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More tracking helps only when the added data can answer a product or operational question. When events and properties accumulate without a clear use, teams take on more storage and processing cost, harder queries, slower investigations, and extra work to govern what gets collected. The goal is not to track as little as possible; it is to collect data with enough useful context to support decisions without creating telemetry sprawl.
What over-instrumentation costs a product team
A tracked event is a record, not an insight. GitLab distinguishes an event—an action that occurred—from a metric, which summarizes or calculates across event information. Recording more actions does not automatically make decisions better; the actions need to support product insight or a more informed business decision.
The costs can show up in several places:
- Storage and processing: More data requires more capacity to collect, retain, and process. AWS identifies excessive collection as an instrumentation anti-pattern: “Over-instrumentation leads to unnecessary data collection, escalating costs, and storage requirements.” AWS Well-Architected guidance recommends prioritizing relevant data tied to customer experience and business outcomes.
- Query complexity: Indiscriminate collection can make it harder to find the signal that matters. Snowflake notes that data volume can increase storage costs and query complexity, slowing investigation.
- Investigation overhead: More fields can be useful when they let a team explore a problem without deploying new instrumentation. But that benefit depends on having captured relevant dimensions; collecting unrelated detail does not make diagnosis more effective.
- Governance work: Separate pipelines can duplicate enrichment, filtering, and routing, while applying redaction and other policies inconsistently. OpenTelemetry describes these problems in its guidance on telemetry infrastructure and processes.
Product analytics and observability overlap, but they are not interchangeable. Behavioral analytics records how people use a product; operational observability uses signals such as metrics, logs, and traces to investigate system behavior. A team should be clear about which question each signal is meant to answer.
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More detail is justified when it changes what a team can learn or do. For product analytics, that might mean understanding whether a customer completes a key flow. For observability, relevant context attached to metrics, logs, and traces can help diagnose an issue or answer an unanticipated question.
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Snowflake explains that correlating metrics, logs, and traces can support ad hoc questions without redeploying instrumentation when the relevant dimensions are already present. That flexibility has a tradeoff: richer, high-cardinality telemetry can increase storage, compute, and network demands and may require more involved tooling. The practical test is whether a dimension is likely to improve a real investigation or decision enough to justify its carrying cost.
A practical review for events and telemetry
- Start with the decision or question. State the customer outcome, product hypothesis, or operational issue the data should inform, and identify what the team might do with the answer. If neither is clear, the event needs a stronger justification.
- Define the trigger and necessary context. Specify what counts as the event and include only the properties needed to interpret it. Keep the distinction clear between the raw action recorded and the metric or decision derived from multiple records.
- Assign an owner and standardize meaning. Give someone accountability for the definition, and use consistent names and formats across teams so that similar events do not mean different things in different systems.
- Check actual use. Review whether the event is queried or informs a decision. Retire redundant or unused instrumentation through a controlled change process, rather than removing it without checking downstream dependencies. This is a practical governance recommendation, not a procedure prescribed by AWS or OpenTelemetry.
- Choose retention by purpose. If verbose data is needed for short-term troubleshooting, keep it for a bounded period suited to that use. AWS recommends aggressive retention for verbose datasets when detailed, short-term diagnostic value must be balanced against ongoing cost.
- Centralize the baseline and bound local variation. OpenTelemetry recommends centrally owned standards with controlled environment- or workload-specific customization. AWS also advises standard definitions and collaborative SLO setting. This supports consistency without assuming every workload has identical needs.
How to assess a tracking or observability platform
There is no universal best tool for every product team, and the cited sources do not provide a comparative product test. Evaluate platforms against the way your team collects, investigates, and governs data:
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- Can you control collection, filtering, redaction, and routing?
- Can teams maintain consistent event schemas or telemetry semantics while accommodating legitimate workload differences?
- Does the query experience fit the questions you actually need to answer, including correlation across relevant signal types?
- Can retention be set to match each use case?
- Are ownership, policy enforcement, and collection visibility clear across teams and pipelines?
- How does the cost model respond to data volume, processing, and high-cardinality fields?
OpenTelemetry notes that OpAMP is Beta in the cited guidance and advises teams to assess implementation maturity and supportability before standardizing on it. That is a reminder to evaluate operational readiness as well as features when choosing a collection-control approach.
What the sources establish—and what they do not
The guidance supports a clear conclusion: collecting data without a defined use can add cost, query friction, investigation delays, and governance burden. It does not establish a measured industry-wide percentage of product teams affected or quantify a universal productivity loss. Snowflake’s discussion of observability is vendor guidance, not independent comparative testing.
GitLab’s internal analytics documentation illustrates why product-analytics details need version and deployment context: it states that event-level collection is available on GitLab Self-Managed and Dedicated from version 18.0, while earlier versions use aggregated metrics, and that relevant identifiers are pseudonymized. These are GitLab-specific implementation details, not general rules for analytics systems; check the current GitLab documentation for its latest scope.
Sources: AWS Well-Architected, “Anti-patterns for strategic instrumentation”; OpenTelemetry, “Infrastructure and Processes in Non-K8s Environments”; GitLab Docs, “Internal analytics”; and Snowflake, “Observability vs. Monitoring”.
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