As our production application systems continuously increase in complexity, the challenges of understanding, debugging, and improving them keep growing by orders of magnitude. The practice of Observability addresses both the social and the technological challenges of wrangling complexity and working toward achieving production excellence. New research shows how observable systems and practices are changing the application performance management (APM) landscape.
Observability Requires Both Technical and Social Approaches
Tooling alone can't solve anything, it's just a necessary part of any solution. Tackling the challenges of managing complex production systems isn't just a technical problem and it isn't just a social problem. We manage sociotechnical systems and any reasonable solution must take that into account in order to be effective.
Observability isn't logs, metrics, and tracing. Yes, those aspects are important. Those tools can help shed light on what's happening in the systems that are critical to your business. However, there's a big difference between having tools that provide instrumentation and using them to achieve better outcomes. Many of today's tools require you to predict the future by knowing in advance what conditions to monitor, which trends to look for, or the correlations you need to make to find application performance hotspots.
The coveted observability sweet spot is finding the unknown unknowns. Observability is a sociotechnical practice that allows you to answer any arbitrary questions about your environment, without needing to know ahead of time what you wanted to ask. However, it's doing the work that proves a bit more challenging for many teams, especially those weaning off legacy tools.
Practicing observability is a journey. It takes time for entire teams to adopt new practices and shift mindsets to a model of shared ownership. Our new study shows how different teams are practicing, or intending to practice, observability within the next two years. The report also examines the challenges teams face and the practices they are implementing as they progress on their observability journey.
Observability Maturity Research Findings
Teams must decide how to start their observability journey. Those early decisions have a high degree of impact because they influence both tool choices and habits during the software development and delivery lifecycle. Teams that adopt recommended observability practise to an advanced degree see greater benefits than less advanced teams. Advanced teams stabilize their systems, spend less time reactively fixing issues in production/refactoring code/resolving technical debt, and spend more time proactively innovating.
The report affirms that adopting observability tools, site reliability engineering (SRE) practices, and a culture of shared ownership translates to efficiencies across the software engineering cycle, better end-user experiences, and ultimately helps teams achieve production excellence.
Outcomes are much more pronounced when teams apply observability mindsets and processes in conjunction with tooling. That combination can lead to a virtuous cycle of reinforcement, presuming those teams are using tools purposely designed to address observability use-cases. Research findings show that most teams adopt a handful of tools across disparate teams to accomplish daily tasks. Yet it's that same juggling of different tools that creates confusion, frustration, an oft-heard complaint of tool bloat, and ultimately leads to slower performance.
Go to Advanced Observability Teams See Big Efficiency Gains - Part 2
The Latest
Navigating observability pricing models can be compared to solving a perplexing puzzle which includes financial variables and contractual intricacies. Predicting all potential costs in advance becomes an elusive endeavor, exemplified by a recent eye-popping $65 million observability bill ...
Generative AI may be a great tool for the enterprise to help drive further innovation and meaningful work, but it also runs the risk of generating massive amounts of spam that will counteract its intended benefits. From increased AI spam bots to data maintenance due to large volumes of outputs, enterprise AI applications can create a cascade of issues that end up detracting from productivity gains ...
A long-running study of DevOps practices ... suggests that any historical gains in MTTR reduction have now plateaued. For years now, the time it takes to restore services has stayed about the same: less than a day for high performers but up to a week for middle-tier teams and up to a month for laggards. The fact that progress is flat despite big investments in people, tools and automation is a cause for concern ...
Companies implementing observability benefit from increased operational efficiency, faster innovation, and better business outcomes overall, according to 2023 IT Trends Report: Lessons From Observability Leaders, a report from SolarWinds ...
Customer loyalty is changing as retailers get increasingly competitive. More than 75% of consumers say they would end business with a company after a single bad customer experience. This means that just one price discrepancy, inventory mishap or checkout issue in a physical or digital store, could have customers running out to the next store that can provide them with better service. Retailers must be able to predict business outages in advance, and act proactively before an incident occurs, impacting customer experience ...
Earlier this year, New Relic conducted a study on observability ... The 2023 Observability Forecast reveals observability's impact on the lives of technical professionals and businesses' bottom lines. Here are 10 key takeaways from the forecast ...
Only 33% of executives are "very confident" in their ability to operate in a public cloud environment, according to the 2023 State of CloudOps report from NetApp. This represents an increase from 2022 when only 21% reported feeling very confident ...