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 min read

Turn talent data into more and better hires (2022 Guide)

There's an almost infinite amount of valuable talent data available, but it's not always accessible and usable for recruiters. This is how to integrate talent data into your sourcing and recruiting engine.

September 29, 2020
Yuma Heymans
March 16, 2022
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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 

To understand the available data points on talent better, we’ll take a look at what those data points can look like.

Data attributes serve as descriptors for data points or data objects.

We’ll zoom in on talent data attributes based on the Person and Company data categories.

Below we give the data attributes that are relevant for recruiting.

Person data attributes

Here’s an overview of the most important person data attributes that you can use for recruitment purposes:

Company data attributes

Here’s an overview of the most important company data attributes that you can use for recruitment purposes:

Public sheet with the company and person attributes

How to organize talent data

Since there’s potentially a lot of candidate data you’ll be working with, you need to organize that data to be able to access it quickly but also to get a sense of the priority of that data.

Let me explain.

One of the major challenges in making use of talent data, and any data really, is to organize the data in a way in which it’s accessible, clear and usable.

If you have any experience working with data, for example in spreadsheets, you know how quickly data can become unstructured, full or errors and as a result unusable.

Some of the challenges in organizing (talent) data are:

  • Data goes through different hands inside and outside the organization, risking data to be manipulated, neglected or misinterpreted.
  • Connections between data systems are not real-time and require manual processing (exporting and importing).
  • Data can be incomplete or missing entirely.
  • Data quality is low, data is shown in the wrong fields or the data itself is simply incorrect.

To get over these challenges and be able to use the data in your daily work, you need data to be accessible and you need a great user experience with the data.

1. Data accessibility

Accessibility is about how easy it is to access the data exactly when you need it. Do you need several passwords to access data? Or do you have a single sign in to access the data? Is the data accessible in one tool? Or is it spread all over different systems?

Data accessibility is about:

  • Access to data systems
  • Sign in experience
  • Managing user roles and user rights

2. Data user experience

User experience is about how a user (recruiter) can interact with the data. Raw data is usually really hard to work with, because it’s not translated to insights and possible actions. 

Having to manually copy and paste unformatted text all the time or having to work with incomplete contact details does not contribute to a great data user experience. Enabling user actions with buttons, automations and the right instructions at the right time is crucial to get value out of data.

Data user experience is about:

  • Visualization of data
  • The data user interface, enabled by buttons and other interactions
  • Automations to minimize manual actions like copying and pasting

How to make use of talent data

In over 99% of the cases talent data is not made accessible, incomplete and actions are not enabled by a great user experience.

In addition, there’s almost always a lack of understanding in how data can be used in the first place.

Important to determine is what kind of data you need and in what level of detail you need it.

Look at the talent acquisition strategy that you have and ask yourself what data is needed to execute on that strategy.

Do you have a sourcing strategy that heavily relies on proactive outreach on social media, then you need the data to support that, like social media links and maybe even social media activity.

Do you follow a mass communication approach that heavily relies on email, then you need email addresses of candidates that are verified for deliverability.

As you could see in the tables of talent data attributes, the possibilities are very extensive with all the available data online.

And that means that the use cases for that data are almost limitless:

  • Enrich candidate profiles with valuable insights like top skills and startup readiness scores
  • Bring sophistication to your screening process by using trusted matching algorithms to do the first filtering and analysis of profiles
  • Enrich contact details of sourced candidates with email addresses so you can differentiate your outreach approach
  • Get strategic insights by doing high level analytics over your entire talent pool to  improve your targeting and outreach strategy

Quick wins in using talent data

There is a lot that can go wrong in setting up a data driven way of working based on talent data. 

If you want to set up your talent data architecture and way of working yourself, you need people in the organization who are dedicated to doing this.

An alternative to assigning data responsibilities to team members, you can start making use of more reliable and relevant talent data by acquiring the right tooling.

So what kind of tooling do you need?

It all depends on your organization and your goals.

Some organizations already have access to quite some internal and external data. In that case, the focus should be on the user experience of that data, for an example by using the right Application Tracking System (ATS) and message sequencing tooling to prompt the right way of working and make systems work in an integrated fashion.

But typically organizations, big or small, do not have access to data let alone a great data user experience.

To get started with talent data here’s a suggestion for a possible set of tooling you can use to begin with talent data driven recruiting:

  1. Sourcing tool
  2. ATS
  3. Optional: analytics tool

1. Sourcing tool

A sourcing tool is the gateway to external data, which is always the richest data source out there because a lot more data is available externally than internally. A sourcing tool should enable you to find and reach candidates that are not in your internal database yet.

The sourcing tool you choose should be:

  • Searching in different sources: don’t settle with a LinkedIn only database, there are tools available that search the web including other sources like GitHub or Stack Overflow (great to find engineers).
  • Enriching profiles: there’s so much more to a candidate than one single social media profile, find a tool that gives a full view on a candidate with full skills data, instead of just a name, job title and summary.
  • Enables outreach: your sourcing tool should find contact details like email and enable outreach with for examples message templates and sequences.

Example of a data driven sourcing tool

HeroHunt.ai: data driven sourcing engine that finds the best tech candidates across the entire web, with rich information, verified contact details and personalized outreach templates.

✅ Finds candidates across platforms like LinkedIn, GitHub and Stack Overflow

✅ Enriches candidate profiles with valuable information to filter on and screen on

✅ Provides contact details and outreach templates, with one click sending of emails

2. ATS

An Application Tracking System (ATS) is software that recruiters use to keep track of candidates and the touchpoints with them in the recruiting process. It’s very similar to how Customer Relationship Management (CRM) software works, and for that reason some recruiters choose to use a CRM (also, definitions are used interchangeably in the industry). 

The ATS you choose should be:

  • Integrated: your ATS of choice should integrate easily with other systems to enable a real-time and two way connection of data sharing so you don’t have to import and export data all the time to sync different systems. Your ATS should at least be able to integrate with your sourcing tool and email.
  • Supports data driven working: your ATS should be able to work with the data that you choose to use in your process. Some ATS’s do not allow for custom fields. Bummer if you collect nice and rich profiles in your sourcing process and you can’t leave that data in your ATS because it doesn’t let's you save and use the different types of data that you collect. So you need an ATS that is able to process and present the data that you acquire.
  • Is highly automated: most ATSs still work in a very manual fashion; contacts are created manually, profiles should be completed with information manually, process steps are changed manually… For you to work in a data driven way, your ATS should support things like automated data import from profiles based on a LinkedIn URL, automated status changes or labels based on your actions and data enrichment.

Example of a data driven ATS

Recruitee is an ATS that allows for collaborative hiring that supports sourcing processes next to hiring processes and allows for automation of repeatable processes, also integrates with several third party tools.

✅ Has integration marketplace with plugin tools that sync with ATS data

✅ Supports automatic data enrichment of candidates and their profile

✅ Automates repetitive processes with workflows and task automation

3. Optional: analytics tool

People analytics software enables, typically larger, recruiting organizations to analyze people (talent) data at a strategic level. Organizations use it to get a high level overview of their current workforce, the available talent in the market (talent market mapping) and to find possible underutilized sources of candidates.

The people analytics tool you choose should:

  • Give strategic level insights: the added value of people analytics software is that you can generate high level overviews based on people data that can give insights into the as-is and the to-be state of talent sourcing efforts.
  • Make use of external data: your people analytics tool should give you access to external sources for analysis so you can benchmark your current situation with the market. Ideally it provides reference data, like average market salaries, of similar companies so you get an idea of how much your talent base and methods differ from the market.
  • Work with your existing tools: your current tools are important input for people analytics software to give the right insights, so pick a tool that integrates with the tools you already use to have it serve as an additional layer of analytics to turn day-to-day data to strategic analytics and insights.

Example of a people analytics tool

ChartHop is a people analytic tool that allows for analysis of HR and more specifically recruiting data across the entire organization. It integrates with the most important recruiting tools in the market.

✅ Provides strategic insights based on real life operational data

✅ Provides benchmarking data on data points like compensation

✅ Integrates with recruiting tools in the market so data across the organization can be used for analysis

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