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And now here's your host, Eric Kavanaugh.
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
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, 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?
So thanks er, thanks for having me.
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.
It is across many.
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 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.
Requirement. Now.
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
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.
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.
Right, that's funny.
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 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. 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 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 the services you're providing to your businesses. That makes sense.
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 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 make it look more cloud native, you can really move
those workloads around. And that gives tremendous agility to the organization, right.
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, 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
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 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
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.
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
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.
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.
The benefit of that?
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?
How much am I spending on GPUs?
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.
Or COEs, right, because the way we think.
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 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, but also all workloads in the cloud.
Yeah, I mean, this is it's fascinating. Go ahead, man.
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 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 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 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.
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 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
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 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 AI models have been around for decades.
Forever, forever.
This is extremely efficient at understanding particular business use cases, right.
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 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, 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 other ninety percent underneath underneath the water line? Did you That ain't correct?
Right?
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.
That stuff, right, Well, yeah, go ahead, go ahead to that.
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.
But the actual usage of.
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 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.
And that's I think something that is captured in this as well.
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.
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, I mean like if I'm taking if I'm driving my kids to school every day, right, my Honda Civical work just fine.
Right right?
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 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.
Mm hmm.
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.
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.
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,
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.
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
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.
Respect, Welcome back to Inside Analysis. Here's your host, Eric Tabanac.
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 does it cost?
Well?
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 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.
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 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 easy to buy resources on the cloud.
That's too easy, right, that's the problem. It's too easy.
It is so easy, right.
And like your kid buying squish mellows with your credit card. I want to think about that.
They're just pressing the button, right.
That's right, more mellows and.
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
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.
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? Where am I spending money?
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, 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 operating model?
Right right?
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?
Right?
Right?
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?
You know?
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 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
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.
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.
But when we're talking about sort of doing new things.
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 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 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, 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? Because the whole foundational premise of the cloud was that your cost with your business and do that you actually have to measure.
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
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.
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, 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, whether it's on the revenue side or in the cost it's all about optimization and data is the key to that optimization.
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 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.
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
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.
What you've now ended up with is a.
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 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 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 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.
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
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 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?
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.
Know, we build reports based on.
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.
Whereas this has now.
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, 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 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.
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 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?
Oh? Absolutely, you know and and and you know, and executives are getting trained that you can work in a in a conversational way.
Right.
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.
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 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, 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.
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
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.
Expected Welcome back to Inside Analysis. Here's your host, Eric Tabanaugh.
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 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 these details. The devil's in the details, right.
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, 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 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.
Yeah, but but I think.
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, 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.
You're going to get? And how are you going to monitor and measure that?
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? 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.
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
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, anyone can be successful when it's a good.
Day that's a quote too.
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.
You learn, man, that's brutal. It's painful to think about your time. Go ahead.
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
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.
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 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.
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.
Right.
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 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.
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
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. What's your advice there, Shwatan.
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
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.
So the iight, what.
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 bring that specialization from a managed perspective has really come into play.
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 populate it in your center of excellence and have someone shepherding that and watching over it.
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 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.
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, 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 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 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 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?
Right?
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 in security. What do you think, Ben.
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 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 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?
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?
Is it?
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?
Right?
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?
Right?
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 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, even if you take the cloud out of it, just think of your driving licenses and now digital.
Your tickets are now digital.
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.
But it's a nuclear and race.
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.
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 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.
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, 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 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?
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 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 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 with doing that and then cascading that out or guiding everybody is really a key critical thing.
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?
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 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.
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.
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