The term “cloud computing” was first coined in the mid-1990s. Now cloud computing is a pillar of the modern business landscape. In 2018, enterprises spent an average of $3.5 million on cloud apps, platforms, and services. Today, IT departments allocate more than 30% their budgets to cloud computing, and nearly 80% of enterprises have a portion of their enterprise computing infrastructure in the cloud.
All of that activity in the could needs to be monitored. And just when we all got used to doing that, we now have cloud observability. Are you kidding? What’s the difference? It’s actually significant.
Monitoring gives software teams instrumentation. This allows them to gather data about their systems, which in turn provides the ability to respond fast when errors occur.
In short, with monitoring, you know when something isn’t working as intended.
Conversely, observability instruments teams with the ability to gather actionable data. You discover not only when an error occurred, you learn why it happened in the first place. When you know the “why”, you’re much better equipped to quickly and proactively resolve issues, which is critical in our constantly changing, extremely competitive business environment.
Enterprises need observability because systems are too complex now to just passively monitor them. The tiniest delay or interruption in production, communication, or fulfillment can be economically catastrophic. But with observability, you can look at all of the data from all of your systems, holistically, to dictate a proactive course of action. You prevent problems before they occur.
Of course, many IT professionals – justifiably – may be skeptical about observability. You can’t really blame them. After all, technology probably tops the list of the industries that generate the most hype and empty promises, both on the B2B and B2C side. It’s perfectly legitimate, then, to question if “observability” is just a re-packaging of current monitoring tools.
But that’s not the case at all. It’s time for IT professionals to acknowledge that observability is the real deal. That’s because in today’s enterprise, the amount and variety of the data that must be collected is too much for mortal human beings to manage. Observability allows systems to manage themselves via artificial Intelligence and machine learning. And with intelligent algorithms, you can understand how services and applications performed in the past, which allows you to predict the potential of future events.
Overall, observability helps you to:
– Deliver high-quality software at scale
– Consistently innovate
– Optimize technology investments
– Analyze the real-time performance of the digital aspect of the business
– See events in context in order to make more informed and accurate decisions
The aforementioned benefits are not minor victories. Over the past 10 years, we’ve witnessed an astonishing rate of innovation, which has piled pressure on software teams like never before. Because of the need to get to market faster than the competition, they have to constantly develop new features and functionality. And they must do so with fewer resources than in the past.
Companies also face increased expectations from their customers. They now have little, or any, patience for slow or poor experiences. If an app doesn’t perform as they hoped, they’re likely to uninstall it and try the competitor’s version.
Another challenge has come with the emergence of DevOps and automation, where enterprises have been deploying autonomous teams responsible for specific internal services. The good news is that automation reduces repetitive work and improves reliability. The bad news is that anything automated has the potential to fail. Teams need to monitor internal systems just the same as they would applications that serve their customers.
Those trends – pressured software teams, higher customer expectations, and the rise of DevOps – have carved out the requirement for observability. Enterprises are dealing with more complexity and higher risk. Team members now have to learn disciplines often beyond their core expertise, which inevitably leads to skills gaps within the organization; the amount of new and different technologies to learn is too much for a single person.
Software teams need one solution – a solution that reduces complexity and risk, that provides context for data, yet is also easy to use. That solution is cloud observability.
We agree that it’s often difficult to embrace the new. But if we could do it with the cloud in the mid-1990s, we can open our arms to observability today.