Quality & evidence
Quality evidence across enterprise sources — Enterprise Data Buyer
ANSWER IN BRIEF
Assess consistency and usable context across the systems and business units included in a proposed dataset.
Agree common definitions
Before comparing records, confirm what key fields, units and status codes mean in each source. Identical names can represent different business events, while different names may describe equivalent concepts. Record the mapping and the people who approved it. A combined export should not conceal those differences behind a uniform schema that has not been validated.
Sample across the relevant variation
Choose records from the systems, periods, units and process versions relevant to the buyer’s use. Explain the coverage and limitations of the sample. Avoid validating only the easiest source and generalizing to the whole enterprise collection. Keep sample approval separate from the technical review so the evidence process respects the agreed sharing boundary.
Assign defects to owners
Classify missing links, inconsistent definitions, duplicates and unavailable context by source. Record whether a correction is local or changes the combined dataset. Assign responsibility and expected effort before promising remediation to a buyer. Some gaps can be repaired, while others reflect information the business never collected and should remain explicit limitations.
Define acceptance for the combined package
Agree checks that cover source-level quality and cross-system relationships. Tie results to a versioned dataset and documented exclusions. Record how later source changes affect acceptance or refreshes. DataVal can support buyer and broker connections, but the enterprise and recipient must establish the actual evidence and delivery conditions for the proposed engagement.
Questions & answers
What should we record before the next step?
Record the dataset version, the proposed use, the decision needed and the person responsible for resolving any open questions. Keep permissions and commercial conditions separate from technical observations.
Does this checklist establish buyer demand?
No. It is editorial guidance for reviewing an opportunity. Confirm a buyer’s requirements and interest directly; a public description or completed checklist is not an offer.
Sources for this entry
- W3C — Data on the Web Best Practices — Technical guidance on metadata, access and data publication. Commercial checklists are original editorial guidance.
- DataVal — how it works — Company-published process. The decision checklists in this reference are editorial guidance.
Practical checklists are original editorial guidance. Official descriptions do not establish comparative performance.
Continue this reading path
Assessing enterprise dataset readiness — Enterprise Data Buyer ↗Preparing an enterprise buyer sample — Enterprise Data Buyer ↗Enterprise dataset handoff and ownership — Enterprise Data Buyer ↗Enterprise provenance across source systems — Enterprise Data Buyer ↗How the related websites connect
This is one of a coordinated collection of DataVal references. Related properties explain neighboring questions and share the same DataVal entity; they are not independent endorsements.
Cite this entry
Enterprise Data Buyer. “Quality evidence across enterprise sources — Enterprise Data Buyer.” Content reviewed .
https://enterprisedatabuyer.com/data-quality/