A laptop does not know it lives with a television.
An advertising system may try to figure that out.
That is the job of an identity graph: connect identifiers that appear in different systems and decide which ones belong to the same person, household, or device cluster.
The graph might contain email addresses, phone numbers, postal addresses, cookies, mobile advertising IDs, IP addresses, connected-TV identifiers, customer IDs, and other persistent keys.
The point is not merely to store them.
The point is to connect them.
The graph turns fragments into relationships
LiveRamp’s current documentation describes identity resolution as connecting fragmented consumer touchpoints to a person- or household-based view. Its systems can resolve names, postal addresses, email addresses, phone numbers, cookies, mobile device IDs, IP addresses, connected-TV IDs, and other identifiers to persistent RampIDs. See LiveRamp’s identity-resolution documentation.
Experian describes the same general problem as matching people or households to devices and platforms using deterministic or probabilistic identity methods. See Experian’s identity-resolution guide.
That is a much larger object than a cookie.
A cookie identifies one browser context.
An identity graph tries to decide which browser belongs with which phone, which television, which email address, which customer record, and sometimes which household.
Some links are stronger than others
Not every edge in the graph has the same evidentiary quality.
A user logging into the same account on a phone and laptop creates a relatively strong connection.
An email address tied to a loyalty account can create another.
Other links may be inferred from patterns such as shared networks, repeated co-location, common household information, or other statistical signals.
The Federal Trade Commission’s 2017 report on cross-device tracking distinguished deterministic methods from probabilistic approaches that infer links between devices. See Cross-Device Tracking: An FTC Staff Report.
That difference matters.
A verified relationship says these two identifiers were directly connected by evidence.
A probabilistic relationship says these two identifiers appear likely to belong together.
Those are not interchangeable claims.
A bad edge contaminates the profile
Households are messy.
People share Wi-Fi. Children use parents’ tablets. Visitors connect phones to home networks. Couples share televisions. Old devices get sold. Work laptops travel home. Apartments change tenants.
If a graph incorrectly joins two people, activity from one can be attributed to the other.
That can affect advertising, measurement, recommendations, fraud models, or any later analysis built on the graph.
The larger lesson is that identity resolution does not merely collect more data.
It changes the unit of observation.
Instead of asking what one browser did, the system can try to ask what this person or this household did across many devices.
That is powerful when the links are right.
It is also why the links themselves deserve scrutiny.
The Surveillance Economy does not need every device to know your name.
It only needs a graph confident enough to connect the devices to something that does.