Data Accuracy & Methodology

OpenDataUSA aggregates data from multiple public sources across federal, state, and county government databases to create a comprehensive, searchable platform of publicly available records. We understand that when people search for information on our platform, they need to trust that what they find is as accurate and complete as possible.

This page explains our data collection process, how we match records across different databases, the inherent limitations of public records data, and what we do to maintain the highest standards of accuracy and transparency. We believe that being open about our methodology is not just good practice — it is essential to earning and keeping your trust.

All of the data displayed on OpenDataUSA originates from government databases and public registries at the federal, state, and county levels. We do not create, fabricate, or infer data about individuals. Instead, we organize and index information that is already part of the public record.

Our primary data sources include:

• Voter Registration Files: Publicly available voter rolls maintained by state election offices, containing names, addresses, party affiliation, and voting history where permitted by state law.
• Property & Assessor Records: County-level property ownership data, assessed values, deed transfers, and parcel information from local assessor and recorder offices.
• FEC Campaign Finance Data: Individual and organizational political contribution records filed with the Federal Election Commission, including donor names, employers, and contribution amounts.
• USPTO Patent Database: Patent filings and grants from the United States Patent and Trademark Office, including inventor names, assignees, and filing dates.
• Business Filings: State-level corporate registrations, LLC filings, trade name registrations, and annual reports from Secretaries of State offices.
• Professional Licensing Boards: State-regulated professional licenses including healthcare providers (via NPI/NPPES), real estate agents, contractors, attorneys, and other licensed professions.
• PPP Loan Disclosures: Small Business Administration Paycheck Protection Program loan recipient data, including business names, loan amounts, and forgiveness status.

We obtain this data through three primary channels:

One of the most significant technical challenges in aggregating public records is determining when records from different databases refer to the same individual. A person may appear in voter registration files, property records, FEC donation records, and professional licensing databases — each with slightly different formatting, abbreviations, or details. Our identity resolution system works to connect these records accurately.

Our matching process relies on combinations of the following data points:

• Full Legal Name: We compare first, middle, and last names across databases, accounting for common variations such as initials, suffixes (Jr., Sr., III), and alternate spellings.
• Address History: Current and historical addresses help establish whether two records with the same name likely belong to the same individual, particularly when someone has moved between jurisdictions.
• Date of Birth Indicators: Where available, age ranges or birth year data provide strong matching signals. Not all public records include this information, but when present, it significantly improves match confidence.
• Geographic Location: Proximity and geographic overlap between records help narrow matches, especially for common names in large metropolitan areas.

Common Name Disambiguation

Names like "James Smith," "Maria Garcia," or "David Johnson" appear thousands of times across national databases. Matching records for common names requires particular care. Our system uses a weighted scoring approach that considers multiple attributes simultaneously. A "James Smith" who appears at the same address in both voter records and property records with a consistent age range is far more likely to be the same person than two "James Smith" entries in different states with no overlapping attributes.

Confidence Scoring

Not all record matches carry the same level of certainty. Our system assigns a confidence score to each potential match based on the number and quality of overlapping attributes. Matches supported by multiple independent data points (name, address, and age range, for example) receive higher confidence scores than those based on name alone.

Critically, records that cannot be matched with sufficient confidence are not displayed on our platform. We would rather show incomplete results than risk attributing records to the wrong person. This conservative approach means that some legitimate connections between records may not appear in search results, but it significantly reduces the risk of false associations.

We strive for accuracy in everything we present, but it is important for users to understand the inherent limitations of public records data. Transparency about these limitations is part of our commitment to honest, responsible data presentation.

We respect every individual's right to control how their information appears on our platform. If you find information about yourself on OpenDataUSA that you would like removed or corrected, we offer straightforward processes to address your concerns.

Transparency is a core principle at OpenDataUSA. We believe that a data aggregation platform has a responsibility to be open about what data it collects, where that data comes from, and how it is processed. Here are the commitments we make to every user:

Publicly Available Sources Only

We are committed to using only publicly available information. Every data point on our platform originates from a government database, public registry, or official open-records source. We do not purchase data from private data brokers, scrape social media profiles, harvest information from private databases, or access any data that is not already part of the public record.

No Social Media Scraping

We do not scrape, index, or display information from social media platforms including Facebook, Instagram, Twitter/X, LinkedIn, TikTok, or any other social networking service. The information on our platform comes exclusively from government and regulatory sources.

Source Documentation

We maintain detailed documentation of every data source category we use, including the originating agency, the type of data collected, and how frequently it is updated. You can review this information on our Data Sources pages, which cover:

• Federal government databases (FEC, USPTO, SBA, and more)
• State government records (voter files, business filings, professional licenses)
• County and municipal records (property, assessor, deed data)
• Professional registries (healthcare providers, licensed professionals)