Data enrichment
Enrichment appends third-party attributes to records so that fit can be evaluated from data the buyer did not have to supply.
Enrichment exists to shorten forms. Every field removed from a form raises conversion, and every field that can be inferred from a company domain does not need to be asked. That trade is the entire business case, and it is measurable: form length against completion rate against downstream acceptance.
The governance requirement is precedence. When enriched data disagrees with data the customer entered, one source has to win, and it should usually be the customer. Systems that let a nightly enrichment job overwrite self-reported fields produce records that contradict what the buyer told a salesperson last week.
Where it breaks
An enrichment job overwrites a manually corrected account owner every night, and nobody can work out why the field keeps reverting.
Related terms
- Revenue data model
- The revenue data model is the set of objects, relationships and required fields that every revenue-facing process reads from and writes to.
- Lead scoring
- Lead scoring is a model that ranks records by expected conversion, and it is only valid when it is calibrated against realized outcomes.
- Ideal customer profile (ICP)
- An ICP is a testable specification of the accounts a company can serve profitably and repeatably, expressed in attributes the revenue system can actually evaluate.
- Data quality
- Data quality is the measured fitness of records for the decisions made from them, completeness, accuracy, consistency, timeliness and uniqueness, each measured against a stated requirement.
Field notes on this
- The MQL debate needs an acceptance record
A recorded acceptance decision makes lead-quality disputes inspectable. It is a signal, not a complete diagnosis of demand performance.