What Is Data Enrichment?

[Jahangir307]

Data enrichment takes the aggregated data and enhances it with additional layers of context, either by correcting, validating, or appending new data points. For example, if a phone number is tied to app usage data and a mailing address, enrichment might add estimated income, likely interests, political leanings, or behavioral scores.

Key enrichment techniques include:

Reverse lookup: Identifying name, address, or email vietnam phone number list from a phone number.

Appending behavioral traits: Based on app behavior or geolocation data.

Linkage: Connecting a phone number to social accounts or online identities.

Predictive modeling: Inferring traits such as creditworthiness or churn risk.

3. How Aggregation and Enrichment Work Together
These two processes are often used in tandem:

Aggregation collects the fragments.

Enrichment turns the fragments into a narrative.

Imagine an app with access to a user’s phone number, location, and device type. Aggregation pulls in additional data from ad exchanges and third-party SDKs. Enrichment then links that phone number to a real-world identity, adding behavioral segments, purchase history, and interest categories. This enriched profile can now be sold, targeted, or analyzed.