Good evening. How are you all doing?
Are you feeling fine? Can you stand a little more?
You must have seen a lot of presentations today.
Today, we're going to talk a little bit about
how edge computing actually is affecting how we live
and work and how we actually can improve by
putting compute close to where the data is.
Does it sound good?
Yes. Okay. Well, let's go. It actually starts with all the data.
Where do you think all the data is?
And the data turns out to actually be
at the edge where we all live and work and do things.
Factories run on data, as we heard today from Fidelma's talk,
wineries run on data. Everything we do, this show runs on data.
And much of that data, if we insisted on sending it back to our data center
or any other central location,
we actually run the risk of destroying the value of the data.
And that's the important point.
The data is the first thing we examine and we realize some of that data
must be where it is.
And I give you an example. Manufacturing data, for example,
you have less than a millisecond to make heads and tails of it in real time.
There is no time to move the data.
Therefore, compute must be coming close
to where the data is being born by sensors and the like.
And that's actually the key. We are using more and more sensors.
If you go on Ebay today, you'll be able to buy
a 1080P camera for less than $15.
And what happens if you put that everywhere,
then you get an awful flow of data coming in
and people are using that. Anywhere here who owns a Tesla?
Yeah, a few? And guess what? We have eight cameras on the Tesla today.
We're throwing up more and more data for every car that drives around.
So just imagine that as the general rule for everything we do.
When we give a computer eyes, we can begin to do the things we couldn't do before.
But that data has to be processed in real time.
And this is the key point to understand.
With all the data growth we're talking about, turns out 30% of the data
is a real-time requirement. And that could be real-time
in your data center, it could be real-time over in a cloud provider's data center.
But it can also more increasingly be at the edge.
So let's take a look at how is the data distributing itself? Is it at the edge?
Where is it? It turns out, and people talk about data explosions all the time.
I don't think it's an explosion. I think it's an ever expanding universe of data.
Its not an explosion. It doesn't go away after it says "boom" and goes away.
No, it doesn't do that. It continuously expands.
So here from where we bite,
we had at the turn of the century when we
actually aggregate the growth rate over the last 23 years,
we're finding that by 2025, 20% of the data is sitting in a data center,
30% of the data is sitting in a public cloud,
and 50% of the data is sitting somewhere else.
And it cannot be moved at least 30% of it because it has to be operated upon
in real time.
That's why we talk about edge computing moving data center firepower
to where the data is, and not moving it at all.
So it's a question is that a lot of people are asking,
"Edge, edge. Where is the edge? Whats edge computing?"
I got that question this morning where we had a break out.
Define edge computing. And edge computing really is a location-specific conversation.
Where is the data? Is it sitting here, or is it sitting somewhere else?
As my old mentor used to say, there are only two states of data.
It's either here, or not here. It's somewhere else.
But if it's here, we have to have compute firepower at the ready.
Now, we all walk around with a supercomputer
in our pockets. So it's fairly easy to actually do that today.
I can tell you that a computer of yesteryear,
like just the ones we used to rack and stack around
the year 2000 when Google came of age, and so on.
Well, 24 racks of those today we can do in what is a coffee table size.
So computing has become more powerful in less space. Okay.
That's a good thing because here's the problem we're trying to solve.
So if all this data is at the edge,
if we insist on moving the data, and you heard this because we all
had a CEO or someone, or a CFO, or a CTO, say we're going to go
cloud first. Well, that means moving the data to the cloud.
What about the opposite? If we actually move the cloud to where the data is?
Because when we do that, we actually don't encounter the delay of moving the data.
Think of this as a Federal Express package.
You actually have to send it from point A to point B. While doing so, you waste time.
What if you don't have any time?
What if you have to actually begin to understand what the data is
inside a second?
Then it's no good. It takes 20 seconds to get it from point A to point B.
Does that make sense?
Yeah. Okay. Good. So this is a bad model.
Not all workloads, as we discussed, should be treated this way.
Some workloads are perfectly happy
if it's like, oh, I just need to
take out my data from the month and aggregate it at the end of the month.
Well, we have a whole month to move the data then. It's not a big deal.
But data that's real time, not so much. So we lose opportunity.
We compromise when we move the data out there. So that's no good.
So what do we do? How do we flip this? You know what I'm going to say
because I've been saying it already.
Move the darn compute to the edge. If you do that,
you have success. Now you can begin to get business value from the data.
So just think about that for a minute.
I have value in data.
If I compute upon it in real time where it lives,
I can get increased business efficiency out of it.
If I don't, if I insist on going with a centralized paradigm,
I destroy the value of the data. Making sense?
All right guys, great. So who is doing that?
I have a couple of examples of how we improve the way we live and work.
RaceTrac, a great customer of ours,
they are actually putting computers, they have done it for years,
putting computers into every single gas station and convenience store.
And today, what they do is they actually keep an eye on,
"Is someone not using pump number 10? What's going on?
And they get an automated alert from a camera feed
that sees there's not normal activity at pump number 10
and they send an automated crew out to repair. And sure enough they find
that there's a malfunction, there's something wrong with that pump.
And then of course, have you had that experience driving up to a gas station
and you go, I can't believe it.
They could at least put an out of order sign on it, right?
And it just doesn't work. And you get upset. You get a bad experience.
Well, they combat that with real-time computing
in a distributed fashion across 750-plus stores.
Okay, good example.
What about manufacturing? I mentioned this to you before.
This is actually an example from my own manufacturing plant in Eastern Europe.
We actually go and manufacture servers where you configure them.
So each server is configured to spec.
That means one server is different than the next server.
Might be the same model that some other customer ordered,
but it has different CPU, different D-ram, different flash, and so on.
Well, we actually have the ability to see that, I don't know if you
can see in this drawing here of this picture,
we have five cameras sitting on the top left corner of the picture.
Can you see that? Right under where it says Relimetrics. It turns out
that camera gantry is moving up and down
in real time and actually looking at the product. And from a data perspective,
how much data do you think we're throwing off?
We are throwing off 75 megabytes every second.
And we actually do not have 21 seconds to move the data.
We tried it. We tried to move the data to a central location. It took too long.
We only have one second to make heads and tails of this.
We cannot use 21 seconds. 21 seconds,
You might as well have old Johnson standing there with a clipboard and a loop.
Not a good idea.
So what we need to do is begin to look at this from a systemic standpoint and say,
all right, when you're manufacturing 45,000 servers a month
in five different parallel manufacturing lines,
you want to catch any and everything that comes down the pike
that's misconfigured, poorly seeded connectors, stuff like that.
But why? Because we don't want to send that to you
and then have you turn around and say,
"The server I bought didn't work. It was a bad experience."
Then you go around and say,
"You know what, I didn't like that HPE experience."
And it's bad for our experience together.
It's bad for the brand.
And it's true for anything and everything we all do.
So if you catch it early, you can save a lot of money. Now, it doesn't sound a lot.
You can see here we saved 96 seconds per unit. It sounds like--
96 seconds, give me a break. That's not a lot, right?
When you multiply that by 45,000 servers a month you get a different picture.
So here, I calculated before versus after.
So before, it took six minutes and 28 seconds on average to manufacture a server.
After, it took four minutes and 55 seconds.
you start looking at that, you go,
"Not a big difference." But like I said,
it's more than $100 million worth of additional capacity per month.
And the reason that this is an important
conversation to have is this is a conversation
we technologists have to have with the decision-makers back home.
Because when we do,
we can then figure out how it helps technology, helps the way we live and work,
helps us increase our revenue, decrease our costs, and mitigate our risk.
And that's what we all want.
So by monetizing the technology in this fashion,
you begin to actually get a good gauge for how it will help what you do.
Okay, good. So here's another good example, Carestream. What do they do?
Well, it's a partner of ours
we announced, I think last year. Carestream, 112 countries worldwide.
They use real-time AI in hospital, in the clinic,
to actually look at data, look at X-ray data.
You come in, you take an X-ray, the doctor looks at your X-ray and he says,
wait a minute,
I need to filter out the cardiovascular system so I can see what's going on.
Okay. They use real-time AI filters
to almost dissect you. It sounds a little gross, right?
But dissect you like we used to dissect frogs in school,
they do that today digitally with AI in real time.
That has to happen in the actual clinic.
So here you have a doctor with the X-ray camera
and AI inference engine sitting in each, they actually have them
on little carts that they can drive around inside the clinical hospital.
And that hospital then aggregates that fleet of diagnostic engines
inside the four walls of the hospital.
But when you operate across 100-plus countries worldwide,
there is more to this story. Because there's not just one hospital,
it's several hospitals.
Does that make sense?
So when you look at that, you say wait a minute,
there's a one-to-many relationship inside the hospital
and a one-to-many relationship between hospitals.
Because what do hospitals do typically? They have a chain of hospitals. Why?
Because they are distributed enterprise. They have to be where the sick people are.
Same thing with restaurants, same thing with Home Depot,
same thing with anybody who is doing anything for the local community.
So here, classic example how we help the way we live and work.
So if you take a look at this, you go,
"Does that mean there's no reason to use public cloud at all?"
No, there is. But you use that as an aggregator over aggregators.
So I hope you can see here. There are three levels of aggregation.
and the cloud is sort of like the aggregator in
the back where I put backup of patient data, and so on.
And the further I move away from that edge, the more latency-tolerant I get.
In the front of this equation, I'm latency intolerant.
I need to be able to use my data right there and then.
Okay, so you get this, so you say, fine,
can we get into this example in more detail? Yes, there's a stack involved here.
You heard Fidelma talk about the stack needs to be aligned.
If you have a misaligned stack, you have sub-optimal examples
or sub-optimal results, I would say, of what you're doing.
And if that happens across 100 locations,
imagine you miss opportunities for $1,000 across 1,000 locations.
How much missed opportunity do you have?
A million bucks, right?
You get the point.
The point here is that the worst part about, or
the best part depending how you look at it,
the best part if we actually do it right
it's a gift that keeps giving. Because every store,
every location adds to the bottom line.
All right, great. So in this case here we use Ezmeral
which is a Kubernetes distribution.
We also use the Ezmeral Data Fabric.
What's the interesting thing about the Ezmeral Data Fabric?
It provides a global name space across all locations
so that the data scientists can get at the data
through one pane of glass, even though the data is distributed across
thousands of locations. How is that achieved? And you say,
"This thing about global name space, sounds very fancy. What is it?"
You know a file system, right?
File system has meta data, file names,
data that was created, what sub folder it's in, and so on.
That meta data is shared amongst all the participants,
all the participants in the global Data Fabric.
And when you do that, that means
you have the phone book now. You can get to talk to any location and get at the data.
Okay, sounds good?
All right. So that's an example of a stack.
I'd like to conclude on this little thing, Home Depot, a big, big customer of ours.
We love them dearly. We help them run their business more efficiently.
Hopefully you have a better Home Depot experience.
So I like to drill into just the impact of what this means
when you actually do it across 2,324 stores, and growing.
You've got to check this number of them weekly or monthly.
They keep opening more stores.
So, here's the thing. What do they want to do?
So, imagine that you are fixing, let's say, your grandma's credenza
and you are putting in new poles on it.
And you have found some poles you like, and you go to your Home Depot store.
But with these poles, the way they've been packaged,
they don't come with the right screws or the right washers.
Well, you need to find that too. And if you forget about it,
then you have to go back and go,
oh, bad experience. I can't believe I'd go back to Home Depot again.
So what does Home Depot do to give you a better experience?
They actually have the people in the store,
the service personnel, walk around with
an iPad with information about inventory and
what goes with what. Pretty much the way we go to Amazon.
We order people who like cat food also like cat toys, that kind of thing.
Is done in association and analysis of what's
taking place in real time in that store.
Why? Because that store has inventory.
It's at the fingertips of the personnel in the store through an iPad.
Now, imagine how this works.
It works with the database in the store running on
servers in the store that's tied into inventory management.
And it also has Wi-Fi so we can cover the whole store.
Not only that. You can snap up
your app in your smartphone and you can interact in real time with the store.
Better experience, and guess what it does for Home Depot from a business standpoint?
It stops you from having to go home
dissatisfied because you only got half of what you needed.
And they actually can then, what we call, upsell you, which is a good thing
if it's done in the service of what you're trying to achieve.
They can make sure you have everything you need.
So you look at that, you say,
can we take a look at what does this mean for Home Depot?
So just before the show, I went up and saw their public information.
I'm not disclosing anything I'm not supposed to.
And I looked at how much revenue did they do in last quarter?
And we know how many stores they have. That's also public information.
We're finding out there's $13.69 million in revenue per store on average.
And you say, what's that 15% underneath there?
Well, that's the stuff they do online.
So if we're talking about an in-store experience,
that doesn't count. So we got to back that out.
So what we are talking about is close to $14 million per store.
So one percentage point of additional screws, or hammer and nails, whatever it is
we managed to help our customers with
lifts the revenue of the store. We would do that across 2,000 stores or
what do we have, 2,324 stores here.
You actually end up with $317 million in a digital upside
One percentage point.
That does not only give us a better Home Depot experience, it helps Home Depot
increase their revenue. Isn't that neat?
So, this is about the right data in the right place
calculated and computed upon in the right period of time.
All right. So you get this idea. This is very sexy. What are we talking about?
We're talking about what we call an OODA loop? What's an OODA loop?
It's a military strategy was invented
many, many, many years ago by the jet fighter pilots.
Actually, in a dog fight, he who has the most data the quickest has an advantage.
So think about your own OODA loop for your business.
How fast can you actually get data
from real-time sensors and scanners?
How fast can you use things like
artificial intelligence to diagnose what's going on?
And how quickly can you actually implement the solution?
The faster this loop goes,
the more revenue you can attain,
the more value you can create, the more you can actually save lives
if that's what your game is. It doesn't matter. It's the quicker you can do this,
the better. Time, we none of us get any time back.
Time is something we can't keep in our pocket.
But we can utilize the time more efficiently by letting this
OODA loop spin faster and create more value for the businesses
we work for and with. Does that make sense? All right, good.
So this is the whole idea. When you do this across the entire globe that you operate,
then every location,
every little thing counts and every little thing is aggregated.
And you need, of course, some sort of operating environment
by which to manage and orchestrate this whole thing.
And that, ladies and gentlemen, is where GreenLake's job is.
whether you own and
operate the gear, or whether you want us to own and operate it,
you have visibility across your entire distributed enterprise.
And what's interesting is,
as we increasingly are putting AI to work at the edge,
so to improve what we do, how we live and work, guess what?
This means we become distributed autonomous enterprises.
There'll be more and more automation, more and more stuff being taken care of for us,
just like we're beginning to see that autonomous
cars are going to be around the corner.
We're going to see autonomous enterprises emerge in the
next decade. And I'm excited about being here.
So I think we have the technology, we have the software, we have the partnership,
we have you guys. We are in good shape to really make a dent in the universe.
So with that, thank you so much for your kind attention.
I appreciate your time with me today.