How Intelligent Observability Unleashes Innovation

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Companies are constantly on the hook to push the frontiers of innovation. Just look at what happens when businesses blunder modernization: Nokia rested on its hardware laurels without improving its cell phone software, Xerox put all its eggs in the copier machine basket, Blockbuster was late to the online transformation game and the list of failed businesses goes on.

In our world of digital business, innovation usually means adding new technology components and capabilities to a business’s digital products and services. As a matter of fact, continuously delivering the latest and greatest technology has become imperative to meeting rising customer expectations, keeping pace with competitors and staying relevant in a rapidly advancing world.

There’s a caveat to this hunger for advancement though: the user experience must be seamless. New functionalities must flow into the production environment without discontinuities or downtime. After all, modern consumers demand always-on digital products and services to transact, interact, purchase and access at their leisure. If the products or services consumers trust go down or perform poorly, fickle customers will find another solution and often never come back.

Delivering unceasing digital innovation and change while providing continuous availability is no easy task. Just ask a development and operations (DevOps) practitioner or site reliability engineer (SRE). An ever-changing stack comes with significant risks for incidents and outages as the continuous flow of new components into a production environment can disrupt critical apps and services and derail the supporting system infrastructure.

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How can DevOps practitioners and SRE teams overcome pressures to deliver business value through technological innovation while maintaining a gold standard for system uptime?

Why Intelligent Observability Is Critical 

In an environment of continuous change, around-the-clock system vigilance is essential. Monitoring ensures that incidents caused by implementing innovations are quickly detected and remediated. But the volume and velocity of data produced by modern systems make it nearly impossible for the human mind to root out incidents or detect early signals of an outage.

In addition to the sheer amount of data, the systems behind this data are becoming more distributed, ephemeral and complex as more IT assets move to the cloud. These complexities result in incident-prone systems that are extremely difficult for humans to monitor.

The DevOps and SRE teams developing innovative technology and overseeing uptime need the modern monitoring that AI-driven observability provides. Observability enables IT teams to understand overall system behavior by making visible the metric and event data from across services, applications and infrastructures. AI and ML then establish a continuous learning cycle that garners insights from data to set a baseline for normal operating conditions and understand what needs attention.

Although these tools bolster system reliability, no technology can fully safeguard against incidents and outages. When disruptive issues do occur, AI-powered observability detects a departure from routine behavior, provides valuable context to the anomaly and notifies team members. DevOps and SRE teams, armed with deep insights into the problem, can rapidly get to the root cause of the issue, resulting in increased uptime and decreased mean time to remediation (MTTR).

How Observability Unlocks Knowledge 

Incidents and outages happen. And they can wreak havoc on a business’s bottom line. In fact, Gartner estimates that each minute of downtimes costs businesses an average of $5,600 per minute. Considering the amount of money on the line, the pressure is on for DevOps and SRE teams to fix issues quickly and mitigate the damage they can cause.

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How can DevOps and SRE teams operate expeditiously, efficiently and unemotionally when faced with a critical system outage?

Knowledge helps teams take a calm, rational approach to high-stress, high-stakes situations like system outages. And knowledge — in this case, context around the incident and actionable insight into the fix — is what intelligent observability provides. It essentially turns heaps of overwhelming and unwieldy data from across an organization’s IT stack into actionable information.

How Observability Minimizes the Cognitive Load

We mentioned how intelligent observability’s quick fixes can rescue businesses from bruised reputations, devalued stocks and finicky customers. But automation has yet another economic advantage. Intelligent observability tools automate out the toil associated with observability. With repetitive tasks taken off their plates, IT teams can focus on the kinds of innovations that customers crave and businesses require for growth.

Automation also reduces the cognitive load that plagues IT teams and eventually leads to stress and burnout. Intelligent observability reduces alert noise, surfacing only the significant incidents that need attention. As a result, teams can take a more proactive posture instead of spending their days in firefighting mode.

Modern systems — with their growing complexities and constant upgrades — require modern monitoring solutions. Intelligent observability is the only way to provide customer-delighting technologies and maintain a high level of assurance in our rapidly advancing digital economy.

About the author: Chris Boyd is an experienced Engineering Leader, Observability Fanatic, and loves to challenge the status quo. Driven by improving the lives of fellow technologists when working with Observability products, he takes pride in the teams he builds and the innovative solutions they develop together. You may know him from his work as the Direction of Site Reliability Engineering at GoDaddy from their early days to their successful IPO launch. He currently resides in Mesa, AZ, and is vice president of engineering for Moogsoft, a leader in AI and Service Assurance. 

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