How Do Observability Platforms Work?
The functionality of observability platforms is rooted in their ability to collect and analyze three primary types of data: metrics, logs and traces.
Metrics are numerical data points — such as CPU usage, memory consumption and network latency — that offer a snapshot of a system’s overall health. Logs provide detailed records of events occurring within the system, including errors and transactions, making them essential for troubleshooting and root cause analysis. Traces map the flow of transactions within apps, enabling the identification of bottlenecks and inefficiencies in their performance.
Together, these data types form the backbone of effective observability, and collection can be agent-based or agentless.
“Agent-based systems involve deploying software on specific servers or devices to collect detailed data,” Panchal says.
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These agents then transmit the information to a centralized observability platform. Agentless systems use the built-in functionalities of devices or operating systems to do the same. Both approaches reduce deployment complexity.
Modern observability platforms also leverage open-source frameworks such as OpenTelemetry.
“OpenTelemetry is enabling standardized data collection, making it easier for organizations to implement observability without being locked into vendor-specific solutions,” Panchal says.
Once the data is within the observability platform, it’s indexed, filtered and correlated, generating actionable insights.
“Automation is a key feature,” Panchal says. “For example, when recurring issues are detected, automated responses can restart affected services, reducing downtime and manual intervention.”
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How Does Data Observability Fit In?
Data observability focuses on ensuring the quality, reliability and contextual relevance of the data being analyzed. This is particularly important for agencies, where inaccurate or incomplete data can lead to flawed decision-making and inefficiencies.
“Good data observability ensures that the data being analyzed is reliable, complete, consistent and valid,” Panchal says. “This is critical for identifying anomalies and performing root cause analysis.”
Data observability plays a vital role in ensuring that observability platforms provide accurate and actionable insights and involves validating data quality, contextualizing metrics and logs, and tracing issues back to their sources.
“If there’s a performance drop, data observability helps provide contextualization and determine whether the issue is with the network, server or application,” Panchal says.
Data observability further supports broader IT initiatives such as artificial intelligence adoption and cloud migration.
“AI relies on clean, well-structured data to generate meaningful insights,” Panchal says. “If the data is messy or redundant, it slows down AI processes and reduces the effectiveness of automated solutions.”
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Helping Agencies Eliminate Tool Sprawl
One of the most pressing challenges for agencies is tool sprawl, the proliferation of disconnected tools across IT silos. This often leads to inefficiencies, redundant data and increased costs.
Observability platforms address tool sprawl by consolidating multiple monitoring functions into a single, unified system.
“Tool sprawl happens when different teams purchase tools for their specific needs without considering the bigger picture,” Panchal says. “An application team might monitor servers while a server team does the same, resulting in overlapping but inconsistent data.”
Consolidation leads to cleaner data and standardization and correlation of events for more efficient data management, he adds.
Observability platforms also help agencies phase out outdated monitoring tools that are siloed and limited in scope, and enable IT teams to work more effectively by fostering collaboration and providing a single source of truth.
“With a unified platform, agencies can identify performance issues across all IT systems,” Panchal says. “This lets them speed troubleshooting, resolve issues faster and ultimately deliver better services to end users.”
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