> ## Documentation Index
> Fetch the complete documentation index at: https://docs.revenuebase.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Organization
> Organization-level firmographic data — industry, size, revenue, location, and more.
The [Data Dictionary](/docs/data-features/data-dictionary#org_latest) has the most up-to-date field definitions for this table. Use it as your primary reference for column names and descriptions.
**Column names in this table have no `_ORG` suffix.** Use `HEADQUARTERS_CITY`, `INDUSTRY_LINKEDIN`, etc. exactly as listed below — do not append `_ORG` to any field when querying `RELEASE.ORG_LATEST` directly. The `_ORG` suffix appears only in `INSIGHTS_LATEST` and the pre-joined `VELOCITY_*` tables, where it disambiguates organization fields after a merge — never in `ORG_LATEST` itself.
The `organizations` table contains organization-level records with firmographic data. Use it for
account targeting, ICP filtering, segmentation, and enriching your CRM with organization-level
attributes. One row per organization; join to contacts and insights via `RBID`.
## Where data comes from
Organization records are built from public profiles, websites, regulatory filings, and
third-party data providers. Industry and size are normalized for consistent filtering.
Location and financials are updated with each release; fill rates for revenue and funding are
higher for public and venture-backed organizations.
## Organization counts by region
The map below shows current organization counts by country. Use it to understand coverage and plan
account-based campaigns by region.
## Table stats
| Metric | Value |
| -------------------- | ---------------------------------------------------------------------------------- |
| **Total records** | See map above; updated with each release |
| **Update frequency** | Monthly (new delivery on the 1st of each month) |
| **Primary key** | `RBID` |
| **Main foreign key** | `VELOCITY_BASE_UNLIMITED_LATEST.rbid_org` and `INSIGHTS_LATEST.rbid_org` join here |
## Data dictionary
For the complete and most current field reference, see the [Data Dictionary](/docs/data-features/data-dictionary#org_latest).
Fill rate is the percentage of rows where a field is non-null. Rates vary by segment (e.g. US
and public organizations often have higher fill rates for financials and industry).
### Identifiers
| Field | Description | Example |
| ----------------- | -------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- |
| `RBID` | RevenueBase's unique identifier for an organization. The same value is placed in a field RBID\_ORG of people dataset | rb-oacksydgz |
| `DOMAIN` | Domain of the organization's website | company.com |
| `DOMAIN_TLD` | Top-level domain of the organization's website | .com |
| `WEBSITE` | Organization URL | [https://www.company.com](https://www.company.com) |
| `LINKEDIN_URL` | LinkedIn URL for the organization | [https://www.linkedin.com/company/company\_name](https://www.linkedin.com/company/company_name) |
| `LINKEDIN_URL_ID` | LinkedIn unique identifier for the organization | 76647045 |
### Organization basics
| Field | Description | Example |
| ------------------------- | ------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `COMPANY_NAME` | Name of the organization | Dell Technologies |
| `COMPANY_NAME_LANGUAGE` | The language the name of the organization is written | en |
| `ABOUT_US` | Description of the organization | Upstream Fullvision is a full service design firm in Honolulu Hawaii. Specializing in homes and small business design, 3D rendering/virtual tour, graphic design and beyond. Upstream Fullvision will provide the best service to turn your concept into reality. |
| `COMPANY_ENTITY_TYPE` | Legal entity type of the organization | Privately Held |
| `COMPANY_LEGAL_TYPE` | Legal type of the organization | \[ ""Corporation"" ] |
| `FOUNDED` | Year of the organization's founding | 1997 |
| `PHONE` | Contact phone number for the organization | +1 972-000-0000 |
| `IS_LINKEDIN_URL_CLAIMED` | Indicates if the LinkedIn URL is claimed by the organization | TRUE |
| `IS_WEBSITE_FOR_SALE` | Indicates if the website is listed for sale | FALSE |
| `IS_WEBSITE_WORKING` | Indicates if the organization's website is currently active | TRUE |
| `LOGO_URL` | URL to the company logo on LinkedIn | [https://media.licdn.com/dms/image/](https://media.licdn.com/dms/image/)... |
### Industry & classification
| Field | Description | Example |
| ------------------------------------------- | ------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `CATEGORY_CRUNCHBASE` | Organization industry categories from Crunchbase | \["SaaS", "Enterprise Software"] |
| `CATEGORY_G2` | Organization software categories from G2 | \["CRM", "Sales Automation"] |
| `INDUSTRY_LINKEDIN` | Industry description from LinkedIn | \[ ""Software Development"" ] |
| `INDUSTRY_NAICS_CODE` | NAICS Code | \[ ""484121"", ""541512"", ""541511"", ""561499"" ] |
| `INDUSTRY_NAICS_DESCRIPTION` | NAICS Description | \[ ""General Freight Trucking, Long-Distance, Truckload"", ""Computer Systems Design Services"", ""Custom Computer Programming Services"", ""All Other Business Support Services"" ] |
| `INDUSTRY_NAICS_2022_CODE` | NAICS 2022 industry classification code | \["511210"] |
| `INDUSTRY_NAICS_2022_DESCRIPTION` | NAICS 2022 industry classification description | \["Software Publishers"] |
| `INDUSTRY_SIC_CODE` | Standard Industrial Classification code | \[ ""7373"" ] |
| `INDUSTRY_SIC_DESCRIPTION` | Standard Industrial classification description | \[ ""Computer systems design"" ] |
| `INDUSTRY_UK_STANDARD_2007_CODE` | UK Standard Industrial Classification 2007 code | \["62012"] |
| `INDUSTRY_UK_STANDARD_2007_DESCRIPTION` | UK Standard Industrial Classification 2007 description | \["Business and domestic software development"] |
| `PREDICTED_INDUSTRY_NAICS_2022_CODE` | RevenueBase's predicted NAICS 2022 industry code | \["511210"] |
| `PREDICTED_INDUSTRY_NAICS_2022_DESCRIPTION` | RevenueBase's predicted NAICS 2022 industry description | \["Software Publishers"] |
| `SPECIALTIES` | Specialties or key areas of focus of the organization | \[ ""technology"", ""education and implementation"", ""data synchornization"", ""product data management"", ""advisory services"", ""innovation"", ""data quality analytics"" ] |
### Size & financials
| Field | Description | Example |
| ------------------------------- | ------------------------------------------------- | ---------------- |
| `EMPLOYEE_COUNT_MIN` | Minimum number of employees | 5001 |
| `EMPLOYEE_COUNT_MAX` | Maximum number of employees | 10000 |
| `EMPLOYEE_COUNT_RANGE` | Range of employee count | 5001 to 10000 |
| `EMPLOYEE_PROFILES_ON_LINKEDIN` | Number of employee profiles in LinkedIn | 7196 |
| `REVENUE_MIN` | Minimum revenue of the organization | 1000000 |
| `REVENUE_MAX` | Maximum revenue of the organization | 9999999 |
| `REVENUE_RANGE` | Range of the organization's revenue | `$50M to <$100M` |
| `LINKEDIN_FOLLOWERS` | Number of LinkedIn followers for the organization | 2039 |
### Addresses
| Field | Description | Example |
| ----------------------------- | ---------------------------------------------------- | --------------------- |
| `LOCATION_STREET` | Street address of the organization's location | 2440 Sand Hill Rd 300 |
| `LOCATION_CITY` | City where the organization is located | New York |
| `LOCATION_STATE_NAME` | State name of the organization's location | California |
| `LOCATION_STATE_CODE` | State code of the organization's location | CA |
| `LOCATION_POSTCODE` | Postal code of the organization's location | 94612 |
| `LOCATION_COUNTRY_NAME` | Country name of the organization's location | United States |
| `LOCATION_COUNTRY_CODE` | Country code of the organization's location | US |
| `LOCATION_COUNTRY_REGION` | Region of the organization's location | NORAM |
| `LOCATION_CONTINENT` | Continent of the organization's location | North America |
| `LOCATION_IS_PRIMARY` | Indicates if the location is the primary one | TRUE |
| `LOCATION_COUNT` | Number of locations associated with the organization | 1 |
| `HEADQUARTERS_STREET` | Street address of the organization HQ | 1 Infinite Loop |
| `HEADQUARTERS_CITY` | City where the organization's HQ is located | New York |
| `HEADQUARTERS_STATE_NAME` | State where the organization's HQ is located | NY |
| `HEADQUARTERS_STATE_CODE` | State code where the organization HQ is located | CA |
| `HEADQUARTERS_POSTCODE` | Postal code of the organization HQ | 95014 |
| `HEADQUARTERS_COUNTRY_NAME` | Country where the organization's HQ is located | United States |
| `HEADQUARTERS_COUNTRY_CODE` | Country code where the organization HQ is located | US |
| `HEADQUARTERS_COUNTRY_REGION` | Global region where the organization HQ is located | NORAM |
| `HEADQUARTERS_CONTINENT` | Continent where the organization HQ is located | North America |
**Location vs. Headquarters:** For small organizations, location and HQ fields may point to the same address. For organizations with multiple branches, the `LOCATION_*` fields represent one of the locations listed on the organization's LinkedIn page, while `HEADQUARTERS_*` fields are derived from the headquarters information on the LinkedIn organization page.
### Metadata
| Field | Description | Example |
| ------------ | ------------------------------------------ | ---------- |
| `UPDATED_AT` | The organization record's last update date | 2024-01-15 |
## Joining this table
Join `org_latest` to `per_latest` on `RBID` = `RBID_ORG` to attach firmographics to people. Join to `insights_latest` on `RBID` = `RBID_ORG` to attach buying signals and technographics.
### Organization + Person (accounts with decision-makers)
```sql theme={null}
SELECT
co.RBID,
co.COMPANY_NAME,
co.DOMAIN,
co.INDUSTRY_LINKEDIN,
co.EMPLOYEE_COUNT_MAX,
COUNT(c.RBID) AS contact_count
FROM RELEASE.ORG_LATEST co
JOIN RELEASE.PER_LATEST c ON c.RBID_ORG = co.RBID
WHERE co.INDUSTRY_LINKEDIN LIKE '%Software%'
AND co.EMPLOYEE_COUNT_MAX BETWEEN 50 AND 500
GROUP BY co.RBID, co.COMPANY_NAME, co.DOMAIN, co.INDUSTRY_LINKEDIN, co.EMPLOYEE_COUNT_MAX
HAVING COUNT(c.RBID) >= 1;
```
### Organization + Insights (firmographics + signals)
```sql theme={null}
SELECT
co.COMPANY_NAME,
co.DOMAIN,
co.INDUSTRY_LINKEDIN,
co.EMPLOYEE_COUNT_MAX,
i.CRM_TECH_ORG,
i.SALES_ROLE_COUNT_ORG,
i.HAS_CIO_ORG,
i.LAST_FUNDING_AMOUNT_ORG
FROM RELEASE.ORG_LATEST co
LEFT JOIN RELEASE.INSIGHTS_LATEST i ON co.RBID = i.RBID_ORG
WHERE co.EMPLOYEE_COUNT_MAX BETWEEN 100 AND 1000;
```
## How to calculate fill rates
Use this pattern to compute fill rates for any field or segment. Replace the sample fields with the columns you care about.
```sql theme={null}
SELECT
COUNT(*) AS total_records,
ROUND(COUNT(DOMAIN) * 100.0 / COUNT(*), 1) AS domain_fill_pct,
ROUND(COUNT(INDUSTRY_LINKEDIN) * 100.0 / COUNT(*), 1) AS industry_fill_pct,
ROUND(COUNT(EMPLOYEE_COUNT_MAX) * 100.0 / COUNT(*), 1) AS employee_count_fill_pct,
ROUND(COUNT(HEADQUARTERS_COUNTRY_NAME) * 100.0 / COUNT(*), 1) AS hq_country_fill_pct
FROM RELEASE.ORG_LATEST;
```
## Sample queries
### Find target accounts by ICP (mid-size software in the US)
**What you’re finding:** Organizations that match your ideal customer profile so you can build an account list or sync to a CRM.
**Why these fields:** `industry` and `employee_count` define ICP; `revenue_range` adds budget signal. `hq_city` and `hq_state` support territory or regional campaigns. We select only the columns you’d need for account creation or routing.
**Logic:** Filter by industry, size band, country, and revenue band. Order by `employee_count` to prioritize larger accounts within the band.
```sql theme={null}
SELECT
COMPANY_NAME,
DOMAIN,
INDUSTRY_LINKEDIN,
EMPLOYEE_COUNT_MAX,
REVENUE_RANGE,
HEADQUARTERS_CITY,
HEADQUARTERS_STATE_NAME,
HEADQUARTERS_COUNTRY_NAME,
HEADQUARTERS_COUNTRY_CODE
FROM RELEASE.ORG_LATEST
WHERE
(INDUSTRY_LINKEDIN LIKE '%Software%' OR INDUSTRY_LINKEDIN LIKE '%Information Technology%')
AND EMPLOYEE_COUNT_MAX BETWEEN 50 AND 500
AND HEADQUARTERS_COUNTRY_CODE = 'US'
AND REVENUE_RANGE IN ('$10M to <$50M')
ORDER BY EMPLOYEE_COUNT_MAX DESC NULLS LAST;
```
### Normalize LinkedIn URN
**What you're finding:** The `LINKEDIN_URL_ID` field contains LinkedIn's numeric organization identifier. Some integrations (e.g. LinkedIn Ads, Sales Navigator APIs) require the full URN format (`urn:li:organization:`) rather than the raw ID or URL.
**Why these fields:** `LINKEDIN_URL_ID` is the source value; the query constructs the URN by prepending the standard LinkedIn organization URN prefix. Rows without a `LINKEDIN_URL_ID` are excluded.
```sql theme={null}
SELECT
RBID,
COMPANY_NAME,
LINKEDIN_URL,
LINKEDIN_URL_ID,
'urn:li:organization:' || LINKEDIN_URL_ID AS LINKEDIN_URN
FROM RELEASE.ORG_LATEST
WHERE LINKEDIN_URL_ID IS NOT NULL;
```
### Check fill rates for a segment
**What you’re finding:** How complete key fields are for a subset (e.g. one industry or region) so you know what to expect when building lists.
**Why these fields:** Same as the global fill-rate query, but with a `WHERE` clause so percentages reflect only the segment you care about.
```sql theme={null}
SELECT
COUNT(*) AS total_records,
ROUND(COUNT(DOMAIN) * 100.0 / COUNT(*), 1) AS domain_fill_pct,
ROUND(COUNT(INDUSTRY_LINKEDIN) * 100.0 / COUNT(*), 1) AS industry_fill_pct,
ROUND(COUNT(EMPLOYEE_COUNT_MAX) * 100.0 / COUNT(*), 1) AS employee_count_fill_pct,
ROUND(COUNT(REVENUE_MAX) * 100.0 / COUNT(*), 1) AS revenue_fill_pct
FROM RELEASE.ORG_LATEST
WHERE HEADQUARTERS_COUNTRY_NAME = 'United States'
AND INDUSTRY_LINKEDIN LIKE '%Software%';
```