Data… used by many, understood by few.

It’s all around us and available for use, but not many professionals don't understand the value of data and how they can access it, organize it and use it to their advantage.

Also in the recruiting space there are very few sourcers and recruiters who really master the use of data in their process.

But when you use talent data the right way, it can give not only strategic advantages by providing insights, but it can also serve as a means of sourcing talent in a smarter and much more efficient way.

For example, if you as a recruiter are able to review the top three skills of a candidate in the blink of an eye, can filter on candidates who have been part of a fast growing company and know how to get their verified contact details with a click of a button, you’re likely to win in today’s world of recruiting.

This guide will help you understand talent data better, how you can access it and how you can make better use of it.

1. What types of data are there
2. Talent data attributes: what relevant talent data looks like
3. How to organize talent data
4. How to make use of talent data

What types of data are there

The first step in understanding the value of talent data is looking at what types of data are available and what those different categories of data can mean in sourcing and recruitment.

Data can be categorized in many different ways: 

• Quantitative vs qualitative data
• Fit vs intent data
• Public vs private data
• External vs internal data
• Company vs company data

Quantitative vs qualitative data

Quantitative data are basically numbers. 

Qualitative data is descriptive in nature and usually in the form text. 

Quantitative data in recruiting is usually used as a filter, like the number of experience years that someone has in a certain position.

Qualitative data is usually used for a recruiter to interpret the ‘quality’ and relevance of information on a candidate in respect to a job.

Fit vs intent data

Fit data is data about how well the candidate matches (fits) the job and the company.

Intent data is data about how interested the candidate is in applying for the job.

In other words fit data in recruiting is used to determine if a candidate qualifies and intent data to determine the level of probability of a candidate to say yes to a job.

Intent data can be further categorized into behavioral data (what people do and when they do it; actions) and contextual data (the situation in which people show their behavior; time, location, device…).

Public vs private data

Public data is data that is available and accessible to the public (arguably without any limitations).

Private data is data that is intended to be shared only with a specific set of people and sometimes with no one at all.

An example of public data is a LinkedIn profile that you can find online. An example of private data is a secured profile, website or database only accessible with a password.

External vs internal data

External data is data which comes from external sources like social media, external databases, websites and service providers that provide data.

Internal data is data coming from your organization itself, like data on performance reviews, hires, contracts, internal skills and internal mobility.

Person vs company data

Person data is data about an individual, like their name, job title and top skills.

Company data is data about a company that includes things like industry, company size and business.

Person data is used to search individuals and assess them on their background, interests and personality. Company data is typically used by recruiters in the context of an individual to help assess the relevancy of their experience at the companies they have worked. 

All these types of data can be analysed by either humans (recruiters) or machines (Artificial Intelligence).

Talent data attributes: what relevant talent data looks like

Knowing the categories is one thing. Knowing which specific attributes actually change a hiring decision is another.

Most talent databases hold far more fields than anyone ever uses. The skill is not collecting more attributes, it’s knowing the handful that change who you contact and what you say to them. Everything else is noise that makes your database look impressive and your shortlist no better.

In practice, the attributes that earn their place answer three questions: who is this person, what have they actually done, and how do I reach them.

Identity and reachability attributes

These are the attributes that let you find the same person twice and get a message in front of them.

Name, current location and a stable profile URL form the backbone. The profile URL matters more than people expect: names are not unique, titles change every couple of years, but a LinkedIn or GitHub URL is a durable key you can match records against later. If you store only a name and a title, you will create a duplicate the next time that person shows up.

Contact attributes sit on top of that: a work or personal email, a phone number, and the channels where the person is actually active. The critical attribute here isn’t the email itself, it’s its verification status. An email guessed from a company naming pattern and a verified, deliverable email are different data points. Treating them as the same thing is how sourcing teams quietly wreck their sending domain reputation and then wonder why response rates collapsed.

Experience and skill attributes

This is the group most recruiters over-collect and under-use.

A job title on its own is weak data. ‘Software Engineer’ at a 40-person startup and ‘Software Engineer’ at a bank describe two very different jobs, and seniority labels are inflated at wildly different rates across companies. Title becomes useful only when you pair it with tenure (how long in the role), progression (what they did before it) and the context of the employer.

Skills are the attribute people most want and most often get wrong. Self-reported skill lists on a profile are a statement of intent, not evidence. The stronger version is skill data inferred from what someone actually shipped: the repositories they contribute to, the products they’re credited on, the tools named in their own descriptions of their work. That’s the difference between someone who listed a technology and someone who used it for three years.

Company attributes as candidate context

Company data becomes talent data the moment you use it to interpret a person.

Headcount, industry, funding stage and growth rate tell you what an experience line on a profile really means. Someone who joined a company at 30 people and stayed until it hit 400 lived through something specific, and you can find those people by filtering on the company’s growth curve rather than on anything in their profile text. That is the ‘fast growing company’ filter mentioned at the top of this guide, and it works because it uses company data to ask a question person data can’t answer.

The same logic applies in reverse. A candidate leaving a company that just had a hiring freeze or a layoff round is a different prospect, in terms of intent, than one at a company that just raised. Neither fact appears on their profile. Both live in company data.

How to organize talent data

Organizing talent data is mostly one discipline: making sure one person equals one record, and that the record tells you how old it is.

Everything else follows from that. Teams that skip it end up with three copies of the same candidate, contacted by two different recruiters, using an email that stopped working eighteen months ago. No amount of clever filtering fixes a database that can’t tell you who is in it.

Four practices carry most of the weight:

• Identity resolution: match on stable keys (profile URL, verified email) rather than name plus company, and merge rather than duplicate.
• Timestamp everything: every attribute should carry the date it was captured, so you know what to trust.
• Normalize the fields you filter on: locations, seniority and skills need a controlled vocabulary or your filters silently miss people.
• Record the source: where an attribute came from determines how much weight it deserves.

Timestamping deserves the most attention because talent data decays faster than almost any other business data. People change jobs, move cities and change emails, and every one of those events silently invalidates fields in your database without telling you. A record with no capture date is a record you have to re-verify from scratch, which in practice means nobody does. A record that says ‘this email was verified in March’ lets you make an informed call about whether to trust it in November.

Normalization is the other quiet failure. If half your records say ‘NYC’, some say ‘New York’, and some say ‘New York, United States’, a location filter returns a confident, clean and badly incomplete list. The recruiter never sees the people who were dropped, which is what makes this class of error so expensive: it fails silently and looks like a result.

Where the data should live

The practical answer for most teams is that the ATS holds people you are in process with, and a sourcing database or CRM holds people you are not.

Mixing those two is the most common structural mistake. An ATS is built around a requisition: candidates enter against a job and are dispositioned. A talent pool is built around a person who might be right for something in two years. Force the second into the first and you get thousands of ‘rejected’ records that are really just ‘not right for that one job’, and a database nobody wants to search.

Wherever it lives, note that organizing talent data is also where the legal obligations land. If you hold data on people in the EU, you need a lawful basis for keeping it, a retention period you actually enforce, and a way to honour a deletion request. Retention is the one most sourcing databases fail: keeping a profile forever because storage is cheap is a decision, and it is one you have to be able to defend.

How to make use of talent data

All of the above is setup. The payoff is that good talent data changes three things: who you contact, in what order, and what you say.

Most teams only use it for the first one. Filtering is the obvious application and the shallowest. The real returns come from prioritization and personalization, because those are where data stops being a search input and starts being a judgement.

Prioritization: deciding who to contact first

Fit data tells you who qualifies. Intent data tells you who will answer.

A list of 200 qualified people is not a sourcing plan, because you can’t write 200 good messages. The useful move is to rank that list using the intent and contextual signals from earlier: how long someone has been in their current role (people are most movable around the two to three year mark in many fields), whether their employer just went through a funding event or a freeze, whether they’ve quietly started engaging with content in your space. None of this tells you a person is looking. It tells you where to spend your first twenty messages instead of your last twenty.

This is also where machines genuinely beat humans. Ranking 200 records against a dozen weighted signals is tedious and error-prone for a person and trivial for a model. Recruiters are better than any model at the judgement calls the ranking sets up.

Personalization: deciding what to say

The attribute you use to find someone and the attribute you use to open a conversation should usually be different ones.

You might filter on ‘Python, five years, Berlin’, but nobody wants to receive a message that recites that back to them. The data worth putting in the message is the specific thing: the product they shipped, the transition their company just went through, the talk they gave. That data lives in the qualitative fields most databases treat as decoration.

This is the clearest argument for keeping qualitative data at all. Quantitative attributes get you the list. Qualitative attributes get you the reply.

The loop back to internal data

The last step is the one almost everyone skips: feeding outcomes back in.

Your ATS knows which sourced candidates got to interview, which got offers and which stayed past a year. That’s internal data, and it’s the only data you have that nobody else can buy. Connect it back to the attributes you filtered on and you can finally answer the question that matters: which of our filters actually predict a hire, and which ones just feel right?

Most teams discover that some long-held belief about what makes a good candidate doesn’t survive contact with their own hiring data. That is uncomfortable and it is the single highest-value thing talent data can tell you. It’s also why external data alone has a ceiling: it can find you everyone, but only your own outcomes can tell you who to look for.

Where to start

If you take one thing from this guide, take the order of operations.

Fix identity and timestamps before you buy more data, because more data poured into a database that can’t deduplicate makes the problem worse rather than better. Then get the handful of attributes that actually change decisions (verified contact status, tenure, employer growth, evidenced skills) rather than every field on offer. Then use them to rank and personalize, not just to filter. Then close the loop with your own hiring outcomes.

Very few recruiting teams do all four. The ones that do aren’t working with better data than everyone else. They’re working with data they can trust, which turns out to be the rarer thing.