Looking ahead to the observability landscape in 2022 and beyond, IT leaders will have to make a critical decision about how to manage complexity in their tech stacks. Organizations must decide how to create and monitor their expanding architectures as the wave of microservices and containers grows.
In today’s competitive marketplace, every company in every industry sector is under enormous pressure to provide exceptional customer, employee, and partner experiences. It’s not enough to release a great product; businesses must also deliver consistent innovation as rapidly as feasible while maintaining efficient services and operations. The ability of a company’s engineers to develop, produce, deploy, and run great software is critical to its long-term success.
While engineering teams want to focus on innovation and agility, they’re frequently tasked with locating and rectifying errors in complex tech stacks. In the form of shipping delays, slow reactions to outages, and poor customer experiences, this complexity has a real and immediate financial impact. The time engineers could have spent providing new value for users, which would have led to continuous business growth, is perhaps the most significant cost.
While engineering teams understand the value of monitoring performance and detecting abnormalities, today’s digital experiences are comprised of a network of microservices that struggle to connect and communicate with one another in the observability landscape. Engineers can often see limited glimpses of their tech stack due to a patchwork of analytical tools, but not enough to figure out why a mistake is occurring, let alone how to solve it.
According to New Relic’s “2021 Observability Forecast,” 90 percent of IT leaders and engineers believe observability is vital to their company’s performance, with 94 percent saying it is critical to their work. True full-stack observability has now become mission-critical to the success of modern enterprises, allowing engineers to obtain a holistic perspective of their operations and make quick improvements.
According to this research, the data-driven observability landscape will gain substantial traction in the coming year and beyond. The following are three reasons for this:
In the evolving observability landscape, fragmented monitoring can’t keep up with the growing number of outages
The era of monolithic, do-it-yourself tech stacks has ended. Modern engineering teams are rapidly adopting a dizzying array of tools, both proprietary and open source.
This explosion of tools has resulted in a slew of new issues. Engineers must spend an unnecessary amount of time sewing together siloed data and context hopping between tools, rather than helping teams innovate quicker and enhancing mean time to resolution (MTTR) and mean time to detect (MTTD). Following a year marked by widespread outages affecting applications, cloud services, and internet providers, IT executives have realized the value of observability in resolving costly outages.
Customer strategies change as a result of usage-based pricing
Many monitoring tools with subscription pricing schemes prevent IT leaders, developers and engineers from ingesting all of their data. Pricing is difficult to estimate and scale, and is too expensive for most businesses. As a result, teams sacrifice visibility: 60% of New Relic study respondents only monitor telemetry data at the application level, leaving enormous volumes of data unmonitored throughout their software stack. Modern observability tools, however, are moving toward usage-based consumption and pricing models. These new services give teams complete visibility into their telemetry data while only charging them for what they utilize. This pricing approach helps engineers to acquire a comprehensive picture of their operations and realize the benefits of true observability by removing upfront speculation on usage and potential overage penalties.
Observability enhances service and reliability
Return-to-office plans have been disrupted by new COVID-19 variants and changing corporate policy during the last two years, demonstrating the need for digital services. Application data can provide more depth and insight into real-world performance in today’s always-on world. A rise in web traffic or application demand, for instance, is usually accompanied by a rise in transaction volumes. This rise can be observed and tracked across application components as well as revenue figures, highlighting the importance of observability in generating and quantifying business success.