Modern enterprises have instrumented everything in pursuit of observability – collecting floods of metrics, logs, and traces from every system. Yet CIOs are finding that more data does not automatically mean more insight. In fact, an observability overload is leaving IT teams drowning in telemetry “clutter,” while costs skyrocket with little proportional gain in outage prevention or performance improvement. Surveys consistently confirm this challenge: over 80% of organizations struggle with the cost, complexity, and sheer volume of their observability data and tools.
Paradoxically, the very practice meant to enhance clarity is instead creating confusion and cost. CIOs stand at a crossroads – they must rethink their approach to observability, shifting focus from collecting everything to gaining actionable intelligence from the data that matters. In this analysis, I’ll explore why less is more in observability and how a new philosophy of “controllability” can help IT leaders escape the current cost spiral while delivering real business value and future-proofing their stack for scale and AI.
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The Observability Clutter
The explosion in telemetry has outpaced most teams’ ability to use it effectively. Microservices, multi-cloud environments, and containerized apps generate massive volumes of logs, metrics, and traces—often collected “just in case.”
But this flood of data comes at a cost. A 2023 survey found that 72% of organizations use more than nine observability tools, leading to fragmented insights and alert fatigue. Another 2024 report revealed a staggering 62 distinct observability tools in use across respondents, with 70% of teams juggling four or more tools in their stack. Each tool adds its own siloed dashboards and alerting logic, overwhelming engineers with fragmented views. As a result, tool sprawl—not visibility—has become the top barrier to effective observability.
Even worse, the deluge of telemetry frequently obscures critical signals. When everything is instrumented, nothing stands out. It’s not uncommon for engineering teams to spend more time sifting through irrelevant alerts than solving the actual problem.
Forward-thinking IT leaders now recognize that simply aggregating mountains of raw telemetry is not the goal. Observability’s value lies in actionable insight, not data collection. Top teams are evolving from just collecting data to “actively governing, shaping, and optimizing telemetry” to focus on what’s truly important. In practice, this means raising the signal-to-noise ratio: filtering out low-value data, consolidating tools, and homing in on the key indicators that illuminate system health and user experience. The aim is intelligent observability – capturing less data overall, but more of the right data. This “less is more” approach can actually improve incident detection and root-cause analysis by removing noise that obscures issues.
The Cost Spiral
The cost of observability is rising faster than infrastructure itself. Gartner reports that more than one-third of enterprises now spend over $1 million annually on observability, with some exceeding $10 million. In one well-publicized case, a major tech firm was charged $65 million per year by a single observability vendor. While extreme, it reflects a broader trend: telemetry bills are becoming untenable, often accounting for up tooften more than 25% of a company’s total cloud spend.
What’s worse, most of this data is never used. Logs accumulate without analysis. Metrics are collected at high frequency, only to be down sampled or ignored. This is observability as an unchecked utility—a pipeline of one-way ingestion that few teams can justify or govern.
The result? CIOs are under pressure to explain—and reduce—these costs. According to research, 84% of technology leaders believe they’re overpaying for observability. Yet few have the mechanisms in place to change that.
Observability Needs a Control Layer
This is where the concept of controllability comes in. Rather than treating observability as a passive data sink, controllability adds an active layer of intelligence—one that governs telemetry in real time.
Think of it as a shift from one-way “dumb pipes” to adaptive systems with feedback loops. These systems continuously assess what data is useful, what’s redundant, and what should be prioritized or suppressed. When done right, controllability increases the signal-to-noise ratio while reducing volume, cost, and complexity.
At its core, controllability is built on three principles:
1. Active Telemetry Management
Instead of statically collecting all data, controllable systems observability adjusts in real time. For example, they this might increase trace sampling during a production incident, then scale back during normal operations. Unused logs can be filtered out or rerouted. Low-value metrics can be deduplicated or down sampled on the fly.
2. Feedback Loops
Telemetry pipelines become self-aware, adapting to changes in system behavior. This might mean auto-tuning log levels during a deployment or dynamically routing only high-severity events to high-cost storage. Feedback loops ensure observability is right-sized for the moment—not one-size-fits-all.
3. Open Standards and Vendor Neutrality
To support this adaptability, controllability must be built on open foundations. OpenTelemetry (OTel) is emerging as the de facto standard for vendor-neutral instrumentation, already adopted by nearly half of enterprises. It enables organizations to decouple data generation from proprietary tools, offering freedom to optimize observability without being locked in.
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Real Business Impact
This goes beyond technical hygiene. Instead, it’s about business-critical transformation. CIOs embracing controllability are seeing measurable improvements across four key dimensions:
- Cost Control: Intelligent telemetry governance, dynamic sampling, and tool consolidation are cutting observability costs by 30–50% in many cases. More importantly, costs become predictable and scalable, avoiding the spike-and-surprise cycle.
- Operational Clarity: With less noise and fewer redundant alerts, teams detect issues faster and resolve them more effectively. One company reported a 90% drop in false alerts after trimming low-value telemetry, improving mean time to resolution (MTTR).
- AI Readiness: Clean, curated observability data is the fuel for effective AIOps and predictive analytics. Controllability delivers the structured, relevant telemetry that advanced analytics and AIOps demand.
- Resilience and Agility: When observability is adaptive, organizations can roll out changes, respond to incidents, and scale infrastructure with confidence. Monitoring becomes an enabler—not a bottleneck.
A New Model for CIOs
Transitioning to controllability doesn’t require ripping out existing tools. In fact, the best approach is incremental and pragmatic. CIOs can begin realigning their observability strategy by focusing on two key areas: strategic shifts and a cultural mindset that supports purposeful telemetry.
These are the immediate steps CIOs can lead to reduce observability clutter and cost while improving insight:
- Inventory and Rationalize: Identify which data sources and tools are delivering value—and which aren’t. Remove redundant dashboards, unify telemetry streams, and benchmark data usage against spend by department, domain, and applications. This sets the stage for cost optimization and simplification.
- Implement Governance: Set telemetry policies that define instrumentation and semantic convention standards, data retention periods, sampling thresholds, and telemetry quotas per team, application, and/or business unit. Encourage teams to assess whether their data collection aligns with business value and use lightweight governance mechanisms to enforce standards.
- Standardize and Automate: Move toward OpenTelemetry for consistent, vendor-neutral instrumentation. Adopt a control plane that applies real-time rules, enforces data policies, and adapts telemetry pipelines as systems evolve—without requiring constant manual oversight.
- Pilot Adaptive Approaches: Select high-value applications or environments to pilot adaptive observability techniques. These might include automated trace sampling during rollouts or real-time filtering of known “noisy” logs. Use feedback from these pilots to inform broader rollout strategies.
Beyond the tools and telemetry lies a human challenge: getting teams aligned with a new observability philosophy. CIOs must foster a culture of “observability with purpose.”
- Foster a Culture of Intentional Observability: Communicate to engineering teams and business stakeholders alike why observability is evolving and requires intent- to know why you are collecting this data in the first place. Emphasize that it’s not about limiting visibility—it’s about focusing effort and spend where it delivers the most value. Encourage teams to highlight pain points in the current monitoring setup (such as alert fatigue or blind spots), and celebrate wins where optimization improves performance or clarity.
Less Is More
Enterprise observability is at a turning point. The old model—collect everything, store everything, pay for everything—is collapsing under its own weight. CIOs now have an opportunity to rewrite that model.
By focusing on controllability, they can transform observability from a reactive cost center into a proactive source of business insight. They can reduce data volume while improving service quality. They can cut costs without sacrificing reliability. And they can prepare their organizations for the demands of AI, automation, and real-time digital operations.
In a world flooded with data, less isn’t just more—it’s essential.
