From Reactive to Proactive: Building a Winning Observability Strategy

Observability complexity is climbing as cloud environments multiply and signals stay siloed. In chasing root causes, many IT teams toggle between incompatible tools and juggle multiple dashboards. Even the most experienced professionals can feel stuck in slow, reactive processes.

An observability strategy can move your organization in a better direction. A well-articulated strategy defines how to transform metrics, events, logs and traces into shared, actionable signals. It connects uptime, latency and cost efficiency to business outcomes like customer experience, compliance and spend accountability. When fully implemented, an observability strategy can help operations teams shift from firefighting to proactive prevention. This is business observability in practice.

The need for effective observability strategies is growing. Modern observability adoption has tripled since 2020, according to Gartner, reaching 30% of enterprises in 2024. Forward-leaning teams embrace observability as a performance multiplier, with the potential to improve reliability and accountability across every service. By building a clear observability strategy, you can better manage service health, staff burnout and operating costs — without introducing new silos or creating more complexity.

What is observability? (And why it isn’t just monitoring)

Monitoring tracks the threats that you know to expect. Using static thresholds or known failure modes, it flags symptoms such as CPU spikes, HTTP 500s or pod restarts. When monitoring alerts trip, you have to jump between dashboards and reverse engineer what happened. This kind of approach worked decently as long as infrastructure stayed predictable.

Cloud native systems change constantly. Deployments happen quickly, and dependencies regularly adjust. Incidents emerge across orchestrators, APIs and ephemeral services, and it’s impossible to envision every possible failure in advance. Today’s teams need the ability to ask new questions as unpredictable issues unfold.

Observability connects and contextualizes metrics, events, logs and traces — four key data types known as MELT — in a unified view. That correlation is key to turning scattered telemetry into actionable insights. This approach directly supports real-world goals like protecting SLOs, preventing outages and controlling spend.

When signals align across services and dependencies, incident response becomes faster and more focused. You gain the ability to investigate system behavior in real time. This shift reduces guesswork and allows engineers to focus more directly on resolution.

The four pillars of observability

Modern systems produce a flood of telemetry. When every signal is treated as equal, the volume quickly becomes overwhelming. Observability-focused teams manage this overflow by elevating what matters most. They define and prioritize the signals that explain behavior, enable action and align with business goals.

MELT data forms the foundation of many observability strategies. Each pillar of MELT captures a different dimension of system health, and the strongest observability strategies actively correlate the four streams.

Metrics

Metrics help describe the ways that systems behave over time. Metrics are compact, quantitative and ideal for tracking service-level performance. Latency percentiles, error rates and uptime percentages are examples of metrics, all of which help you monitor the actual experience of users.

Metrics vary, and they should not all carry the same weight. For example, when judging performance under load, p95 and p99 latency trends offer better insight than container CPU graphs. Prioritizing the right metrics for your business helps with defining meaningful thresholds, keeping teams focused and setting realistic budgets.

Events

Events capture changes like deployments, configuration updates, autoscaling moves or system restarts. These state transitions can help explain sudden slowdowns, failures or recoveries.

On their own, events may not raise alarms. But when paired with metrics and logs, they help you establish cause-and-effect patterns. By emphasizing event context, an observability strategy is more likely to reveal which changes preceded incidents and had the biggest impacts.

Logs

Logs provide detailed, timestamped records from individual components. Raw logs can be very helpful in postmortem investigations and in compliance tracing. Unfortunately, they are also noisy, unstructured and storage-intensive. As a result, they are poorly suited to serve as real-time signals.

The solution isn’t to discard logs — it’s to treat them as a searchable dataset. By enforcing schemas, applying filters and redacting sensitive personal data at ingestion, you can reduce log volume and improve value without compromising traceability.

Traces

Traces show the full journey of a request, which helps connect the dots across services. Traces visualize call sequences, latency at each hop and ownership boundaries. This end-to-end view is critical in distributed architectures, since slowdowns rarely occur in isolation.

Teams use traces for debugging, but they also support ownership routing and blast radius analysis. When integrated with metrics and events, tracing guides navigation through a distributed system. These insights are essential for reducing MTTR and improving handoffs.

Building your observability strategy: A step-by-step guide

When crafting an observability strategy, every organization’s starting point will look different. Whether you prioritize SLOs, cost control, cloud native tools or otherwise, the most important piece is rooting the strategy to your organization’s specific needs. 

The following steps below offer a clear, adaptable framework that can help with this process. They each prioritize signal clarification, complexity reduction and strategic observability investments.

Step 1: Define your business goals and KPIs 

Effective observability starts with clarity. You begin by translating business priorities into measurable goals related to system behavior, then identifying the specific signals that impact those goals.

For reliability, meaningful insight will likely derive from metrics such as p95 and p99 latency, error rate tied to SLO burn or availability targets. For cost control, measuring usage — like cost per 1,000 requests or idle waste by namespace — will provide actionable feedback. And for growth planning, capacity headroom or minutes-to-risk will help with flagging emerging constraints.

By aligning telemetry to specific outcomes with organization-wide relevance, you help frame observability as a business driver. Dashboards will track what matters most across IT operations as well as finance and development.

Step 2: Choose the right tools and platform 

Observability tools should simplify workflows rather than add friction. Many organizations begin with proprietary monitoring products, which can provide quick visibility but may result in fragmented views as environments grow. Over time, this can make it harder to coordinate issues across systems and slow down response.

A more sustainable approach is to lean on open tooling that integrates with existing infrastructure while advancing you toward unified visibility. Frameworks like OpenTelemetry, alongside components such as Prometheus or Fluent Bit, give you a flexible and consistent foundation for collecting signals. When paired with a platform that correlates metrics, events, logs and traces in a single context, these tools support agility while avoiding lock-in.

If you run across mixed estates, prioritize seamless hybrid cloud observability to keep context intact across environments. Platforms like SUSE Cloud Observability can help by building upon open tooling foundations and organizing your data around service topology and ownership. This approach provides deep awareness of workloads and their dependencies. As a result, you can better connect symptoms to upstream or downstream causes, even in fast-changing, multi-environment landscapes.

Step 3: Standardize data collection and management 

Observability only succeeds when data is consistent. If every team collects signals differently, correlation breaks down and costs rise without producing clarity. Standardization turns telemetry into observability best practices you can scale across services and environments.

A useful starting point is a data contract. It defines the metadata that every record must carry, such as service name, environment, deployment identifier and owning team. With these labels in place, signals can be routed to the right owners, dashboards can stay reliable and cost reporting can map directly to responsibility.

Pipelines add another layer of control. By filtering, redacting and routing telemetry at the source, you can help keep volume manageable while also protecting sensitive data. Retention tiers will help to further balance cost and readiness — for example, keeping seven days of hot data for rapid investigation, thirty days of warm data for trend analysis and longer-term archives for audits. 

Step 4: Foster an observability culture

Tools make observability possible, but culture makes it effective. When teams embrace shared practices and accountability, telemetry becomes a driver of real outcomes. Culture determines whether telemetry drives action or sits unused in dashboards.

One effective practice is linking observability data directly to team ownership. When you label services with their responsible group, alerts can flow to the right inbox, reducing confusion and wasted time. It can even make incident reviews more collaborative.

Embedding observability into regular routines will further reinforce its value. Over time, teams that review SLO burn rates or replay traces as part of incident analysis will shift from reactive firefighting to proactive reliability. These rituals can build trust across groups, shorten recovery time and reduce burnout. 

Creating a centralized observability team is a growing trend in observability, rooted in advice from Gartner’s Prepare for the Future of Observability report. Establishing a dedicated group can further increase accountability and ensure continual revisiting of observability tools, practices and overall strategic alignment.

Common observability challenges to avoid

Even with solid planning, enterprise observability initiatives can stall or introduce new issues. As organizations scale, recurring operational problems may resurface. By anticipating the following challenges, you can prepare mitigation strategies in the short term and foster long-term resilience.

Tool sprawl

When teams adopt tools independently, platform engineers are left maintaining multiple data formats, agents, dashboards and alert rules. This duplication makes central visibility difficult and costs unpredictable. Consolidating around shared platforms or standard views will help ensure that signals remain accessible and actionable instead of siloed. 

Missing or inconsistent telemetry labeling

Logs, traces and metrics without consistent attributes — such as service name, environment or owner — can create gaps in accountability and routing. By standardizing label keys through telemetry pipelines, you can reduce drift and help engineers reduce lost time during triage. Ephemeral workloads amplify volume, meaning that effective container observability necessitates consistent labels and retention tiers. In addition, consistent telemetry labeling strengthens cost reporting and compliance evidence.

Alert fatigue

Excessive alerts, especially from noisy container-level metrics, can overwhelm on-call staff and reduce response effectiveness. Consider shifting the focus to service-level indicators like SLO burn, minutes-to-risk or other user-impact measures. This can reduce paging volume and help maintain attention on real risks.

Context gaps during incidents

When data streams remain unlinked, engineers must spend extra time flipping between dashboards to piece together a narrative. If you connect MELT data through shared identifiers or a topology-aware view, you help engineers establish context in real time. As a result, you can accelerate investigations and shorten MTTR.

Telemetry without guardrails 

Telemetry costs can grow quietly but quickly. When signals are ingested and stored without contract-based controls, you are more likely to be surprised by high usage-based billing. Clear data contracts and retention tiers will more clearly demonstrate observability’s unique costs. 

Put your observability strategy into action with SUSE

Even teams with a strong commitment to observability can struggle with fragmented data, reactive workflows and unclear ownership. For many organizations, a unified model is key to transforming isolated efforts into coordinated action.

Platforms like SUSE Cloud Observability meaningfully connect signals, systems and teams. By aligning MELT data with service topology and ownership, SUSE can help you triage faster, escalate less and automate with greater accuracy. Live dependency maps can show how services relate and who’s responsible. Alerts can route directly to the right team, supported by the context needed to resolve and review issues.

At scale, SUSE helps you apply observability strategy without adding overhead. Built-in pipelines can filter, redact and shape telemetry at the source. Policies can manage ingest, retention and cost alignment — without custom plumbing.

Built to work with the best open source monitoring tools and anchored in open standards like OpenTelemetry, SUSE reduces lock-in risk and integrates cleanly with your stack. Curated dashboards, alerts and runbooks can help shorten ramp-up and support goals like SLO protection, spend control and reliability across environments.

Ready to multiply your performance? Try SUSE Cloud Observability free on the AWS Marketplace.

Observability strategy FAQs

What is the primary goal of an observability strategy?

The primary goal of an observability strategy is to ensure that raw metrics, events, logs and traces can be turned into shared decisions that support business goals. A strong strategy prioritizes first-class signals, aligns teams and builds context that enables faster, more confident action.

How do you measure the success of an observability strategy?

You measure the success of an observability strategy by tracking priorities — like uptime, performance or spending — that are tied to your observability strategy. You might use metrics like MTTR, escalations and cost efficiency in your measurements.

Is observability a replacement for monitoring?

No, observability is not a replacement for monitoring. Monitoring tracks known risks with predefined thresholds. Observability helps teams investigate unknown problems by connecting signals across systems, which complements monitoring.