You have a list of 50 LinkedIn profiles for a hard-to-fill role. Going through it, you find that only 10 have a valid email and not a single direct phone number shows up. The profiles look right, but you can't prioritise with any confidence, personalise your message, or tell which contact details are still live. Sourcing found the people. Incomplete data is what stops you turning them into conversations.
That is where data enrichment for recruiting starts. It isn't about filling a spreadsheet with extra columns. It's about adding the context you need to decide who to contact first, through which channel and with what message — while keeping verified facts, inferences and still-to-be-confirmed data clearly apart.
An introduction to data enrichment that actually helps you close roles
A busy recruiter doesn't need another layer of complexity. They need to shorten the time between spotting a profile and making first meaningful contact. When an agency is running several open roles, every incomplete record means researching the company again, double-checking the job title, hunting for a contact channel and confirming whether the information is still current.
The problem isn't only operational. Bad data can lead you to email a dead corporate address, mix up two professionals with similar names, or present an unverified deduction as established fact. The accuracy principle as it applies to personal data is more nuanced than it looks: information has to be adequate for the purpose of the processing, and that assessment extends to any inferences and predictions generated along the way. We cover this in our guide to sourcing tools and GDPR.
From profile found to contact you can act on
The base record is usually a professional profile: name, job title, company, location, experience and visible skills. Enrichment adds layers that make that profile usable — identifying the company, normalising the role title, a validated professional email, a phone number, mobility signals, or a clear marker of which information came from a source and which is an inference.
The difference shows up in prioritisation. For a data role, for example, finding someone whose title contains the right keyword isn't enough. The recruiter may need to check the type of company, the level of responsibility, the actual location and the most appropriate contact channel before investing time in a sequence.
Rule of thumb: a field only earns a place in your workflow if it changes a sourcing, prioritisation or outreach decision.
This guide follows that path, from the basic concept through to AI automation. The goal is to help you design a process that works for an agency, a staffing firm or a Talent Acquisition team, without turning enrichment into a pile of data you can't justify. Traceability, minimisation and human review belong in the process from day one — particularly in Spain, where the GDPR and Organic Law 3/2018 shape what you can subsequently do with the information.
What data enrichment means in recruiting, and how it works
Think of a jigsaw. The LinkedIn profile is an important piece, but it doesn't complete the picture. Data enrichment brings in additional pieces, as long as they are relevant, verifiable and compatible with the purpose of the hiring process.
The workflow breaks down into four moves:
- Extraction. You start from a search and collect the profile's visible data: title, company, location, career history and skills.
- Matching. You connect that profile to company or professional information from other sources.
- Verification. You check whether the email, phone number, domain or job title show signals of being current and coherent.
- Prioritisation. You use the confirmed data to rank profiles and decide which contact deserves attention first.
Completing, normalising and inferring are not the same thing
Completing means adding a data point that was missing — a professional phone number attached to a profile, say. Normalising means putting information into a consistent format, such as folding "Talent Acquisition Partner", "TA Partner" and "Recruitment Partner" into whichever taxonomy your team uses. Inferring means drawing a conclusion from signals, like estimating that someone has international experience because they have worked across several markets.
The first two operations can improve the consistency of a record. The third needs more care. An inference should never appear in the ATS as though it were a confirmed fact, nor turn automatically into a knockout filter.
Quality is what decides the value
A record with many fields can be less useful than one with a handful of reliable data points. For every attribute, store at least three things: the value, the source and the validation status. It's also worth noting when it was obtained or last reviewed, wherever the tool and your internal policy allow it.
The result isn't a longer record. It's a record that answers concrete questions: who do I contact, through which channel, which part of their experience fits, which data point needs checking? That's the difference between enriching to work better and hoarding information nobody uses.
Data sources for enriching profiles in Spain without losing traceability
The source has to be chosen for the purpose. To understand a company's context, official public sources give you a different foundation from a private contact-data vendor. To personalise an approach, a professional profile carries different signals from a commercial register. Mixing them without documenting the origin creates confusion and makes it harder to answer an access or rectification request.
Spain's DIRCE, run by the national statistics institute (INE), works as an official reference for cross-checking the Spanish business landscape. The public ecosystem there also includes BORME, the tax agency (AEAT), the Commercial Register, GLEIF, BDNS and PLACSP. Together, these sources let you work with identifiers such as the Spanish company tax number (CIF) and add firmographic, administrative and business-context data with clearer traceability than an opaque database. If you source outside Spain, the equivalent official registers in each country play the same role.
What each type of source gives you
| Source or category | Practical use in recruiting | Control worth keeping |
|---|---|---|
| DIRCE (INE) | Cross-check sector and company characteristics | Record the date and purpose of the check |
| BORME | Review corporate filings and directorships | Keep company filings separate from contact data |
| Commercial Register | Confirm corporate information | Avoid reusing data beyond the defined purpose |
| Company website | Identify structure, departments and activity signals | Save the URL and separate facts from interpretation |
| Private vendors | Obtain contacts or operational signals | Check provenance, recency and permitted use |
| Professional profile | Understand experience, role and work context | Don't treat a public signal as universal permission |
BORME shows up in Spanish-market methodologies as a source capable of structuring roughly 9.2 million companies and 17 million directorships, according to this analysis of B2B data sources in the Spanish market. That volume illustrates how much cross-checking capacity is available — it does not mean all those records are relevant to a recruiting campaign.
Traceability starts before you import
Before enriching, define what you're trying to resolve. If you're looking for candidates at one specific company, confirming sector, size, location and the relationship between the role and the vacancy may be enough. If you're sourcing for a staffing firm, it may matter more to distinguish between work sites, functional areas and hiring managers.
Our guide to people search engines works as an operational reference for organising the search. Either way, don't depend on a single database. Cross-check the signals that matter, keep the origin, and drop the fields that don't support a defensible decision.
Verifying emails and phone numbers to improve deliverability
Finding a contact is not the same as having a usable one. An email can be correctly formatted and still bounce. A phone number can have every digit you'd expect and be disconnected, belong to someone else, or simply be the wrong channel for a professional approach.
Verification has to combine different checks. Each method answers a different question, and none of them turns an inference into a fact.
Five checks before you reach out
Syntax validation. Check that the email has a valid structure and rule out obvious typos. It's a basic filter, not confirmation that the mailbox exists.
Domain check. Confirm the domain exists and is still active. A corporate domain can change after an acquisition, a restructuring or the professional's departure.
SMTP ping. A technical check can indicate whether the mailbox responds or accepts mail. Read it with caution: some servers hide or throttle that response.
Catch-all detection. Some domains accept messages sent to addresses that don't exist. Flag those contacts as uncertain and don't treat them like a confirmed mailbox.
Phone validation. Check the format, the dialling code and whatever activity signals are available. A valid number doesn't by itself prove it's the right way to reach that person.
Our guide to contact data for recruiting helps you organise this layer of the work. The central principle is to separate verified, likely, unavailable and needs review, rather than presenting every result with the same level of confidence.
The recruiter's checklist
Before launching a sequence, confirm:
- Recency: do the job title and company still match?
- Source: can you explain where the email or phone number came from?
- Purpose: will the contact be used within a professional recruitment activity?
- Channel: is the medium you've chosen reasonable for that context?
- Message: is the personalisation based on visible facts rather than sensitive assumptions?
- Objection: is there a mechanism to stop future contact?
Deliverability improves when a team avoids bounces and stops pushing on questionable data. Message quality counts too. A correct email address carrying a generic pitch doesn't solve the response-rate problem.
