Organization

The Data Dictionary 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.

Hide View interactive map: organization counts by country

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. 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
`LINKEDIN_URL`	LinkedIn URL for the organization	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/…

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)

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)

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.

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.

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:<id>) 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.

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.

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%';

Getting Started

Base Tables

Pre-Joined Tables

Joining Tables

Where data comes from

Organization counts by region

Table stats

Data dictionary

Identifiers

Organization basics

Industry & classification

Size & financials

Addresses

Metadata

Joining this table

Organization + Person (accounts with decision-makers)

Organization + Insights (firmographics + signals)

How to calculate fill rates

Sample queries

Find target accounts by ICP (mid-size software in the US)

Normalize LinkedIn URN

Check fill rates for a segment

​Where data comes from

​Organization counts by region

​Table stats

​Data dictionary

​Identifiers

​Organization basics

​Industry & classification

​Size & financials

​Addresses

​Metadata

​Joining this table

​Organization + Person (accounts with decision-makers)

​Organization + Insights (firmographics + signals)

​How to calculate fill rates

​Sample queries

​Find target accounts by ICP (mid-size software in the US)

​Normalize LinkedIn URN

​Check fill rates for a segment

Where data comes from

Organization counts by region

Table stats

Data dictionary

Identifiers

Organization basics

Industry & classification

Size & financials

Addresses

Metadata

Joining this table

Organization + Person (accounts with decision-makers)

Organization + Insights (firmographics + signals)

How to calculate fill rates

Sample queries

Find target accounts by ICP (mid-size software in the US)

Normalize LinkedIn URN

Check fill rates for a segment