Data Quality Issue Guide
The Data Quality Issue Guide helps data consumers document a suspected discrepancy, compare it with the intended meaning, and prepare safe, reproducible evidence for the right owner. It distinguishes quality questions from privacy and security incidents.
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Rolled out to every employee at AutoZone (125,000), PetSmart (50,000+), A.S. Watson and Raley's (20,000) — and at larger retailers we are not permitted to name.
Built for: Retail and supply chain · Financial services · Healthcare operations · Manufacturing · Higher education
Overview
The Data Quality Issue Guide is a content page for employees who use shared datasets, reports, tables, or fields and need to raise a suspected quality issue. It gives readers a sequence for naming the data, recording the observation and comparison conditions, explaining the expected meaning, describing a safe reproduction, and identifying a likely owner. Its evidence table distinguishes useful context from credentials, secrets, unrestricted exports, and unnecessary personal records.
The fictional Northstar Supply example makes the handoff concrete: an analyst sees 1,240 orders and then 1,198 on a rerun using the same stated week and region filter. The example frames the expected measure as orders placed, not orders shipped, while making clear that the discrepancy does not establish a root cause. The page also distinguishes the likely contributions of a dataset owner, data platform team, and reporting analyst, then directs readers to their organization’s established data-support route.
Use this guide when a result appears inconsistent, a field or measure is unclear, or an authorized colleague needs enough detail to compare results. Do not use it to report personal-data exposure, unauthorized access, or a security event; those concerns belong in the established privacy or security reporting route. This page is an authored reference, not a submission form, live queue, system of record, or commitment to an owner or response time. Customize the route and role labels to match verified local procedures before publishing.
Standards & compliance context
- The guide’s evidence boundary advises readers to exclude credentials, secrets, unrestricted exports, and personal records that are not needed; follow organizational privacy and data-handling policies for any permitted evidence.
- Suspected personal-data exposure, unauthorized access, or a security event should go through the organization’s established privacy or security reporting route rather than this data-quality handoff.
- This authored reference does not itself satisfy legal or regulatory reporting obligations, establish a system of record, or promise an investigation response; confirm local requirements and routes before publication.
- Keep headings descriptive and the evidence table readable to support accessibility, and verify the published page against WCAG 2.1 AA, including keyboard access and contrast.
General regulatory context for orientation only — verify current requirements with counsel or the relevant agency before relying on this template for compliance.
What's inside this template
Report a suspected data-quality issue
Start here to understand the guide’s scope and distinguish a data-quality handoff from privacy or security reporting.
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A focused handoff guide for people who use shared data. Use this guide to describe a result that appears inconsistent, explain what you expected it to mean, and give the responsible owner a safe way to compare it. It covers data-quality routing—not privacy or security incident reporting. Fictional example: Northstar Supply is a cross-functional organization using a shared weekly orders dataset. Its scenario illustrates how to make a report reproducible; it does not describe a real investigation.
First, identify what you are seeing
Use these scenarios to decide whether you are looking at a discrepancy, a difference in definitions, or an interpretation question.
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Compare the observation with the intended meaning before routing it. A likely quality discrepancy A weekly total changes when you rerun the same report with the same date range and region filter. Record both results and the comparison conditions. A definition difference One report counts orders placed while another counts orders shipped. Check which event the measure is intended to represent before treating the difference as an error. An interpretation question You are unsure whether a field includes cancelled records. Name the field and the decision you need to make, then ask the dataset owner to clarify its meaning.
Prepare a reproducible handoff
Gather the names, comparison conditions, expected meaning, and safe reproduction details an authorized owner needs.
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Separate what you observed from what you expected. Name the data Identify the dataset, report, table, and field using the names shown in the resource you used. State the observation Record the result, when you saw it, the comparison period, and the filters or grouping applied. Explain expected meaning Say what you expected the value to represent and the definition, report note, or business context behind that expectation. Describe a safe reproduction Write the steps, query description, or report settings another authorized colleague can use to compare the result. Do not include credentials or secrets. Identify the likely owner Start with the dataset owner for meaning and ownership questions. A data platform team may help with shared pipeline or refresh behavior; a reporting analyst may help with report filters and presentation.
Evidence boundaries
Check what makes a handoff useful and what information should stay out of it.
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An authored reference for preparing a safe, useful handoff—not a live queue or system of record. Useful evidence Keep out of the report Dataset and field names; comparison period; filters or grouping; observed result; expected meaning and its basis; steps or a query description needed to reproduce. Passwords, tokens, or other credentials; secrets; unrestricted exports; personal records or identifiable data copied when they are not needed.
Worked example: Northstar weekly orders
See how a fictional analyst presents two results without claiming to know their cause.
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A fictional example showing how to compare like with like. What was observed A Northstar Supply analyst sees a weekly orders total of 1,240, then gets 1,198 on a rerun. Both runs use the same stated week and region filter. The expected measure is orders placed during that week, not orders shipped. First result: 1,240 orders. Rerun result: 1,198 orders. Comparison: same week and same region filter. Expected meaning: count by order placement date. Why this is a useful handoff The matching date range and region filter give the owner a specific comparison to reproduce. The difference merits investigation but does not establish a root cause. Ask the dataset owner to confirm the measure definition and intended date field. Share both results and the report settings through the established data-support route. Do not assume whether refresh behavior, source changes, or another factor explains the difference.
Who may help
Choose a likely first role based on whether your question concerns data meaning, shared platform behavior, or report presentation.
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Start with the role closest to the question. The dataset owner is the first point of accountability for meaning and ownership; other roles can help when the evidence points to platform behavior or report presentation. Dataset owner Clarifies the dataset’s intended meaning and ownership, then identifies the appropriate next handoff. Data platform team Investigates shared pipeline or refresh behavior when the issue may affect more than one report or data consumer. Reporting analyst Checks report filters, grouping, and presentation when the underlying data may be represented differently.
Route the prepared evidence
Use the established data-support route for the dataset, and redirect privacy or security concerns to their separate reporting process.
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Use your organization’s established data-support route for the relevant dataset. This guide does not submit a report, assign an owner, or promise a response time. Provide the dataset and field names, observed result, comparison conditions, expected meaning, and safe reproduction steps. Ask the owner to confirm the interpretation and advise on the next step. If the concern involves personal-data exposure, unauthorized access, or a security event, use the organization’s established privacy or security reporting route instead.
How to use this template
- Replace the fictional example and role descriptions only where needed, then add your organization’s verified data-support route and the appropriate privacy or security reporting destination.
- When a discrepancy or interpretation question arises, identify the dataset, report, table, and field using the names shown in the resource you used.
- Record what you observed, when you saw it, the comparison period, and the exact filters or grouping applied, then state the expected meaning and its basis.
- Write reproducible steps, report settings, or a query description that an authorized colleague can use to compare the result, excluding credentials, secrets, and unnecessary personal records.
- Send the prepared evidence through the established route, starting with the dataset owner for meaning or ownership questions and involving platform or reporting roles when the evidence points to their area.
- Ask the owner to confirm the interpretation and advise on the next step; route privacy or security concerns separately and do not treat the example as proof of a cause.
Best practices
- Use the exact dataset, report, table, and field names shown in the source so the owner can locate the item without guessing.
- Capture the date range, filters, grouping, and observation time for each result rather than describing a number as simply wrong.
- State what the measure should represent and cite the definition, report note, or business context behind that expectation.
- Compare like with like by checking whether reports count the same event and use the same date field before escalating a difference.
- Describe safe reproduction steps instead of attaching unrestricted exports or copying records that are not needed for the comparison.
- Keep credentials, tokens, secrets, and other access information out of every handoff.
- Treat a discrepancy as a question for investigation, not as evidence that a refresh, source change, or specific failure caused it.
- Keep this page’s data-quality route distinct from established privacy and security incident reporting procedures.
What this template typically catches
Issues teams running this template most often surface in practice:
Common use cases
Frequently asked questions
What kinds of issues should I use this guide for?
Use it for suspected inconsistencies in shared data, unclear field or measure definitions, and questions about report filters, grouping, or presentation. The guide helps you describe what you observed and what you expected it to mean. It is not a privacy or security incident reporting procedure.
What evidence should I include in a handoff?
Name the dataset, report, table, and field as they appear in the source you used. Include the observation, when you saw it, the comparison period, filters or grouping, expected meaning and its basis, and steps another authorized colleague can use to reproduce the comparison. Keep credentials, secrets, unrestricted exports, and unnecessary personal records out.
Who should review a suspected data-quality issue first?
Start with the dataset owner for questions about meaning and ownership. A data platform team may be relevant when shared pipeline or refresh behavior could be involved. A reporting analyst may help check filters, grouping, and presentation; the guide does not assign these roles or create a support queue.
How often should I use or review this guide?
Use it whenever a result appears inconsistent, a definition is unclear, or another authorized person needs to reproduce your comparison. The page does not set a review cadence or response-time commitment. The organization should assign a content owner and review the guidance when its data-support route or escalation procedures change.
Does this guide satisfy regulatory reporting requirements?
No. It provides a practical structure for a data-quality handoff, not a regulatory reporting process or legal determination. Follow applicable organizational policies and jurisdiction-specific requirements for records, privacy, and security. Route suspected personal-data exposure, unauthorized access, or security events through the established privacy or security reporting route instead.
What is a common mistake when reporting a discrepancy?
Treating two figures as comparable without checking whether they use the same date field, filters, grouping, or measure definition can send the investigation in the wrong direction. State both what you observed and what you expected, and explain the basis for that expectation. A difference is evidence to investigate, not proof of a specific cause.
Can I customize this template for our organization?
Yes. Replace the fictional Northstar Supply example with a clearly identified example suited to your data consumers, and name your verified data-support route. Clarify local role names and escalation paths without implying this page submits a report or assigns an owner. Keep the evidence boundary and privacy/security distinction visible.
Does the page integrate with a ticketing system or data platform?
The template is an authored guide, not a live queue, system of record, or connected reporting workflow. It does not submit evidence, assign owners, or promise a response time. Add only verified links or connections through your organization’s supported site setup, and explain the actual destination to readers.
How is this better than sending an informal message?
The page prompts the reporter to include the data names, comparison conditions, expected meaning, and safe reproduction steps an owner needs to assess the question. That reduces ambiguity compared with a message that only says a number looks wrong. It still relies on the organization’s established route and the appropriate owner to investigate.
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Rolled out to every employee at AutoZone (125,000), PetSmart (50,000+), A.S. Watson and Raley's (20,000) — and at larger retailers we are not permitted to name.
