Enigma's graph-model-1 represents the most comprehensive, accurate, and expressive representation of the U.S. business landscape available today. This whitepaper details the robust, multi-layered data quality framework that powers graph-model-1, explaining the methodologies, validation processes, and quality control mechanisms that ensure our data accurately reflects the real world.
For organizations relying on business data for critical functions—whether for compliance, marketing, risk assessment, or strategic planning—the quality of that data directly impacts operational effectiveness and business outcomes. Enigma's rigorous approach to data quality delivers measurable advantages, with validation metrics that consistently exceed industry standards.
Business data is only as valuable as it is accurate, descriptive, timely, and reliable. The quality challenges inherent in business data are substantial:
These challenges are compounded when attempting to create a complete picture of the U.S. business landscape, which includes over 30 million active businesses operating across diverse industries, locations, and organizational structures.
While millions of business entities exist on paper, Enigma's graph-model-1 applies rigorous activity criteria to identify the 13 million "Marketable Brands" that demonstrate genuine market presence. This distinction represents businesses with verified operational signals, revenue generation, and complete attribution data. By identifying dormant entities, shell companies, and paper-only registrations, we ensure our customers build strategies on businesses with actual commercial activity.
Enigma's graph-model-1 addresses these challenges through a knowledge graph approach, combining multiple high-quality data sources with sophisticated entity resolution and linking models, all governed by a comprehensive quality assurance framework. This approach allows us to maintain data accuracy at scale, even as the business landscape continually changes.
At the foundation of our system is high-quality data from trusted sources. Enigma carefully vets all data sources to ensure our core records and decisioning rely only on data we can trust. We prioritize authoritative sources such as:
Even with these trusted sources, we don't assume all information is correct or consistent across sources. We implement rigorous validation:
Enigma's data pipeline is designed to maintain freshness while ensuring quality:
Our data pipeline employs sophisticated statistical models for:
Entity Resolution: Resolving over 600 million raw brand records down to more than 45 million distinct brands and operating locations
Entity Linking: Connecting more than 8 million brands to their associated legal entities
Attribute Prediction: Determining key attributes like industry classification (NAICS codes)
For each model, we develop comprehensive ground truth datasets for validation and won't implement models unless they exceed high precision thresholds. We continuously benchmark new models against baseline heuristics and existing approaches to ensure improvements.
When models don't align with reality (e.g., a recent merger not yet reflected in registry data), we utilize EnigmaDB to manually correct assertions. These corrections not only improve current data but inform future model training.
Enigma employs a multi-faceted approach to validation:
We maintain carefully curated ground truth datasets:
These datasets are actively maintained and expanded, providing reliable benchmarks for each release. They're designed to cover diverse business types, industries, and edge cases.
We track key metrics across releases, including:
Our systems automatically flag deviations beyond defined thresholds, triggering investigation before release.
Our pipeline incorporates two types of quality gates:
Blocking Checks: These enforce zero-tolerance or threshold-based requirements that halt pipeline progress until resolved. Examples include:
Alerting Checks: These monitor trends without blocking releases, providing visibility into data health over time.
We implement over 492 data checks across 188 datasets and pipeline stages. These checks become increasingly stringent as data moves through the pipeline, ensuring issues are caught early.
Each week when we refresh our pipeline, the data undergoes comprehensive monitoring:
Before datasets go live, we verify completeness and consistency against previous releases:
This approach helps identify subtle quality issues that might not trigger threshold alerts but could indicate emerging problems.
We combine automated checks with human expertise:
Each month, we manually review 2,000-5,000 random samples per attribute, with precision targets such as:
graph-model-1 maintains exceptional quality metrics:
This level of accuracy represents a significant achievement in small business revenue estimation, where traditional methods often fail due to:
For businesses with annual revenues under $1M, industry standard estimates often vary by factors of 2-3x or more, making Enigma's ±30% precision particularly valuable for organizations seeking to effectively segment and target the SMB market. This granularity enables:
Our quality framework delivers tangible business outcomes:
With graph-model-1, organizations can trust that their decisions—whether for compliance, marketing, risk assessment, or strategic planning—are grounded in accurate, high-quality data.
The business impacts are substantial:
Enigma's comprehensive approach to data quality isn't just a technical achievement—it's a business driver that delivers measurable value across use cases.
To learn more about how Enigma's graph-model-1 can power your organization's decisions with confidence, contact us today.