Assessment of Employee Attrition and Maximizing Employee Retention: Leveraging Business Intelligence Tools for Data-Driven HR Decision Making
Abstract
Data-Driven Decision Making(DDDM) through Artificial Intelligence Tools in Human
Resource Management is the tactical method for a business enterprise's optimistic and systemic
administration. This expedition pursues to identify the most common and paramount triggering
attributes, the knowledge gap between Data-Driven Decision Making through AI Tools, HRM (Human
Resources Management), and an organization's Employee Attrition Rate.
How artificial intelligence might be anticipated to avert employee turnover. By applying
classification, impact, and employee behavioral analysis, Decision Tree Analysis to qualitative and
quantitative & Decision rules framing to the company, HR leaders can gain accurate insights into the
perception of the company's employer brand. The employee Attrition Case Study Dataset used is an
anecdotal dataset that tries to figure out the most triggering variables that determine employee
behavioral aspects toward attrition. Six approaches are employed to categorize attributes.
Employees' monthly earnings, age, average monthly hours worked, distance from home,
cumulative working years, years with the firm percentage of pay increase, The number of firms that
worked, Stock options level, job function, and an array of other criteria must be considered. A feature
importance extraction approach is designed to study each latent factor. The findings also show feasible
hypotheses that help enhance employee engagement, reinvent the worker dynamic, and higher levels
of risk decrease attrition rates.
All significant variables in employee attrition in the Indian IT business are in employee attrition.
This research adds various Business Intelligence tools like Microsoft Power BI, Tableau, WEKA, and
KNIME for Data-Driven Decision Making; based on the findings, our expedition goes with Retention
Strategies Impact Analysis on particular groups of employees to the theory development of behavioral
elements in People Analytics based on Artificial Intelligence.
