Is data enrichment the same as data cleansing?

No. Cleansing corrects and standardises what is already in a record — deduplicating, fixing formats, removing dead addresses. Enrichment adds attributes the record never had. Most teams need both, and cleansing usually runs first so that enrichment matches against a tidy key.

How often should records be re-enriched?

It depends on the field. Job titles and employers change as people move, so contact-level attributes go stale faster than company-level ones such as industry or founding year. Teams usually re-enrich the whole database on a schedule and individual records on demand when they enter an active sequence.

What is data enrichment?

Data enrichment is the process of adding missing or updated attributes to a record you already hold — appending a job title, company size, industry or verified email to a name and a domain. Enrichment fills gaps in existing records rather than creating new ones.

Why it matters

A CRM record with a name and an email but no industry, headcount or seniority cannot be routed, segmented or scored. Most go-to-market work — territory assignment, campaign audiences, lead routing rules — depends on fields that were never captured at the point the record was created.

How data enrichment works

You supply what you have — usually an email address, a company domain, or a name and an employer.
The record is matched against a reference dataset: deterministically where a unique key such as a domain exists, probabilistically where one does not.
Matched attributes are appended as new fields alongside your originals, so nothing you uploaded is overwritten.
Each appended field carries the date it was last verified, so you can decide for yourself what is fresh enough to act on.

What enrichment can add, by what you start with

Enrichment inputs and the attributes each can typically return
What you already have	What enrichment can typically add
Work email address	Full name, job title, seniority, department, employer, public profile
Company domain	Industry, employee range, revenue range, headquarters location, technologies in use
Name and company	Verified work email, direct dial, mobile, job function
Existing CRM account	Updated headcount, current industry codes, current technologies, current address

What to look for

A match rate quoted against the whole file rather than against the part of the file the provider could actually attempt — the two are different numbers.
Whether appended fields overwrite yours or sit beside them. Overwriting destroys the one value you knew was correct.
A verification date on every field. A field with no date is a claim with no expiry.
Coverage in the markets you sell to specifically. A provider that is strong in one region can be thin in the next.
What happens to a row that does not match. In a spreadsheet, an honest blank and a confident guess look identical.

Related terms

Data enrichment — How Bulkdata.ai appends fields to a file or a CRM record you already hold.