People Data Labs is an aggregator of B2B person and company data. We source our globally compliant person dataset via our "Data Union".

The "Data Union" is our proprietary data sharing co-op. Customers opt-in to sharing their data and warrant that their data is fully compliant with global data privacy regulations. Some data sources are provided as a one time dump, others are refreshed every time we do a new data build. Our data sources come from a variety of verticals including HR Tech, Real Estate Tech, Identity/Anti-Fraud, Martech, and others. People Data Labs works with customers on compliance based topics. If a customer wishes to ensure anonymity, we work with them to anonymize the data.

Our company data has identifying information (name, website, social profiles), company attributes (industry, size, founded date), and tags + free text that is useful for segmentation.

People data provides complete people information and gives the ability to link individual information to organizations and roles.

Free dataset containing about 24M companies. Originally compiled by People Data Labs, released under a free license. Data Schema and more information on te dataset at: https://docs.peopledatalabs.com/docs/free-company-dataset This version has been downloaded on 2025-07-28

Our proprietary People Data is a mobile user dataset that connects anonymous IDs to a wide range of attributes, including demographics, device ownership, audience segments, key locations, and more. This rich dataset allows our partner brands to gain a comprehensive view of consumers based on their personas, enabling them to derive actionable insights swiftly.

People Data Graph

• Record Count: 900 Million
• Capturing Frequency: Once per Event
• Delivering Frequency: Once per Month
• Updated: Monthly

People Data

Reach Our extensive data reach covers a variety of categories, encompassing user demographics, Mobile Advertising IDs (MAID), device details, locations, affluence, interests, traveled countries, and more. Data Export Methodology We dynamically collect and provide the most updated data and insights through the best-suited method at appropriate intervals, whether daily, weekly, monthly, or quarterly.

Business Needs

Our People Data caters to various business needs, offering valuable insights for consumer analysis, data enrichment, sales forecasting, and retail analytics, empowering brands to make informed decisions and optimize their strategies.

Dataset

This dataset was created by People Data Labs

Contents

Nymblr offers access to over 80 million US-based verified B2B contacts with valid work emails, personal emails, work phones & direct dials, and social profiles. Easily enrich your contact data in real-time with our APIs or provide us with flat files with your records and we'll do the work for you.

Nymblr makes it easy to enrich emails, phone numbers, social media profiles and receive additional data attributes including:

Job Title Seniority Level (C-Level/Owner, VP, Director, etc.) Job Department (Sales, Accounting, Marketing, Finance, etc.) Skills Company Name/Company Domain Company Industry Company SIC Company Revenue Company Size Location (State, and City)

Contact us to get a free trial today! No commitments required.

Full profile of 10,000 people in the US - download here, data schema here, with more than 40 data points including - Full Name - Education - Location - Work Experience History and many more!

There are additionally 258+ Million US people profiles available, visit the LinkDB product page here.

Our LinkDB database is an exhaustive database of publicly accessible LinkedIn people and companies profiles. It contains close to 500 Million people and companies profiles globally.

Number of listed companies per 1,000,000 people in Romania was reported at 4.2062 in 2020, according to the World Bank collection of development indicators, compiled from officially recognized sources. Romania - Number of listed companies per 1,000,000 people - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Number of listed companies per 1,000,000 people in Thailand was reported at 10.64 in 2020, according to the World Bank collection of development indicators, compiled from officially recognized sources. Thailand - Number of listed companies per 1,000,000 people - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

People Authority (PA) compiles a comprehensive database of individual profiles. The data is sourced from various feeds, including contributions from external datasets and third-party providers. Current data providers encompass public officials and directors, analysts and brokers, contacts from buy and sell-side, and insiders. The aggregated data forms a single, authoritative entry for each individual. Each entry is given a unique, permanent identifier, created through the mastering of essential attributes, both public and private, to accurately identify and describe the person. The Permid enables users to make extensive and detailed connections about individuals. Our approach advocates a standardized, federated collection system. No single entity is in charge of overseeing the individuals in PA. Various content groups utilize PA as the definitive source to reference an individual, and, if necessary, they can create or update records directly in the database using contribution tools.

Contant:

This data set is collected from www.ambitionbox.com in this data have more then 9000 companies data and 10 columns

About the columns

is in the file description

Acknowledgements

Data collected from www.ambitionbox.com website No One helped me in collecting the data

Number of listed companies per 1,000,000 people in Chile was reported at 10.15 in 2020, according to the World Bank collection of development indicators, compiled from officially recognized sources. Chile - Number of listed companies per 1,000,000 people - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

The GaiaLens system uses a combination of intelligent web crawlers and Machine Learning to engineer innovative proprietary features such as diversity metrics at scale and in real-time.

Surveying public sources of company personnel records, we identify metrics such as board and management diversity. Matching this with compensation data from annual reports, we calculate gender and ethnicity pay gaps. This sounds simple but is incredibly challenging, especially at scale and in real-time for over 17,000 global companies.

Here are the people metrics that we collect data for and their respective population percentages across our c.17,000 coverage (at the time of writing July 2022):

• Independent Lead Director Flag: 97%
• Independent Chairperson Flag: 97%
• Percentage of Women on the Board: 100%
• Percentage of Women Executives: 97%
• Percentage of Independent Directors on Board: 97%
• Woman CEO Flag: 95%
• Woman Chairperson Flag: 85%
• CEO Changes per FY: 78%
• Percentage of Women in Management: 67%
• Board Gender Pay Gap: 43%
• Ethnic Diversity on the Board: 38%
• Percentage of non-white executives: 37%
• Management Gender Pay Gap: 24%
• Ethnic Diversity within Management: 28%
• Management Ethnicity Pay Gap: 14%
• Board Non-White Pay Gap: 19%
• CEO vs Employee Pay ratio: 78%

The dataset contains information about world's biggest companies.

Among them you can find companies founded in the US, the UK, Europe, Asia, South America, South Africa, Australia.

The dataset contains information about the year the company was founded, its' revenue and net income in years 2018 - 2020, and the industry.

I have included 2 csv files: the raw csv file if you want to practice cleaning the data, and the clean csv ready to be analyzed.

The third dataset includes the name of all the companies included in the previous datasets and 2 additional columns: number of employees and name of the founder.

In addition there's tesla.csv file containing shares prices for Tesla.

Dataset

This dataset was created by Nobel Barua

Released under Other (specified in description)

Contents

The Doing Business Search provides access to information on entities and individuals that do business with the City of New York. http://www.nyc.gov/portal/site/DBusinessSite

Context

This dataset contains a list of sales of a company.

Fields Include

Row ID
Order ID
Order Date
Ship Date
Ship Mode
Customer ID Customer Name
Segment Country City
State
Postal Code Region
Product ID
Category
Sub-Category
Product Name
Sales
Quantity
Discount Profit

Factori houses an extensive dataset of US Person data, providing valuable insights into individuals across various demographic and behavioral dimensions. Our US Person Data section is dedicated to helping you understand the breadth and depth of the information available through our API.

Data Collection and Aggregation Our Person data is gathered and aggregated through surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points. This ensures that the data you access is up-to-date and accurate.

Here are some of the data categories and attributes we offer within US Person Graph: - Geography: City, State, ZIP, County, CBSA, Census Tract, etc. - Demographics: Gender, Age Group, Marital Status, Language, etc. - Financial: Income Range, Credit Rating Range, Credit Type, Net Worth Range, etc. - Persona: Consumer type, Communication preferences, Family type, etc. - Interests: Content, Brands, Shopping, Hobbies, Lifestyle, etc. - Household: Number of Children, Number of Adults, IP Address, etc. - Behaviors: Brand Affinity, App Usage, Web Browsing, etc. - Firmographics: Industry, Company, Occupation, Revenue, etc. - Retail Purchase: Store, Category, Brand, SKU, Quantity, Price, etc.

Here's the data schema: Person_id first_name last_name gender age year month day full_address city state zipcode zip4 delivery_point_bar_code carrier_route walk_sequence_code fips_state_code fips_county_code country_name latitude longtitude address_type metropolitan_statistical_area core_based_statistical_area census_tract census_block
census_block_group primary_address pre_address street post_address address_suffix address_secondline address_abrev
census_median_home_value home_market_value property_build_year property_with_ac property_with_pool property_with_water property_with_sewer general_home_value property_fuel_type household_id census_median_household_income
household_size occupation_home_office
dwell_type household_income marital_status length_of_residence number_of_kids pre_school_kids single_parent working_women_in_house_hold homeowner children
adults generations net_worth education_level education_history occupation occuptation_business_owner credit_lines credit_card_user newly_issued_credit_card_user credit_range_new credit_cards loan_to_value and alot more...

This dataset contains the owner information for all the accounts listed in the Business License Dataset, and is sorted by Account Number. To identify the owner of a business, you will need the account number or legal name, which may be obtained from theBusiness Licenses dataset: https://data.cityofchicago.org/dataset/Business-Licenses/r5kz-chrr. Data Owner: Business Affairs & Consumer Protection. Time Period: 2002 to present. Frequency: Data is updated daily.

This pie chart displays public companies per CEO gender using the aggregation count. The data is filtered where the company is People. The data is about companies.