So you’ve decided a centralized telemetry pipeline might be right for your organization. Before you start evaluating vendors or building an internal solution, there’s critical preparation work that will determine whether your implementation succeeds or becomes yet another layer of complexity. 

This checklist covers the 10 essential steps organizations must take before implementing a telemetry pipeline. Skip these, and you’ll likely end up with an expensive piece of infrastructure that solves the wrong problems. 

 ☑️ 1. Define Clear, Quantifiable Objectives 

Before evaluating vendors, get brutally clear on why you need a centralized pipeline. 

Strong reasons include: 

Reducing ingest and storage costs by a specific percentage, achieving vendor independence through OpenTelemetry adoption, establishing central governance over data policies, controlling telemetry volume and quality from one location, or enabling multi-destination routing without duplicating agents. Avoid vendor lock-in by maintaining complete data ownership and the flexibility to route to any destination.

Red flags to avoid: 

“Everyone else is doing it,” “Our vendor recommended it,” or “We want to modernize.” 

If you cannot quantify at least one outcome, adoption will likely stall. 

☑️ 2. Conduct a Comprehensive Telemetry Inventory 

Treat this like a financial audit, not a technical exercise. Most organizations don’t know what data they generate, who owns it, why it exists, or who actually uses it. 

For each signal type (logs, metrics, traces), document: source (service, platform, network, security, mainframe, IoT), daily volume, cost per destination, consumer (SRE, SecOps, application teams, compliance), retention requirements, and business criticality. 

This inventory typically surfaces 20–40% of data that can be eliminated or reduced, though industry reports suggest as much as 75% of ingested telemetry goes unused in many organizations. If this exercise feels painful, that’s exactly why you need a pipeline.

☑️ 3. Define Edge vs. Central Processing Boundaries 

Centralized doesn’t mean everything flows through one box.

Determine upfront what stays at the edge (crash protection, minimal sampling, fail-safe buffering) and what moves to the central pipeline (policy-based reduction, routing, enrichment, normalization).

Trying to centralize everything immediately is how pipelines become single points of failure.

☑️ 4. Standardize Data Formats, Not Tools 

You don’t need to standardize vendors first; you need to standardize data shape. 

Essential standards to establish: 

Log structure (JSON, consistent field naming), metric naming conventions, trace attributes, and resource metadata (service.name, environment, region, owner). 

OpenTelemetry provides an excellent baseline, but the key is schema discipline. If every team sends different shapes of data, your pipeline becomes a trash compactor. The goal is standardized data shapes and vendor-neutral formats that give you freedom to route telemetry anywhere without proprietary lock-in. 

☑️ 5. Establish Telemetry Policies as Code 

Write explicit policies before adoption: What log levels are allowed in production? What sampling rates apply to different services? Which data contains PII and requires redaction? What routes to SIEM vs. observability vs. cold storage? What data is forbidden entirely? 

Your policies must be: 

Versioned in source control, reviewable through standard processes, testable before deployment, and rollback-safe. 

If these rules live in tribal knowledge or Slack threads, a centralized pipeline will expose the chaos instead of fixing it. 

☑️ 6. Assign Clear Ownership and Accountability 

A centralized pipeline will fail if ownership is fuzzy.

You need a designated platform owner (typically Platform Engineering or Observability team) and a clear RACI matrix between app teams, SRE, SecOps, Compliance, and Finance. 

Someone must own cost outcomes, policy enforcement, pipeline reliability, and change management. If no one owns these responsibilities, the pipeline becomes “someone else’s problem.”

☑️ 7. Prepare for Organizational Change 

Central pipelines change power dynamics, and you need to prepare teams psychologically, not just technically.

Be transparent about changes: 

Application teams will have less unlimited data freedom, security teams gain stronger enforcement leverage, finance will have visibility into cost drivers, and SRE becomes a gatekeeper for telemetry decisions. 

Without early communication and clear visibility into decisions, teams will bypass the pipeline and reintroduce shadow agents. Adoption failure is usually political, not technical.

☑️ 8. Build Observability Into the Pipeline Before Enforcement 

The fastest way to lose trust: “We dropped your logs. Trust us.”

Before enforcing policies, implement visibility into what data is being dropped and why, clear attribution showing which policies are affecting which teams, self-service dashboards for teams to understand their telemetry flow, and feedback loops so teams can challenge or refine policies.

Transparency builds trust. Opacity breeds rebellion.

☑️ 9. Start with Limited Scope and Iterate 

Avoid the temptation to migrate everything at once.

Begin with a single team or service as a pilot, non-critical data types first, clear success criteria and feedback mechanisms, and documented learnings to inform broader rollout.

Gradual adoption allows you to refine policies, validate assumptions, and build organizational confidence before expanding scope.

☑️ 10. Evaluate Total Cost of Ownership, Not Just License Price 

Pipeline costs extend far beyond the vendor invoice.

Consider: 

Engineering time for maintaining configurations, troubleshooting issues, and handling migrations. Incident risk—what happens when telemetry is lost or delayed? Knowledge concentration—how many people understand the full data flow? Migration drag—how difficult is it to change backends or adopt new tools? Compliance exposure—can you prove data handling meets regulatory requirements?

A DIY approach might appear free, but you’re already paying in hidden costs. 

Modern purpose-built pipelines can reduce total observability costs by 30-40% while simultaneously increasing reliability through features like infinite buffering that prevent data loss during traffic spikes or destination outages. The question isn’t whether you can afford a pipeline solution—it’s whether you can afford to keep running a homegrown pipeline operation inside your organization. 

What’s Next? 

Now that you understand what preparation looks like, how do you know if you’re actually ready? In our final post, we’ll provide a comprehensive self-assessment scoring framework across six critical dimensions: cost and economics, technical complexity, knowledge and maintainability, operational risk, governance and compliance, and organizational scale. 

This assessment will help you determine whether you should keep your telemetry infrastructure simple, start exploring options, or immediately evaluate alternatives. The scoring framework is designed to be brutally honest about where you are—and what that means for your next steps. 

Learn more about Apica Flow and cost savings here: https://www.apica.io/cost-savings/