You may have heard of Talent Intelligence. It is an emerging technology and getting a lot of attention lately. Talent Intelligence is the next evolution in HR tech, but how is it defined and what exactly does it do?
Define Talent Intelligence
Talent Intelligence isn’t a new concept. Logically, it is an aspect of Business Intelligence. Business Intelligence (BI) is defined as, “the methods and technologies that gather, store, report, and analyze business data to help people make business decisions” and “the information gathered by those methods.” It makes sense that information on talent within the organization is gathered, stored, analyzed and reported on to help make better business decisions.
So a simple definition is:
Talent Intelligence is the methods and technologies that gather, store, report, and analyze talent data to help people make talent decisions.
You might think, “Great! We do that. We have Talent Intelligence. #winning”
Or you are more likely thinking, “Easier said than done.”
So, which camp are you in?
We have Talent Intelligence! #Winning
Easier said than done. #Skeptic
Talent Intelligence is Nuanced
While the Talent Intelligence definition above fits at its core, the world of Talent Intelligence is a lot more nuanced than the traditional scope of Business Intelligence work. The difference is in the data sources. Data from other business functions like sales, marketing, and logistics is easily structured. As a result, it is easily documented, measured, and computed to derive insights. Talent Intelligence focuses on a much more complex subject. The data needed to produce insights on talent is messy. Talent data is free form and open to varying degrees of interpretation. It is not easily gathered, structured, or measured, so computing derived insights is difficult.
Why is Talent Data so Messy?
The reason talent data is so complex is quite simple. Us. People. Humans.
Humans possess the highest level of cognitive ability of any species on the planet. We all have different ways of thinking, describing, and communicating about ourselves. Which adds layers and layers of complexity to the type and amount of information available.
- Have you ever interviewed two people who have the same job title, and the same number of years of experience in that job title, but it seems like they were working two completely different roles?
- Is the way you describe your job on your LinkedIn profile, the same way you would describe your job to your boss or the same information available on your resume?
- Have you customized your resume/cv or resume format to apply to a specific position? How many times? How many different versions of your same resume exist?
- If you are looking for a Software Engineer, is it a DevOps Engineer, a Front-End Engineer, a Back-End Engineer, a Principal Engineer, a Software Developer, Full Stack Engineer, QA Engineer, Release Engineer, Security Engineer….. Are these interchangeable at any point if they wanted to be? How many variants of skills and job titles could there be? How many will there be?
All of these different scenarios create talent data. Talent data that is vastly inconsistent, frequently incomplete, and commonly inaccurate. Ultimately leading to a muddled and messy data set. This dirty data is incredibly hard to organize and contextualize in order to distill useful insights. But, if talent data is so messy and difficult to understand, where does that leave Talent Intelligence?
Data Science Enables Talent Intelligence
Intelligence is enhancing everything these days. The TV we watch, the music we listen to, the ads we see, and even the traffic lights we get stuck behind are enriched by intelligence and designed to serve an optimized experience at every turn. This became possible because computer hardware got smaller and exponential increases in computing power led to rapid advancements in data science and machine learning (ML) that are now available to the masses. It was only a matter of time before these concepts and processes were applied to solving talent data challenges.
Artificial intelligence (AI) disciplines like similarity scoring, latent semantic analysis, natural language processing, item-to-item recommenders, and more allow us to find meaningful insights across several facets of very large and complex data sets and to do so at a scale never previously imagined, giving Talent Intelligence room to thrive. Talent data is being analyzed across all aspects of the talent lifecycle and enables us to understand skills and talent landscape trends in a way we never could before by organizing, grouping, and extracting key insights to help make better people decisions.
Better Understanding Through Intelligence
Talent Intelligence goes beyond just measuring results to creating more strategic and actionable insights. It provides a way to understand individuals, the workforce, and the evolution of the skills and labor market ranging from how a person might perform in a particular job, to what skills a company needs to seek out or train for to prepare for the future. In summary,
Talent Intelligence is actionable data-based insights needed to make more informed human capital decisions.
Previously, technology was unable to bring the level of structure needed to get this level of information and context in relation to people decisions. However, as technology has evolved we now have the capacity to apply cutting-edge data science and machine learning to the talent landscape, candidate profile data and so much more to revolutionize how we approach Human Capital Management.