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Dataset Card for Business Points of Interest (POI) Data
Every physical business location, pinpointed on the map and layered with the firmographic detail to know exactly what's there — company identity, contact details, industry, leadership, size, and revenue, in one record.
Dataset Details Dataset Description
Techsalerator's B2B POI Database covers points of interest for companies and entities across markets worldwide — real-world business locations verified, standardized, categorized, and enriched through a multi-source geographic and commercial intelligence methodology. Each record pairs precise geolocation (latitude/longitude, standardized address) with over 200 business attributes, so a single row tells you not just where a business is, but who it is, how big it is, and how to reach it.
Because every record carries full firmographic depth, the database can be filtered down to the exact universe you need — by location, revenue, employee count, years in business, and 40+ additional criteria — rather than delivered as one undifferentiated dump. Coverage and delivery are configured per market; get in touch for availability in a specific country or region.
Curated by: Techsalerator LLC Funded by: Techsalerator LLC Shared by: Techsalerator LLC Language(s): English (schema/labels); native-language business names, addresses, and local fields per market License: Techsalerator Dataset License (see LICENSE) — commercial/proprietary. This card and any sample files are for evaluation; full database access requires a license. Geographic coverage: Global — available by country/region; contact for current market availability Dataset Sources Repository: This repository (methodology and field reference) Full dataset / licensing: techsalerator.com/request-a-quote Contact: info@techsalerator.com Uses Direct Use
Site selection and retail expansion, geomarketing and trade-area analysis, logistics and delivery network optimization, location intelligence and mobility analytics, navigation and mapping enrichment, competitive intelligence, real estate and insurance underwriting, telecommunications network planning, emergency response planning, and AI/LLM systems that need grounded, geolocated business data.
Out-of-Scope Use
Not intended as a sole source for legal, financial, or credit decisions. Revenue and employee figures are sourced or modeled and carry a reliability code — treat accordingly rather than as verified financial statements. Not a consumer contact list: contact fields are business-context data, and outreach using them must still comply with applicable local and international marketing/privacy law (see Personal and Sensitive Information below).
Dataset Structure
Each POI record links a physical location to its owning business through 200+ fields. A representative subset:
Identity & Registration UniqueID UniversalPublicationId CompanyName TradeName DirectoryName NationalID NationalIdentificationTypeCode NationalIdentificationTypeCodeDescription NationalIDIsVat YearStarted NumberOfFamilyMembers LegalStatusCode LegalStatusCodeDescription StatusCode StatusCodeDescription
Location & Geospatial Address1 Address2 PostCode City CityCode Province ProvinceCode Region RegionCode Country CountryCode Latitude Longitude
Contact Language PhoneOrMobile Phone DNCMPhone Fax Mobile DNCMMobile Email Website WebDomain WebsiteIpAddress
Digital & Social Presence WebSocialMedialinksFacebook WebSocialMedialinksTwitter GenericLinkedInLink
Industry Classification PrimaryLocalActivityCode LocalActivityTypeCode MarketabilityIndicator
Leadership CEOName CEOTitle CEOFirstName CEOLastName CEOGender CEOLanguage
Size EmployeesHere EmployeesHereReliabilityCode EmployeesHereReliabilityCodeDescription EmployeesTotal EmployeesTotalReliabilityCode EmployeesTotalReliabilityCodeDescription
Financials SalesVolume Currency SalesVolumeDollars SalesVolumeEuros SalesVolumeReliabilityCode
The full schema totals 200+ fields; the remaining fields extend these same categories (additional identifiers, classification detail, and selection/filter attributes) and are available on request.
There are no train/test/validation splits — this is a flat, entity-level table, one row per physical business location.
Dataset Creation Curation Rationale
Mapping coordinates alone tell you a business exists at a point on a map; they don't tell you what to do with that information. This dataset was built to close that gap — combining authoritative geographic data with commercial business intelligence so every location record supports real decisions (where to open a store, who to contact, how big the account is) rather than just plotting a pin.
Source Data Data Collection and Processing
Primary source acquisition starts with official governmental geographic datasets: national mapping agencies, municipal open-data portals, postal authorities, transportation authorities, tourism boards, land registries, and public government publications. These provide reliable geographic references, addresses, administrative boundaries, and official location identifiers.
Official geographic data is then supplemented with commercial business directories, company websites, franchise networks, licensed commercial providers, publicly available business listings, regulatory publications, real estate records, and other legally available sources describing physical business locations.
Every location goes through address normalization and geospatial validation: addresses standardized to international postal conventions, duplicates consolidated, coordinates validated, administrative regions harmonized, and postal codes verified.
Each POI is categorized using a hierarchical classification model covering thousands of business and venue types — retail, restaurants, hotels, healthcare, education, financial services, logistics, manufacturing, government, entertainment, transportation, professional services, industrial facilities, religious institutions, and more.
Where available, records are further enriched with operating status, contact information, websites, opening hours, brand affiliation, and chain relationships. Chain-business locations are linked to their parent organization, so both individual sites and the broader corporate network can be analyzed together.
Quality assurance includes automated duplicate detection, coordinate validation, address matching, reverse geocoding verification, administrative boundary consistency checks, and scheduled refreshes that catch newly opened businesses, relocations, closures, and category changes. Multiple authoritative sources are reconciled against each other to maximize positional accuracy and minimize stale records.
Who are the source data producers?
National and municipal government geographic authorities (mapping agencies, postal authorities, transportation authorities, tourism boards, land registries), supplemented by commercial business directories, company websites, franchise/chain networks, regulatory publications, real estate records, and licensed commercial data providers in each covered market.
Annotations
This dataset does not contain manually created annotations. Derived/estimated fields (e.g. SalesVolumeDollars, EmployeesTotal where not directly sourced) are flagged via their respective ReliabilityCode fields.
Annotation process
N/A — no manual annotation process; field reliability is established through the cross-source reconciliation and quality-assurance process described above.
Who are the annotators?
N/A
Personal and Sensitive Information
Yes. Records include business contact details (address, phone, email, website, social links) and named individuals in an executive capacity (CEOName, CEOFirstName, CEOLastName, CEOTitle, CEOGender) tied to their business role, sourced from public and legally available business sources — not from private or consumer contexts. The DNCMPhone and DNCMMobile fields flag numbers registered on do-not-call/marketing-suppression lists, to support compliant outreach. Users are responsible for ensuring any use of contact data complies with applicable local and international data protection and marketing law (e.g. GDPR where applicable).
Bias, Risks, and Limitations
Positional and business-attribute accuracy depend on the freshness and completeness of underlying government and commercial sources, which can vary by market and business type — dense urban areas and well-digitized sectors (retail, hospitality) tend to have stronger coverage than rural or informal small businesses. Revenue and employee-count fields are partly modeled rather than fully verified; always check the paired ReliabilityCode before treating a figure as exact.
Recommendations
Check ReliabilityCode fields before relying on modeled sales or employee figures. Screen against DNCMPhone/DNCMMobile before any outreach campaign. For high-stakes use (compliance, credit, legal), verify critical records against the primary registry or source rather than relying solely on this dataset.
Citation
BibTeX:
bibtex @misc{techsalerator_poi_2026, title = {Business Points of Interest (POI) Data}, author = {{Techsalerator LLC}}, year = {2026}, publisher = {Techsalerator LLC}, url = {https://huggingface.co/datasets/techsalerator/poi-data} }
APA:
Techsalerator LLC. (2026). Business Points of Interest (POI) Data [Data set]. Hugging Face.
Glossary POI (Point of Interest) — a verified real-world physical location (commercial, governmental, institutional, or public) with standardized address and coordinates. DNCM (Do Not Contact Me) — flag indicating a phone/mobile number is registered on a do-not-call or marketing-suppression list. ReliabilityCode — indicates how a figure (sales, employees) was sourced: actual, low-end estimate, estimated, or modeled. Chain linkage — the relationship connecting an individual POI location to its parent/corporate organization. MarketabilityIndicator — flag for whether Techsalerator rates the business record as marketable. More Information
Full methodology is documented in POI_Data_Methodology.pdf in this repository. For a custom data pull — filtered by location, revenue, employee count, years in business, or 40+ other criteria — contact info@techsalerator.com or request a quote at techsalerator.com/request-a-quote.
Dataset Card Authors
Techsalerator LLC
Dataset Card Contact
info@techsalerator.com · techsalerator.com/request-a-quote
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