Company Insights

Overview

Complete, worldwide company database of over 30 million businesses. Includes basic company information such as name, location, category, and industry, along with a number of aggregated trends such as employee count and average tenure.

Data InformationValue
Refresh CadenceMonthly
Historical Coverage2010 - Present
Geographic CoverageGlobal

Schema

People and Resume Data is a Multi-Table dataset which allows you to access just the data you need. PDL.COMPANY.COMPANY is the primary table. You can join additional tables using the COMPANY_ID identifier. The schema below is for the PDL.COMPANY.COMPANY table. For more information on how to access Multi-Table datasets via API, review our docs page.

NameDescription
LATEST_FUNDING_STAGEThe stage of the company’s most recent funding event
MIC_EXCHANGEThe MIC code for the company's ticker exchange
ULTIMATE_PARENTThe ID of the ultimate owning company
DATASET_VERSIONDATASET_VERSION
SIZENumber of employees at the company (range)
LINKEDIN_IDMain LinkedIn profile ID for the company
FOUNDEDThe founding year of the company
TICKERThe company ticker for public companies
INDUSTRYThe self-reported industry of the company
COUNT_LINKEDIN_FOLLOWERCount of LinkedIn followers
LINKEDIN_SLUGLinkedIn slug for the company
AVERAGE_EMPLOYEE_TENUREAverage years of employee tenure
COUNT_FUNDING_ROUNDSNumber of funding rounds announced
GICS_SECTORGICS sector classification for public companies
COMPANY_IDUnique identifier for the company
ULTIMATE_PARENT_TICKERUltimate parent's stock symbol (if public)
LINKEDIN_URLMain LinkedIn profile URL for the company
NAMECompany's main common name
DISPLAY_NAMECompany's displayed name
TOTAL_FUNDING_RAISEDTotal amount of funding raised in USD
EMPLOYEE_COUNTCurrent number of employees
LAST_FUNDING_DATEDate of the most recent funding event
TYPECompany type
FACEBOOK_URLMain Facebook profile URL for the company
WEBSITEPrimary company website
IMMEDIATE_PARENTDirect owner of the company
ULTIMATE_PARENT_MIC_EXCHANGEMIC exchange of the ultimate parent (if public)
TWITTER_URLMain Twitter profile URL for the company
SUMMARYCompany description
INFERRED_REVENUEEstimated annual revenue in USD
HEADLINECompany's headline summary

Additional fields, such as the new one's below, are accessible by tables of the same name:

NameDescription
top_next_employersThe top ten companies employees moved to, and how many employees moved there, across all time periods
top_previous_employersThe top ten previous companies employees worked for previously, and how many current employees were previously employed by them, across all time periods
top_next_employers_12_monthThe top ten next employers, counting only employee changes within the last 12 months
top_previous_employers_12_monthThe top ten previous employers, counting only employee changes within the last 12 months
employee_count_by_sub_roleThe number of current employees broken down by Job Title Sub Role.
employee_growth_rate_12_month_by_sub_roleThe twelve month rate of change by Job Title Sub Role
employee_count_by_classThe number of current employees broken down by Job Title Class
employee_growth_rate_12_month_by_classThe twelve month rate of change by Job Title Class

Key Concepts

Reading in PySpark

People Data Labs Company data has a slightly tricky encoding with multiple lines and non-escaped quotes inside quotes that really trip PySpark up (Pandas handles it fine).

You can use this way of reading the data:

df = spark.read.option("header", "true")\
.option("multiLine", "true")
.option("escape", """)
.option("quote", '"')
.csv(path-do-company-data)