Data Accuracy and Coverage

More does not automatically produce better identity intelligence

A dataset can have enormous reach and still contain stale identities, weak linkages, incomplete profiles, or attributes that should not be used with confidence.

AtData is built to balance both sides of the equation:

Recognize more identities. Return better-supported intelligence about them.

That requires broad coverage, multiple sources, identity resolution, ongoing validation, behavioral activity, and the discipline to distinguish between what is known and what is unavailable.

Accuracy is not one percentage

“Accuracy” is often used as if every piece of identity data can be measured the same way.

It cannot. Different data requires different measures.

An email-validation result may be evaluated against deliverability. An identity linkage depends on whether identifiers correctly belong together. Demographic enrichment has a match rate. Behavioral intelligence depends on the recency and breadth of observed activity.

AtData therefore evaluates data quality across several dimensions:

• Recognition

  Have we encountered the identity before?

• Match

  Can the submitted identity be connected to relevant records or attributes?

• Confidence

  How strongly does the available evidence support the connection or output?

• Recency

  Is the intelligence current enough for the decision being made?

• Coverage

  For what portion of the relevant population can AtData return useful information?

Recognize identities at enterprise scale

A data provider can’t add meaningful context to identities it can’t recognize. AtData’s historical email network provides broad reach.

Currently a 99% recognition rate for North American email addresses across our fraud intelligence and activity history on approximately 98% of email identities evaluated through our network.

That breadth matters because an identity that cannot be recognized has little historical context available.

Recognition enables AtData to begin answering the questions that come next:

• When was this identity first observed?
• Is it still active?
• What other identifiers are associated with it?
• How broadly has it appeared?
• Does its behavior look consistent?
• What additional information can be confidently connected?

Explore Activity Network

Build coverage through connected identity

Coverage becomes more useful when records can be connected rather than simply accumulated.

AtData’s email identity graph contains more than 2.4 billion records, including multi-sourced linkages among email addresses, names, and mailing addresses. A larger identity graph creates more opportunities to recognize a customer. Accurate linkages determine whether those opportunities are useful.

Those relationships support capabilities such as:

• Email-to-person matching
• Postal-to-email matching
• Alternate-email discovery
• Customer-profile consolidation
• Identity-graph enrichment
• Online-to-offline resolution

Resolve Identities

Use multiple sources to strengthen the picture

No individual source provides a complete view of a consumer. AtData combines information from multiple data environments and then standardizes, filters, and resolves those inputs before connecting them to identity.

That foundation can include:

• Email identity records
• Activity observations
• Name and postal information
• Demographic and household data
• Behavioral data
• Purchase and interest information
• Domain intelligence
• Risk and fraud signals

The objective is to return information sufficiently supported to improve the decision being made.

Keep intelligence current, not merely complete

A correct identity relationship can become less useful as the underlying data changes.

People adopt new emails. Addresses become dormant. Households move. Domains disappear. Activity changes. Fraud infrastructure appears and vanishes quickly.

AtData combines historical identity depth with more than 150 billion deterministic activity signals processed each month, creating a continuously refreshed behavioral layer around our identity intelligence.

That enables us to distinguish between:
What has historically been true and What appears relevant now.

For many enterprise applications, both matter.

More coverage should create better decisions. Not more false confidence.

When data is unavailable, uncertain, or unsupported, the correct output may be no result. False matches can be more damaging than missing information because they introduce incorrect evidence into:

• Customer profiles
• Personalization
• Identity graphs
• Fraud models
• Analytics
• AI systems
• Decision strategies

AtData’s methodology is designed around connecting supported evidence rather than manufacturing completeness.
Observed data should remain observable.
Derived signals should remain identifiable.
Model outputs should remain distinct from both.

That distinction helps enterprise teams understand what they are using and choose how much confidence to place in it.

Behavioral Models and Scores

Measure AtData against your data.

Enterprise data quality should ultimately be proven against the population and decision that matter to your organization. AtData works with customers to evaluate factors such as:

• Recognition rate
• Match and fill rates
• Data recency
• Identity-resolution lift
• Validation outcomes
• Model performance
• Operational impact

The objective is not simply to demonstrate that AtData has more data. It is to determine whether AtData produces more useful intelligence from the identities you actually encounter.

Breadth gives you the opportunity to know. Quality determines whether you should act.

AtData combines extensive email identity coverage, multi-source resolution, longitudinal history, and continuously refreshed activity to help organizations recognize more identities without treating every connection as equally certain. Because coverage is valuable only when the intelligence behind it can be trusted.