Checklist

Operational Data Pipeline & Quality Checklist

Interactive checklist to evaluate and remediate common problems in operational data pipelines before dashboards or models are built. Collects status, remediation notes, suggested owners, and who should be involved so teams can act and track improvements.

Interactive Tool

Operational Data Pipeline & Quality Checklist

Use this interactive checklist to assess the health and trustworthiness of an operational data pipeline before building dashboards or models. For each item, mark whether it meets expectations, add concise remediation notes, and nominate an owner or team to drive fixes. The form saves a JSON record for review, follow-up, and trend analysis.

Suggested participants: OT, Engineering, Data Team, Quality, Security, and Operations depending on the item. Typical cadence: run as part of deployment, pre-launch readiness checks, or periodic pipeline health reviews.

Which data feeds, environments, or models are covered by this check?
Is there a named person or team responsible for these data and its quality?
If none exists, who should own remediation?
Concrete next steps, target date, or blockers. Keep short and actionable.
Do teams agree where each metric or dataset originates and which system is authoritative?
List ambiguous metrics, proposed authoritative source, and who must align.
Are timestamps consistent across feeds (timezone, event vs ingestion time, clock sync)?
Examples: convert all to UTC, reconcile event vs ingestion timestamp, or add alignment transform.
Are there documented rules for imputation, forward-filling, backfilling, or flagging missing values?
Describe expected behavior and immediate fixes if rules are missing.
Have units been normalized and documented so metrics are comparable?
List mismatched units and the conversion plan.
Can reviewers trace a metric back to its raw inputs and see applied transforms?
Add links to docs, notebooks, or pipeline jobs and list missing lineage.
Is it clear how long raw and processed data are kept and why?
State retention durations, GDPR or regulatory needs, and immediate actions.
Are only authorized teams able to read, modify, or publish datasets?
List any over-permissive roles or gaps to remediate.
Are there alerts for schema drift, throughput drops, or unusual value distributions?
Briefly describe existing monitors or the plan to add them.
Do samples reflect production behavior and cover edge conditions?
Document sampling strategy and any gaps to address.
Select all teams that should participate in fixes or reviews.
A quick synthesized judgment of how much you would rely on this data for operational decisions today.
1.0 10.0
Who completed this checklist?
Date of this assessment (YYYY-MM-DD).
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