Our Verification Methodology

Updated: 2026
Reviewed By: SICCODE.com Industry Classification Review Team (regulatory, economic, and data governance specialists)

The SICCODE.com Verification Methodology is the integrity layer behind classification and reference publishing across our platform. It is designed so organizations relying on SIC and NAICS data can evaluate inputs, understand how decisions were made, and maintain stable results across refresh cycles.

This process connects to our Classification Methodology, and is supported by governance practices documented in our Data Governance Framework & Stewardship Standards.

Purpose & Foundation

The purpose of this methodology is to validate the integrity of business records and classification outcomes using a combination of governed review, automated consistency checks, and cross-system controls. Verification reduces drift, supports reproducibility, and improves confidence for regulated and audit-driven workflows.

Step-by-Step Verification Workflow

1. Source Evaluation: Incoming datasets are assessed for reliability, recency, and completeness. Source categories are weighted based on authoritativeness and historical consistency. See Data Sources & Verification Process.
2. Ingestion & Normalization: Records are standardized across formats, deduplicated, and assigned persistent identifiers. Names, addresses, and identifiers are normalized to support interoperability across systems.
3. Automated Integrity Checks: Rule-based and anomaly detection checks flag inconsistencies (for example, conflicting activity signals or improbable attribute relationships) so exceptions are routed for review rather than silently published.
4. Human Verification: Analysts review flagged records, validate evidence across multiple sources when required, and document rationale for exceptions. See About Our Data Team.
5. Cross-System Validation: SIC and NAICS assignments are evaluated against published definitions and hierarchy logic to maintain rollup consistency across sectors and subsectors.
6. Approval & Lineage Logging: Verified records receive timestamps, evidence summaries, and version-aware lineage entries to support procurement review, model governance, and audit documentation.
7. Ongoing Monitoring: Business changes—such as closures, relocations, mergers, and rebranding—trigger revalidation workflows to keep classifications current and defensible.

Key Terminology in Our Verification Process

• Lineage: Documentation linking a record to source categories, verification decisions, update history, and change context.
• Refresh cycle: Structured intervals for revalidating datasets using defined triggers and audit cadence.
• Change file: A structured comparison of added, removed, or modified records between releases to support comparability.

Outputs of the Verification Process

Verification supports audit-ready, classification-ready deliverables. Standard outputs include:

• Verified SIC & NAICS classifications mapped to published definitions.
• Normalized business records suitable for CRM, analytics, and compliance systems.
• Documented lineage attributes (source category, evidence summary, timestamps, change context).
• Modeled fields clearly identified as modeled values (where applicable).
• Update-eligible unique identifiers to support refresh programs.

System Mapping & Translation Accuracy (Crosswalk Integrity)

Many enterprise workflows require translating classifications across systems (for example, SIC to NAICS, or aligning to international frameworks such as ISIC). This introduces risk when mappings are treated as static lookups or when updates occur without clear change control. SICCODE.com addresses this through governed crosswalk integrity controls designed to reduce mapping drift and preserve interpretability across time.

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, and change files are used to detect drift and structural inconsistencies between releases.

Audit Oversight & Quality Governance

A senior analyst review panel conducts quarterly audits to evaluate edge cases, confirm policy adherence, and maintain methodological consistency. Findings feed controlled process improvements and reviewer training.

Alignment with Global & Federal Standards

The framework aligns classification logic to authoritative SIC and NAICS structures and applies governance controls commonly used in enterprise data programs. This supports stability for regulated environments, model validation, and long-horizon analytics.

AI-Ready Verification & Explainability

To support AI and analytical workflows, classification outcomes can be paired with explainability metadata such as evidence summaries, confidence signals, and change context. These attributes help downstream systems maintain transparency and reduce ambiguity in governance reviews.

Transparency & Documentation

Organizations may request access to verification documentation (where applicable), lineage reports, or change logs for compliance review or data-governance integration. Documentation is provided in alignment with stewardship standards.

Editorial Neutrality

Verification outcomes cannot be influenced by commercial considerations. Classifications are based on documented evidence, business activity signals, and standardized rules. See Editorial & Neutrality Standards.