Talent Acquisition Analytics – The challenges
Analytics in Talent Acquisition is extremely challenging and is not the same as it is in other domains. One of the challenges is “attributes”. When attributes are clear then we get good results from analytics. But in the case of recruitment the attributes are not clear, they are vast and they keep expanding.
I dont know if this the right jargon i.e. attributes which is used by the Data Scientists. What I mean by attributes are the qualities or categorizations or parameters by which we judge a product or service or even feeling. Movies are classified into different genres such as Thrillers, Sci-fi, Documentaries, Drama and so on. Mobiles have attributes such as weight, pixels, RAM, dimensions, processor and so on. We use simple characteristics such as Like, Don’t Like, Hate for expressing feelings.
These characteristics or attributes are what we use in analytics. We analyse which of the attributes are more preferred by customers. Which attributes are given higher weightage in the purchasing decisions. Which of the attribute are related and impact one another. Which product is being liked most or hated most. In which season what attribute gains preference. If one has a preference for one attribute in one product what is the likelihood that some other attribute in some other product would be preferred so on and on.
These attributes based analytics helps in supporting decision making (bringing choices that are most likely to be preferred by us), making predictions on future success or failure. Thus a whole lot about talent acquisition analytics revolves around these attributes.
Fuzziness of attributes in the Talent Acquisition Analytics space
Unlike the products space or the social media space, talent space suffers from a very big challenge. We do not have well defined attributes. What factors or words or qualities are we going to use for talent? Just stop for a while and think of describing your talent. How would you describe them? What words or phrases would you be using? Surely you will start with the easier ones. Where you studied, what you studied, where you worked, what titles you had. So far it is fine. Now you will enter into the fuzzy area. Your mind starts thinking of the softer and intangibles and finds it difficult to articulate them clearly. You will think “I am good in Excel”. “I can solve puzzles well”. “I can write complex code”. “I can research for information”. “I know banking industry well”. The more you think, you will surely be able to bring many things that you think are talent. After all you have developed the talent, you have it and it is just that it needs to be articulated.
Each one of us can do this, express our talent in words and phrases. But, we will all do it in our ways. We will use words and phrases that we are used to or comfortable with. There is no one way for stating them. There is no common parameters like pixel and weight we can use for describing the talent in a common way and be measured commonly. The less tangible part of talent which include knowledge, skills, experiences, activities, behaviors, traits have no common attributes on which all of us express our talent.
So far analytics in talent has dealt with mostly on the more tangible elements of the talent profile i.e. education, experience, titles and age. But if talent as a whole needs to be effectively analyzed, then we would need to overcome the challenges of creating attributes for the less tangible areas of talent. We need to bring some kind of order to the fuzziness. We need to bring some kind of commonness in the words and phrases. We, then, can hope to meet the challenges in talent acquisition analytics space.