Talent classification is a simple concept to grasp but can be a difficult practice to adopt without the right tools.
We refer to talent classification as the process of categorizing human capital according to shared qualities or characteristics. This process helps you recognize, differentiate, and understand the talent you have at your disposal. Decision-making in talent acquisition and strategic workforce planning becomes much more straightforward with this level of insight.
There are 3 core technological concepts at work with talent classification, but it's not essential to fully understand the nuances of each. Thankfully, you'll be able to use talent classification without knowing the equation for multiple linear regression or what the ADAM optimization algorithm does.
Here's a look at what makes talent classification work:
NLP is a form of AI capable of analyzing large amounts of written (or spoken) words. This is the technology that allows computers to essentially read text. Some tasks that are commonly associated with NLP include parsing, named entity recognition, and natural language understanding.
A DNN is a set of algorithms, modeled loosely after the human brain, that is designed to recognize patterns. This is the component of talent classification where computer understanding & categorization takes place. DNN's are good at classification (obviously), clustering, and predictive analytics to name a few.
A taxonomy is a classification scheme that typically follows a tree structure. In talent classification, this is the part where you decide which candidate/employee labels and tags are important to you and how they relate to one another. You can build a taxonomy that represents how your organization thinks about talent. The taxonomy can represent skills, roles, experience levels or any other attribute you can derive from natural language.
The actual experience of talent classification is really simple. In a talent acquisition context, let's say you have resumes coming into your ATS as candidates apply. Talent classification would use the above-mentioned technologies to analyze each of those resumes and apply tags to them within your ATS. This means that every single talent record within your ATS has been automatically reviewed and organized.
Anyone managing data in an HRIS or involved in the hiring process will be able to appreciate how big of an impact neatly organized talent data would have on their organization. Forget sourcing the same candidates from LinkedIn over and over. Forget trying to finesse keyword search to find good candidates. Forget spending hours reading and tagging resumes manually. Learn more about talent classification in our new ebook.