Skills is the currency of organizations and countries even. In a data driven world being able to track, analyse and use data on skills is paramount for improving the value of this currency. Skills, in the way we are used to using them, poses serious challenges on this front.
Skills (also referred as competencies or talent) is an ubiquitous term. Everyone has notions on skills. Everyone has skills. Everyone expresses them. And everyone expresses them in their own and with terminologies they are comfortable with or used to. And given the explosion of technologies as well business models, both skills and occupations have exploded. What all this means is that we are now in a situation where crunching information on skills has become extremely difficult. This has several profound implications.
The most common and wide spread problem is that of immense time and effort and often ending in frustration with finding right people for the right job and vice versa – both within the organization as well outside of it in the talent market at large.
What has happened? Why is skills information of poor quality?
Issue with Semantics
Different people could call or write the same thing in different ways for example one may say he is into Digital Marketing. The same thing may be referred by others in different ways for example Internet Marketing, Web Marketing, Online Marketing. Thus the probability of matching a resume where one mentions “Web Marketing” to a Job requirement which mentions “Digital Marketing” is very low.
Titles cause confusion
“What do you do?”
“I am an Account Manager”
How would one interpret this? Account Management is applicable or a role both Accounts function and in Sales function.
Such problems are widespread in the skills space. And this is one reason we end up getting inappropriate job recommendations.
Adding prefixes or suffixes make big difference when it comes to skills. But often we unable to capture them well and thus the data is improper. If two persons have mentioned teaching in their resumes or profile and a parsing system is able to capture this as their skill it could be a problem if one was into Teaching Physics and one into Teaching Singing. Teaching has now taken a different meaning or has become a different skill.
Fuzzy landscape of skills
Ask “Which city do you live in?” to those living in New York and all will respond in same term “New York”
Now if we ask “What do you do?” to a set of software engineers we are likely these different answers: I am Systems Analyst, I am an IT professional, I am into Web development, I am a LAMP stack developer.
The problem emerges from the fuzziness of the structure of the skills space. It is not a well defined and well understood structure like Continent -> Countries -> State -> Districts -> Cities -> Localities and thus people tend to use what they are familiar with.
These are some of the common but important challenges we face on skills. And these impede our ability crunch or analyse information for people functions like recruitment, learning and development, career planning and others.
Addressing these are important particularly in today’s era driven by data and machine intelligence.
So how do we do this?
IYS addresses this problem. It has spent years of research and created a taxonomy of skills and occupations that addresses these and one that can be used by all.