Data on skills is difficult to capture. Unlike data on titles, education, etc., data on skills is difficult to capture and use. There are several reasons for this. Primarily, ‘skills’ itself is a confusing word with different people having different notions of what it means. It doesn’t help that there exists other terminologies such as competencies, capabilities, and talent. One can get lost in semantics.
In IYS, we use the term ‘skills’ to refer to any one of these; competencies, capabilities, and talent.
Skills are difficult to measure and quantify. Another issue with skills is the difficulty of measuring it or quantifying it. How do we quantify programming or designing? In a data driven world, it is easy to expect quantification and data-fication. I have seen people discard skills themselves for want of an objective measure. This is wrong. There are ways to reduce subjectivity and make measures on skills proficiencies more credible.
Titles can be confusing. The questions, “What do you do?” and “What is your occupation?” can confuse some people. People may use the verb and noun interchangeably. Often to the question, “What do you do?” I givethe answer, “I am a Software Developer”.
Titles can mean different things to different people. If somebody says they are into designing, different people may interpret this in their own way based on their perspective. For example, they might think about graphic designing, product designing, interior designing, or fashion designing.
Over-generalising or over-complexifying. Often, hiring managers tell recruiters to find a UI Developer- which is too broad- or give a long job description, which is mostly copied and pasted from somewhere and too textual, including unnecessary details.
The universal problem of people referring to the same thing with different terms. For example, online marketing can be referred to as web marketing, digital marketing, or internet marketing.
There is no proper common structure for the skills space. In geography, for example, we are quite familiar with the structure of the continent, country, state, district, city, and locality. There is no such structure in the skills space.
Skills are written in different ways. Angular.Js is written also as AngularJS, E-Commerce as ecommerce also, DotNet as .Net also, XRay and X Ray
Prefix may or may not be used. For example, MS Excel and Excel, Apache Hadoop and Apache Hadoop.
All these problems are important to note because these are the ways in which the skills are expressed in the digital form in resumes, job descriptions, forms in ATS, recruitment systems, learning Systems and others.
This means that the confusion is widespread, which in turn makes it difficult to crunch data. Applying data analytics properly becomes difficult and so problems of matching people to jobs, for example, persist.