Intelligent observability: The answer to enterprise data overload

Digital transformation has opened the floodgates to data, more than most teams can realistically manage. To put things into perspective, the amount of data created, consumed, and stored is expected to soar to 394 zettabytes by 2028. That is enough to store nearly 39 trillion high-definition movies, or more than 4,800 films for every person on Earth. This fierce growth is overwhelming, especially as technologies like artificial intelligence (AI), with its data-hungry nature, become more central to operations.

For DevOps and Site Reliability Engineering (SRE) teams, the challenge is making sense of vast streams of data while spotting anomalies before they escalate into major incidents. The problem is not about the sheer volume of data but the noise that comes with it. That is why around 80% of professionals report feeling overwhelmed as they constantly switch between tools, juggle non-stop notifications, and try to piece together a complete picture from scattered data sources.

Amid this data deluge, intelligent observability emerges as the solution, bringing order to the chaos. It also empowers teams to deliver near-uninterrupted services by enabling proactive identification and the prevention of potential disruptions, as well as rapid resolution of issues when they occur.

The Paradox of Data and Digital Silos

Today, organisations use a myriad of tools to manage different components of their digital products, including writing code, auditing websites, or improving the customer experience. The reasons for choosing these tools vary from ease of use to past experience and their compatibility with existing workflows. However, the nature of these tools creates data silos, making information about past issues and resolution paths challenging to locate and stitch together.

When a site or product goes down, SRE and DevSecOps teams are forced to jump between tools and information sources to determine the cause of the issue. Even if each tool uses AI, the data generated still lives within silos. The scattered nature of this information makes it hard to trace the root cause of an issue quickly and accurately. It delays a team’s ability to rapidly execute the steps needed to resolve it.

Although data from these tools is helpful when systems work in isolation, this setup can become a challenge when the whole system crashes. Systems run in a highly interconnected mode, so the failure of one part can have a domino effect on one or more other parts. Siloed data makes it difficult to get a holistic view of things, slowing down response times and mean time to resolution. This is where the paradox of data takes hold: empowering when things are stable, but overwhelming when things go wrong.

This is where intelligent observability can help. By breaking down data silos and connecting the dots across systems, teams can move from reactive firefighting to proactive problem-solving.

Connecting the Dots With AI-Powered Tools

The Observability Forecast by New Relic indicates that 41% of respondents identified the adoption of AI technologies as a key driver for observability. Intelligent observability brings telemetry data from different workflows into one place, supporting DevOps and SRE teams to make sense of vast amounts of information. This enables them to spot anomalies early, before they turn into major incidents.

Intelligent observability also enables teams to move away from juggling multiple dashboards and switching between tools. This unified approach creates clear, actionable insights instead of overwhelming technology teams with raw data. It helps them cut away layers of information quickly to get to the root cause of the problem and fix it.

By using agentic AI integrations, intelligent observability can automate repetitive tasks and deliver insights inside existing workflows. For example, integrating observability with GitHub Copilot can detect errors in code changes or even resolve tickets automatically, reducing the cognitive load on teams and enabling them to focus on strategic work. It also uses techniques like retrieval-augmented generation or RAG and predictive analytics to spot patterns and forecast issues. By looking at historical data to anticipate problems, teams can prioritise the most critical incidents instead of being overwhelmed by alerts.

In short, intelligent observability gives teams exactly what they need: real-time alerts and prioritised recommendations that cut through the noise and layers. These features prevent alert fatigue and help teams focus on what matters most.

The Smart Way to Stay Resilient and Ready

In the age of AI and data overload, intelligent observability is key to operational success. By unifying data, automating workflows, and enabling scalability while fostering collaboration, organisations will get the most out of AI without drowning in complexity. It helps teams move from constantly reacting to incidents to preventing them before they happen.

As data grows, intelligent observability becomes a crucial ally for businesses aiming to succeed and earn customer trust. Intelligent observability helps teams stay in control, improve reliability, and deliver smooth digital experiences.

Organisations must also recognise that intelligent observability is not a one-time deployment but an evolving capability. As tech stacks grow more complex, observability must keep pace by adapting to new tools, environments, and attack surfaces. Investing in training, alignment across DevOps and security teams, and AI readiness are all essential to maximise the long-term benefits of observability systems.

The author is the Senior Director of Software Engineering with New Relic.