Matt McElwain and Justin Lagern

Matt McElwain: I'm Matt McElwain, and I am one part of the Instana team. Go ahead.

Justin Lagern: My name is Justin Lagern. I'm a Senior Solutions Engineer at IBM Instana, and we'll be demonstrating to you just a little bit about our product, kind of how it fits in the market, and just some of the basics of it.

So just an interesting story before we get going. I have four kids at home, and they asked me where I was going and what I was going to be speaking on. I told them I was going to a Minecraft conference, and I'm the most popular guy at my house right now. So my kids are pretty excited. I'm going to talk about Minecraft today.

Matt McElwain

So Instana was born because we wanted to help customers eliminate visibility gaps as they modernized their IT. And it's a well-known fact that software vendors haven't made any substantial investments in modernizing their tooling in some time.

From 2019 to 2020, we saw nearly a three-fold increase in the number of business channels that were transitioned to digital. So does anybody remember the pandemic? Yeah. Well, one of the positive outcomes of the pandemic is it kind of put that growth into hyperdrive, right? It forced a whole bunch of businesses to do things that they normally wouldn't have done in a very short amount of time. And so we saw roughly 10 years of digitalization in just one year. And that's amazing, and this was all out of necessity because everybody was at home: work from home, homeschooling, and those Amazon orders kept going on your Prime membership, and those Amazon orders kept showing up at your doorstep. So not only did we help Jeff Bezos fund his trips to outer space, but the pandemic also helped to accelerate that growth.

So there are two different terms that are generally confused quite frequently, and that's observability and monitoring. Observability isn't monitoring. Monitoring tells you what's wrong, and observability tells you why. In monitoring, we tell our systems what to look for, and in observability, we remove the telling, and instead we allow the platform to be able to collect the needed information so we can answer that very important question of why.

Justin Lagern

Yep. And to add to that too, monitoring often encompasses a lot of the basic KPIs. You're talking about some of your basic metrics, a few things that you've been looking at for years. While observability really is more focused on the broader aspects of your platform, so more on some of the large-scale things: looking at how your services are interacting with one another, how everything is communicating, and how that's affecting your system as a whole. So instead of looking at individual metrics, you're looking at the whole or the larger picture.

Matt McElwain

So as everybody's on their journeys to modernize, it also increases the complexity of our environments. And that complexity decreases visibility. So as you adopt things like containerization and modern cloud architecture, it comes with a lot of benefits, but it's also more difficult to manage.

What you're looking at here is a chart of a modern microservices application. They can be small, they can be medium, and they can be large. If your tooling isn't keeping up with your development efforts, you end up with a lot of gaps. That's what the white space is on our chart here. And problems with gaps are that you can't fix what you can't see. It's really the main reason why it's so critical that you have tooling that keeps up with you. If your tooling isn't keeping up with that pace, then it's holding you back.

So this is probably my favorite slide in the whole presentation. I don't know if any of you are familiar with the Aberdeen Group, but it's a widely quoted study. This is the impact of one second of application latency. Just one second in page load time results in a 7% loss in customer conversion, and also a 16% decrease in customer satisfaction. Stop and think about that for a moment, on the implications of revenue. Just to give you an example to put everything into context for you: if you have a website that's generating roughly about a hundred thousand dollars a day, and you're hit with that one-second issue, this translates roughly into 2.5 million dollars a year that you're going to lose. So should we be concerned? Should we be concerned with that one second? Yes, absolutely.

Justin Lagern

Yep. Next we'll talk about roughly just how Instana really helps with all of this. Primarily, Instana is built on three pillars of observability. That's going to be your automation, your contextualization, and then finally your intelligent action.

If I can apologize to figure out how to use the clicker. There we go: automation, contextualization, and intelligent action.

Starting with automation, this really comes on several points for Instana. First, we want to make the system as easy to use as possible, and that really comes from just working in the system and in the field. You don't want to have to waste time with your APM tool. Tools like this are meant to help facilitate your product. We want to help your developers, your SRE teams. We don't want to get in the way.

First and foremost, we want to make sure that installation of our product is as simple as possible. Instana operates on a single-agent platform, meaning one agent is installed either at the platform or operating system level. We then automatically detect everything in your system. Where this goes to the next step is not only are we doing this at installation, but we're continuously monitoring for new changes. Let's say, as an example, if you have a new system spinning up or new microservice or adding to an environment, you don't want to have to come back to your tool and then reconfigure all of this. We're going to automatically detect that for you, automatically process what we can, and then, of course, if there is anything we do need to put a little hands-on, maybe set up a configuration, we're going to alert you to that so you don't have to worry about it.

And then that comes with the actual contextual information itself. Like we talked about with the observability versus monitoring, all this information is great, but we need to be able to understand it in context. For us, that comes through a couple different key points, primarily with our data graph. We're looking at everything happening behind the scenes. We stitch all the individual transactions and information from every part of your stack, starting not only on the infrastructure, going into the application layer, but then finally within the actual front end itself.

And then finally, taking intelligent action for each of these. Again, all of this information is great, but if we can't point you in the proper direction, showing you what is wrong and giving you some potential active steps on how to fix that, what value is it to you? So we use a couple different machine-learning and AI technologies to really look at things like the seasonality of your application over time, seeing where each of the transactions are coming from, and if you do run into a problem, helping you cut through the white noise, showing you exactly where you should be looking and what you should be focusing on.

So just a couple of our key differentiators here. First and foremost, we of course have high-grade data fidelity. Like we talked about, we do have one-second metric granularity, meaning we are giving you as much information as we can, as fast as we can.

Next, of course, we're looking at specifically the individual granularity in our tracing as well. We haven't mentioned yet that we actually take a no-sampling approach to all of this, meaning essentially we're not actually cutting through each of your information, deciding what is most important. We will get everything, send it to our backend, and make sure that, worse comes to worst, if you are in, say, a war room or even a troubleshooting scenario, you have the guarantee that we're keeping track of every individual transaction and giving you exactly what you need.

Next, of course, we do have a very predictable pricing model, which we'll kind of skim over here. But essentially we're not charging by individual processes, user bases, or anything going through the system. We charge simply on the hosts themselves.

And then next, of course, we have our automated discovery, as I mentioned before, helping us always give you that contextual information. And then we are also a leader. We were named within the leader quadrant of the Gartner Magic Quadrant this past year, and we continue to grow in that every year. And then finally, we have intuitive UI, which is really built for the future. We don't want you to have to clunkily go through each individual system or try to fiddle with it. We want to make it as intuitive as possible for you.

Matt McElwain

So for everything so far that we've shared in the slides, this is one of the reasons why IBM had their sights on us. They wanted to modernize their product portfolio and look for the gaps in their own observability offerings. They wanted to find somebody that could help correct those gaps, and that's when they came to us, a known leader in the observability space. As you can see, we have varying-size customers. This is just a snapshot of some of the many in various verticals and sizes, and we're very proud, as Justin said, to be up in the upper quadrant for Gartner Magic Quadrant.

So Instana delivers real impact. If we look at some of these figures, these are all based on data that we've collected from existing customers and the feedback that we were given. We've seen improved application availability, quicker resolution of incidents, which we'll get to with one of the case studies that we have in a second. We've seen a 52% decrease in MTTR, 69% in mean time to detection, and plus five on availability observability. Enterprise observability is a core ingredient to deliver on risk mitigation, revenue protection, and efficiency gain.

Some of the other, as far as from a revenue perspective: we've seen customers do three times the number of deployments that they had previously done before implementing Instana. We've seen cloud efficiency, and we've seen a 59% reduction in cloud spend. We touched on efficiency. We've seen improved operational efficiency, and we're fostering that DevOps culture, which is fantastic.

Justin Lagern

One thing I do want to mention on this as well is we talk a lot about these metrics like mean time to detection, mean time to resolution, but it's very easy to lose focus on why that is really important. Obviously, of course, the broad marketing points: it saves money, you're saving time. But look, I was in the DevOps space for the first 10 years of my career. I've crawled through logs. I've gone through all of it. What it really means is it's saving your developers and your SREs time. If they're saving even an hour every day, that's time and effort they could be putting into their actual work, again, not wasting time trying to make your products go, but making sure that they're making new features. They're making sure everything is the way it's supposed to be, and that's the real value of these points.

Matt McElwain

So on to a customer profile. For nondisclosure and everything, I can't give you the actual company name, but this is a customer that we've had since 2019. I personally work with them as their assigned technical account manager. They're currently running two different environments. They have a prod and a non-prod environment.

From an adoption standpoint, this is a fantastic customer because they have gone through and they've created over 200 application perspectives, which I think Justin touched on, but that's our way that we're able to go and give you a logical view of an application that you deem as important: 22 websites, 158 alert channels, 147 custom alerts. The metrics speak for themselves.

So why Instana with this particular customer? My internal champion that I have there, he was hired into the role, and the current tool set that they had wasn't set up correctly, and it was far too difficult to unravel the mess. He was looking to work smarter and not harder. The application that they had in there before was roughly a 700 to 800 thousand dollar investment, and they rarely had people going and logging in to look at data because they couldn't find anything useful.

So he rallied the troops and he conducted a bake-off between Dynatrace, Datadog, New Relic, and Instana. During the bake-off, they felt like they were getting more for less money, and we touched a little bit about our predictable pricing model. It was very easy to deploy. We have an intuitive interface, and it was a fast ramp-up for his users. So it essentially means that he was spending less time training users on the platform, so he could focus on the things that really were important to him and his team.

He was also impressed with the fact that through the sales cycle, they were dealing with technical folks that understand what was going on. We have a robust API, which Chris has done a tremendous amount of work with. When he saw gaps in functionality that would have impacted his existing workflow and how his team worked, he used our API to be able to write a wrapper over the top of it. So most of the functions that he performs on a daily basis, or a good bulk of them, are self-service, which is fantastic. As a result, they've seen broad adoption. They have a lot of users logging in. More people are logging in, and they trust the data that they're seeing, which is a really positive outcome.

Justin Lagern

Yeah. And just to add a little bit to that too, this is something we see all the time with our prospects. As a solutions engineer, I'm on the front end working with new prospects, making sure the system works for them and really fits their needs. What we often see is wide-scale adoption larger than the initial scope for things like the actual POC, and what they're looking at. The reason for that is really simple, right? Instana is intuitive. It's easy to use. Not only does it give you what you need, but it's giving a lot of insight that you really didn't even know you needed before. And all of that in one simple package, again, very easy to use, very simple to set up, just means Instana works.

So we do have a couple minutes. I'd like to open the floor to a few questions if you guys have any, but if not, thank you guys for listening to our talk.