Chrystal Taylor:
Welcome to SolarWinds TechPod. I’m your host, Chrystal Taylor, and with me as always is my co-host, Sean Sebring.
Sean Sebring:
Hello, Chrystal.
Chrystal Taylor:
Hello. And today we’re seeking an answer to a question, a major question in the industry, which is basically what is the difference between monitoring and observability? And to help us explore this topic we have with us, Jeff Stewart, our GVP for product management here at SolarWinds. So Jeff, can you tell us a bit about yourself?
Jeff Stewart:
Hey, Chrystal. Hey, Sean. Thanks for having me. Excited about this topic today. Sure. Just a little bit about my background. I’ve been at SolarWinds for almost 15 years now in a mix of different roles, mostly between our solution engineering group and our product management or product strategy groups. Prior to that, I was a network engineer for a university who used SolarWinds’ various offerings and built a relationship with the company and joined in 2010.
Chrystal Taylor:
Awesome. So you’ve been around SolarWinds for quite a long time.
Jeff Stewart:
I’m definitely getting some gray hairs.
Chrystal Taylor:
I’ve also been around SolarWinds for a long time and we’ve been living in this monitoring space and I feel like nmonitoring is a great place to start for us because it’s an established part of the industry. It’s an established thing for every part of the industry, whether you’re in networking or systems management or cloud management or anything, you have some form of monitoring in place and that’s something that you need in order to accomplish basically anything if you don’t want to be left hanging in the wind because you didn’t know something was going on. So let’s dive into what monitoring is, and I think that the simplest explanation for that is with you asking a question to a device, piece of software, whatever, a piece of technology, and you’re getting an answer back and you’re keeping track of that and that seems like the simplest answer. Would you have anything you want to add to that? What monitoring means to you?
Jeff Stewart:
I think that overall, I think that that’s a pretty good definition, really just understanding, like you said, reaching out to a device, understanding if that device is responding, maybe pulling back some metrics to understand utilization of certain things, but really just kind of a query and a response. And from there making a determination of what’s happening.
Chrystal Taylor:
SolarWinds has been in the monitoring space for 25 years, so we are pretty well established there. And I think that over the past couple of years as we’ve been making the shift to observability, there’s been a lot of questions about why we’re doing that and what it means, and if we’re walking back from doing monitoring, which of course we’re not. Anyone who’s used our products will know that we didn’t just throw everything away. So I think that it’s really an intriguing proposition to talk about what observability is, and primarily I think it’s really interesting because there’s not really an industry-wide accepted definition for observability. It’s a term I think that got borrowed from another industry and then it has taken on a life of its own. And I have a few different definitions that I’ve pulled from across the industry, other observability vendors, and then we have our own, obviously our own thinking on what observability is. And you and I, specifically Jeff, have talked about this before for SolarWinds Day and that kind of thing, what observability is. So I will ask you I guess to start with what you think observability is.
Jeff Stewart:
Okay, yeah. For me, I look at observability as really the evolution or maturing of monitoring, taking it to the next level. So instead of just querying a device and getting a response, it’s a more mature version of that to make better informed decision making or potentially better understand the overall performance of an environment or an experience for your users. And so really I think it’s more so about understanding in-depth how things are performing.
Chrystal Taylor:
Sean, do you have an idea of what observability is? I know observability is not your usual space, so I’m curious to hear your take as kind of almost an outsider’s perspective of what observability is.
Sean Sebring:
And you’re not far off. Outsider is not what most people call me, but no, no, no. I was thinking about how my views would be always trying to force the perspective of how it relates to what I do. And so an initial thought was monitoring is a form of reporting, but it’s more of a talkative reporting. I’m saying observe this metric and then if something happens, then talk back to me. And so when I think about what is observability, I agree with Jeff, evolution is actually a great way to do it, similar to how computing evolved in the fact that multiple processes went from running, whereas it used to be a single process.
Monitoring in the very same way, now we can correlate multiple monitorings into an observability so that we’re not just saying, you device, what are you doing? We can say, you device, you’re part of a greater service. What is the service’s health? And so we’re talking to the service more than just the individual node and its specific metric. So it’s to me, I agree it’s an evolution and there’s a lot of crazy technology behind it and I can see how a lot of… Man, we never don’t bring AI into this, but I can see how AI is going to play a massive role in the continued evolution, the rapid evolution of what observability can do.
Chrystal Taylor:
Well, you say that and I think that I agree. I know we never don’t bring AI into everything, but I think that it is… We can’t ignore it. It is there and it’s necessary at the rate that everything is expanding and evolving. If we relied on individual people to look through all of the various data, well, we’d be where we were 10 years ago, which is that you miss a lot of stuff and you’re reactive because you don’t see it before it happens. And I think that we need the computers to do some of that work for us. We need it. There’s not really an option to not have the computers do some of the work for us, so we as humans can’t possibly process-
Sean Sebring:
I got my best resume. I mean, I don’t have a resume out there right now, but…
Jeff Stewart:
I think that’s also led to this explosion in the market around how much data can I collect about my systems, my infrastructure, the performance of an experience. And so that vast amount of data is only going to increase, and I think that’s where we’ll talk about AI again. I think that’s where AI is certainly going to help in the observability market is as we start collecting data from various places and build this wealth of a data repository or data lake, we’re going to need AI to help us really be more efficient with understanding what’s happening in the environment, why is it happening, how do I prevent that moving forward and maybe even get into some more predictability of performance in the environment.
Sean Sebring:
You bring up something that I think AI will also play a big role in until there’s more unification of this and that’s the telemetry. AI can help with a lot of that data translation, whereas if we’re collecting a bunch of different data from different objects, we’re going to get a lot of different languages. And again, I think AI… What do you think, Jeff, in that? And do you think that in the end the language will just be AI? I mean trying to make a common language is a huge part of what makes things not work as well as they could or should today. So what are your thoughts around that?
Jeff Stewart:
Yeah, I mean I definitely think having a common language, a common understanding is an area where AI will help us. Again, especially as we think about these different data sets that are coming together, how do we enrich those data sets to have that common understanding of those different data sets and what’s happening? And so I think that is potentially a great area of application for AI moving forward.
Chrystal Taylor:
I am intrigued by what you said there. It’s basically what we’re talking about is seeing how all of the data is connected without… You earlier, I think Sean, you said something about looking at the system as a whole and basically that’s what observability allows you to do that monitoring previously didn’t allow you to do. No matter how many dashboards and reports and things that we built in the past that were more manual, they required a level of and still do, they require a level of understanding of your own system that is getting surpassed by the rate at which we acquire new technology and the rate at which it is evolving itself. When you update a switch, you might get new metrics and things that you didn’t have before. You might get more logging information that you weren’t prepared for before. So those dashboards that you may have made manually or reports that you made manually or connections that you had to make manually are immediately out of date every time you do some kind of an update or every time there’s a new vulnerability out there or every time there’s some new thing happening out there, which is constantly and all the time.
The rate at which technology changes is only increasing. And I was sitting here thinking you were talking about data lakes and processing them and large amounts of data and things like that, Jeff, and I was thinking about, I don’t remember where I saw it, but there’s a historical metric somewhere talking about the rate at which we process data and the big jumps that it’s made. Maybe in 1980 it was a gigabyte a day and then by where we’re at now, which is insanely large amounts, petabytes, probably hundreds of thousands of petabytes a day and it’s only getting faster every day and it’s just such an exponential rate that we could not possibly keep up as human beings. We’re not able to.
Jeff Stewart:
Yes, it’s crazy and I love that you bring the pace of technology advancement into the conversation because if I look at the last five years compared to the previous 10, things are accelerating at a rapid pace. Complexity in our space is growing like crazy. And so our ability to really stay on top of that and understand how we manage that complexity, it’s a real challenge for professionals in our field. And so I think that’s where application of observability, application of AI can really assist in that space.
You also mentioned something about, you didn’t directly say it, but it made me think of a conversation I had yesterday with an analyst and it was around how IT is still somewhat siloed or fragmented. And even in this conversation, Sean is an expert in the ITSM space and service desk, and my background is network engineering and Chrystal, you have your background and so as IT professionals, we could be experts in that certain area and have a good understanding of how things are performing, but if we think about the larger picture pulling that all together, we would have to come together to really understand that. And I think that’s also what observability is doing in the market is pulling all of that together.
Sean Sebring:
I like the way you put that. I look at it from an analogy perspective is each department reports up to a different level of leadership and then eventually we’re trying to make a big picture at the C-suite or a CEO level. We’re all trying to feed up to make sure we’re accomplishing the right vision.
Chrystal Taylor:
You’re saying that Sean-
Sean Sebring:
I would never collaborate with you guys. I’m staying with my title.
Chrystal Taylor:
Never. Absolutely not. No, you’re saying that actually it’s just making me think about what we were talking about earlier, which is the normalization of data, which is having it all speak the same language. It’s exactly the same thing as what you just mentioned. The systems engineer and the network engineer don’t necessarily speak the same language. Their jargon is different. The technical things that they’re talking about are different and the way that they relate to each other, they think about it differently. And so you need that next level up to start putting those pieces together in a way that makes more sense. You need people to translate the jargon from each area in order for it to make sense. And you just mentioned Jeff, that’s sort of what observability is helping you do as well. There is a school of thought though that, and you mentioned this earlier, Sean, of the normalization, you lose something sometimes. There’s a school of thought that you’re losing things and I’m thinking about SIEM tools which work hard to normalize log data because it looks different from all kinds of different places.
Going back to the same thing, a network device sends its syslog and its syslog looks a certain way and it looks very different than a Windows event log. You’re looking at things very differently and all the data comes in different ways, so you need to normalize it in order to process it, but sometimes that normalization means that parts of the data are left behind almost. They’re not processed necessarily in the same way. And that gets better as we go. We continue to learn and we continue to change the way that that normalization happens. But do we think that that is a risk with moving to something like observability that we lose out on any insights because it doesn’t have the same depth level?
Jeff Stewart:
I think that certainly a risk that exists. My hope would be that the systems, the observability offerings are intelligent enough to understand precisely what data they need as they normalize things. And I think that’s where expertise in the market and how you develop the offerings is critical, but certainly things could get overlooked. And so I think that’s maybe a risk that you have to think about as you implement your offering. And I think that may be another topic that we touch on later is operational resilience and how do you identify that risk and is that risk acceptable in your environment? I don’t know, Sean, what do you think about that one?
Sean Sebring:
So this is actually accidentally a perfect segue for what I wanted to talk about because operational resilience might be the term for this, but again, I come from my perspective and so when I think about things like this, we just expressed that technology is changing at an insane pace. Processes themselves, when I say processes, it could be frameworks as practiced, they change much less rapidly, maybe more so than they used to. We’re not as fixed as professionals to say let’s keep doing things the way we used to, but they still change much slower in my observation than the technology is, because, again, technology is just blowing it out of the water. So it makes the development of something like a framework or a practice guide for how should we be doing this? It makes it so much harder because the technology is moving so insanely quickly.
But if we’re talking about unifying languages or setting standards for practices, I feel like frameworks are almost necessary, but it’s going to be something that’s hard to keep up with. And so with the term of operational resilience, it sounds like it could be the ITIL of moving things forward, because, again, I just looping it back to how I perceive things, I’m like, what is the best practice for this? And I think it’s being written just a couple steps behind the development of the technology rather than ahead of the technology. Whereas typically you identify a need and build the technology. Now we’re building the technology and we’re like, how should we even be using this? So I feel like we’re a little bit behind, and so I am curious. Operational resilience, it’s something new, but I’m curious how a framework or a practice if this is going to be maybe a starting point for that.
Jeff Stewart:
Yeah, I think absolutely it should be. I just want to hit on your point there around where the technology’s at and where we’re at. And you see this as you engage with folks in the field, they’re going through implementation of something. It could be an observability offering, it could be something else, but based on a problem that they’re trying to solve at a point in time without thinking about future problems potentially. And I think the pace of technology is increasing those future problems that they need to be thinking about. And so can a framework help as you implement certain things to be more ready for future changes or future problems? And I think operational resilience, I think there’s a framework around that, that I think can help with some of that.
Sean Sebring:
One thing that, speaking from framework perspective though, the thing I like about the way it’s being developed so quickly right now and almost in a thousand different directions is the level of innovation is able to be so much higher because we’re not thinking in the mindset of stick to the framework. Instead, there’s more innovation taking place potentially because people aren’t fixated on, oh, I should be doing it like this in this direction. So there’s potential risks there with frameworks, but frameworks are also necessary to help, especially smaller or emerging or developing organizations or software, things like this, or even to get them started on the development of something new and innovative. But I think it’s really neat just to think about this. Yeah, Chrystal, I totally was interrupting you there. Go ahead.
Chrystal Taylor:
It’s fine. It’s fine. We do this all the time. It’s normal. I think that that’s really interesting how you just phrased that. You kind of danced around saying it, but you’re basically saying that the framework has to be adaptable. And that typically in my experience is not how people who are very framework-centric think about frameworks. They don’t want them to be adapting. They want them to-
Sean Sebring:
Hey, I’m on the call, be careful.
Chrystal Taylor:
This is the framework and you have to work within that. So it’s an interesting perspective. I do want to be clear though that I was being a devil’s advocate earlier, and I do believe that observability reduces your risk of missing things. I didn’t talk about it earlier, but I do think that that is true because as I mentioned earlier, human beings are much more capable of bypassing things than the computers. But I think that whereas operational resilience, what does that mean to us? We haven’t really defined it. And I think that thinking about the system as a whole rather than the individual parts, if we’re talking about going back to our earlier premise, you’re talking about monitoring is a level of we’re looking at the individual parts. I’m a network engineer, I need to know how my network devices are doing. If the network device says it’s fine, it’s not my problem, it’s someone else’s problem. Hand it over the fence to the systems administrator or hand it over to the DBA or whoever, like the age-old blame game, it’s not my problem. I’m not part of it.
Observability takes a little bit of that away because you’re looking at all of those parts in one place that you can say, this is the whole system from beginning to end. We’re looking at all of the layers of the parts of the process. So for instance, if it is your web store, for instance, if you’re an e-commerce site, you have an e-commerce site and you have all the way from the backend database and you’re watching through all the different various layers of servers and the network that is taking out of your, wherever the servers are hosted, if they’re hosted online or in the cloud or on-prem or whatever, all the way through to how your website is performing to how your users are using your website, you get to see the full picture. And I think that observability pulls that together and helps reduce missing those things.
And then we’re talking about resilience and I feel like observability contributes more to resilience than monitoring even did in the past, which would have been more… We’ve been talking about being behind the technology and we’re in a state of almost reactive versus proactive. And this is a long-standing thing in monitoring where you have reactive and proactive monitoring where you can ask a question and get an answer back from a device or a piece of software or whatever. And that’s active monitoring, but there is passive monitoring, which is typically like logs and net flow and that kind of thing where you’re getting it all the time. And I feel like we are now that piece of technology where we are more reactive than proactive and I don’t know if we’re going to get ahead of it anytime soon. We’ve seen this already with legislation. We’ve seen it already with regulations. We’ve seen it already with other frameworks like security frameworks are trying to catch up with how fast the technology is evolving and changing and moving. All of these things are playing catch up. So do we think that building a case towards operational resilience is something that we can effectively catch up to the technology? That’s a question that’s rambling, but I got there eventually.
Jeff Stewart:
It’s a great question. I don’t know if we’ll ever catch up, but when I think about operational resilience and how I think about a business’s ability to respond to and to recover from disruptions. Now that can apply to IT, that can apply to facilities, that can apply to many parts of your business. And also I think there are varying degrees of how good you are at operational resilience. And so even with just monitoring and being reactive, it’s your ability to respond and recover to that disruption. And so I may have received a notification that a device is down, and so what steps do I take next to go recover? Now if I’m more mature or more advanced, maybe my observability offering is potentially predicting things that are about to happen and I’m getting ahead of that. And so I’m a bit more resilient or faster to identify issues and recover from issues leading to less disruption in the business. And so I think there’s a scale in operational resilience and depending on how good you are at applying these frameworks, identifying areas of risk and planning and practicing for those disruptions will determine where you are on that scale of maturity. And I do think you have to continuously evolve and learn within the framework and be a bit flexible, which we talk about people that are into frameworks and process sometimes have trouble being flexible.
Sean Sebring:
I think the most important thing you said to me was practice. Literally that’s the biggest takeaway. And I’m an analogy guy. I’m going to do it. If you guys want to pick it apart or say yes, this piece, no, that piece, totally fine. But I’m thinking of observability now as we’ve continued the conversation, and I’m going to try that word one more time. I’m thinking of observability as big brain control versus what if each individual organ had its own brain. So in the way that we’ve been handling each thing right now to have the system which is us run, we were operating more like each organ had its own brain and it would say, here’s what’s wrong with me and I’m going to fix just me versus if it was being observed from up here. And you even talked about predictive, my eyes can see something that’s going to injure my hand and my hand knows to brace or react in a different way because of that observation.
So the ability to have a more holistic view of things and know where to send the resources is going to help with that operational resilience, which is just survival in our case as bodies. But again, they have to work together. I need these systems because we’re filled with systems as creatures, these systems all need to work together. And if one system’s not talking to the other, that’s a huge problem, and that happens in our bodies as much as it happens in IT in an organization. So again, analogy there, I’m just like what this is providing is just a higher level observation, not to put the term in there of everything that’s going on in order for us to stay more resilient.
Chrystal Taylor:
I like that because it also leans into what we’ve talked about in the past, which is that you can do things to improve your resilience as well. So if we’re still talking about the bodies analogy, you can exercise, you can do…
Sean Sebring:
Practice!
Chrystal Taylor:
… brain puzzles, you can do… There are things to strengthen your resilience. You can do activities which increase your reflex speed and things like that. There are things that you can do to increase your body’s resilience and I think the same is true for your systems. If you think of your entire business as a system, every part of it. Jeff mentioned several other parts of the business besides IT, facilities and HR and sales and all these parts that in IT we don’t like to think about and pretend like they don’t exist, they’re all parts of the whole. And if one part is failing, one part of your body is failing, then the rest of the parts are also going to fall behind. That’s just how it works.
That’s true in a business as well as a human being. If one part of the organization is not doing well, then the rest falls apart. And if we are looking at observability and other things, I know there are methods of monitoring and observing how things are behaving, how processes are going in HR and facilities. You have processes and procedures in each of these places and how well those things are doing, ways to measure that in some capability, so pulling all that information together into one state, and we’re not quite there yet, but I think that we could get there in the next 10 years where we really are, maybe less than that considering how rapidly things are expanding. I think we could eventually get to that state where it is all really integrated and we can see, oh, the Wi-Fi router on floor two is out, the entire sales floor is going to be unproductive for an hour until they get that fixed or whatever. If there are ways where we can start tying that data to, a) it ties it to real business use cases where your business will have to care about things that maybe you care about.
If you think your Wi-Fi routers are behind or need to be updated or whatever, and you’re not getting the business use case, that’s a way to make your business use case, apply it to something that makes the business money, you immediately will get more attention. But I think it’s really an interesting thing to think about as we expand and improve on operational resilience and observability and keep on expanding those things to encompass the entire business and not just our IT infrastructure.
Jeff Stewart:
And I think some of that is already starting to happen in the market and in the industry. Going back to earlier in our conversation and the number of data sources and objects or areas that we’re collecting data from, we’re starting to see a real desire to tie all of that data together to be able to focus on business outcomes. What is the impact of this in terms of a business outcome? Is it good, is it bad? If it’s bad, how do I improve that? And so I think we are starting to field more requests from different areas outside of IT than I’ve seen in the last couple of years, but about them getting their data set into a place where it can be leveraged.
Sean Sebring:
So is it now far enough in the conversation that we can put Jeff on the spot and say, so what does observability mean?
Jeff Stewart:
I don’t know. Let’s start with Chrystal. What does observability mean?
Sean Sebring:
Nice, nice.
Chrystal Taylor:
What is observability? I think of observability as a state rather than a thing. Monitoring is also a state, it is an active state if you are monitoring, if you are observing things. It is a state of collecting data from a bunch of different sources for observability. It’s collecting data from a bunch of different sources, however you do that, actively, passively, or however you need to. And seeing the state of the entire thing, being able to predict how things are doing. Being able to say, I’m looking at the entire picture and there’s my problem. Hopefully before everyone’s complaining about it. That’s always been the goal in monitoring. I’ve been working in monitoring for almost 15 years and that’s always the goal. Prevent rather than just react.
We need to be able to react, but the reality is that our businesses would much rather us prevent problems than to just react to them because any major outage that you have in any company is going to cost that company money. So if you can prevent them, if you can somehow say, this is the state of this thing and in six months we’re going to need more storage or we’re going to need to add more VMs to this cluster or whatever the case may be. We’re going to add more containers, we need to add more services, we need to pare this back because no one’s using this anymore, going the other direction. If you have that ability, then that’s what you’re looking for.
So observability for me is a state of being. You have visibility into everything that you need to have visibility into, and the software hopefully is helping put together the pieces so you’re not having to do that mental work yourself. And also the reality is that I don’t know all of the things that exist in my infrastructure. I might have forgotten about something. This is why we do scans and discoveries and that kind of thing. Someone might bring a device in and plug it in somewhere and I wouldn’t know about that. Some other team isn’t going to inform you when they decide to stand something up necessarily. So being able to get that information and have that in front of you and be able to say, this is the state of things in my infrastructure, in my whatever you want to call it. That’s what I think observability is. Really long answer.
Sean Sebring:
Five slides by Chrystal Taylor on the definition of observability.
Chrystal Taylor:
I could do it.
Jeff Stewart:
Here’s my one-liner slide. One slide. The ability to understand the quality of experience of a service that we’re delivering.
Chrystal Taylor:
Oh, I like that.
Jeff Stewart:
That’s how I think of observability.
Sean Sebring:
That’s brilliant.
Jeff Stewart:
And the more data points or the better- the ability that I have to get this information into a single place and correlate it, the better I can understand that experience that I’m delivering on my services. And that service could be ecom, it could be an internal service for my stakeholders here at SolarWinds, but I want to ensure that that experience is delighting my customers internal or external all the time. And so I think observability is a very powerful tool that allows me to understand that experience and what that’s like. And then help me really identify when that experience is not great, and specifically what is causing that experience to not be great, and how do I fix it? How do I resolve it very quickly and even in an automated fashion? Maybe that wasn’t one slide.
Chrystal Taylor:
That was great.
Sean Sebring:
No, no, no. The quick answer-
Chrystal Taylor:
It’s one slide, but you talk for five minutes, it’s fine. That’s how I do things. I’m a minimal slides person.
Sean Sebring:
I wasn’t trying to overly agree with Jeff, but I love it because that to me, if we go back to the very beginning of the episode, we said how does it relate to monitoring? Observability is an evolution of monitoring. And I think that we could, regardless of what the term would be, monitoring is saying, you component, tell me about you. And then observability as an evolution is saying, all of you, components are part of a service. Service, tell me about you. And I’m super curious for what the new term’s going to be for the evolution on this where we say organization, you’re comprised of many different services, tell me about you organization. It’s another layer, and I think that evolution will happen infinitely more quickly than we went from monitoring to observability.
Jeff Stewart:
Yeah, totally agree.
Chrystal Taylor:
I want to ask a specific question. So I think what’s really interesting is I’ve been looking at these other definitions that people have for observability. I think it’s really interesting to think about when observability came on the scene in technology, it was largely or only used basically for developers. It was developers getting observability into the state of their code without having to make code changes. Obviously we all agree that it has changed since then. Maybe when they originally came on that didn’t make a lot of sense for it to just be for developers. So Jeff, I wanted to ask you for people who are still out there and they think that this is only for developers or DevOps professionals or SREs that are owning their own applications, what do they get out of observability if they’re not those people?
Jeff Stewart:
It can’t just be about that in my opinion. I think it has to be, again, just taking it up to, or we’ll take it back to what Sean said, it has to be about the organization. And so while there can be observability that’s very focused and narrow on certain areas, I don’t think that gives you the organizational perspective of how things are going or how things are performing. And so yeah, you can say observability is DevOps, originated with DevOps, that’s great, but I think it extends well beyond that. And I think that if you just look at the market and where it’s being applied and how it’s being used, I mean it’s much bigger than I think it was originally defined as.
Sean Sebring:
I’d say just like service management started off as an IT practice and has evolved into more of an enterprise, every HR provides services they need to manage those. Just like that took place. We could say that where it started with IT here, maybe observability did start with DevOps and Dev because they were already more service-oriented when it came to what they were keeping an eye on. Because to make an application run, you have to manage several components, whereas if you’re just in the network side, you’re just looking at your components. So for them, there was the need to create a framework and a practice around to make sure my app is running. I’ve got database, I do also have network. There’s a handful of things. So they were a little bit more maybe service-oriented, so it’s wonderful it came from them. That doesn’t mean that’s what it is for, just like service management isn’t exclusively for IT, especially not any longer. So I think it’s great that it started there. But yeah, I agree with Jeff. It should be adopted across the organization at this point.
Chrystal Taylor:
I think that the short answer then is stop gatekeeping good technology. Let other people use it for whatever they want to use it for. I think that-
Sean Sebring:
They just want their patents, you know?
Chrystal Taylor:
Yeah, right. Why get upset? That’s not how you use service management. That’s not how you use observability. I don’t think there’s any room for that in technology realistically.
Sean Sebring:
But my framework!
Jeff Stewart:
Observability for all.
Chrystal Taylor:
Yeah. If you can see a use for it, I think it only helps everyone else if there’s some new use case that comes up for it and then we’re like, I never thought of doing that. That’s really cool. Didn’t know it could do that. Let’s explore that some more. How can we apply that to other things? I think it only helps technology when we open the gates and explore what the possibilities are.
With service management, I know exactly what you’re talking about, Sean. We have been talking about it for a few years now of expanding it to other departments and stuff like that. Other places have processes and things that benefit from service management capabilities. And the same thing is true for observability. I mean, I mentioned it earlier. I foresee a future where we can do that, where we can say, the sales floor is doing this this way, and every laptop that’s over there, this one’s running slow, we need to do something about that. It’s impacting productivity, whatever the case may be. I foresee a future where that’s true, where we can look at the whole state of the whole business, and I don’t think we’re that far away from it.
Sean Sebring:
I agree. It’s all data correlation. Data.
Chrystal Taylor:
Data.
Sean Sebring:
I don’t know if that was the same voice I used earlier. We’ll see.
Chrystal Taylor:
Well, I think this has been an interesting exploration really into, really the state of data and how that affects us and how that affects monitoring, observability, and then now operational resilience and how we can improve our state of operational resilience, because really that’s what we’re all looking for.
Thank you so much, Jeff, for coming on and sharing your thoughts and your expertise with us. I appreciate you joining us today.
Jeff Stewart:
Thank you very much for having me. I enjoyed the conversation.
Sean Sebring:
Thanks, Jeff.
Chrystal Taylor:
Thank you listeners for joining us on another episode of SolarWinds TechPod. I’m your host, Chrystal Taylor, joined by my co-host, Sean Sebring.
Sean Sebring:
Thanks Chrystal.
Chrystal Taylor:
And if you haven’t yet, make sure to subscribe and follow for more TechPod content. Thanks for tuning in.
