---
license: cc-by-nc-4.0
task_categories:
  - text-classification
  - feature-extraction
  - tabular-classification
language:
  - en
  - multilingual
tags:
  - companies
  - business
  - lead-generation
  - b2b
  - firmographic
  - company-data
  - credit-scoring
  - financial-data
  - global-companies
pretty_name: "World Company Database — Premium 1M (Revenue + Credit Score)"
size_categories:
  - 100K<n<1M
configs:
  - config_name: default
    data_files:
      - split: train
        path: premium-1m-companies.parquet
---

# World Company Database — Premium 1M Sample

**1,000,000 curated companies** with verified revenue data and credit scores, extracted from the [S.C.A.L.A. Score](https://score.get-scala.com) global company database containing **272+ million records**.

## What Makes This Premium

Unlike random company samples, **every record in this dataset** has:

- **Actual revenue data** (revenue > 0) — no empty financial fields
- **Credit score >= 50** — only creditworthy, financially assessed companies
- **Sorted by revenue** — the largest companies in each country come first

This is the top slice of 2.3 million financially enriched records out of 272M+ total.

## Dataset Description

This dataset provides high-quality structured firmographic and financial data for 1 million companies across 13 European countries + the US, useful for:

- **Financial analysis & benchmarking** — Every record has real revenue, many have net income, assets, and equity
- **Credit risk modeling** — All records have S.C.A.L.A. credit scores (50-100) and letter grades
- **Lead generation & B2B prospecting** — Filter by country, sector, size, and financial health
- **Market research** — Analyze business landscapes with actual financial data
- **ML training** — High-quality labeled data for revenue prediction, credit scoring, sector classification

## Schema

| Column | Type | Description | Coverage |
|--------|------|-------------|----------|
| `name` | string | Company legal/trading name | 100% |
| `city` | string | City / municipality | varies |
| `country` | string | ISO 3166-1 alpha-2 country code | 100% |
| `legal_form` | string | Legal entity type (SAS, SA, SRL, etc.) | varies |
| `sector` | string | Industry sector code | varies |
| `sector_desc` | string | Sector description (human-readable) | varies |
| `status` | string | Company status (active, inactive, etc.) | varies |
| `founded` | string | Year or date of incorporation | varies |
| `employees` | integer | Number of employees | varies |
| `revenue` | bigint | Annual revenue (local currency) | **100%** |
| `net_income` | bigint | Net income (local currency) | varies |
| `total_assets` | bigint | Total assets | varies |
| `equity` | bigint | Shareholders' equity | varies |
| `financial_year` | integer | Year of financial data | varies |
| `score` | integer | S.C.A.L.A. credit score (50-100) | **100%** |
| `grade` | string | Credit grade (A/B/C/D/E/F) | varies |
| `source` | string | Data source identifier | 100% |

Note: `tax_id` and `address` fields are excluded from this public sample for privacy. Available via the Score API.

## Country Distribution

| Country | Records | Avg Score | Avg Revenue | Max Revenue |
|---------|---------|-----------|-------------|-------------|
| FR | 400,000 | 53.5 | 18M | 214B |
| NO | 200,000 | 60.4 | 60M | 941B |
| IT | 200,000 | 77.0 | 20M | 190B |
| PT | 80,000 | 64.2 | 2.4M | 29B |
| SE | 50,000 | 65.0 | 17M | 26B |
| BE | 30,000 | 65.0 | 28M | 92B |
| DK | 27,000 | 65.1 | 270M | 425B |
| CZ | 6,000 | 70.0 | 1B | 424B |
| EE | 3,000 | 59.1 | 4.7M | 2.2B |
| US | 2,000 | 80.3 | 12.9B | 717B |
| LV | 1,000 | 76.3 | 82M | 1.4B |
| ES | 500 | 58.4 | 492M | 62B |
| FI | 500 | 54.2 | 169M | 25B |

Revenue values are in local currency (EUR for most countries, NOK for Norway, SEK for Sweden, CZK for Czech Republic, USD for the US, DKK for Denmark).

## Usage

### Python (pandas)
```python
import pandas as pd
df = pd.read_parquet("hf://datasets/Alessandro114/world-company-database/premium-1m-companies.parquet")
print(df.shape)  # (1000000, 17)
print(df['revenue'].describe())
```

### Python (DuckDB)
```python
import duckdb

# Top 10 companies by revenue
duckdb.sql("""
    SELECT name, country, revenue, score, grade
    FROM 'hf://datasets/Alessandro114/world-company-database/premium-1m-companies.parquet'
    ORDER BY revenue DESC
    LIMIT 10
""").show()

# Country breakdown
duckdb.sql("""
    SELECT country, COUNT(*) as companies, AVG(score) as avg_score,
           AVG(revenue) as avg_revenue
    FROM 'hf://datasets/Alessandro114/world-company-database/premium-1m-companies.parquet'
    GROUP BY country ORDER BY companies DESC
""").show()
```

### Python (datasets)
```python
from datasets import load_dataset
ds = load_dataset("Alessandro114/world-company-database")
```

## Full Database Access

This is a **premium 1M sample** from a database of **272+ million companies** across 265 countries.

The full database contains 2.3M+ companies with financial data, and 272M+ total company records.

For full access with advanced filtering, enrichment, and real-time updates:

- **Score API**: [https://score.get-scala.com/api](https://score.get-scala.com/api) — RESTful API with country, sector, revenue, and employee filters
- **Bulk exports**: Available for enterprise customers
- **Custom enrichment**: Tax ID validation, financial data, credit scoring

Built by [S.C.A.L.A.](https://get-scala.com) — the enterprise AI operating system.

## License

This dataset is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).

- **Non-commercial use**: Free with attribution
- **Commercial use**: Requires API access — see [score.get-scala.com](https://score.get-scala.com)

## Citation

```bibtex
@dataset{scala_score_premium_2026,
  title={World Company Database - Premium 1M Sample with Revenue and Credit Scores},
  author={S.C.A.L.A.},
  year={2026},
  url={https://huggingface.co/datasets/Alessandro114/world-company-database},
  license={CC BY-NC 4.0}
}
```