The IT Intelligence Foundation For Digital Business Transformation Builds from HPE InfoSight AIOps

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The IT Intelligence Foundation
For Digital Business Transformation
Builds from HPE InfoSight AIOps
A discussion on how HPE InfoSight has emerged as a broad and inclusive capability for AIOps across an
expanding array of HPE products and services.
Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: Hewlett Packard
Enterprise.
Dana Gardner: Hello, and welcome to the next edition of the BriefingsDirect AIOps innovation
podcast series. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host and
moderator for this ongoing discussion on how artificial intelligence (AI) increasingly supports IT
operations.
One of the most successful uses of machine learning (ML) and AI for IT efficiency has been the
InfoSight technology developed at Nimble Storage, now part of Hewlett Packard Enterprise
(HPE). Initially targeting storage optimization, HPE InfoSight has emerged as a broad and
inclusive capability for AIOps across an expanding array of HPE products and services.
Stay with us now as we welcome a Nimble Storage founder, along with a cutting-edge machine
learning architect, to examine the expanding role and impact of HPE InfoSight in making IT
resiliency better than ever.
To learn more about the latest IT operations solutions that help companies deliver agility and
edge-to-cloud business continuity, we’re joined by Varun Mehta, Vice President and General
Manager for InfoSight at HPE and founder of Nimble Storage. Welcome, Varun.
Varun Mehta: Nice to be here, Dana.
Gardner: We’re also here with David Adamson, Machine Learning Architect at HPE InfoSight.
Welcome, David.
David Adamson: Thank you very much.
Gardner: Varun, what was the primary motivation for creating HPE
InfoSight? What did you have in mind when you built this
technology?
Data delivers more than a quick fix
Mehta: Various forms of call home were already in place when we
started Nimble, and that’s what we had set up to do. But then we
realized that the call home data was used to do very simple
actions. It was basically to look at the data one time and try and
find problems that the machine was having right then. These were
Mehta

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very obvious issues, like a crash. If you had had any kind of software crash, that’s what call
home data would identify.
We found that if instead of just scanning the data one time, if we could store it in a database and
actually look for problems over time in areas wider than just a single use, we could come up
with something very interesting. Part of the problem until then was that a database that could
store this amount of data cheaply was just not available, which is why people would just do the
one-time scan.
The enabler was that a new database became available. We found that rather than just scan
once, we could put everyone’s data into one place, look at it, and discover issues across the
entire population. That was very powerful. And then we could do other interesting things using
data science such as workload planning from all of that data. So the realization was that if the
databases became available, we could do a lot more with that data.
Gardner: And by taking advantage of that large data capability and the distribution of analytics
through a cloud model, did the scope and relevancy of what HPE InfoSight did exceed your
expectations? How far has this now come?
Mehta: It turned out that this model was really successful. They say that, “imitation is the
sincerest form of flattery.” And that was proven true, too. Our customers loved it, our
competitors found out that our customers loved it, and it basically spawned an entire set of
features across all of our competitors.
The reason our customers loved it -- followed by
our competitors -- was that it gave people a much
broader idea of the issues they were facing. We
then found that people wanted to expand this
envelope of understanding that we had created
beyond just storage.
And that led to people wanting to understand how their hypervisor was doing, for example. And
so, we expanded the capability to look into that. People loved the solution and wanted us to
expand the scope into far more than just storage optimization.
Gardner: David, you hear Varun describing what this was originally
intended for. As a machine learning architect, how has HPE InfoSight
provided you with a foundation to do increasingly more when it comes
to AIOps, dependability, and reliability of platforms and systems?
Adamson: As Varun was describing, the database is full of data that
not only tracks everything longitudinally across the installed base, but
also over time. The richness of that data set gives us an opportunity
to come up with features that we otherwise wouldn’t have conceived
of if we hadn’t been looking through the data. Also very powerful from
InfoSight’s early days was the proactive nature of the IT support
because so many simple issues had now been automated away.
That allowed us to spend time investigating more interesting and
advanced problems, which demanded ML solutions. Once you’ve cleaned up the Pareto curve
of all the simple tasks that can be automated with simple rules or SQL statements, you uncover
Adamson
The reason our customers loved
[InfoSight] – followed by our
competitors – was that it gave
people a much broader idea of
the issues they were facing.

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problems that take longer to solve and require a look at time series and telemetry that’s
quantitative in nature and multidimensional. That data opens up the requirement to use more
sophisticated techniques in order to make actionable recommendations.
Gardner: Speaking of actionable, something that really impressed me when I first learned about
HPE InfoSight, Varun, was how quickly you can take the analytics and apply them. Why has
that rapid capability to dynamically impact what’s going on from the data proved so successful?
Support to succeed
Mehta: It turned out to be one of the key points of our success. I really have to compliment the
deep partnership that our support organization has had with the HPE InfoSight team.
The support team right from the beginning prided themselves on providing outstanding service.
Part of the proof of that was incredible Net Promoter scores (NPS), which is this independent
measurement of how satisfied customers are with our products. Nimble’s NPS score was 86,
which is even higher than Apple. We prided ourselves on providing a really strong support
experience to the customer.
Whenever a problem would surface, we would
work with the support team. Our goal was for a
customer to see a problem only once. And then
we would rapidly fix that problem for every
other customer. In fact, we would fix it
preemptively so customers would never have
to see it. So, we evolved this culture of
identifying problems, creating signatures for
these problems, and then running everybody’s
data through the signatures so that customers would be preemptively inoculated from these
problems. That’s why it became very successful.
Gardner: It hasn’t been that long since we were dealing with red light-green light types of IT
support scenarios, but we’ve come a long way. We’re not all the way to fully automated, lights-
out, machines running machines operations.
David, where do you think we are on that automated support spectrum? How has HPE InfoSight
helped change the nature of systems’ dependability, getting closer to that point where they are
more automated and more intelligent?
Adamson: The challenge with fully automated infrastructure stems from the variety of different
components in the environments -- and all of the interoperability among those components. If
you look at just a simple IT stack, they are typically applications on top of virtual machines
(VMs), on top of hosts -- they may or may not have independent storage attached – and then
the networking of all these components. That’s discounting all the different applications and
various software components required to run them.
There are just so many opportunities for things to break down. In that context, you need a
holistic perspective to begin to realize a world in which the management of that entire unit is
managed in a comprehensive way. And so we strive for observability models and services that
We evolved this culture of
identifying problems, creating
signatures for these problems, and
then running everybody’s data
through the signatures so that
customers would be preemptively
inoculated from these problems.

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collect all the data from all of those sources. If we can get that data in one place to look at the
interoperability issues, we can follow the dependency chains.
But then you need to add intelligence on top of that, and that intelligence needs to not only
understand all of the components and their dependencies, but also what kinds of exceptions
can arise and what is important to the end users.
So far, with HPE InfoSight, we go so far as to pull in all of our subject matter expertise into the
models and exception-handling automation. We may not necessarily have upfront information
about what the most important parts of your environment are. Instead, we can stop and let the
user provide some judgment. It’s truly about messaging to the user the different alternative
approaches that they can take. As we see exceptions happening, we can provide those
recommendations in a clean and interpretable way, so [the end user] can bring context to bear
that we don’t necessarily have ourselves.
Gardner: And the timing for these advanced IT operations services is very auspicious. Just as
we’re now able to extend intelligence, we’re also at the point where we have end-to-end
requirements – from the edge, to the cloud, and back to the data center.
And under such a hybrid IT approach, we are also facing a great need for general digital
transformation in businesses, especially as they seek to be agile and best react to the COVID-
19 pandemic. Are we able yet to apply HPE InfoSight across such a horizontal architecture
problem? How far can it go?
Seeing into the future: end-to-end visibility
Mehta: Just to continue from where David started, part
of our limitation so far has been from where we began.
We started out in storage, and then as Nimble became
part of HPE, we expanded it to compute resources. We
targeted hypervisors; we are expanding it now to
applications. To really fix problems, you need to have end-to-end visibility. And so that is our
goal, to analyze, identify, and fix problems end-to-end.
That is one of the axis of development we’re pursuing. The other axis of development is that
things are just becoming more-and-more complex. As businesses require their IT infrastructure
to become highly adaptable they also need scalability, self-healing, and enhanced performance.
To achieve this, there is greater-and-greater complexity. And part of that complexity has been
driven by really poor utilization of resources.
Go back 20 years and we had standalone compute and storage machines that were not
individually very well-utilized. Then you had virtualization come along, and virtualization gave
you much higher utilization -- but it added a whole layer of complexity. You had one machine,
but now you could have 10 VMs in that one place.
Now, we have containers coming out, and that’s going to further increase complexity by a factor
of 10. And right on the horizon, we have serverless computing, which will increase the
complexity another order of magnitude.
That is our goal, to
analyze, identify, and fix
problems end-to-end.

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So, the complexity is increasing, the interconnectedness is increasing, and yet the demands on
businesses to stay agile and competitive and scalable are also increasing. It’s really hard for IT
administrators to stay on top of this. And that’s why you need end-to-end automation and to
collect all of the data to actually figure out what is going on. We have a lot of work cut out for us.
There is another area of research, and David spends a lot of time working on this, which is you
really want to avoid false positives. That is a big problem with lots of tools. They provide so
many false positives that people just turn them off. Instead, we need to work through all of your
data to actually say, “Hey, this is a recommendation that you really should pay attention to.”
That requires a lot of technology, a lot of ML, and a lot of data science experience to separate
the wheat from the chaff.
One of the things that’s happened with the
COVID-19 pandemic response is the need for
very quick response stats. For example,
people have had to quickly set up web sites
for contact tracing, reporting on the diseases,
and for vaccines use. That shows an
accelerated manner in how people need
digital solutions -- and it’s just not possible
without serious automation.
Gardner: Varun just laid out the complexity and the demands for both the business and the
technology. It sounds like a problem that mere mortals cannot solve. So how are we helping
those mere mortals to bring AI to bear in a way that allows them to benefit – but, as Varun also
pointed out, allows them to trust that technology and use it to its full potential?
Current complexity requires automated assistance
Adamson: The point Varun is making is key. If you are talking about complexity, we’re well
beyond the point where people could realistically expect to log-in to each machine to find,
analyze, or manage exceptions that happen across this ever-growing, complex regime.
Even if you’re at a place where you have the observability solved, and you’re monitoring all of
these moving parts together in one place -- even then, it easily becomes overwhelming, with
pages and pages of dashboards. You couldn’t employ enough people to monitor and act to spot
everything that you need to be spotting.
You need to be able to trust automated exception [finding] methods to handle the scope and
complexity of what people are dealing with now. So that means doing a few things.
People will often start with naïve thresholds. They create manual thresholds to give alerts to
handle really critical issues, such as all the servers went down.
But there are often more subtle issues that show up that you wouldn’t necessarily have
anticipated setting a threshold for. Or maybe your threshold isn’t right. It depends on context.
Maybe the metrics that you’re looking at are just the raw metrics you’re pulling out of the system
and aren’t even the metrics that give a reliable signal.
With the COVID-19 pandemic
response, there is the need for very
quick response stats. … That shows
an accelerated manner in how people
need digital solutions – and it’s just not
possible without serious automation.

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What we see from the data science side is that a lot of these problems are multi-dimensional.
There isn’t just one metric that you could set a threshold on to get a good, reliable alert. So how
do you do that right?
For the problems that IT support provides to us, we apply automation and we move down the
Pareto chart to solve things in priority of importance. We also turn to ML models. In some of
these cases, we can train a model from the installed base and use a peer-learning approach,
where we understand the correlations between problem states and indicator variables well
enough so that we can identify a root cause for different customers and different issues.
Sometimes though, if the issue is rare enough, scanning the installed base isn’t going to give us
a high enough signal to the noise. Then we can take some of these curated examples from
support and do a semi-supervised loop. We basically say, “We have three examples that are
known. We’re going to train a model on them.” Maybe it’s a few tens of thousands of data
points, but it’s still in the three examples, so there’s co-correlation that we are worried about.
In that case we say: “Let me go fishing in that installed base with these examples and pull back
what else gets flagged.” Then we can turn those back over to our support subject matter experts
and say, “Which of these really look right?” And in that way, you can move past the fact that
your starting data set of examples is very small and you can use semi-supervised training to
develop a more robust model to identify the issues.
Gardner: As you are refining and improving these models, one of the benefits in being a part of
HPE is to access growing data sets across entire industries, regions, and in fact the globe. So,
Varun, what is the advantage of being part of HPE and extending those datasets to allow for the
budding models to become even more accurate and powerful over time?
Gain a wider, global point of view
Mehta: Being part of HPE has enabled us to leapfrog our competition. As I said, our roots are
in storage, but really storage is just the foundation of where things are located in an
organization. There is compute, networking, hypervisors, operating systems, and applications.
With HPE, we certainly now cover the base infrastructure, which is storage followed by
compute. At some point we will bring in networking. We already have hypervisor monitoring, and
we are actively working on application monitoring.
HPE has allowed us to radically increase the scope
of what we can look at, which also means we can
radically improve the quality of the solutions we
offer to our customers. And so it’s been a win-win
solution, both for HPE where we can offer a lot of
different insights into our products, and for our
customers where we can offer them faster solutions
to more kinds of problems.
Gardner: David, anything more to offer on the depth, breadth, and scope of data as it’s helping
you improve the models?
HPE has allowed us to radically
increase the scope of what we can
look at, which also means we can
radically improve the quality of the
solutions we offer to our customers.

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Adamson: I certainly agree with everything that Varun said. The one thing I might add is in the
feedback we’ve received over time. And that is, one of the key things in making the notifications
possible is getting us as close as possible to the customer experience of the applications and
services running on the infrastructure.
We’ve done a lot of work to make sure we identify what look like meaningful problems. But
we’re fundamentally limited if the scope of what we measure is only at the storage or hypervisor
layer. So gaining additional measurements from the applications themselves is going to give us
the ability to differentiate ourselves, to find the important exceptions to the end user, what they
really want to take action on. That’s critical for us -- not sending people alerts they are not
interested in but making sure we find the events that are truly business-critical.
Gardner: And as we think about the extensibility of the solution -- extending past storage into
compute, ultimately networking, and applications -- there is the need to deal with the
heterogeneity of architecture. So multicloud, hybrid cloud, edge-to-cloud, and many edges to
cloud. Has HPE InfoSight been designed in a way to extend it across different IT topologies?
Across all architecture
Mehta: At heart, we are building a big data warehouse. You
know, part of the challenge is that we’ve had this explosion in
the amount of data that we can bring home. For the last 10
years, since InfoSight was first developed, the tools have
gotten a lot more powerful. What we now want to do is take advantage of those tools so we can
bring in more data and provide even better analytics.
The first step is to deal with all of these use cases. Beyond that, there will probably be custom
solutions. For example, you talked about edge-to-cloud. There will be locations where you have
good bandwidth, such as a colocation center, and you can send back large amounts of data.
But if you’re sitting as the only compute in a large retail store like a Home Depot, for example, or
a McDonald’s, then the bandwidth back is going to be limited. You have to live within that and
still provide effective monitoring. So I’m sure we will have to make some adjustments as we
widen our scope, but the key is having a really strong foundation and that’s what we’re working
on right now.
Gardner: David, anything more to offer on the extensibility across different types of architecture,
of analyzing the different sources of analytics?
Adamson: Yes, originally, when we were storage-focused and grew to the hypervisor level, we
discovered some things about the way we keep our data organized. If we made it more
modular, we could make it easier to write simple rules and build complex models to keep
turnaround time fast. We developed some experience and so we’ve taken that and applied it in
the most recent release of recommendations into our customer portal.
We’ve modularized our data model even further to help us support more use cases from
environments that may or may not have specific components. Historically, we’ve relied on
having Nimble Storage, they’re a hub for everything to be collected. But we can’t rely on that
anymore. We want to be able to monitor environments that don’t necessarily have that particular
At heart, we are building
a big data warehouse.

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storage device, and we may have to support various combinations of HPE products and other
non-HPE applications.
Modularizing our data model to truly accommodate that has been something that we started
along the path for and I think we’re making good strides toward.
The other piece is in terms of the data science. We’re trying to leverage longitudinal data as
much as possible, but we want to make sure we have a sufficient set of meaningful ML
offerings. So we’re looking at unsupervised learning capabilities that we can apply to
environments for which we don’t have a critical mass of data yet, especially as we onboard
monitoring for new applications. That’s been quite exciting to work on.
Gardner: We’ve been talking a lot about the HPE InfoSight technology, but there also has to be
considerations for culture. A big part of digital transformation is getting silos between people
broken down.
Is there a cultural silo between the data scientists and the IT operations people? Are we able to
get the IT operations people to better understand what data science can do for them and their
jobs? And perhaps, also allow the data scientists to understand the requirements of a modern,
complex IT operations organization? How is it going between these two groups, and how well
are they melding?
IT support and data science team up for the win
Adamson: One of the things that Nimble did well from
the get-go was have tight coupling between the IT
support engineers and the data science team. The
support engineers were fielding the calls from the IT
operations guys. They had their fingers on the pulse of
what was most important. That meant not only building
features that would help our support engineers solve their escalations more quickly, but also
things that we can productize for our customers to get value from directly.
Gardner: One of the great ways for people to better understand a solution approach like HPE
InfoSight is through examples. Do we have any instances that help people understand what it
can do, but also the paybacks? Do we have metrics of success when it comes to employing
HPE InfoSight in a complex IT operations environment?
Mehta: One of the examples I like to refer to was fairly early in our history but had a big impact.
It was at the University Hospital of Basel in Switzerland. They had installed a new version of
VMware, and a few weeks afterward things started going horribly wrong with their
implementation that included a Nimble Storage device. They called VMware and VMware
couldn’t figure it out. Eventually they called our support team and using InfoSight, our support
team was able to figure it out really quickly. The problem turned out to be a result of a new
version of VMware. If there was a hold up in the networking, some sort of bottleneck in their
networking infrastructure, this VMware version would try really hard to get the data through.
So instead of submitting each write once to the storage array once, it would try 64 times.
Suddenly, their traffic went up by 64 times. There was a lot of pounding on the network,
Nimble, from the get-go, has
had tight coupling between
the IT support engineers
and the data science team.

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pounding on the storage system, and we were able to tell with our analytics that, “Hey this traffic
is going up by a huge amount.” As we tracked it back, it pointed to the new version of VMware
that had been loaded. We then connected with the VMware support team and worked very
closely with all of our partners to identify this bug, which VMware very promptly fixed. But, as
you know, it takes time for these fixes to roll out to the field.
We were able to preemptively alert other people who had the same combination of VMware on
Nimble Storage and say, “Guys, you should either upgrade to this new patch that VMware has
made or just be aware that you are susceptible to this problem.”
So that’s a great example of how our analytics was able to find a problem, get it fixed very
quickly -- quicker than any other means possible -- and then prevent others from seeing the
same problem.
Gardner: David, what are some of your favorite examples of demonstrating the power and
versatility of HPE InfoSight?
Adamson: One that comes to mind was the first time we turned to an exception-based model
that we had to train. We had been building infrastructure designed to learn across our installed
base to find common resource bottlenecks and identify and rank those very well. We had that in
place, but we came across a problem that support was trying to write a signature for. It was
basically a drive bandwidth issue.
But we were having trouble writing a signature that would identify the issue reliably. We had to
turn to an ML approach because it was fundamentally a multidimensional problem. If we looked
across, we have had probably 10 to 20 different metrics that we tracked per drive per minute on
each system. We needed to, from those metrics, come up with a good understanding of the
probability that this was the biggest bottleneck on the system. This was not a problem we could
solve by just setting a threshold.
So we had to really go in and say, “We’re going to label known examples of these situations.
We’re going to build the sort of tooling to allow us to do that, and we’re going to put ourselves in
a regime where we can train on these examples and initiate that semi-supervised loop.”
We actually had two to three customers that hit that specific issue. By the time we wanted to put
that in place, we were able to find a few more just through modeling. But that set us up to start
identifying other exceptions in the same way.
We’ve been able to redeploy that
pattern now several times to
several different problems and
solve those issues in an
automated way, so we don’t
have to keep diagnosing the
same known flavors of problems
repeatedly in the future.
Gardner: What comes next? How will AI impact IT operations over time? Varun, why are you
optimistic about the future?
We’ve been able to redeploy that pattern now
several times to several different problems and
solve those issues in an automated way, so we
don’t have to keep diagnosing the same known
flavors of problems repeatedly in the future.

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Software is eating the world
Mehta: I think having a machine in the loop is going to be required. As I pointed out earlier,
complexity is increasing by leaps and bounds. We are going from virtualization to containers to
serverless. The number of applications keeps increasing and demand on every industry keeps
increasing.
Andreessen Horowitz, a famous venture capital firm once said, “Software is eating the world,”
and really, it is true. Everything is becoming tied to a piece of software. The complexity of that is
just huge. The only way to manage this and make sure everything keeps working is to use
machines.
That’s where the challenge and opportunity is. Because there is so much to keep track of, one
of the fundamental challenges is to make sure you don’t have too many false positives. You
want to make sure you alert only when there is a need to alert. It is an ongoing area of research.
There’s a big future in terms of the need for our solutions. There’s plenty of work to keep us
busy to make sure we provide the appropriate solutions. So I’m really looking forward to it.
There’s also another axis to this. So far, people have stayed in the monitoring and analytics loop
and it’s like self-driving cars. We’re not yet ready for machines to take over control of our cars.
We get plenty of analytics from the machines. We have backup cameras. We have radars in
front that alert us if the car in front is braking too quickly, but the cars aren’t yet driving
themselves.
It’s all about analytics yet we haven’t graduated from
analytics to control. I think that too is something that
you can expect to see in the future of AIOps once the
analytics get really good, and once the false positives
go away. You will see things moving from analytics to
control. So lots of really cool stuff ahead of us in this
space.
Gardner: David, where do you see HPE InfoSight becoming more of a game changer and even
transforming the end-to-end customer experience where people will see a dramatic
improvement in how they interact with businesses?
Adamson: Our guiding light in terms of exception handling is making sure that not only are we
providing ML models that have good precision and recall, but we’re making recommendations
and statements in a timely manner that come only when they’re needed -- regardless of the
complexity.
A lot of hard work is being put into making sure we make those recommendation statements as
actionable and standalone as possible. We’re building a differentiator through the fact that we
maintain a focus on delivering a clean narrative, a very clear-cut, “human readable text” set of
recommendations.
And that has the potential to save a lot of people a lot of time in terms of hunting, pecking, and
worrying about what’s unseen and going on in their environments.
In the future of AIOps, once the
analytics get really good and
once the false positives go
away, you will see things moving
from analytics to control.

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Gardner: Varun, how should enterprise IT organizations prepare now for what’s coming with
AIOps and automation? What might they do to be in a better position to leverage and exploit
these technologies even as they evolve?
Pick up some new tools
Mehta: My advice to organizations is to buy into this.
Automation is coming. Too often we see people stuck in
the old ways of doing things. They could potentially
save themselves a lot of time and effort by moving to
more modern tools. I recommend that IT organizations
make use of the new tools that are available.
HPE InfoSight is generally available for free when you buy an HPE product, sometimes with
only the support contract. So make use of the resources. Look at the literature with HPE
InfoSight. It is one of those tools that can be fire-and-forget, which is you turn it on and then you
don’t have to worry about it anymore.
It’s the best kind of tool because we will come back to you and tell you if there’s anything you
need to be aware of. So that would be the primary advice I would have, which is to get familiar
with these automation tools and analytics tools and start using them.
Gardner: I’m afraid we’ll have to leave it there. We have been exploring how HPE InfoSight has
emerged as a broad and inclusive capability for AIOps across an expanding array of edge-to-
cloud solutions. And we’ve learned how these expanding AIOps capabilities are helping
companies deliver increased agility -- and even accelerated digital transformation.
So please join me in thanking our guests, Varun Mehta, Vice President and General Manager
for InfoSight at HPE and a founder of Nimble Storage. Thanks so much, Varun.
Mehta: Thank you, Dana.
Gardner: And we’ve also been here with David Adamson, Machine Learning Architect at HPE.
Thanks so much, David.
Adamson: Thank you. It’s been a pleasure.
Gardner: And a big thank you as well to our audience for joining this sponsored BriefingsDirect
AIOps innovation discussion. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your
host for this ongoing series of Hewlett Packard Enterprise-supported discussions.
Thanks again for listening. Please pass this along to your IT community, and do come back next
time.
Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: Hewlett Packard
Enterprise.
[Organizations] could
potentially save themselves a
lot of time and effort by
moving to more modern tools.

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A discussion on how HPE InfoSight has emerged as a broad and inclusive capability for AIOps across an
expanding array of HPE products and services. Copyright Interarbor Solutions, LLC, 2005-2020. All rights
reserved.
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• How REI used automation to cloudify infrastructure and rapidly adjust its digital pandemic
response
• How the right data and AI deliver insights and reassurance on the path to a new normal
• How IT modern operational services enables self-managing, self-healing, and self-optimizing
• How HPE Pointnext Services ushers businesses to the new normal via an inclusive nine-step
plan
• As containers go mainstream, IT culture should pivot to end-to-end DevSecOps
• AI-first approach to infrastructure design extends analytics to more high-value use cases
• How Intility uses HPE Primera intelligent storage to move to 100 percent data uptime
• As hybrid IT complexity ramps up, operators look to data-driven automation tools

The IT Intelligence Foundation For Digital Business Transformation Builds from HPE InfoSight AIOps

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