By Ganesh Narasimhadevara, Principal Technologist, APJ, New Relic
It’s hard to find a business that hasn’t already migrated to the cloud or doesn’t have immediate plans to make the shift. Research from Nasscom has found that 53% of India’s enterprises increased their cloud adoption over the past year; enabling them to scale quickly and innovate rapidly. However, innovating with speed requires engineers from across all business functions to increase development velocity, fix issues fast, reduce outages, and ensure smooth customer experiences. This is easier said than done.
In fact, the 2022 New Relic Observability Forecast found that only 40% of businesses in India can proactively detect issues before customers are impacted. The vast majority are also most likely to experience outages multiple times per day that can take more than 60 minutes to detect and even longer to remediate. Businesses involved in building and maintaining software know how challenging it is to differentiate signals from noise. Sifting through a plethora of data during an incident–and before the impact is widespread–is like trying to find a needle in a haystack while the haystack is on fire; inevitably causing downtime and loss of revenue. But this process can be made simple with observability solutions that harness the power of generative AI.
Decluttering mountains of data
Analysing huge volumes of data to form a plan isn’t always easy when engineers are navigating multiple dashboards, documentation, alerts, logs, traces, and anomalies. An all-in-one observability platform that integrates the benefits of generative AI has the potential to turn the tide and remove data silos. Generative AI assistants declutter mountains of noise; enabling engineering teams to find the cause of a problem quickly.
The right generative AI-powered observability solutions act as a force multiplier and when
utilised properly by significantly reduce complex, labor-intensive work. Its conversational capabilities make it incredibly easy to sift through data, find the root cause of issues, and ultimately fix errors in code. By being able to run queries like, “What’s wrong with my browser app?”, engineers gain valuable insights into the state of their systems.
Generative AI is only as reliable as the information it's fed. By design, generative AI assistants become more proficient as they learn, so the more valuable data they are fed, the more reliable the output. When integrated into an all-in-one observability platform that unifies telemetry data, a generative AI assistant can quickly turn vast amounts of data into reliable, actionable insights.
Reduce outages and improve customer experience
The main challenge for engineering teams is making sense of complex systems and infrastructure while keeping these live and running. According to Gartner, an average of one hour of downtime can cost organisations $300,000 USD, which is why it’s no surprise that engineers are under extreme pressure to minimise any interruptions.
Reducing downtime is no easy feat and requires engineers to comb through mountains of
telemetry data that often resides in silos while also having a good understanding of complex
systems that can be hard to troubleshoot. It’s also nearly impossible to manually identify
instrumentation gaps, isolate the root cause of an issue, debug code-level problems, generate reports and dashboards, and maintain data privacy. Generative AI-powered observability solutions have the capability to automate significant parts of these processes.
With high-quality insights that can be scaled quickly, these solutions simplify the first, crucial step: helping engineers easily understand complex systems, craft analysis of issues, and democratise observability by making it accessible to every engineer. Generative AI accelerates the debugging process, allowing engineers to fix issues before they become bigger problems that adversely affect revenue, and ultimately, improve customer experience at every level.
Generative AI has been making waves but its true potential is harnessed when organisations look beyond its use in chatbots and language processing to use the right data sets in engineering teams. The future of observability is already here, and solutions that are built on generative AI are able to create a single source of truth, break down silos, deliver better business outcomes, and enable knowledge workers to work smarter, not harder.
