Our Verification Methodology
Verification Methodology
SICCODE.com uses a governed, multi-step verification process to ensure that every SIC and NAICS classification, business record, and data attribute is accurate, explainable, and aligned with federal standards. This page outlines the complete methodology used to validate sources, normalize records, detect anomalies, and produce audit-ready classification data.
Contents
The SICCODE.com Verification Framework is the foundation behind all classification, enrichment, and data governance operations across our platform. It ensures that organizations relying on SIC and NAICS data—including banks, insurers, regulators, researchers, and enterprise analytics teams—receive information that is accurate, documented, and stable across updates.
Our verification process is tightly connected to our Classification Methodology, overseen by the Industry Classification Review Team, and supported by the governance practices described in our Data Governance Framework & Stewardship Standards.
Purpose & Foundation
The purpose of this methodology is to validate the integrity of every business record and classification decision using a combination of human expertise, automated checks, and cross-system analysis. This framework aligns with standards used by the U.S. Census Bureau, Bureau of Economic Analysis (BEA), and international quality guidelines such as ISO 8000 (Data Quality) and ISO 9001 (Quality Management).
Step-by-Step Verification Workflow
- Source Evaluation: Each incoming dataset is scored for reliability, recency, and completeness. Source categories are weighted based on authoritativeness and historical accuracy. See Data Sources & Verification Process.
- Ingestion & Normalization: Records are standardized across taxonomies, deduplicated, and assigned IDs for long-term tracking. Business names, addresses, and identifiers are normalized to support interoperability across systems.
- Automated Integrity Checks: ML-assisted pattern recognition flags inconsistencies (e.g., invalid addresses, mismatched industry claims, or conflicting data). Business rules validate expected relationships across fields.
- Human Verification: Trained classification analysts review flagged records, validate classification evidence, consult multiple sources, and document rationale for exceptions. Learn more on the About Our Data Team page.
- Cross-System Validation: SIC and NAICS classifications are compared against official definitions, economic rollups, and global reference systems (ISIC) to maintain consistency across hierarchies.
- Approval & Lineage Logging: Each verified record receives a timestamp, reviewer ID, evidence summary, and lineage entry. Lineage ensures traceability for audits, model governance, and procurement documentation.
- Ongoing Monitoring: Business changes—such as closures, relocations, mergers, and rebranding—trigger automatic revalidation workflows.
Key Terminology in Our Verification Process
- Lineage: Documentation linking each record to its sources, verification decisions, update history, and reviewer.
- Refresh Cycle: Structured intervals for revalidating datasets (quarterly for extended data, annual for core hierarchies).
- Change File: A structured comparison indicating added, removed, or modified records between releases.
Outputs of the Verification Process
Clients and partners benefit from verification in the form of complete, auditable, and classification-ready deliverables. Standard outputs include:
- Verified SIC & NAICS classifications mapped to official definitions.
- Normalized business records suitable for CRM, analytics, and compliance systems.
- Documented lineage attributes (source type, evidence summary, timestamps).
- Modeled fields (sales, employees, credit codes) clearly identified as modeled values.
- Update-eligible unique IDs to support 6- and 12-month refresh programs.
Accuracy Benchmarks
SICCODE.com's internal audit benchmark maintains an average verified classification accuracy above 96.8%. Material claims and classifications require verification from at least two independent sources before publication. Change files are used to detect drift, misclassification, or structural inconsistencies between releases.
Audit Oversight & Quality Governance
A senior analyst review panel conducts quarterly audits to ensure compliance with verification policy, evaluate edge cases, and maintain methodological consistency. Audit findings are used for continuous process improvements and analyst training.
Alignment with Global & Federal Standards
Our framework incorporates principles from ISO quality standards, U.S. federal economic classification rules, and best practices in data stewardship. This alignment provides the stability required for regulated environments, AI model validation, and enterprise-scale classification programs.
AI-Ready Verification & Explainability
To support AI and analytical workflows, classification decisions are enriched with explainability metadata. These attributes capture contributing evidence, reviewer rationale, and confidence signals, enabling transparency in automated systems.
Transparency, Access & Documentation
Organizations may request access to verification notes, lineage reports, or change logs for compliance, audit review, or data-governance integration. Documentation is available on a fair-use basis and aligned with our stewardship standards.
Editorial Neutrality
Verification outcomes cannot be influenced by commercial considerations. All classifications are based solely on documented evidence, business activity, and standardized rules. For full details, see our Editorial & Neutrality Standards.