Dark Mode
Wikipedia-Sourced Global Business and Sector Registry
News & Media Articles
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
Analysing the global corporate landscape through the lens of structured Wikipedia data provides a detailed demographic study of businesses worldwide. Extracted from the DBpedia resource using SPARQL querying, these records offer insights into company origins, sectors, and financial standing. The collection serves as a tool for researchers to identify growth factors for established firms and conceptualise the distribution of products and services across various territories. It provides a foundational view of international commerce, allowing for the visualisation of industry demographics as they stood in late 2021.
Columns
- a: The unique DBpedia resource URL associated with the specific company profile.
- name: The official designation or name of the business entity.
- type: The legal or operational classification of the firm, such as a public company or a subsidiary.
- foundation: The historical date or year the organisation was established.
- location: The geographic headquarters or primary base of operations for the company.
- products: The specific goods and services offered by the business to its market.
- sector: The primary industry or economic sector in which the company operates, such as financial services.
- employee: The total count of individuals employed by the business entity.
- assets: The reported financial value of the organisation's total asset holdings.
- equity: The recorded equity or net worth of the company.
Distribution
The information is delivered in a CSV format titled
sparql_2021-11-03_13-04-38Z.csv with a file size of 2.78 MB. It consists of 10,000 valid records structured across 12 columns. The dataset exhibits high integrity, with primary fields like name, sector, and employee count showing 100% validity. While financial fields such as assets and equity have approximately 71% coverage, the core identification and demographic fields are largely complete. The resource is maintained with an expected annual update frequency.Usage
This collection is ideal for performing market research and identifying potential geographic areas for business expansion. It can be used for training machine learning models to categorise firms by industry or for creating visualisations of global corporate demographics. Additionally, analysts can utilise the financial and employee metrics to benchmark companies within specific sectors, such as building materials or financial services, and to study the correlation between a company's age and its asset holdings.
Coverage
The geographic scope is global, with a notable concentration of records from the United States and other major economies. Temporally, the records provide a snapshot of the corporate world from late 2021, although foundation dates span several decades. Industry coverage is diverse, though the financial services sector represents a significant portion (46%) of the data. The records are derived from Wikipedia entries, meaning they reflect companies with significant public profiles.
License
CC0: Public Domain
Who Can Use It
Business analysts can leverage these figures to study market saturation and find opportunities in specific industries. Academic researchers can utilise the demographics to understand the historical evolution of various sectors. Entrepreneurs and consultants may also find the data useful for identifying critical success factors and competitor landscapes when planning new business ventures or advising established firms on growth strategies.
Dataset Name Suggestions
- Global Corporate Demographics and Industry Statistics 2021
- DBpedia Worldwide Company Profiles and Financial Metrics
- Wikipedia-Sourced Global Business and Sector Registry
- International Corporate Asset and Employment Database
- World Company Directory: Foundation, Location, and Industry Analysis
Attributes
Original Data Source: Wikipedia-Sourced Global Business and Sector Registry
QUALITYLoading...
Free
Download Dataset in ZIP Format
Recommended Datasets
Loading recommendations...