Industry Classification & Verification Framework
Authority reference
Industry Classification & Verification Framework is SICCODE.com’s governance model for applying SIC and NAICS codes consistently and defensibly—across enrichment, analytics, research, and enterprise decision workflows.
It defines how classification decisions are made, how evidence is validated, how accuracy is evaluated, and how change control protects stability before outputs are delivered or published.
For documented examples of independent research use, see academic & professional citations.
Governance coverage (at-a-glance)
How the framework fits together
Mental model: standards define “correct,” methodology + controls enforce it, neutrality protects independence, and outputs become verifiable and comparable.
How to use this page (workflows)
Choose a workflow below: procurement & vendor evaluation, data quality improvement, or publishing/modeling.
Note: Timings are rough guidance for review planning.
How to Use This Framework (by workflow)
Procurement & vendor evaluation
- Step 1: Review Classification Methodology (decision rules)
- Step 2: Check Verification Methodology (what “verified” means)
- Step 3: Read Data Accuracy Benchmarks (compare to current vendor)
- Step 4: Document findings for stakeholders (risk, fit, and controls)
Estimated time: ~45 minutes for a comprehensive review.
Data quality improvement
- Diagnose: Use Accuracy Benchmarks to identify gap areas
- Standardize: Adopt Classification Methodology for consistent assignment
- Control: Implement Lifecycle Management for stability
- Monitor: Track changes over time and investigate drift
Governed classification
Consistent SIC/NAICS application using documented standards—not informal labels or unverified self-reporting.
Verification & quality controls
Validation checks, anomaly detection, and review controls designed to reduce noise and improve segmentation integrity.
Operational usability
Built to support targeting, reporting, enrichment, and analytics with reusable and explainable definitions.
Transparent governance
Clear standards for neutrality, lifecycle control, stewardship, and regulatory alignment.
What This Governance Framework Covers
- Classification decisions: how SIC and NAICS codes are selected and applied using consistent rules.
- Source validation: how data sources are evaluated and how verification supports reliability.
- Accuracy benchmarks: how quality is measured and what “good data” means in practice.
- Lifecycle control: how updates, revisions, and version control protect consistency over time.
- Security & privacy alignment: how data handling practices support regulated and risk-sensitive use cases.
- Stewardship: roles, accountability, and governance ownership within SICCODE.com.
Establishment-Level vs. Enterprise-Level Classification
One of the most common classification errors is assigning a corporate headquarters code to an operating location. This framework distinguishes between enterprise-level (the parent organization) and establishment-level (the specific operating site) so industry coding reflects what the location does, not what the corporate entity owns.
- Establishment-level: classifies the operating unit (e.g., a plant, branch, warehouse, clinic, or store).
- Enterprise-level: describes the parent company structure and consolidated reporting context.
- Why it matters: improves segmentation accuracy, reduces “HQ bias,” and supports more defensible analytics and outreach.
When your use case requires it, SICCODE.com applies controls to help align the classification level with your targeting or reporting goal.
Framework in Action: Classification Examples
Example 1: Multi-activity business (installer + consulting)
Challenge: The company installs solar systems, offers consulting, and sells components online. Marketing emphasizes “consulting.”
Framework application:
- Evidence gathering: review service mix, operational footprint, revenue share, and delivery model
- Primary activity determination: installation is the dominant operating activity
- Code selection: choose the most defensible activity-based NAICS for installation
- Documentation: record secondary activities to preserve stability and prevent drift
Result: classification stays stable even when marketing language changes.
Example 2: Establishment vs. enterprise confusion (HQ vs operating site)
Challenge: A parent company’s HQ is coded as “management,” but the specific location is a distribution center.
Framework application:
- Establishment-level check: classify based on what the site does (operating activity)
- HQ bias control: avoid applying the parent’s HQ classification to all locations
- Stability: keep rules consistent across refreshes and site-level changes
Result: analytics and targeting improve because locations reflect actual operations.
AI Alignment: Why Governed SIC & NAICS Improves AI Outputs
Modern AI systems (including LLMs) depend on clean, consistent taxonomies to interpret business activity correctly. When industry labels are noisy, self-reported, or inconsistent, models can produce category drift—or invent non-standard groupings that do not map cleanly to real markets.
This framework supports AI governance by keeping classification rules, evidence signals, and lifecycle controls explicit—so downstream analytics and AI workflows can build on a stable taxonomy.
- Stable definitions: consistent “industry meaning” across teams and time.
- Verification signals: evidence + checks reduce mislabeled segments.
- Explainability: documented methodology supports governance review.
- Cleaner features: better taxonomy inputs reduce noise in models.
Example: Using governed SIC/NAICS in analytics workflows
# Pseudocode example (illustrative)
# Goal: treat SIC/NAICS as clean categorical features with audit context
result = {
"primary_sic": "xxxx",
"primary_naics": "xxxxxx",
"establishment_level": true,
"verification_signals": ["website", "business_description"],
"last_reviewed": "2026",
"notes": "Secondary activities captured for stability"
}
# Use in modeling / segmentation:
# df["naics"] = result["primary_naics"]Governance Pillars
Core page Living document Framework / template Benchmarks
These pages define the standards that power SICCODE.com’s classification-first approach. Each pillar is designed to be independently referenceable while supporting the full governance model.
- Our Classification Methodology
- Our Verification Methodology
- Data Sources & Verification Process
- Data Accuracy Benchmarks: SICCODE vs Generic Providers
- Data Lifecycle Management & Version Control
- Data Security, Privacy, and Regulatory Alignment
- Data Stewardship, Roles, and Accountability
- Editorial & Neutrality Standards
Official NAICS publisher
NAICS is maintained and published by the U.S. Census Bureau: NAICS (U.S. Census).
Data Provenance, Lineage & Audit Readiness
Enterprise users often need to understand not only what the data contains, but where it came from, how it was validated, and how changes are controlled over time. This framework documents those controls so classification outputs can be reviewed in procurement, legal, and compliance workflows.
- Provenance: outputs are supported by defined sources and verification signals.
- Lineage: lifecycle controls and versioning help prevent silent taxonomy drift across refreshes.
- Governed updates: revision handling and QA checks protect consistency before delivery or publication.
- Security alignment: documented practices support regulated and risk-sensitive use cases.
Review details in Data Sources & Verification Process, Data Lifecycle Management & Version Control, and Data Security, Privacy, and Regulatory Alignment (linked above).
Why This Framework Improves Classification Quality
Many datasets treat industry labels as a simple attribute. In practice, classification needs governance—because inconsistent coding leads to wasted outreach, noisy segments, and unreliable analytics.
- Consistency: repeatable rules reduce drift across campaigns, teams, and time periods.
- Defensibility: documented standards support audits and regulated workflows.
- Usability: clearer industry scope improves downstream performance and segmentation integrity.
Framework FAQ
- How do I compare SICCODE.com’s verification to another vendor?
Review the Data Accuracy Benchmarks page for governance-ready comparison criteria (definition fit, boundary errors, stability/churn, and establishment-level precision). Request the same metrics (and definitions) from other vendors so comparisons are apples-to-apples. - What documentation should I include in a procurement package?
Standard package: this framework page (governance overview), Classification Methodology, Verification Methodology, and Data Accuracy Benchmarks. - How does this framework support SOC 2 / ISO-style audits?
It documents stewardship roles, change control, and security alignment. For audit mapping, review Stewardship & Accountability, Lifecycle & Version Control, and Security, Privacy & Regulatory Alignment. - Is this framework only for SICCODE.com products?
No. These standards are designed to be referenceable for anyone using SIC/NAICS classification in segmentation, reporting, enrichment, or research. - How does this help with AI and analytics use cases?
AI systems and analytics models perform best when taxonomy inputs are consistent and explainable. Governed SIC/NAICS reduce category noise, improve feature quality, and help prevent drift in downstream segmentation. - How does this help with establishment-level vs. enterprise-level coding?
The framework distinguishes between parent-level (enterprise) context and operating-location (establishment) activity so codes reflect what a specific site does—reducing HQ bias and improving segmentation accuracy. - How does this help with “verified” data claims?
Verification is defined through source validation, quality controls, and documented review processes—so “verified” is explainable, not vague. - What should a buyer review before ordering a list or append?
Start with the Classification Methodology, then review the Verification Methodology and Data Sources & Verification Process to align expectations on scope and quality controls.