SolarWinds Tech Predictions for 2022: Observability - SolarWinds TechPod 058

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It's a new year, ripe with new trends and technology. Join our Head Geeks as they dive into their 2022 Tech Predictions, beginning with observability – a term becoming prevalent among tech pros. Listen in as they share how cloud investments; the rise of AI/ML services; and the re-evaluation of tools, technology, and processes are driving the observability conversation.  Related Links:
Thomas LaRock


Thomas LaRock is a Head Geek™ at SolarWinds and a Microsoft® Certified Master, Microsoft Data Platform MVP, VMware® vExpert, and former Microsoft Certified Trainer. He has over… Read More
Liz Beavers

Guest | Head Geek

Like many IT professionals, Liz’s entry into the tech industry was unconventional. With plans to pursue a career in public relations, Liz’s career quickly took… Read More
Sascha Giese

Guest | Head Geek

Sascha Giese holds various technical certifications, including being a Cisco Certified Network Associate (CCNA), Cisco Certified Design Associate (CCDA), Microsoft Certified Solutions Associate (MCSA), VMware… Read More

Episode Transcript

Announcer: This episode of TechPod is brought to you by SolarWinds THWACKcamp—our free, annual, digital IT learning event. Join us March 2 and 3 for the 10th annual SolarWinds virtual IT learning event. Register free at

Thomas: Welcome to SolarWinds TechPod. I’m your host Thomas LaRock, SolarWinds Head Geek. On this episode, we will review the SolarWinds tech predictions for 2022. Every year, the Head Geeks at SolarWinds compile a list of their upcoming tech predictions with varying degrees of accuracy. This year, we couldn’t fit all of the predictions into one episode of TechPod, so this is a two-part series and we will dig deeper into each of the Geeks predictions and why they made it. Joining me for part one, focusing on observability are SolarWinds fellow Head Geeks Liz Beavers and Sascha Giese.

Sascha: Hi, guys.

Liz: Thanks for having us.

Thomas: So, let’s just dive right in and we’ll start with one of mine, and that is a prediction around cloud investments which on the surface you might say to yourself, “Yeah, of course people are going to spend money on cloud.” But this prediction is more about a focus on how the money is going to be spent. In other words, starting to examine where your cloud dollars are going. Think of it more not as a cloud spend, but think of it as managing cloud costs. I think we can all agree that money is flowing into the cloud, but do we have any ideas who is actually tracking that spending. And Sascha, I know you have some thoughts around cost control that you might want to share.

Sascha: Well, yeah, I mean, in each cloud system, you find those cost control center, whatever I call them, the fancy names. Problem number one is they are not easy to find. Problem number two is they are not easy to understand. And the thing with cloud services is they have tiny, tiny costs. They have a megabyte of this and the CPU core of this costs a few cents. But it’s very difficult to understand what does it cost in the end so you most likely book services without an understanding of what they actually cost. At some point, you might already be in production or half of your production system over in the cloud and it’s too late. You have to swallow the pill at that point, right. And cost control is really, really difficult for someone in IT because they’re not used to this. Usually you buy your gear and you put it in a data center and that’s it. Now, you suddenly deal with subscription so it’s a difficult topic, I think.

Liz: I absolutely agree with that, Sascha. I think, too, on the heels of 2020, these teams had this rapid pivot and this need to align their resources digitally in order to support the changes in their workforces. So now we’re watching teams go through and actually catch back up and try and contain those costs. So, they need to go back and understand: How much are they actually using? What are the frequency at which these resources are being consumed? And understand again the size and volume of those resources. So, while it’s critical to do, I think we’re in a state of catching up in order to continue to propel forward in 2022.

Thomas: Liz, staying with you for a second, is there any sort of optimization techniques or strategies that you might recommend for people that have done that as you’ve said, they’ve made this hard pivot and maybe now they need to start tracking.

Liz: Absolutely. I think we’ll talk about that a little bit later too, in terms of some of our tool consolidation and evaluation. But I think that that’s the key part in terms of when you’re adopting the cloud or even when folks have migrated. Go back through and understand: How are you transitioning? Does it align with broader organization goals? Is it ultimately achieving those objectives or not? One phrase that I like to take from ITIL ideology is start where you are and really understand what’s actually being practically in use? Where can we do better? Where can we consolidate in order to optimize the resources that we have on hand? So really, a strategic review in my opinion.

Thomas: I like how you mentioned, we’ll talk about it later. It’s amazing how these predictions segue into one another. It’s almost as if, I don’t know, we talk.

Liz: We planned it.

Thomas: It’s almost like it was it was planned. Sascha, with regards to this spending, I was wondering if you’ve seen the same thing that we’ve kind of hinted at here, is kind of a rise of a, say, a handful of new tools coming into a company as a result.

Sascha: Well, well, there’s obviously a lot of new tools coming in, but I would like to come back to Liz for one more second because there’s a major risk, which we might see in cloud scenarios, and that is when stuff doesn’t perform the way it should perform. It’s so easy to just throw more resources on it. Okay. And then you just buy, you rent a couple more CPU cores, whatever, whatever, whatever, instead of spending time and optimizing stuff, right? It’s easier to just, “Hey, yes, here’s another thousand dollars, whatever. Surely it will run better, right?” That’s one thing.

Sascha: But yeah, in regards to tools, we will see a lot of new tools that make hopefully good use of new technologies, which we’ve seen in the past. I mean, look, Tom, your favorite topic is usually anything with data, right? And today I read something interesting. There’s a difference between data management and data collection. That’s actually a quite interesting statement. Many companies just collect and collect and collect and they don’t really know what to do with the data. Just some of them start understanding that the amount of data they collect is kind of a product or a service for itself and can be monetized so they need tools to make that work, or at least some initiatives to make this work. I don’t want to go that DataOps path. That’s not a topic for today, but that would be one of them. But yeah, we will see machine learning more than before. I usually don’t like to say AI, artificial intelligence. It’s so, I don’t know, it’s such a marketing statement, but we are kind of there yet. At least we see good use cases, I think.

Liz: I think on top of needing the tools to optimize, as you were mentioning as well, Sascha, I think we’re also reaching a point where we’re starting to really delegate a responsibility to retain and maintain our cost and our technology initiatives. So really, having that set and defined set of responsibilities for how they’re going to keep tabs on their spend, so actually delegating somebody to maintain and keep an eye on that so we don’t just keep throwing more and more new solutions at the problem, we’re actually looking at how are we leveraging it today and how can we do it better.

Thomas: Well, I would add on top of this, it wasn’t in the prediction, but it was in the back of my mind is I’m going to say, is the rise of multi-cloud. I think multi-cloud by itself is not usually the best approach for any company. But what I’m starting to realize is that companies are multi-cloud without even realizing it. The point being that a company, a business unit inside of a company, decides that there’s a product or a service they want, they make the purchase and they have no idea that who they’re working with, where their backend is. Sometimes it’s just not mentioned. It’s like, “Oh, we do something as a service for you. You don’t have to worry about it, we take care of everything for you.” Then, U.S. East one goes down and now your system’s down with it, and you’re like, “We’re not even using AWS. We’re a Microsoft shop. Everything we have is in Azure. Why are we impacted by an AWS outage?”

Thomas: And now, you’re multi-cloud without knowing it. And I think there’s some inherent costs associated, maybe fixed costs if you’re paying a subscription. But maybe not. I think it’s almost the equivalent of having 17 different streaming services because you think $3 and $4 a month for each one of them is somehow cheaper than paying the full bill for cable television. I think that’s kind of the point is that there’s a sprawl and there’s a few difficulties associated with that, mostly around spending, but also just around architecture. And what is business critical and understanding where does that data really lie? Is that, this cloud, that cloud, are we in both? I just think things are getting a little bit more complex. So when you start looking at those costs, I think you can try to reign in the complexity of your environment as well.

Sascha: Have you ever heard of torrenting, but that’s a different topic. It’s okay. Now, actually I have a nice example for where multi-cloud makes sense. A couple of weeks ago I went to Dubai and visited Gitex. I was speaking to whatever ministry of Saudi Arabia also, and they have like a cloud-first strategy for a while now. Each software that’s rolled out for government or local authority should run in the cloud. And they actually use two different clouds. They have one kind of private cloud where they keep the data locally in country, but it’s basically for storage. They still use public clouds for the front end, for the GUIs because AWS and all their friends just have more and more advanced technologies. So they use both together, which is an interesting use case for multi-clouds, actually.

Thomas: Let’s talk a little bit, so since we talked about these cloud services, one of these services, let’s say, a rise of services, would be artificial intelligence, machine learning services. This is your prediction, Sascha, the rise of AI ML. Why don’t we speak a little bit about what you had in mind and what your prediction is for 2022?

Sascha: Well, I think we will see much more. Stuff gets much more affordable. It’s not so expensive anymore. There’s a higher need. We talked about data and the amount of data that’s increasing. Machines are just faster in dealing with all that data. There’s a few roadblocks. If you look a couple of years back, there was a lot of resistance as automation came into IT for various reasons like I didn’t write that script, whatever, I don’t trust it. I don’t know. The same applies for an AI system. Who wrote it? Who’s behind it? What does it do? We don’t really know it.

Sascha: But meanwhile, we have a couple of very well working frameworks, which are in AWS, et cetera, that can be customized without too much trouble. I think the next thing which we see is the marriage between low code, no code, and AI, so that everyone, including the three noobs of us are probably able to create something which is based on an AI system, but will do some good stuff for our companies.

Sascha: What we see as a typical use case, I think my use case was like a CEO who needs to go into a meeting in half an hour and he needs sales numbers from, I don’t know, from the States, the last quarter. I need this now. So what’s going to happen? He or she is probably calling into the room to the PA to whoever, “I need those numbers now. I need those right now.” And wouldn’t it be so cool if there would be just a chatbot, like, “Hey, Chatty, give me the number of the last three months, blah, blah, blah.” That is an AI use case. We are going to see more and more of them this year. Not this year, that’s just a few more weeks. Next year and the year afterwards.

Liz: Well, and to your point too, Sascha, I think as we saw with automation, the same can be said for AI and machine learning in terms of that apprehension of, well, is this going to take jobs? But again, if we look at what’s been happening over beyond the last 18 plus months, we’ve seen the need to adopt this in order to optimize how teams are operating. So, it’s not so much the scare or the fear behind having that available. It’s how in having these technologies is this going to make us perform more effectively. How can we get answers more quickly so that we don’t incur any issues in our productivity and getting information when we need these deadlines to have these business cases moving forward?

Liz: But I think one of the biggest thing with AI is, and the same could be said as we experience with automation, is don’t just select and implement it for the sake of saying that you’ve adopted it. I think teams are going to need to have a legitimate strategy behind how they’re going to bring it in, how they’re going to use it so that it’s effective to match business goals, what’s the IT strategy, and marry that together so that you’re having the most effective use of these smart technologies moving forward.

Thomas: A little surprised at myself that I didn’t think of a prediction around AI ML, but Sascha did. I don’t know how that escaped me. Sascha, earlier you did mention though, I thought I heard you say AI was a bit of a marketing term?

Sascha: Yeah.

Thomas: Yeah and I won’t disagree. I’m not sure that AI … I wouldn’t say AI is a marketing term. What I would say is that as Liz just mentioned, a company that deploys a solution just so they can say that they’re using AI or trying to make use of AI. I think there is some misinformation or bad information around what really AI is versus machine learning. I see phrases used fairly interchangeably and I’m the person that just sort of asks the question, “Hey, explain to me more about what it is you’re doing that you call machine learning or you call AI?”

Thomas: And so, I believe that there will be a rise in the services and the use of the services, because I see companies like Microsoft Azure and AWS, they are making it far too easy to build predictive models, to use data. As you said, Sascha, there’s low code, no code, there’s so much data, and my attitude is let the machines feast on that. The machines should do what machines are good at so humans can do what we’re good at. And automation is a good thing. I don’t think it automation takes away jobs. I think automation shifts jobs for people to take on new roles, new responsibilities.

Thomas: So, I absolutely believe that there’ll be a rise in a lot of this. I think the two major cloud providers are doing everything they can to make it easier for people to curate and collect data, to feed it into a model, to get predictions back, and for it to be useful in some way. I think that’s the key, right? There are programs out there to help with employee retention, for example, is one. Of course, our wheelhouse would be anomaly detection. We’re going to alert you when there is something that you should pay attention to. Not just alert you for everything, but here, this is something a little bit smarter. This is truly an anomaly. You should go focus on this thing and not the other hundred things that you might normally get alerted on.

Thomas: I’m going to throw this to Liz. What about the use of AI and for security. Do you think that will drive adoption as well?

Liz: I definitely think it will, especially when we go back and so we have the need for enhanced security measures moving forward, be prepared for the unexpected. So having the, just as you mentioned, like anomaly detection and being able to reflect on historical insights. That’s going to make having these AI and these smart technologies in place so that teams can be more preventative in their interactions and in their plans moving forward. So what have we seen previously and how could that influence algorithms moving forward so that we are alerted or aware of items, so that we’re prepared in the future? That’s where I think is a really interesting avenue in terms of how AI and machine learning can potentially influence those security needs, the growing changes in our environments moving forward so that we can be more proactive.

Thomas: Any thoughts from you, Sascha? Same topic, security.

Sascha: I wrote a piece and one of my statements was that we might need an security AI that controls a monitoring AI. But, but it’s a reality because that basically prevents Skynet from happening. Otherwise, we would end in such a situation. But it’s really, once an AI is set up correctly and trained or supervised, it will work fine. I think it’s more of a calculation how much does the system cost versus how much do you pay for 20 analysts in a year? The analysts do cost a little bit of money and they might get sick. The AI won’t get sick, but they might be outages. But fortunately, there’s us. We help. But that’s a different topic.

Thomas: I think security, it’s another great use for AI, for anomaly detection, really tracking something that’s different in your environment. Security is another use case for adoption, right?

Thomas: And I also just think there’s a lot of, there’s market not just for people doing it themselves, but paying a company that can take your data and provide some insights. So I know there are companies that are doing this today. I see them all the time. Hey, we can build you a nice visualization, give you some analysis, do all these things for you. You don’t even need a team of data scientists. You just need to give us access to all your data and we’ll do it for you as a service. I see more and more of those coming out. I think it’s going to really, hockey stick growth, I think, is the term I want to use, over time. And I think next year is a pretty good starting point. I’m going to say this prediction of yours, Sascha, I’m going to say it’ll happen. I agree. I think that’s how we usually do it. Do we agree on this prediction? Liz, do you agree?

Liz: Agreed.

Thomas: I agree.

Sascha: Party on.

Liz: Yeah, I agree. I agree. So let’s talk now … so we’ve talked a little bit about cloud costs, right? Managing those costs, people spending, throwing more and more money over the last 20 months, we’ll say, the digital transformation for a lot of companies, as they tried to quickly move to the cloud for a variety of reasons, one of which being the pandemic. So, money flows into the cloud. And now we’ve talked a little bit about money flowing for specific purposes, AI, ML, and the services, things like security that can be leveraged. I’m also seeing this is going to go right to Liz’s last prediction here, which is the reevaluation of tools and technologies and processes. Because with this push for the cloud, with these new tools, I’m guessing that maybe your IT department has, I don’t know, more than a handful of tools. What do you think Liz? Is that a fair statement?

Liz: I think that’s an understatement, but exactly. I think at this point in time, and we alluded to it a little bit earlier in turn terms of optimizing some of those investments, I think teams are now at a kind of a critical point where you need to go through and do, as I like to call, your application cleanse. What’s working for me? What’s not working for me? Where do we have duplication? What are we not touching? We made such rapid, and in some instances, drastic changes.

Liz: Because a lot of teams, honestly, before the pandemic hit, they had roadmaps for where they wanted to go from a digital transformation perspective. Then, they absolutely had to put that in motion in order to support their geographic disbursement for their teams working remotely and now work from anywhere or hybrid environments. So going back and reevaluating your suite of tools and technology is going to be paramount so that you are making actual, excuse me, that you’re making intelligent and cost effective decisions in terms of what tools you’re actually investing in, what you’re using, and what you need to make changes in moving forward. Teams aren’t drowning in the tools and so that IT itself isn’t burning out on what they’re responsible for maintaining and managing, and at the end of the day so that end users are also effective and successful in their day to day operations.

Thomas: So you mentioned application cleanse. I’ll just ask, does this involve grapefruits or is this like a veggie thing?

Liz: Ginger, turmeric, don’t forget those key spices.

Thomas: This sounds like something I’m sure most people didn’t know they signed up for. Sascha, your thoughts on the tools, the reevaluation of the tools and processes?

Sascha: Well, actually, I think we had a survey on THWACK that might be two or three years ago where we asked our customer base, how many tools they use. I think the network team alone, they had 20 something tools. So as Liz mentioned, it’s an understatement speaking of handful. When we look at 20 tools, I guess a couple of those are just those one-trick ponies. They do one thing, that’s it. That’s pretty much it. But you know where I see a big risk and that is the waste of money. Because when you work in IT, you usually don’t talk to the accounting team. The result is, you acquire a new software, you use it for a year or two years or whatever. You stop using it and you’ll never tell anyone. That means the company keeps on paying maintenance, maintenance, maintenance. That is awesome if we are the guys receiving the money, but for the companies, it’s just tough luck. There’s a lack of communication. And with tool consolidation, you will basically mitigate this scenario.

Liz: I’m so glad that you brought up the communication and ultimately the collaboration between those teams at the end of the day. I was looking back at our SolarWinds IT trends report from this year and 33% of tech pros noted their organizations are working towards improving between IT business goals and corporate leadership. I think the tool consolidation is an excellent place to begin that initiative so that you do have better communication and collaboration moving forward. We’re not using disparate systems and we’re actually talking together and having improved internal strategic alignment moving forward.

Sascha: So you think that different IT silos talk to each other?


Liz: I know.

Sascha: That’s like a revolutionary concept. It’s really.

Thomas: So it’s those silos that have led to the rise of all these tools, right? It’s because the network team kind of were in their own little silos, so they get the handful of tools for what they need versus your database team, versus your sysadmins, versus your functional analysts. So at some point along the career of a company, say, the IT department can hit this tipping point where the largest consumer of IT resources is IT itself as they try to maintain 27, 37 different tools and they go, “Why do we have 800 servers and there’s only 400 employees?” And you go, “It’s because we have way too many tools and there’s so much overlap and consolidation would be a nice thing to happen.” And you also hit a point where it’s like such a mess that you’re not sure you could ever spend the time to truly figure out what’s doing what and what’s unique and what’s necessary.

Thomas: So I really like this prediction Liz, because I think when companies start looking at costs, they’re really going to evaluate what tools are absolutely necessary right now. And if they’ve migrated away from something on-prem to something being as a service, do they really need to keep the tool that was monitoring this thing that was earthed? Or should they just rely on the native cloud offering for whatever service they have to monitor an alert. So, I think this really hits home for a lot of companies. I think people reading this prediction, I think it’s going to resonate with them quite a bit and say, “Yeah, this is the thing we need to do. We’ve got 27 tools when we only need say four.”

Sascha: Let me throw in. For many companies, it might be a good first step to create an inventory. They might not even know what kind of software do we have there because people leave, people buy stuff, they leave the company, and no one else is using it so they don’t know what they have, what they use. That is the first step.

Liz: I think I’m so glad you mentioned that first step too. If I honestly go back and put on my solutions engineer hat, it takes me back even to evaluation days with prospective customers of, what are you using? What is your current stock? What do you need to, again, improve on? Where can we trim the fat so that you can contribute to the holistic vision? So stop looking at the tree that’s in front of you. It’s that view of the forest when you’re going through and doing tool consolidation. Again, not only does it help with IT initiatives and IT goals, but it also can contribute to those larger objectives of the business, and at the end of the day, making our users successful.

Sascha: Absolutely agree, and sounds like a perfect project when there’s no firefighting to do and everything else is fine.

Thomas: Well, we’ll figure it out later because we’re busy with all this other stuff. So naturally that’s usually how this thing evolves, right? Is that there’s never time to do it right. Or you figure you can do it right later. But you never find that time, right? It’s an ongoing battle, but I do believe Liz is correct that in the next coming year, we’re going to see it more of a priority.

Thomas: So thank you both, Liz and Sascha, for joining me on this discussion today on TechPod. If you’d like to hear more of the tech predictions, please listen to part two of the series or check out all of the Head Geeks 2022 predictions, which are available in the show notes. If you enjoy SolarWinds TechPod, we’d love for you to follow, rate, and review the podcast. Thank you for listening and until next time, I’m Thomas LaRock.