Don’t just react: How executives can predict and prevent outages to maximize availability

I’ve seen firsthand the sleepless nights and high-stress environments that come with keeping digital services up and running in production. The stakes are high, and the pressure to deliver fast while maintaining uptime and preventing outages is relentless. With Dynatrace, executives can now benefit from predicting and preventing issues before customers are impacted and reducing the need to react. And when outages do occur, Dynatrace AI-powered, automatic root-cause analysis can also help them to remediate issues as quickly as possible. The end goal, of course, is to optimize the availability of organizations’ software.

I realized that automating root-cause analysis requires a comprehensive approach: observing end-to-end and full-stack with deep insights, unifying all data in real time with up-to-date topology, and applying causal AI that learns instantaneously to handle cloud-native dynamics. Combining multiple types of AI made Dynatrace even stronger, and enables prediction, prevention, and auto-remediation all in one. But business is the top priority, which is why it never made sense to me to just monitor servers. The real business need for executives lies in understanding customer impact, which makes end-to-end observability essential. This is what we uniquely solved for our customers with Dynatrace.

Respond to issues before they impact your customers

For executives, IT outages are a major headache. For issues that can’t be prevented in the first place, the next best option is to resolve issues faster than customers notice. Being faster, however, requires automation.

As the name Dynatrace suggests, dynamic tracing is at the heart of what we do. Dynatrace traces end-user interactions deep into the full stack of server-side activity to understand dependencies, allowing the platform to quantify the impact, qualify the situation, and prioritize actions. A hypermodal, power-of-three approach to AI fuels automatic root-cause analysis to pinpoint the culprit amongst millions of service interdependencies and lines of code faster than humans can grasp.

Cloud technology complexity with billions of dependencies has outgrown human ability to manage and requires AI to analyze and comprehend. Dynatrace AI increases efficiency by magnitudes and prevents alert storms. This means you can avoid finger–pointing and war rooms, and dev teams’ productivity and happiness improve, eliminating business risk alert fatigue. Session replay capabilities provide visual proof and incident context so that teams can more easily understand and act upon the root cause. Automatic root cause analysis with Dynatrace can ultimately reduce mean time to repair (MTTR) by 90% or more.

As responsibilities shift left due to the increased use of cloud-native technologies, development teams take more control over production deployments. While I am excited that the people who create software are also responsible for it—in contrast to “throw over the wall” approaches—it poses consistency and compliance challenges in larger organizations. That’s why we have Dynatrace extended (not shifted) to the left to address both needs: developers have easy and safe access to staging and production deployments while central SRE and DevOps teams have the scalable and automatic observability they need to remain compliant, consistent, and resilient. Finally, a standardized approach to observability coupled with self-service for departmental users reduces tool sprawl and complexity.

Gone are the days when executives could afford for their teams to stare at dashboards 24/7 to manually interpret data and act on runbooks. By unifying observability data and applying advanced AI, Dynatrace progresses to a new generation of AIOps that can predict and prevent issues and leverage automation for self-healing.

Predict and prevent outages with AI

The 2024 State of AI Report found that 89% of technology leaders expect AI to improve incident response, and 88% expect it to improve teams’ ability to predict and proactively resolve service-affecting issues such as application failures and security vulnerabilities. In this journey, many organizations have investigated AIOps tools to improve pattern analysis and noise reduction, as most of these solutions provide only correlation, not true causation. Even worse, the idea that such systems learn from past outages is flawed, as training would require thousands of production outages that no executive can afford.

So, to truly predict and prevent issues, the complexity of systems must be captured instantaneously and continually assessed in full context, through AI that maps causation in real-time. Dynatrace addresses this need with its hypermodal, power-of-three approach to AI, which combines causal, predictive, and generative capabilities in a single framework. This approach eliminates the need for learning from past outages and enables a highly automated software delivery process, maximizing resilience.

IT teams can also embed quality gates into their workflows so they continually meet the thresholds for user experience defined through service-level objectives (SLOs). As a result, they can predict capacity demands based on seasonal patterns and use causal dependencies to automatically capture and prevent problems as they emerge.

Improving availability to meet ever-growing customer expectations requires high grades of automation for scale, agility, and resilience. This includes auto-scaling, overload protection, auto-remediation, auto-rollback, auto-quality-gating, and more. Eventually, the goal is to arrive at self-healing through autonomous cloud operations.

Therefore, platform engineering emerges as a discipline for a holistic approach to software, infrastructure and delivery, with a relentless aim to automate. Automation, however, should not be done in isolation of tech. It needs to execute in the context of the business, which requires insights into business-impacting metrics including end-user experiences, public API call success rates, learning from seasonal changes, and strategic business considerations such as cost vs. performance goals.

That’s where observability from Dynatrace goes far beyond “observing systems.” Dynatrace observability provides AI, analytics, and automation that integrates with platform engineering, continuous delivery, and automated operations. This greatly offloads DevOps, SRE, and operations teams from manual tasks and allows them to shift their work to automation tasks. Note that the work doesn’t get reduced. The key benefit is increased availability and security, faster software delivery, improved productivity, and cloud cost optimization.

New certification and security legislation projects, such as the Digital Operational Resilience Act (DORA) in Europe, are emphasizing the heightened expectations for digital systems availability. DORA further requires continuous compliance and the ability to report on the status, placing a heavy burden on organizations. This is where Dynatrace provides additional help and automation with the new Compliance Assistant app.

Likewise, since availability is affected by not only technical issues but also security threats, observability, and cloud security must converge to minimize availability issues. That is where Dynatrace AI and analytics—on top of unified observability and security data—raise the bar to proactively prevent problems and remediate them faster. I’ll discuss this convergence more in my upcoming blog on security compliance in November.