October 15, 2021 • 24 mins
For those working in IT, they know too well that you’re spending a lot of time fixing problems and putting out fires. And in the world that leans on the digital with each day, it’s becoming a necessity to fix those problems rapidly. That’s where Steven from Instana comes in. Instana changes the conversation from an IT person having to manually fix each issue as it arises, to have the tools to address the issue before it even arises. Steven explains how the sluggish processes of observability and problem solving become trivial in the time of AI-powered insights. By adopting this tool IT experts can worry less about the problems of the day and think about the goals of the future.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Matt Eastwood (00:06):
Did you know that some sources claim the average person
makes roughly 35, 000 decisions a day. That's 2, 000 choices
every hour. That's mind blowing, and even a little bit outrageous.
But what if you have a brain of wires, a
language of zeros and ones? There's a supercomputer in the
(00:27):
Oak Ridge National Lab in Tennessee, dubbed Summit, that dwarfs
our own ability to make rapid calculations. It can spit
out 200 quadrillion calculations in a second. That's 15 zeros,
by the way. That is so fast that if everyone
(00:48):
on the planet we're hashing out calculations every second, it
would take nearly a year to catch up with what
the Summit can achieve in the time that it takes
you to just snap your fingers. We live in a
rapidly changing world, and nowhere is that more apparent than
(01:11):
in the field of information technology and artificial intelligence. Hi,
I'm Matt Eastwood, senior VP of Enterprise IT Research at IDC.
And I'm one of the hosts of Scaling AIOps, artificial
intelligence for IT specialists for business outcomes. It's a joint
venture between IDC and IBM. And joining me is Stephen Elliott, our
(01:33):
analyst on the show, and group vice president of INO,
cloud operations, and DevOps. And we'll be speaking to industry
leaders in a world of AI operations, and learning from
their experiences and their insights in this ever- changing industry.
We hope you find as much value in these conversations
(01:55):
as we did ourselves.
Stephen Elliott (01:57):
This first episode features Steve Waterworth, the technical marketing manager
for Instana, a company that is rewriting how we approach
observability in IT systems. By utilizing tools like Instana,
companies can worry less about the problems of today and
focus on their goals for the future. Let's get started.
Matt Eastwood (02:18):
Just want to make one basic simple observation just to
set up this discussion. The pandemic has clearly raised the
urgency around the importance of transformation in the business. And
as businesses transform themselves digitally, we see this continuum of
interdependent apps and data emerging that stretches from the edge
to the core. And these application portfolios and data volumes
(02:42):
are growing extremely quickly. All of this has an impact
on how we think about managing our IT operations. And
so with that, I'd like to bring in IDC's Stephen
Elliott into the conversation and ask him to explain a
bit about the research that you've been doing in IT
Ops and how the world of online consumer experiences interacting
(03:04):
with businesses, and how that's changed even within just this
last year.
Stephen Elliott (03:08):
Yeah, the research we do at IDC in the IT
operations and SRE and cloud ops team really has a
tremendous focus around these transformational themes that we're seeing specific
to the transition of IT operations, to more of a
automated, proactive set of capabilities. That is not just around
(03:31):
thinking about system reliability and making sure that everything's working
properly, it's really more and more about the customer experience
and the customer journey. And being able to define how
different sets of customers, whether it's internal employees or external
partners, or certainly customers that are using the digital services
(03:51):
to drive revenues and profits, how they're experiencing the company's
products and services.
Matt Eastwood (03:57):
That's perfect. Thank you for that perspective to open this up, Stephen. Next,
I'd like to introduce our guest, Steve Waterworth. And Steve, could
you introduce yourself to the audience and what you do
at Instana.
Steve Waterworth (04:10):
Yeah, I'm Steve Waterworth, I'm technical marketing manager at Instana.
I suppose my main job role is really to take
all the fantastic stuff our engineers do and boil that
down and make it so that normal people can understand
what all the technology is and where the value is
(04:31):
for them. I put it into terms that a business
manager or a senior architect will understand.
Matt Eastwood (04:39):
Perfect. And I know, obviously those conversations in this industry
gets more complicated every day, and we have to have
conversations with lots of different folks that influence the direction
that technology and IT is going in. But Steve, could you just tell
us a little bit more about Instana and exactly what
you guys are doing?
Steve Waterworth (04:55):
Instana is a enterprise observability platform. Observability is generally considered metrics,
logs and traces, and certainly Instana automatically injects metrics, logs and
traces from numerous sources. But what it does then on
top of that, it adds automation, context, and intelligence. So
(05:19):
from the automation point of view, obviously we think we've
already touched on this. Automation is a big drive. There's so
much complexity in modern cloud native applications, and automation is a
big drive there to help manage that complexity. So Instana
has a single agent, it automatically detects what's running around it.
(05:40):
When it detects a suitable target, and there's well over 200
different senses that can connect to those, it will then
connect to that, whether it's a language runtime or a
data store, or a cache, and start pulling out metrics, logs,
and traces.
Matt Eastwood (05:59):
So at its core, what we're trying to achieve here
I think is faster decision- making all across the business. In IT,
but in the business as well. So maybe we could
just dive a little bit more into Instana, and talk
about some of the challenges that we see the IT
professional face today as they work to enable this deeper
(06:19):
and richer decision- making. So Steve, let's start with you
and then I'll go to Stephen on that.
Steve Waterworth (06:24):
Yeah. The challenges they face is the complexity. If we
go back a few years, we might've had a handful
of hosts with a handful of applications running on them.
Now with modern containerized cloud native applications, we've probably got
hundreds of virtual machines, possibly numerous Kubernetes clusters running across
(06:46):
that. And we have thousands of containers running on those
hundreds of virtual machines. So the level of complexity is
many orders of magnitude greater than it used to be.
In fact, it's so complex most of these serious enterprise applications now
are so complex it's pretty much impossible for a human
brain to contain a map of it. And just when
(07:10):
you think that's bad enough, because what we're doing, one
of the reasons for going for container type applications is
agility, is speed. The faster you can get new features
and fixes out the door, the better. Because we want
to do it faster than your competition, maintain that competitive
advantage. So not only do you have something that is
hugely complex, but it's also changing all the time.
Matt Eastwood (07:32):
And that makes great sense. And Stephen, I know you
spend a lot of time talking to IT Ops professionals.
There anything you want to add to that in terms
of some of the challenges that they talk to you
about and what they're facing today?
Stephen Elliott (07:45):
Yeah. No, there's I think a number of things to
build off of the comments here. First and foremost, there's
so many people involved, or that should be involved in
the identification of where a problem exists. And as Steve
mentioned, the increasing complexity of the application architectures, of the
infrastructure architectures, of certainly the use of multiple clouds, most
(08:07):
customers are growing with this and trying to deal with
it. And so you're seeing the need for observability technology
to really bring in and harness all the information that's
being created to help different teams across the IT organization understand,
okay, here's where our problem is. Here's who should help
(08:28):
fix it. And let's go and get it done, with
the potential to actually auto remediate. So you have a
number of tailwinds here that are driving the need for
this type of technology. And it's also a great way
to drive a better data- driven culture. We have a
lot of organizations that call us and we talk to
(08:50):
them about, how do they get more, from reactive to
proactive? How do they get more teams involved in, and
not only more proactively to prevent potential service degradation that
can impact the customer experience, but how do they make
sure that the teams are working off the right information?
How do they think about speeding up the problem identification
(09:13):
resolution cycle? And then of course, ultimately the maturity of
automation of different processes, a problem change, incident management, all
these things are foundational to a great customer experience.
Matt Eastwood (09:30):
All right, I want to pause right there. There are
a lot of insights that we've uncovered into the necessity
of something like Instana. But of course, it's just the
tip of the iceberg. Stay tuned with us as we
try to unpack the complexity of our digital world coming up.
You're listening to Scaling AIOps, a podcast by IDC and IBM for
(09:53):
industry leaders and professionals to better understand how AI is
reshaping the world around us. Again, I'm your host, Matt Eastwood,
along with my cohost Stephen Elliott.
Stephen Elliott (10:07):
We'll be bringing you conversations with industry leaders in the
field of artificial intelligence, who are at the forefront of
some of the technologies shaping how we do IT today.
If you're enjoying what you've heard so far, we ask
that you subscribe to our show wherever you get your podcasts.
Matt Eastwood (10:30):
What we're talking about here, there's really three elements to
this conversation when we think about the role of Instana
in the marketplace. It runs through your system, and it's
clearly providing this level of observability. It is collecting data
to give you a better mapping in terms of how
it relates to the application sets, the workloads that you
(10:52):
manage. And then lastly, it's really about automation. And Steve, I know
you touched on some of this, but I wonder if you
want to touch a little bit more on specifically the
element of how this runs, and the role that really
observability and automation ultimately plays here.
Steve Waterworth (11:10):
Observability is, it's metric logs and data. It's getting important
information out of the application so you can understand the health.
The monitoring side of it is then looking for anomalies
in that data. So if we see a sudden increase
in error or a sudden drop- off in requests. You
(11:30):
want to ensure, from an automation point of view, you
want to ensure that you've got complete coverage. You don't want to
be deploying stuff into production without any observability. So the
automation part of Instana there ensures that nothing's missed. So
the automation has really three benefits. You ensure that nothing's
(11:51):
missed, because it is you're not churning human cycles doing
stuff manually. And of course, you get the accuracy, humans make mistake. One slip
on a keyboard and a misspelling of a tag, and
essentially that piece of information is now invisible. Because when
you search for it, the tags don't match. You get
(12:11):
total consistency with automation, you save time, and you ensure
nothing's missed.
Matt Eastwood (12:19):
And Stephen, as an analyst you spend a lot of
time forecasting and shaping where do you think this will
take all of us. With all this backdrop on what
Instana, what observability allows IT practitioners to do, I'm curious
if you have any thoughts about what the future holds.
Where do you think this could take us in the
(12:41):
future of how we run our businesses?
Stephen Elliott (12:43):
Yeah. This is a great question, because we're seeing lean forward IT
organizations think about both the technology and the operational information
that these solutions can collect, organize, and analyze, as well
as the customer information. So think about information that customers
(13:05):
utilize for, whether it's surveys or just experiential, rating the
satisfaction of the process that they went through with a
particular business. So there's lots of pertinent customer experience information
that you could arguably push into these models to drive
(13:26):
a further broader view of what's going on. Now, there's
no doubt that you think about the future here, and
you think about site reliability engineering. The continued maturation of
DevOps processes. The growing importance of both, not just collecting
the right technology metrics, but being able to articulate, what
(13:47):
are the business metrics that solutions are driving as outcomes, right?
Matt Eastwood (13:52):
I mean, I think what you're really saying there is that
what organizations need to focus on is, how do they
get more of their ITS state and more of their
investment into innovation? And quite a bit less just into
the maintenance of what they have. It seems to me
that data is a big part of this, and we've
touched on the processes and we've touched on the applications
(14:15):
quite a bit. But we all know that organizations are
struggling with all the data, volumes of data that's really
increasingly meaningful to how they operationalize in these increasingly interconnected
worlds. And I'm wondering, Steve, if you could just articulate
for me some of the challenges that you're seeing in
the customer base with the amounts of data that folks
(14:38):
are dealing with, and how that's creating challenges for the
IT practitioners that are working with.
Steve Waterworth (14:45):
Yeah. Certainly, you look at the volume of data, observability data that comes out of
a typical application. It is hundreds and thousands of metrics,
and millions of traces and millions of lines of logs.
It's certainly way beyond the human brain to be able to process all
of that. So this is really where looking to the future
(15:07):
I see more and more reliance there on artificial intelligence
and machine learning to be able to process that data
and understand it. And then when the observability system has
a better understanding of what it is it's looking at,
then that can drive more and more automation. So whether
(15:27):
that's automation in anomaly detection, or automation in actually tuning
the application itself, be that for performance or for your
cloud costs. Because most of these cloud native applications, of course,
are running on a cloud provider. And that little meter keeps spinning. And
because people tend to be a little cautious, so they
always over- provision, and you end up with a whole
(15:49):
bunch of resource that's sitting there doing nothing. But because
it's still driving that meter around, so it still drives
your cloud bill.
Matt Eastwood (15:57):
Yeah. Yeah. And I think he did a good job
of just articulating how cloud is really the operating model
increasingly for a lot of these organizations that layers on
top. It's not a destination, it's very much part of
how people think about building this IT. And I'm just curious,
Steven, if you have anything you'd want to add to
what Steve just said there?
Stephen Elliott (16:16):
Yeah. I think the other piece of this too is
really, forces IT organizations, and whether you're a site reliability
engineer, IT Ops, DevOps engineer, architecture, to really understand who
that end customer is. I think that's why these solutions
are so critical to really driving these revenues, to driving
(16:39):
a brand reputation. Because if your services or digital products
aren't working well, or if customers are having a poor
experience, it's easy to go somewhere else. These are part
of the expansion of opportunities for these different groups, and
certainly for CIOs and their teams to really rethink the
(16:59):
level of information that they need, the importance and criticality
of that information. And then, the types of analytic models
and how they share that across their teams internally.
Matt Eastwood (17:11):
Where I feel we're headed here around automation, and Steve,
I'd like to probe on this with you. Is, you
touched a little bit about what this all means under
the hood. I'm wondering if in your conversations with your
clients, if there's anything that you're seeing them that they're
able to accomplish with the technology that surprised you. Maybe
(17:32):
something that you haven't expected and you're starting to see
more of as folks mature in terms of how they
think about the use of data in IT and the use
of data in business, and what Instana can help them accomplish.
Steve Waterworth (17:44):
Yeah, certainly. So the main use cases are really around
that DevOps and SRE. So we have customers that are
using the data they get out from Instana to then
tweak and tune their application and get performance improvements. So
they can spot the inefficiencies in the code, tune that.
(18:10):
Yeah. Do more with less resource. Again, it goes back
to that cloud. Yeah, you can make it go faster
if you throw a lot of resource at it, but then
that costs you a load more money, and that directly
hits profitability. And what Steve has been saying, it is
all about the customer and the experience that customer's having.
(18:30):
And if the customer is having a bad experience with the
modern online world, your competition is merely a click away.
Your customer will very quickly vote with their feet and
open a new browser tab and go somewhere else. And
that's business lost. So it's very important to know that
your application is serving those requests in a prompt and error-
(18:50):
free manner.
Matt Eastwood (18:53):
When I hear the word automation, it does make me
think a lot about people in process and sometimes the
reluctance that people have to accepting change. So if we
think about the future of observability, and I guess what I'm
trying to get, I want to get a little better
sense of is, how we see this relationship between what's
(19:15):
AI driven and what that AI solution potentially looks like
with the humans that ultimately need to be empowered to
do something with all this information. Do you see something that's
increasingly AI driven, or is it going to be a bit more of a hybrid
between what's happening on the AI side with the human
side of all of this in the IT world?
Steve Waterworth (19:36):
From my personal experience of having worked with a number
of these systems, I generally find that when the AI
first goes in and it starts making suggestions, everybody wants
a manual " Are you sure?" Button. They want a human
in that decision chain. So the AI will spit out a
recommendation and somebody will review it and go, " Yeah, okay. That
(19:59):
looks good." And hit okay. And then after a period
of time when all the suggestions have been good ones,
that level of trust builds up and then they go, "
Okay, well for these things we can turn off the
human part of it. We'll just take away that, " Are
you sure?" bit. And we'll just have the machine go
(20:21):
do it. So, yeah, as always with any new technology,
there's a bit of distrust until it really can prove
itself, and then people become more accepting.
Matt Eastwood (20:33):
As we round out this conversation, let's do that by
focusing a little bit on what comes next. We feel
this future is really around being able to be much
more agile, to be able to scale an organization around
data and make these quicker decisions all across the organization,
actually. But specifically around IT, what do we think is the
(20:56):
future of IT as we build on this conversation around
observability and automation? And I'll start with you, Steve, and then
we'll come to you, Steven.
Steve Waterworth (21:03):
The future in this space is definitely around improved machine
learning and AI. And as that gets better and it
understands application environments better, then that drives more effective and
efficient automation. Which is just going to improve quality, and
basically enables you to go faster with fewer mistakes. Or
(21:26):
as we would say, build better software faster. The faster
you can iterate, then the faster you're bringing new features
and functions to the market and staying ahead of your
competitors. And really, that's what it's all about. We want to
be ahead of our competitors and make more money.
Stephen Elliott (21:43):
Yeah, I think a lot of the cultural perspectives of
operations teams, site reliability engineering, DevOps teams, with the increasing
complexity we've been talking about of multiple clouds and different
app architectures and infrastructures. And even organizational complexity within IT.
These are all things that have to be managed. It's
(22:04):
no longer a choice, you can't ignore it. And so
now, because of the rise of digital first executive teams
and CEOs that are really starting to understand that, frankly,
their technology architecture is probably their business architecture. And so
therefore, that mindset really drives a lot of foundational requirements,
(22:30):
such as analytics, such as automation, such as observability. These
are things that you think about the foundation of a house,
these are really foundations for a great customer experience.
Steve Waterworth (22:46):
Yeah. That's where the observability data is an enabler. We
have a number of customers that have significantly increased their
release frequency because they've got rich and immediate feedback in
data. So they know they can put a release out
and they'll know straight away whether it's any good or
(23:07):
not, so they know whether to roll back and go
forward. And that gives them confidence to go faster and
be more agile. And more and more smaller increments of
improvement to that continuous improvement process.
Matt Eastwood (23:23):
Well, gentlemen, I think that's a perfect place to bring
this conversation to a close. I want to thank the
audience, thank all of you for joining us today and
listening to our conversation. I want to thank Steve Waterworth
from Instana, and Stephen Elliot from IDC, for sharing your
insights and your expertise on the market. It is an exciting
space and it's an exciting time. And hopefully you've gained
(23:44):
something from this conversation today. Thanks for joining us.
Stephen Elliott (23:49):
Thank you.
Matt Eastwood (23:49):
Thank you for listening to our show, Scaling AIOps
for IT specialists for business outcomes. Join us next time
where Steven and I will speak with Oved Lourie, the
global field CTO for Turbonomic.
Stephen Elliott (24:04):
In that conversation, we'll be looking into how best to
manage your resources in IT, especially when there's a lot
on the line. Oved will tell us how it's making
all those applications run better and more efficiently than ever
before. If you've enjoyed this episode of Scaling AIOps, we
ask that you subscribe wherever you get your podcasts.
Matt Eastwood (24:23):
I've been your host, Matt Eastwood.
Stephen Elliott (24:25):
And I'm Stephen Elliott.
Matt Eastwood (24:26):
And thank you for listening to episode one of Scaling
AIOps, a joint venture between IBM and IDC. And we hope to
see all of you very soon.
Did you know that some sources claim the average person
makes roughly 35, 000 decisions a day. That's 2, 000 choices
every hour. That's mind blowing, and even a little bit outrageous.
But what if you have a brain of wires, a
language of zeros and ones? There's a supercomputer in the
(00:27):
Oak Ridge National Lab in Tennessee, dubbed Summit, that dwarfs
our own ability to make rapid calculations. It can spit
out 200 quadrillion calculations in a second. That's 15 zeros,
by the way. That is so fast that if everyone
(00:48):
on the planet we're hashing out calculations every second, it
would take nearly a year to catch up with what
the Summit can achieve in the time that it takes
you to just snap your fingers. We live in a
rapidly changing world, and nowhere is that more apparent than
(01:11):
in the field of information technology and artificial intelligence. Hi,
I'm Matt Eastwood, senior VP of Enterprise IT Research at IDC.
And I'm one of the hosts of Scaling AIOps, artificial
intelligence for IT specialists for business outcomes. It's a joint
venture between IDC and IBM. And joining me is Stephen Elliott, our
(01:33):
analyst on the show, and group vice president of INO,
cloud operations, and DevOps. And we'll be speaking to industry
leaders in a world of AI operations, and learning from
their experiences and their insights in this ever- changing industry.
We hope you find as much value in these conversations
(01:55):
as we did ourselves.
Stephen Elliott (01:57):
This first episode features Steve Waterworth, the technical marketing manager
for Instana, a company that is rewriting how we approach
observability in IT systems. By utilizing tools like Instana,
companies can worry less about the problems of today and
focus on their goals for the future. Let's get started.
Matt Eastwood (02:18):
Just want to make one basic simple observation just to
set up this discussion. The pandemic has clearly raised the
urgency around the importance of transformation in the business. And
as businesses transform themselves digitally, we see this continuum of
interdependent apps and data emerging that stretches from the edge
to the core. And these application portfolios and data volumes
(02:42):
are growing extremely quickly. All of this has an impact
on how we think about managing our IT operations. And
so with that, I'd like to bring in IDC's Stephen
Elliott into the conversation and ask him to explain a
bit about the research that you've been doing in IT
Ops and how the world of online consumer experiences interacting
(03:04):
with businesses, and how that's changed even within just this
last year.
Stephen Elliott (03:08):
Yeah, the research we do at IDC in the IT
operations and SRE and cloud ops team really has a
tremendous focus around these transformational themes that we're seeing specific
to the transition of IT operations, to more of a
automated, proactive set of capabilities. That is not just around
(03:31):
thinking about system reliability and making sure that everything's working
properly, it's really more and more about the customer experience
and the customer journey. And being able to define how
different sets of customers, whether it's internal employees or external
partners, or certainly customers that are using the digital services
(03:51):
to drive revenues and profits, how they're experiencing the company's
products and services.
Matt Eastwood (03:57):
That's perfect. Thank you for that perspective to open this up, Stephen. Next,
I'd like to introduce our guest, Steve Waterworth. And Steve, could
you introduce yourself to the audience and what you do
at Instana.
Steve Waterworth (04:10):
Yeah, I'm Steve Waterworth, I'm technical marketing manager at Instana.
I suppose my main job role is really to take
all the fantastic stuff our engineers do and boil that
down and make it so that normal people can understand
what all the technology is and where the value is
(04:31):
for them. I put it into terms that a business
manager or a senior architect will understand.
Matt Eastwood (04:39):
Perfect. And I know, obviously those conversations in this industry
gets more complicated every day, and we have to have
conversations with lots of different folks that influence the direction
that technology and IT is going in. But Steve, could you just tell
us a little bit more about Instana and exactly what
you guys are doing?
Steve Waterworth (04:55):
Instana is a enterprise observability platform. Observability is generally considered metrics,
logs and traces, and certainly Instana automatically injects metrics, logs and
traces from numerous sources. But what it does then on
top of that, it adds automation, context, and intelligence. So
(05:19):
from the automation point of view, obviously we think we've
already touched on this. Automation is a big drive. There's so
much complexity in modern cloud native applications, and automation is a
big drive there to help manage that complexity. So Instana
has a single agent, it automatically detects what's running around it.
(05:40):
When it detects a suitable target, and there's well over 200
different senses that can connect to those, it will then
connect to that, whether it's a language runtime or a
data store, or a cache, and start pulling out metrics, logs,
and traces.
Matt Eastwood (05:59):
So at its core, what we're trying to achieve here
I think is faster decision- making all across the business. In IT,
but in the business as well. So maybe we could
just dive a little bit more into Instana, and talk
about some of the challenges that we see the IT
professional face today as they work to enable this deeper
(06:19):
and richer decision- making. So Steve, let's start with you
and then I'll go to Stephen on that.
Steve Waterworth (06:24):
Yeah. The challenges they face is the complexity. If we
go back a few years, we might've had a handful
of hosts with a handful of applications running on them.
Now with modern containerized cloud native applications, we've probably got
hundreds of virtual machines, possibly numerous Kubernetes clusters running across
(06:46):
that. And we have thousands of containers running on those
hundreds of virtual machines. So the level of complexity is
many orders of magnitude greater than it used to be.
In fact, it's so complex most of these serious enterprise applications now
are so complex it's pretty much impossible for a human
brain to contain a map of it. And just when
(07:10):
you think that's bad enough, because what we're doing, one
of the reasons for going for container type applications is
agility, is speed. The faster you can get new features
and fixes out the door, the better. Because we want
to do it faster than your competition, maintain that competitive
advantage. So not only do you have something that is
hugely complex, but it's also changing all the time.
Matt Eastwood (07:32):
And that makes great sense. And Stephen, I know you
spend a lot of time talking to IT Ops professionals.
There anything you want to add to that in terms
of some of the challenges that they talk to you
about and what they're facing today?
Stephen Elliott (07:45):
Yeah. No, there's I think a number of things to
build off of the comments here. First and foremost, there's
so many people involved, or that should be involved in
the identification of where a problem exists. And as Steve
mentioned, the increasing complexity of the application architectures, of the
infrastructure architectures, of certainly the use of multiple clouds, most
(08:07):
customers are growing with this and trying to deal with
it. And so you're seeing the need for observability technology
to really bring in and harness all the information that's
being created to help different teams across the IT organization understand,
okay, here's where our problem is. Here's who should help
(08:28):
fix it. And let's go and get it done, with
the potential to actually auto remediate. So you have a
number of tailwinds here that are driving the need for
this type of technology. And it's also a great way
to drive a better data- driven culture. We have a
lot of organizations that call us and we talk to
(08:50):
them about, how do they get more, from reactive to
proactive? How do they get more teams involved in, and
not only more proactively to prevent potential service degradation that
can impact the customer experience, but how do they make
sure that the teams are working off the right information?
How do they think about speeding up the problem identification
(09:13):
resolution cycle? And then of course, ultimately the maturity of
automation of different processes, a problem change, incident management, all
these things are foundational to a great customer experience.
Matt Eastwood (09:30):
All right, I want to pause right there. There are
a lot of insights that we've uncovered into the necessity
of something like Instana. But of course, it's just the
tip of the iceberg. Stay tuned with us as we
try to unpack the complexity of our digital world coming up.
You're listening to Scaling AIOps, a podcast by IDC and IBM for
(09:53):
industry leaders and professionals to better understand how AI is
reshaping the world around us. Again, I'm your host, Matt Eastwood,
along with my cohost Stephen Elliott.
Stephen Elliott (10:07):
We'll be bringing you conversations with industry leaders in the
field of artificial intelligence, who are at the forefront of
some of the technologies shaping how we do IT today.
If you're enjoying what you've heard so far, we ask
that you subscribe to our show wherever you get your podcasts.
Matt Eastwood (10:30):
What we're talking about here, there's really three elements to
this conversation when we think about the role of Instana
in the marketplace. It runs through your system, and it's
clearly providing this level of observability. It is collecting data
to give you a better mapping in terms of how
it relates to the application sets, the workloads that you
(10:52):
manage. And then lastly, it's really about automation. And Steve, I know
you touched on some of this, but I wonder if you
want to touch a little bit more on specifically the
element of how this runs, and the role that really
observability and automation ultimately plays here.
Steve Waterworth (11:10):
Observability is, it's metric logs and data. It's getting important
information out of the application so you can understand the health.
The monitoring side of it is then looking for anomalies
in that data. So if we see a sudden increase
in error or a sudden drop- off in requests. You
(11:30):
want to ensure, from an automation point of view, you
want to ensure that you've got complete coverage. You don't want to
be deploying stuff into production without any observability. So the
automation part of Instana there ensures that nothing's missed. So
the automation has really three benefits. You ensure that nothing's
(11:51):
missed, because it is you're not churning human cycles doing
stuff manually. And of course, you get the accuracy, humans make mistake. One slip
on a keyboard and a misspelling of a tag, and
essentially that piece of information is now invisible. Because when
you search for it, the tags don't match. You get
(12:11):
total consistency with automation, you save time, and you ensure
nothing's missed.
Matt Eastwood (12:19):
And Stephen, as an analyst you spend a lot of
time forecasting and shaping where do you think this will
take all of us. With all this backdrop on what
Instana, what observability allows IT practitioners to do, I'm curious
if you have any thoughts about what the future holds.
Where do you think this could take us in the
(12:41):
future of how we run our businesses?
Stephen Elliott (12:43):
Yeah. This is a great question, because we're seeing lean forward IT
organizations think about both the technology and the operational information
that these solutions can collect, organize, and analyze, as well
as the customer information. So think about information that customers
(13:05):
utilize for, whether it's surveys or just experiential, rating the
satisfaction of the process that they went through with a
particular business. So there's lots of pertinent customer experience information
that you could arguably push into these models to drive
(13:26):
a further broader view of what's going on. Now, there's
no doubt that you think about the future here, and
you think about site reliability engineering. The continued maturation of
DevOps processes. The growing importance of both, not just collecting
the right technology metrics, but being able to articulate, what
(13:47):
are the business metrics that solutions are driving as outcomes, right?
Matt Eastwood (13:52):
I mean, I think what you're really saying there is that
what organizations need to focus on is, how do they
get more of their ITS state and more of their
investment into innovation? And quite a bit less just into
the maintenance of what they have. It seems to me
that data is a big part of this, and we've
touched on the processes and we've touched on the applications
(14:15):
quite a bit. But we all know that organizations are
struggling with all the data, volumes of data that's really
increasingly meaningful to how they operationalize in these increasingly interconnected
worlds. And I'm wondering, Steve, if you could just articulate
for me some of the challenges that you're seeing in
the customer base with the amounts of data that folks
(14:38):
are dealing with, and how that's creating challenges for the
IT practitioners that are working with.
Steve Waterworth (14:45):
Yeah. Certainly, you look at the volume of data, observability data that comes out of
a typical application. It is hundreds and thousands of metrics,
and millions of traces and millions of lines of logs.
It's certainly way beyond the human brain to be able to process all
of that. So this is really where looking to the future
(15:07):
I see more and more reliance there on artificial intelligence
and machine learning to be able to process that data
and understand it. And then when the observability system has
a better understanding of what it is it's looking at,
then that can drive more and more automation. So whether
(15:27):
that's automation in anomaly detection, or automation in actually tuning
the application itself, be that for performance or for your
cloud costs. Because most of these cloud native applications, of course,
are running on a cloud provider. And that little meter keeps spinning. And
because people tend to be a little cautious, so they
always over- provision, and you end up with a whole
(15:49):
bunch of resource that's sitting there doing nothing. But because
it's still driving that meter around, so it still drives
your cloud bill.
Matt Eastwood (15:57):
Yeah. Yeah. And I think he did a good job
of just articulating how cloud is really the operating model
increasingly for a lot of these organizations that layers on
top. It's not a destination, it's very much part of
how people think about building this IT. And I'm just curious,
Steven, if you have anything you'd want to add to
what Steve just said there?
Stephen Elliott (16:16):
Yeah. I think the other piece of this too is
really, forces IT organizations, and whether you're a site reliability
engineer, IT Ops, DevOps engineer, architecture, to really understand who
that end customer is. I think that's why these solutions
are so critical to really driving these revenues, to driving
(16:39):
a brand reputation. Because if your services or digital products
aren't working well, or if customers are having a poor
experience, it's easy to go somewhere else. These are part
of the expansion of opportunities for these different groups, and
certainly for CIOs and their teams to really rethink the
(16:59):
level of information that they need, the importance and criticality
of that information. And then, the types of analytic models
and how they share that across their teams internally.
Matt Eastwood (17:11):
Where I feel we're headed here around automation, and Steve,
I'd like to probe on this with you. Is, you
touched a little bit about what this all means under
the hood. I'm wondering if in your conversations with your
clients, if there's anything that you're seeing them that they're
able to accomplish with the technology that surprised you. Maybe
(17:32):
something that you haven't expected and you're starting to see
more of as folks mature in terms of how they
think about the use of data in IT and the use
of data in business, and what Instana can help them accomplish.
Steve Waterworth (17:44):
Yeah, certainly. So the main use cases are really around
that DevOps and SRE. So we have customers that are
using the data they get out from Instana to then
tweak and tune their application and get performance improvements. So
they can spot the inefficiencies in the code, tune that.
(18:10):
Yeah. Do more with less resource. Again, it goes back
to that cloud. Yeah, you can make it go faster
if you throw a lot of resource at it, but then
that costs you a load more money, and that directly
hits profitability. And what Steve has been saying, it is
all about the customer and the experience that customer's having.
(18:30):
And if the customer is having a bad experience with the
modern online world, your competition is merely a click away.
Your customer will very quickly vote with their feet and
open a new browser tab and go somewhere else. And
that's business lost. So it's very important to know that
your application is serving those requests in a prompt and error-
(18:50):
free manner.
Matt Eastwood (18:53):
When I hear the word automation, it does make me
think a lot about people in process and sometimes the
reluctance that people have to accepting change. So if we
think about the future of observability, and I guess what I'm
trying to get, I want to get a little better
sense of is, how we see this relationship between what's
(19:15):
AI driven and what that AI solution potentially looks like
with the humans that ultimately need to be empowered to
do something with all this information. Do you see something that's
increasingly AI driven, or is it going to be a bit more of a hybrid
between what's happening on the AI side with the human
side of all of this in the IT world?
Steve Waterworth (19:36):
From my personal experience of having worked with a number
of these systems, I generally find that when the AI
first goes in and it starts making suggestions, everybody wants
a manual " Are you sure?" Button. They want a human
in that decision chain. So the AI will spit out a
recommendation and somebody will review it and go, " Yeah, okay. That
(19:59):
looks good." And hit okay. And then after a period
of time when all the suggestions have been good ones,
that level of trust builds up and then they go, "
Okay, well for these things we can turn off the
human part of it. We'll just take away that, " Are
you sure?" bit. And we'll just have the machine go
(20:21):
do it. So, yeah, as always with any new technology,
there's a bit of distrust until it really can prove
itself, and then people become more accepting.
Matt Eastwood (20:33):
As we round out this conversation, let's do that by
focusing a little bit on what comes next. We feel
this future is really around being able to be much
more agile, to be able to scale an organization around
data and make these quicker decisions all across the organization,
actually. But specifically around IT, what do we think is the
(20:56):
future of IT as we build on this conversation around
observability and automation? And I'll start with you, Steve, and then
we'll come to you, Steven.
Steve Waterworth (21:03):
The future in this space is definitely around improved machine
learning and AI. And as that gets better and it
understands application environments better, then that drives more effective and
efficient automation. Which is just going to improve quality, and
basically enables you to go faster with fewer mistakes. Or
(21:26):
as we would say, build better software faster. The faster
you can iterate, then the faster you're bringing new features
and functions to the market and staying ahead of your
competitors. And really, that's what it's all about. We want to
be ahead of our competitors and make more money.
Stephen Elliott (21:43):
Yeah, I think a lot of the cultural perspectives of
operations teams, site reliability engineering, DevOps teams, with the increasing
complexity we've been talking about of multiple clouds and different
app architectures and infrastructures. And even organizational complexity within IT.
These are all things that have to be managed. It's
(22:04):
no longer a choice, you can't ignore it. And so
now, because of the rise of digital first executive teams
and CEOs that are really starting to understand that, frankly,
their technology architecture is probably their business architecture. And so
therefore, that mindset really drives a lot of foundational requirements,
(22:30):
such as analytics, such as automation, such as observability. These
are things that you think about the foundation of a house,
these are really foundations for a great customer experience.
Steve Waterworth (22:46):
Yeah. That's where the observability data is an enabler. We
have a number of customers that have significantly increased their
release frequency because they've got rich and immediate feedback in
data. So they know they can put a release out
and they'll know straight away whether it's any good or
(23:07):
not, so they know whether to roll back and go
forward. And that gives them confidence to go faster and
be more agile. And more and more smaller increments of
improvement to that continuous improvement process.
Matt Eastwood (23:23):
Well, gentlemen, I think that's a perfect place to bring
this conversation to a close. I want to thank the
audience, thank all of you for joining us today and
listening to our conversation. I want to thank Steve Waterworth
from Instana, and Stephen Elliot from IDC, for sharing your
insights and your expertise on the market. It is an exciting
space and it's an exciting time. And hopefully you've gained
(23:44):
something from this conversation today. Thanks for joining us.
Stephen Elliott (23:49):
Thank you.
Matt Eastwood (23:49):
Thank you for listening to our show, Scaling AIOps
for IT specialists for business outcomes. Join us next time
where Steven and I will speak with Oved Lourie, the
global field CTO for Turbonomic.
Stephen Elliott (24:04):
In that conversation, we'll be looking into how best to
manage your resources in IT, especially when there's a lot
on the line. Oved will tell us how it's making
all those applications run better and more efficiently than ever
before. If you've enjoyed this episode of Scaling AIOps, we
ask that you subscribe wherever you get your podcasts.
Matt Eastwood (24:23):
I've been your host, Matt Eastwood.
Stephen Elliott (24:25):
And I'm Stephen Elliott.
Matt Eastwood (24:26):
And thank you for listening to episode one of Scaling
AIOps, a joint venture between IBM and IDC. And we hope to
see all of you very soon.
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