Artificial Intelligence - People Analytics

People  Analytics

Technology has undergone tectonic change over the last few decades. New streams like Artificial Intelligence, Machine Leaning and Deep Learning have emerged due to the advancement in computing. In this connected world, the impact of technology has been witnessed by different fields. Human Resources too has been affected by the advent of the modern technology. A new paradigm in the form of People Analytics has emerged which might just change the way things are carried out in the HR ecosystem.

HR technology which was used traditionally was basically aimed at optimizing the operational activities and carrying out simple fixes. However, a new HR Technology wave is going to hit HR which aims at dismantling the old ways of doing things. An army of geeks are trying to use their mathematical skills in solving real life HR related problems. Further, companies are seriously looking forward to the amazing opportunities that people analytics has to offer and are investing their monetary and manpower resources at their disposal. The  aim of the advanced HR Tech based solutions is not just to automate the process of collecting data and storing it in a well-defined way but to unlock the hidden meanings which the data has to offer. For ex- The feedback (textual or Numeric) which employees share with the companies about a certain issue can be analyzed using text analytics which will help in understanding the various underlying trends using minimal resources. Further, high level data analytics can be carried out using sophisticated tools to forecast the employee turnover, expected performance and hiring trends. Overall, people analytics provides a wide range of solutions to the problems plaguing the HR ecosystem in a cost effective way with minimal human intervention and in a short period of time

The usage of people analytics is wide but as it is said the devil is in the detail. Majority of the people analytics solutions are based on certain hypothesis. The design of such hypothesis requires human intervention. Such an intervention is a big source of bias which may skew the final output. There is a high probability that the design of the hypothesis may lead to fundamental attribution error. In such a context, the negative outcomes related to certain process may be attributed to the people and not to the situational dynamics. For ex- A manager of a company is a bad leader and he/she is majorly responsible for the attrition in the department. However, if the system is designed in a way to look into the profile of employees who leave and not into the situational attributes, the final result will emphasize upon certain personal traits of the employees who leave and not to the situational context (here the bad manager) for the high attrition in that department. Further, implementing people analytics requires a dedicated cadre of data scientists in the HR department. Lack of essential manpower would end up hurting the implementation process which may inturn affect its usability.