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:

"verification": {
    "score": 0.9955,
    "components": {
      "data_freshness": 1,
      "source_quality": 1,
      "data_footprint": 0.9867
    }

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).