Information governance / interactive case study
See how missing information and possible duplicates become clear review priorities. This interactive walkthrough uses eight fictional catalog records and the published results of a read-only audit.
8 / 8records pass validation
75%documented provenance
75%SHA-256 coverage
7records routed for review
01 / Interactive governance review
Walk through the sample results, then explore why each record needs attention and what the reviewer should check next.
The interactive walkthrough needs JavaScript. Read the sample findings below or explore the linked project.
Audit stages
Read onlyShows precomputed sample results from the public Python audit. No files are scanned or uploaded.
Catalog evidence
Readysynthetic_catalog.csv / read-only
Explore missing information and possible duplicates. The walkthrough shows review recommendations; a person makes each catalog decision.
Review recommendation
High priorityASSET-004 / HIGH
Confirm the source reference and identity before any reuse, relocation, merge, or deletion.
02 / Reproducible evidence
The figures below come from the repository’s included eight-record synthetic fixture. They demonstrate the audit model—not a production catalog.
See which records are complete and which need better source information before reuse.
The queue puts missing identity information first, followed by records that need duplicate review.
Evidence, not authority
Similar names and matching file fingerprints identify records worth comparing. A reviewer decides whether they belong together or should remain separate.
Read-only by design
The audit organizes the findings and suggests next steps. The person responsible for the collection retains the final decision.
03 / Governance workflow
Five steps turn a broad quality review into focused work, with clear reasons for each recommendation.
Confirm the expected schema, controlled values, identifiers, review dates, and optional checksum format.
Calculate coverage, provenance, and confidence so evidence gaps can be compared consistently.
Surface exact-name, normalized-name, and exact-checksum candidates using conservative matching rules.
Rank records by the evidence they need and attach a clear, controlled next step.
Keep reuse, relocation, merge, retention, and deletion decisions with a human steward.
04 / Why this project matters
Collections often bring together archive imports, department copies, and records with incomplete source information. Similar filenames may hide important differences.
I built this case study to make those review questions easier to work through: show what is known, explain what needs attention, and give the reviewer a clear next step.
Scope boundary
Every figure comes from the repository’s eight-record synthetic fixture, not a production catalog. This walkthrough displays published sample results; it does not run the Python audit or inspect your files. Catalog data is not saved or uploaded. Only your light/dark theme preference may be saved in this browser. Review priorities are recommendations, not a risk certification or permission to change records.