Verification
Description:
The level of confidence Enigma has that the identity attributes describing the business are accurate, as a proxy for the legitimacy of a business. This score takes into account recency of updates, prevalence across data sources, and quality of underlying data sources.
Child attributes (and data file structure):
Column Name | Data Type | Description | Example |
---|---|---|---|
score | float | A score between 0 and 1 of the freshness and quality of the underlying data sources. | 0.81 |
components | object | The components of the score: data_freshness (score between 0 and 1 representing how recently the underlying data sources were updated), source_quality (score between 0 and 1 representing the quality of the underlying data sources) and data_footprint (score between 0 and 1 representing how many data sources the data comes from). | data_freshness : 1.0,source_quality : 1.0,data_footprint : 0.53 |
JSON Sample:
"names": [
{
"ENIGMA TECHNOLOGIES INC"
}
],
Coverage:
- Business: 100%
- Business Locations: 100%
Time structure:
- Current point in time. No historical information.
Timeliness:
- This attribute updates twice a month, each time Enigma updates the dataset.
Data sources:
- This score takes into account all of the data sources that Enigma uses.
Methodology:
- The score is made up of three components
- Data freshness: How recently have the data sources been updated?
- Source quality: How high is the quality of the underlying data sources that the business shows up in?
- Data footprint: How many data sources does the information for the business record come from?
- For all scores between 0 and 1, 0 is the lowest score (poorest data source confidence) while 1 is the highest (strongest data source confidence).
Updated 4 months ago