What is data enrichment?

Definition

Data enrichment augments records with information that was not present in the original source. For customer and account data, enrichment can add company details, classifications, contact information, or signals that improve segmentation and decision-making.

Enriched data can improve AI context, but its source, freshness, accuracy, and usage rights still need to be governed. More attributes are useful only when they are relevant and trustworthy for the task.

ELI5

Data enrichment adds useful details from other sources to records that already exist. It can make a dataset easier to search, group or use for decisions.

For example, a customer record with only a company name can be enriched with its industry and location from a trusted directory. An AI system may then have better context, but the added information still needs a known source, current date, suitable usage rights and accuracy checks.

Acronyms and aliases

data enhancement synonym

Frequently asked questions

How is data enrichment used in customer systems?

It supplements customer or account records with additional attributes that can support targeting, prioritization, personalization, and analysis.

What are the risks of data enrichment?

The added information may be inaccurate, outdated, improperly licensed, or unnecessary, so teams should validate provenance, permissions, and relevance.

Videos explaining data enrichment