HR Analytics Simulation Data

Columns

• Employee ID (EmpID): A unique identifier for each employee within the organisation.
• First Name: The employee's first name.
• Last Name: The employee's last name.
• Start Date: The date the employee began working for the organisation. Data ranges from 7 August 2018 to 6 August 2023.
• Exit Date: The date the employee left or exited the organisation, if applicable. Approximately 49% of records have missing exit dates. Data ranges from 19 November 2018 to 6 August 2023.
• Title: The employee's job title or position (e.g., Production Technician I, Production Technician II).
• Supervisor: The name of the employee's immediate supervisor or manager.
• Email (ADEmail): The employee's organisational email address.
• Business Unit: The specific business unit or department the employee belongs to (e.g., NEL, SVG).
• Employee Status: The employee's current employment status (e.g., Active, On Leave, Terminated). Approximately 82% of employees are Active.
• Employee Type: The type of employment the employee holds (e.g., Full-Time, Part-Time, Contract).
• Pay Zone: The pay zone or salary band for the employee's compensation (e.g., Zone A, Zone B).
• Employee Classification Type: The employee's classification type (e.g., Exempt, Non-exempt, Temporary).
• Termination Type: The type of termination if an employee has left the organisation (e.g., Resignation, Layoff, Retirement). Approximately 49% are 'Unk' (unknown).
• Termination Description: Additional details or reasons for the employee's termination, if applicable. Approximately 49% of records are null.
• Department Type: The broader category or type of department associated with the employee's work (e.g., Production, IT/IS). Production accounts for 67% of records.
• Division (Division Description): The division or branch of the organisation where the employee works (e.g., Field Operations, General - Con).
• DOB (Date of Birth): The employee's date of birth.
• State: The state or region where the employee is located. Massachusetts (MA) accounts for 88% of records.
• Job Function (JobFunctionDescription): A brief description of the employee's primary job function or role (e.g., Laborer, Technician).
• Gender (GenderCode): A code representing the employee's gender (e.g., M for Male, F for Female, N for Non-binary). Female accounts for 56% and Male for 44%.
• Location (LocationCode): A code representing the physical location or office where the employee is based.
• Race (or) Ethnicity (RaceDesc): A description of the employee's racial or ethnic background (e.g., Asian, Black).
• Marital Status (MaritalDesc): The employee's marital status (e.g., Single, Married, Divorced).
• Performance Score: A score indicating the employee's performance level (e.g., Excellent, Satisfactory, Needs Improvement). 79% of employees Fully Meet expectations.
• Current Employee Rating: The current rating or evaluation of the employee's overall performance, typically on a scale of 1 to 5. The mean rating is 2.97.
• Training Date: The date on which a training session took place.
• Training Program Name: The name or title of the training programme attended by the employee.
• Training Type: The categorisation of the training, indicating its purpose or focus (e.g., Technical, Soft Skills, Safety).
• Training Outcome: The observed outcome or result of the training for the employee (e.g., Completed, Partial Completion, Not Completed).
• Trainer: The name of the trainer or instructor who facilitated the training.
• Training Duration (Days): The duration of the training programme in days.
• Training Cost: The cost associated with organising and conducting the training programme.
• Applicant ID: A unique identifier for each job applicant.
• Application Date: The date the applicant submitted their job application.
• Phone Number: The applicant's contact phone number.
• Address: The applicant's street address.
• City: The city where the applicant resides.
• Zip Code: The postal or ZIP code associated with the applicant's address.
• Country: The country where the applicant resides.
• Education Level: The highest level of education attained by the applicant.
• Years of Experience: The number of years of professional experience the applicant has.
• Desired Salary: The salary the applicant wishes to receive for the job.
• Status: The status of the applicant's application (e.g., Submitted, Under Review, Rejected, Selected).
• Survey Date: The date on which the engagement survey was administered to employees.
• Engagement Score: A calculated numerical score representing the level of employee engagement based on survey responses.
• Satisfaction Score: A numerical score indicating employee satisfaction with various aspects of their job and workplace.
• Work-Life Balance Score: A numerical score reflecting employee perceptions of the balance between work and personal life.

Distribution

Usage

• Human Resources Analytics: Analysing employee demographics, performance, and turnover trends.
• Machine Learning Applications: Developing models for predicting employee attrition, optimising recruitment processes, or forecasting training needs.
• Training Effectiveness Evaluation: Assessing the impact of professional development programmes and guiding future training investments.
• Recruitment Efficiency Studies: Gaining insights into the effectiveness of various sourcing channels and candidate profiles.
• Employee Engagement and Satisfaction Analysis: Identifying key drivers of engagement and areas for workplace improvement based on survey data.

Coverage

License

Who Can Use It

• HR Analysts: To track key HR metrics, understand workforce dynamics, and inform strategic HR decisions.
• Data Scientists/Machine Learning Engineers: For building predictive models related to employee retention, performance, and recruitment.
• Organisational Development Specialists: To assess training programme outcomes and gauge employee engagement levels.
• Academics and Researchers: For studies on human capital, workplace behaviour, and talent management.
• Recruitment Managers: To analyse recruitment pipeline efficiency and candidate quality.

Dataset Name Suggestions

• Workforce Lifecycle Dataset
• HR Analytics Simulation Data
• Employee Management Data Hub
• Organisational Talent Insights
• Synthetic Employee Data Collection

Attributes