A synthetic portfolio of three people-data governance case studies spanning identity and access management, demographic-data lineage, and workforce identity reconciliation across HRIS, analytics, background screening, and client reporting.
The lab is designed to show how technical controls become business decisions. Each project includes an executive-facing visual, documented review logic, synthetic data, and an auditable workflow that can be understood by HR leaders, HRIS teams, business analysts, compliance partners, and program sponsors.
Identifies stale, expired, orphaned, terminated-worker, privileged, and ownership-related account risks across HR systems. The project converts account-level exceptions into a risk-ranked remediation queue and a full operating protocol covering reviewer decisions, immediate protective action, evidence, service levels, escalation, sponsor reporting, and cycle closure.
Primary capabilities: IAM governance, HRIS analysis, access review, risk classification, remediation planning, Excel, Python
Uses verified historical sources to reconstruct missing or unsupported demographic records while preserving source lineage, identifying conflicts, and leaving unresolved records unassigned. The model explicitly prohibits inference from names, nationality, location, language, photographs, or AI.
Primary capabilities: SQL, SQLite, EEO data integrity, privacy-aware controls, lineage, reviewer workflow, Excel
Reconciles global person IDs, country-issued employee IDs, client assignments, background-check records, and Tableau analytics IDs. The project detects apparent ghost-worker exceptions, duplicate display IDs, cross-border mapping conflicts, and client-reporting risks while establishing a controlled release, implementation, UAT, cutover, and certification process.
Primary capabilities: HRBP risk advising, HRIS integration analysis, business requirements, identity crosswalk design, client assurance, program governance, Python, SQL, Excel
The three projects address different dimensions of the same enterprise question: Can the organization establish that the right person, the right attributes, and the right access are represented accurately across systems?
| Governance dimension | Portfolio project | Leadership question |
|---|---|---|
| Access | HR Identity & Access Governance Audit | Who should have access, and what requires remediation? |
| Attribute lineage | EEO Demographic Data Lineage | Can sensitive employee data be traced to a defensible source? |
| Workforce identity | Workforce Identity & Client Reporting Integrity | Can the organization prove that client-facing worker records belong to legitimate, cleared, assigned workers? |
| Professional lens | Evidence in the lab |
|---|---|
| HR Business Partner | Connects data defects to workforce readiness, client confidence, leader decisions, accountability, and communications. |
| HRIS Analyst | Designs reconciliation logic, source hierarchies, identity crosswalks, access controls, exception reports, and audit outputs. |
| HR Business Analyst | Defines current state, future state, requirements, data dictionaries, source-to-target mappings, business rules, and acceptance criteria. |
| Program Manager | Structures workstreams, risk tracking, stakeholder ownership, executive decisions, interim controls, and implementation governance. |
- All people, IDs, companies, systems, clients, demographic values, account records, background checks, and findings are synthetic.
- The project methods are portfolio reconstructions of recurring enterprise problem patterns; they do not reproduce any former employer's confidential data, tenant design, client roster, security configuration, or investigation record.
- Audit flags are review triggers, not accusations or findings of misconduct.
- Proposed corrections require documented human review and approval.
- Product and vendor names are used only to describe a conceptual integration context.
people-data-governance-lab/
├── README.md
├── LICENSE
├── .gitignore
├── run_all.py
├── 01_hr_identity_access_audit/
├── 02_eeo_demographic_data_lineage/
└── 03_workforce_identity_client_reporting_integrity/
Python 3.10+ is sufficient; the scripts use only the Python standard library.
python run_all.pyThe scripts regenerate the CSV outputs used by the reviewer workbooks.