In this special guest feature, Abel Gonzalez, Director of Product Marketing, Sumo Logic, lays out where Observability is going for the enterprise as well as explaining where we’ve been and why it’s important. Abelardo is a leading expert in application performance management with over 19 years of experience in product management, product marketing, and professional services. His areas of expertise include how performance affects the user experience as well as utilizing analytics to quantify the impact of performance on business objectives. Abelardo holds a master’s degree from Governors State University, and earned his Bachelor of Arts degree from St. Edward’s University.
Consumers have grown accustomed to getting what they need, fast.
When we place an order on Amazon, we opt for same-day delivery. When we run a Google search, we anticipate results within a matter of seconds. We rely on Facebook for a quick and seamless user experience every time.
The pressure is on for all companies—not just tech giants—to move at lightning speed, and many are realizing that their old tech stacks won’t be able to keep up. This has forced businesses to take a hard look at their technical debt and determine how best to address it. But as companies rearchitect their applications to move faster, they’re bringing on new challenges, like adopting agile releases, managing hybrid-cloud environments, and processing the mountains of data associated with this increased speed.
How can businesses address common challenges and ensure that their new applications are both performant and secure?
The answer is observability.
To get familiar with observability, let’s first take a step back. The concept and terminology of observability have only recently been applied to information technology and cloud computing. The term originated in the discipline of control systems engineering, where observability was defined as a measurement of how well a system’s internal states could be inferred from its external outputs.
Today, observability refers to the ability to measure a system’s internal state by inferring it from external outputs. To achieve this, vast amounts of machine data including logs, metrics, traces, and events are collected and then connected together to derive actionable insights. Observability lets companies gain a true understanding of the problems modern apps can experience that legacy monitoring tools can miss. Simply put, legacy tools are no longer able to make sense of all these new data, and observability is bridging the gap.
Observability adoption has been on the rise as more organizations work to modernize their applications. According to Gartner, “by 2024, 30 percent of enterprises implementing distributed system architectures will have adopted observability techniques to improve digital business service performance, up from less than 10 percent in 2020.”
As this upward trend in adoption continues, it begs the question: What does the future of observability look like?
It’s undoubtedly getting more complex. Take smart grills, for example. Something that was once simple—turn the grill on and keep an eye on the food—is now connected to Wi-Fi and generates telemetry that has to be sent to an application to then process, analyze, and relay information to a user’s device. Data on the smart grill’s temperature, humidity level, cook time, and more, must be analyzed and communicated to the consumer in a quick and streamlined manner that provides them with actionable insights (i.e. the steak is done—it’s time to take it off of the grill).
The complexity and scale required for a company to build something like a smart grill is staggering, and a growing variety of everyday products like this are coming online every day. As more devices come online, large amounts of telemetry are generated and application networks become increasingly taxed.
Now and in the future, companies will need cost-effective ways to manage this steady increase in telemetry flowing into their observability platform. The business advantage of having a single repository will be better cost management, data correlation, and deeper insights from analyzing the entire data set. Technologies like AI and machine learning can then leverage this single platform so businesses can spend less time identifying and fixing issues and more time innovating and delighting users. Per the example above, the smart grill is clearly generating a large amount of data. But, of those data, what is most important? What does the user need to know so that their steak is grilled to perfection? This is where the true value of observability lies.
Building AI and machine learning into these self-healing systems is essential. With so much data being generated and processed, minimal manual intervention is the goal. Observability platforms will empower companies to be more proactive and self-remediate, while providing valuable insights that help create better customer experiences and positive business outcomes.
Another way businesses are addressing increased complexity is through open source. Open source prevents vendor lock-in and gives companies portability and the freedom to do whatever they want with their data. In fact, the open source services industry is expected to reach nearly $33 billion by next year as more companies adopt these solutions.
Customer expectations are rapidly evolving. As more devices come online with new features and functionality, consumers will continue to expect an exceptional experience, and observability is integral to providing it. At the end of the day, it’s critical to connect observability back to the end goal of the business—to serve its customers, community, and shareholders. Because that’s really what it’s all about.
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