Developing breathtaking apps isn't easy. You have to generate amazing ideas, incubate projects, hire best-in-class talent, and scale your solution across various hiccups and road bumps. So, this shouldn't be too surprising: 99.5 percent of apps fail. But here's what is surprising: most apps don't fail because of bad ideas, budgetary issues, or scaling woes. According to surveys, nearly 90 percent of users leave apps due to ongoing performance issues — not a lack of interest, time, or money.
If you don't catch outages, bugs, and glitches early and often, they will sink your apps. In fact, Google estimates that elite performers are over 4x more likely to have monitoring solutions that incorporate observability into their overall system health. And these elite performers are over 6,500 percent faster at recovering from incidents.
Synthetic monitoring isn't a value-added service; it's the table stakes. But how do you utilise synthetic monitoring in practice? And what does a robust synthetic monitoring ecosystem even look like?
The Cost of a Little Downtime
Let's talk about the "real" cost of downtime. Over the years, we've seen a ton of numbers thrown around in this arena. From our research, the cost of downtime exceeds $100,000 per hour intangible costs. But what about the intangibles? Every employee takes around 23 minutes to fully mentally recover from a small work interruption. And downtime immediately impacts your reputation, customer satisfaction, and overall security presence, which can lead to significant downstream costs. According to Gartner, the actual cost of downtime is over $300,000 per hour when you include these intangibles into the equation.
But here's the kicker: this was pre-COVID-19. Today, downtime also interrupts nearly every employee, especially in our digitally-connected business ecosystem. So, small outages can completely cripple your company. A little bit of downtime can cause a lot of damage.
Eliminating Downtime Via Synthetic Monitoring
Let's make the connection. Synthetic monitoring gives you observability into your stack, reduces downtime, and helps create elite performing dev teams. That's a big deal. In fact, for many companies, that's a multi-million-dollar type of "big deal." Here's the great part: synthetic monitoring doesn't have to be hard. In fact, we can break down the value of synthetic monitoring in three simple steps:
Step 1: Discovering Outages & Issues Before Your Users
For starters, you need to figure out if any users (across your ecosystem) are having an issue. Ideally, you would have one solution capable of bridging monitoring across every app ecosystem. But some people choose to purchase (or code) a new monitoring solution for each app environment. You want to know:
- Can users complete their work right now?
- Can users complete their work in an acceptable timeframe?
These are the two big questions. You need to know if you have an outage. But you also need to know if you have any issues impacting your users' workflow. To answer these two questions, synthetic monitoring utilises "bots." These are essentially automated testing scripts that mimic users' actions across all workflows constantly. If they encounter an issue, they shoot you a message (or video). This is what we call active monitoring. It's always happening in the background. Generally, your synthetic robot army will catch issues well before users — giving you time to correct them before they hit your users (and your wallet).
Synthetic monitoring should be, by nature, cheap. Unfortunately, that's not always the case. Some solutions require significant support staff, training, and a bunch of coding to work in single (and especially multiple) environments. But you can find no-code solutions that require virtually no support staff while still giving you end-to-end assurance and robust monitoring capabilities.
Step 2: Identifying Failure Points
At 2 Steps, we're very big proponents of problem localisation. You obviously need to know if you have any issues. But you probably also want to know where that issue is coming from, right? Which sub-systems are problematic? Is it your network? Is it Citrix? Is it your app? To answer these questions, we recommend the following steps:
- Use video playback: This is huge! Video recordings of synthetic checks give you an extra layer of visibility into errors. With 2 Steps, you not only get the log and some of the more deep technical "jumbo" scents straight to your Splunk dashboard. But you get video playbacks that show you exactly what happened (which can give you some visual clues into which sub-system may be causing the issue).
- Use logical checkpoints: We recommend running your synthetic scripts in logical "chunks." So, you could have a script constantly checking startup sequences, login sequences, and connection sequences. When something goes wrong, you can see which chunk had the issue and start diagnosing the problem. Sometimes, multiple chunks will fail. And, if the entire app is down, every chunk may fail. But using these logical checkpoints allows you to see which areas are problematic and which areas are functioning perfectly.
- Use smart positioning: You probably won't get away with this if you're not using a no-code solution like 2 Steps. But we highly recommend positioning your robots across multiple network locations. So, you can deploy one as close to the app source as possible. And you can deploy some further away. If a robot fails in a distant location but not close to your app, you can start looking for network issues. If both fail, it's probably an app issue.
- Use VD analysis: With many businesses leveraging virtual desktops, you should position checks within your VD environment and outside of your VD environment. You need to know if there's a problem with the VD or DaaS (e.g., Citrix, Azure, etc.). Or if the app itself is a problem. Remember, you need to set up a different logical chunk for logging into the VD. This can help you detect issues caused by VD outages or network-to-VD interactions.
Step 3: Remediating the Problem(s)
Finally, you need to solve the problem. If you followed the steps above, you should have a pretty good idea of where a problem is located. 2 Steps also provides a ton of historical metrics to help you predict failures (see: AI Ops). These can be extremely helpful. But this part is more about building smart DevSecOps teams and leveraging talent and communication to tackle issues across your tech ecosystem — which is a little beyond the scope of this post (see DevSecOps).
Simplify App Monitoring With 2 Steps
Are you ready to solve your downtime issues, eliminate pesky glitches and bugs, and build smarter, faster, and more robust apps? Get in touch with us for a 15-minute consultation. 2 Steps is a no-code synthetic monitoring solution capable of working across any app environment, in any stack, and with any employee. What does that look like in action? See for yourself.