Selected work
MY ROLE
ROLE / Governance architecture · measurement
SCOPE
DATA GOVERNANCE · PYTHON
TOOLS
TOOLS / Python standard library · CSV/JSON
OUTCOME
PROOF / Eight-record synthetic fixture
SANITIZED NATIVE ARTIFACT
Governance audit matrix
BASELINE
75% documented provenance
SIGNAL
75% SHA-256 coverage
CONTROL
0.74 mean confidence
OUTCOME
PROOF / Eight-record synthetic fixture
CASE
04
DATA GOVERNANCE · PYTHON
Governance evidence you can calculate.
A read-only audit CLI that measures provenance, confidence, integrity coverage, and catalog-similarity signals across a reproducible synthetic dataset.
75% documented provenance
75% SHA-256 coverage
0.74 mean confidence
CASE MAP
04
BUILD
01
Create a repeatable, read-only view of catalog quality across schema health, provenance, evidence coverage, confidence, and record similarity while preserving source records.
02
A deterministic Python audit validates the CSV contract, calculates coverage, provenance, and confidence, then groups exact-name, normalized-name, and checksum similarities for focused review.
03
Controlled enums and identifiers, ISO review dates, SHA-256 validation, version-aware normalization, clear confidence thresholds, machine-readable findings, and read-only operation keep results consistent and review-ready.
04
On the included eight-record synthetic fixture, all records validate; 75% show documented provenance, mean confidence is 0.74, and the tool creates focused review groups across exact names, normalized names, and checksums. The result is a reproducible governance audit with transparent, fixture-based evidence.