
Speaker 4: out there and they're not being used, just knowing what's
Speaker 4: not being used and turning it off, that's probably half
Speaker 4: the battle right there. What do you think?
Speaker 5: Right?
Speaker 6: And I think AI is helping beyond what Ben said,
Speaker 6: there's two other things he is helping with. One is
Speaker 6: actually you know, being able to go over the large
Speaker 6: amount of meddata that we have to understand who is
Speaker 6: using what, what is actually you know, normal and what
Speaker 6: isn't normal?
Speaker 8: Right?
Speaker 6: But then the other side of that also is that
Speaker 6: it is also another additional surface area that we do
Speaker 6: need to secure because you know what llms they come
Speaker 6: with their own kind of security they will look at
Speaker 6: and you know how prompt injection, how do you actually
Speaker 6: ensure that you know, the data that you're training on
Speaker 6: is not skewed? How are you ensure that it's not
Speaker 6: exposed externally? So you know, I think this is one
Speaker 6: of those things where you have to do it, but
Speaker 6: it is good. You know, it is going to continue
Speaker 6: getting more and more complex as we start to become
Speaker 6: more dependent on everything that is digital, right, because I
Speaker 6: mean just think about it the proportion of things that
Speaker 6: we were doing digitally five years ago versus now. You know,
Speaker 6: even if you take the cloud out of it, just
Speaker 6: think of your driving licenses and now digital.
Speaker 5: Your tickets are now digital.
Speaker 6: So the surface area of both the data as well
Speaker 6: as the interactions have skyrocketed. And I you know, I
Speaker 6: think the security side of that is trying very hard
Speaker 6: to keep up.
Speaker 5: But it's a nuclear and race.
Speaker 4: Yeah, that's right, and been I'll throw it back over
Speaker 4: to you. It is a constantly changing battlefield out there,
Speaker 4: and you have to stay on top of things, and
Speaker 4: you have to be very agile and willing to admit
Speaker 4: when you're wrong about stuff. I think you know, probably
Speaker 4: pride leads to as many security breaches as anything else.
Speaker 4: What do you think, Oh my.
Speaker 3: Gosh, oh absolutely, No one wants to say I left
Speaker 3: the door open, right, no one? No one said, no
Speaker 3: one else and so and so. Yeah. I do think
Speaker 3: like the rate by which people learn is which implies
Speaker 3: that I'm the open with what's working and what hasn't worked,
Speaker 3: is going to be a key determinant to how secure
Speaker 3: things are because you've got to have closed feedback groups
Speaker 3: associated with what happened how do we fix it or
Speaker 3: what's working and how do we do more of that? Right,
Speaker 3: It's all about learning absolutely well.
Speaker 4: And we'll just kind of close on this because I
Speaker 4: think it's very exciting. I'll throw it over to show
Speaker 4: Talk and then Ben for final thoughts. It's very exciting
Speaker 4: to know that we can use these AI engines, whether
Speaker 4: it's old fashioned predictive models for example, or all this
Speaker 4: Jenni stuff. Jenni is great for just sort of stochastic
Speaker 4: use cases for just exploring ideas and looking for things. Yes,
Speaker 4: you have to verify it because they do hallucinate, but
Speaker 4: still you need people. I mean, you know I personally
Speaker 4: I advocate for an information strategy group in every organization,
Speaker 4: like almost like a center of excellence around analytics are
Speaker 4: just strategic thinking because things change so much. I mean,
Speaker 4: look at these like chatchipt. They're adding in new layers,
Speaker 4: they're doing new things all the time. And of course
Speaker 4: it's a black box now right, it's not open AI,
Speaker 4: it's closed AI. But just having a team focused on
Speaker 4: how to use these technologies I think is a huge,
Speaker 4: huge win for companies to do what do you think?
Speaker 6: Absolutely and and it's also because as you put things
Speaker 6: into production, you have to continuously review how those should
Speaker 6: be affected or up updated with that, right, And I'll
Speaker 6: give you an example of you know, we internally in
Speaker 6: rag space actually built a GENII two around a year
Speaker 6: agowards as I searched the you know, information co worker
Speaker 6: for enterprises that we use in rackspace, and a year
Speaker 6: ago when we built it, you know, a lot of
Speaker 6: these abstractions around how you do guardrails, how you do
Speaker 6: cost monitoring did not exist, so we wrote that ourselves
Speaker 6: and now look, you know eighteen well twelve to fifteen
Speaker 6: months later, a lot of these abstractions exist. We need
Speaker 6: to put in now is other kinds of guardrails around
Speaker 6: sort of learning of different kinds of data and things
Speaker 6: like that. So we have to constantly go back and
Speaker 6: look at, you know, what should I rip out, what
Speaker 6: should I use new? Because I want to get the
Speaker 6: benefits of the continuously lowering price of inference and of
Speaker 6: the better models which are doing that. So it is
Speaker 6: a continuously ongoing thing. And having this sort of operating
Speaker 6: model which defines a center of excellence which is tasked
Speaker 6: with doing that and then cascading that out or guiding
Speaker 6: everybody is really a key critical thing.
Speaker 4: Yeah, great, great point in one minute left. Closing thoughts
Speaker 4: from you, Ben, what's your advice on companies who know
Speaker 4: they have to do something and are just not sure
Speaker 4: where to start. What's your advice?
Speaker 3: Real? Couple of key things. One, look at your workloads
Speaker 3: down optimizer workloads so you can free up some money
Speaker 3: to pay for these things. Two, as you're looking at AI,
Speaker 3: it is an emerging field, but you have to have
Speaker 3: an operating model about how you're going to run it.
Speaker 3: It is not one and done. It is you just
Speaker 3: you just you have just now created a new set
Speaker 3: of employees, and just like a new set of employees,
Speaker 3: you have to do care and feeding and life cycles.
Speaker 3: So that right, that's what I would suggest.
Speaker 4: Excellent, excellent advice. Will look up this survey, folks. New
Speaker 4: research by RAX based Technology revisals, hybrid cloud and AI
Speaker 4: integration are key drivers for IT innovation in twenty twenty five.
Speaker 4: Look them up online. Great talking to you, gentlemen. You've
Speaker 4: been listening to Inside Analysis.
Speaker 9: Now you can listen to KCAA radio anytime on your
Speaker 9: smartphone device. Call seven two oh eight three five three
Speaker 9: oh nine nine seven two oh eight three five three
Speaker 9: oh nine nine.
Speaker 10: What is your plan for your beneficiaries to manage your
Speaker 10: final expenses when you pass away life insurance, annuity, bank accounts,
Speaker 10: bestment account, all required deaf ativity which takes ten days
Speaker 10: based on the national average, which.
Speaker 11: Means no money's immediately available.
Speaker 3: This car is stress and arguments.
Speaker 11: Simple solution the beneficiary liquidity clan use money you already
Speaker 11: have no need to come up with additional funds. The
Speaker 11: funds grow tax deferred and pass tax free to your
Speaker 11: name beneficiary. The death benefit is paid out in twenty
Speaker 11: four to forty eight hours out a desert out a
Speaker 11: defitite as at one hundred three zero six fifty eighty.
Speaker 1: Six TBO Tea Club's original pure powdery Rco Super tea
Speaker 1: comes from the only tree in the world that fungus
Speaker 1: does not grow on. As a result, it naturally has
Speaker 1: anti fungal, anti infection, anti viral, antibacterial, anti inflammation, and
Speaker 1: anti parasite properties. So the tea is great for healthy
Speaker 1: people because it helps build the immune system, and it
Speaker 1: can truly be miraculous for someone fighting a potentially life
Speaker 1: threatening disease due to an infection, diabetes, or cancer. The
Speaker 1: tea is also organic and naturally caffeine free. A one
Speaker 1: pound pack age of T is forty nine ninety five,
Speaker 1: which includes shipping. To order, please visit to Hebot club
Speaker 1: dot com. To Hebo is spelled T like tom, A
Speaker 1: h ee b like boy.
Speaker 4: Oh.
Speaker 1: Then continue with the word T and then the word club.
Speaker 1: The complete website is to Hebot club dot com or
Speaker 1: call us at eight one eight sixty one zero eight
Speaker 1: zero eight eight Monday through Saturday nine am to five
Speaker 1: pm California time. That's eight one eight sixty one zero
Speaker 1: eight zero eight eight to ebot club dot com.
Speaker 12: How you doing this is Gary Garver. In today's society,
Speaker 12: the majority of people are not getting enough sleep.
Speaker 1: I know I'm not.
Speaker 12: If you're like me and having problems getting a good
Speaker 12: night's rest, whether it's health or stress related, I have
Speaker 12: a solution for you.
Speaker 4: South Pacific Sleep Lab.
Speaker 12: South Pacific Sleep Lab will do an evaluation of your
Speaker 12: sleep pattern and will provide a comprehensive study so you
Speaker 12: can start getting a RESTful, peaceful not asleep. They take
Speaker 12: all types of insurance which will cover your cost of
Speaker 12: the event evaluation, and they will even provide transportation to
Speaker 12: their offices at no cost.
Speaker 4: To you.
Speaker 12: For more information, contact Tony at three one zero nine
Speaker 12: nine nine one eight eight seven. That's three one zero
Speaker 12: nine nine nine one eight eight seven. Tony even stays
Speaker 12: awake all night, twenty four hours a day, seven days
Speaker 12: a week, so you can sleep better and rest easy.
Speaker 5: South Pacific sleep left.
Speaker 12: Start feeling better and getting a great night of sleep today.
Speaker 7: Hi, y'all, merle here. Good news for once. My neighbors
Speaker 7: is jealous of me. You want to know why because
Speaker 7: my grass is growing and looking green and I can
Speaker 7: see on my sofa out in front yard and I
Speaker 7: don't even have to overwater it anymore. You know how
Speaker 7: I did it. I listened to damn water Boys underwater
Speaker 7: Zone every Thursday night on KCIA. Well, I got me
Speaker 7: a smart controller and now a water's at night yard
Speaker 7: looks darn tooting. No more sleeping around and hooking up
Speaker 7: my horse to my neighbors pigott in the middle of
Speaker 7: and his dog won't buy me anymore. And you can
Speaker 7: do it too. Listening is easier than ever. KCIA is
Speaker 7: now screaming online.
Speaker 9: It's streaming what it's streaming? You dumm it?
Speaker 7: Well, I don't know much about streaming, but they doing
Speaker 7: it apparently at kca radio dot com. So AnyWho listen
Speaker 7: to the water Zone and fix your Yata brant right
Speaker 7: here at KCIA, the station that leaves no listener behind.
Speaker 1: KCAA Radio has openings for one hour talk shows. If
Speaker 1: you want to host a radio show, now is the time.
Speaker 1: Make KCIA your flangship station. Our rates are affordable and
Speaker 1: our services are second to none. We broadcast to a
Speaker 1: population of five million people plus. We stream and podcast
Speaker 1: on all major online audio and video systems. If you've
Speaker 1: been thinking about broadcasting a weekly radio program on real
Speaker 1: radio plus the Internet, contact our CEO at two eight
Speaker 1: one five ninety eight hundred two eight one five ninety eight.
Speaker 1: You can skype your show from your home to our Redlands,
Speaker 1: California studio, where our live producers and engineers are ready
Speaker 1: to work with you personally. A radio program on KCAA
Speaker 1: is the perfect work from home advocation in these stressful times.
Speaker 1: Just time Kcaaradio dot com into your browser to learn
Speaker 1: more about hosting a show on the best station in
Speaker 1: the nation, or call our CEO for details. Two eight
Speaker 1: one five ninety nine ninety eight hundred. Located in the
Speaker 1: heart of San Bernardino, California. The Teamsters Local nineteen thirty
Speaker 1: two Training Center is designed to train workers for high demand,
Speaker 1: good paying jobs and various industries throughout the Inland Empire.
Speaker 1: If you want a pathway to a high paying job
Speaker 1: and the respect that comes with a union contract, visit
Speaker 1: nineteen thirty two Trainingcenter dot org to enroll.
Speaker 4: Today.
Speaker 1: That's nineteen thirty two Trainingcenter dot org.
Speaker 13: NBC News Radio, I'm Lisa Carton. President Trump says he
Speaker 13: expects to find hundreds of billions in wasteful spending at
Speaker 13: the Education and Defense departments. Trump made the prediction in
Speaker 13: a pre Super Bowl interview with Fox News, during which
Speaker 13: he outlined plans for Elon Musk and the Department of
Speaker 13: Government Efficiency to expand its probe of fraud, waste, and
Speaker 13: abuse in the federal government.
Speaker 3: I'm going to tell him very soon, maybe in twenty
Speaker 3: four hours, to.
Speaker 5: Go check the Department of Education.
Speaker 13: He's going to find the same thing, Trump said. The
Speaker 13: funding freeze for the US Agency for International Development is
Speaker 13: only the beginning. Heavy security is in place for today's
Speaker 13: Super Bowl in New Orleans. President Trump is expected to
Speaker 13: become the first sitting president to attend the Big game.
Speaker 13: Extra protection includes an enhanced security zone near Bourbon Street,
Speaker 13: which is where the deadly New Year's Day terrorist attack happened.
Speaker 13: Governor Jeff Landry created the zone, allowing law enforcement officers
Speaker 13: to search bags of folks entering the area. Kickoff for
Speaker 13: the game between the Kansas City each Chiefs and the
Speaker 13: Philadelphia Eagles is set for six thirty pm Eastern. Homeland
Speaker 13: Security Secretary Christy Nomes says illegal immigrants already at Guantanamo
Speaker 13: Bay will remain there until officials can reach agreements to
Speaker 13: return them to their home countries. Nomes said this could
Speaker 13: mean some may be imprisoned at the US military detention
Speaker 13: facility in Cuba for weeks.
Speaker 4: My goal is that people are not in these facilities
Speaker 4: for weeks and months, that there's a short term stay,
Speaker 4: they're able to go, incarcerate them, take them, follow the process,
Speaker 4: and get them back to their country.
Speaker 13: She stressed. Those being housed there currently are accused of
Speaker 13: serious violent crimes and have been afforded their right to
Speaker 13: do process. Another wave of the flu has hit the nation.
Speaker 13: Scott Carr has an update.
Speaker 8: Forty five states now reporting high or very high flu activity.
Speaker 8: Health officials say a rapid rise in cases is causing
Speaker 8: doctor visits for flu related symptoms to reach their highest
Speaker 8: in fifteen years.
Speaker 13: You're listening to the latest on NBC News Radio.
Speaker 7: News on KCAA Lomel and sponsored by Teamsters Local nineteen
Speaker 7: thirty two. Protecting the Future of Working Families Teamsters nineteen
Speaker 7: thirty two, dot org