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Workforce Retention and Survey Insights Data

Data Science and Analytics

Tags and Keywords

Hr

Attrition

Engagement

Workforce

Retention

Trusted By
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Workforce Retention and Survey Insights Data Dataset on Opendatabay data marketplace

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About

Gain extensive insights into workforce dynamics through this robust collection of HR analytics data. This dataset facilitates the exploration of critical factors influencing employee retention, job satisfaction, and organisational health. It features a dual-layered approach: one segment focuses on detailed employee survey responses regarding supervision, inclusivity, and performance accountability, while the other captures essential traits such as attrition flags, travel commitments, educational background, and compensation metrics. By analysing these records, organisations and researchers can uncover patterns in engagement and turnover, empowering data-driven decisions to improve workplace culture and operational efficiency.

Columns

File: employee-survey-insights-analysis.csv
  • Response ID: Unique sequential identifier for each survey response.
  • Status: Indicates if the response is Complete or Incomplete.
  • Department: The respondent's functional area (e.g., Planning and Public Works, Sheriff's Department).
  • Director: Binary flag (0/1) indicating Director role.
  • Manager: Binary flag (0/1) indicating Manager role.
  • Supervisor: Binary flag (0/1) indicating Supervisor role.
  • Staff: Binary flag (0/1) indicating Staff role.
  • Question: The full text of the survey question asked.
  • Response: Numerical value of the answer (0 to 4).
  • Response Text: Textual interpretation of the answer (e.g., Strongly Disagree, Agree).
File: employee-traits-by-department-and-role.csv
  • EmployeeId: Unique identifier for the employee.
  • Attrition: Binary indicator (0=no, 1=yes) of whether the employee has left.
  • Age: Age of the employee.
  • BusinessTravel: Frequency of work-related travel.
  • DailyRate: Daily salary rate.
  • Department: Functional department associated with the employee.
  • DistanceFromHome: Commute distance in kilometres.
  • Education: Level code (1-Below College to 5-Doctor).
  • EducationField: Field of study.
  • EnvironmentSatisfaction: Satisfaction level (1-Low to 4-Very High).
  • Gender: Employee gender.
  • HourlyRate: Hourly pay rate.
  • JobInvolvement: Involvement level (1-Low to 4-Very High).
  • JobLevel: Hierarchical job level (1 to 5).
  • JobRole: Specific role title.
  • JobSatisfaction: Satisfaction score (1-Low to 4-Very High).
  • MaritalStatus: Civil status of the employee.
  • MonthlyIncome: Monthly earnings.
  • MonthlyRate: Monthly rate.
  • NumCompaniesWorked: Total prior employers.
  • Over18: Confirmation of age majority.
  • OverTime: Overtime status.
  • PercentSalaryHike: Percentage of recent salary increase.
  • PerformanceRating: Performance score (1-Low to 4-Outstanding).
  • RelationshipSatisfaction: Satisfaction with workplace relationships.
  • StandardHours: Standard working hours.
  • StockOptionLevel: Level of stock options held.
  • TotalWorkingYears: Cumulative years of employment.
  • TrainingTimesLastYear: Number of training sessions attended.
  • WorkLifeBalance: Balance score (1-Low to 4-Outstanding).
  • YearsAtCompany: Tenure at the current company.
  • YearsInCurrentRole: Tenure in the current position.
  • YearsSinceLastPromotion: Time elapsed since the last promotion.
  • YearsWithCurrManager: Time spent reporting to the current manager.

Distribution

The dataset is structured as structured text files (CSV format). The survey analysis file contains approximately 14,700 records, exhibiting a 99% completion rate among responses. The data encompasses varying scales, including binary flags, Likert scales (1-4), and continuous numerical values for salary and time metrics.

Usage

  • Exploratory Data Analysis (EDA): Identifying correlations between demographic traits and attrition rates.
  • Predictive Modelling: Building machine learning models to forecast employee turnover.
  • Organisational Psychology: Assessing the impact of management styles and environment on job satisfaction.
  • Sentiment Analysis: Evaluating survey response texts to gauge workforce morale regarding diversity and inclusion.
  • Dashboarding: Creating visual reports on departmental performance and salary distributions.

Coverage

  • Demographic Scope: Covers various ages, genders, marital statuses, and education levels ranging from Below College to Doctorate.
  • Occupational Scope: Includes multiple departments such as Planning and Public Works, Sheriff's Department, and Human Resources, covering roles from Staff to Directors.
  • Geographic Note: Distance from home is recorded in kilometres.
  • Data Availability: The survey data includes both valid and a small percentage (1%) of incomplete responses.

License

CC0: Public Domain

Who Can Use It

  • HR Analysts: To monitor engagement metrics and identify flight risks.
  • Data Scientists: To train classification models for attrition prediction.
  • Business Intelligence Developers: To construct executive dashboards for workforce planning.
  • Academic Researchers: To study social science trends in employment and workplace satisfaction.

Dataset Name Suggestions

  • HR Analytics Employee Engagement and Attrition Metrics
  • Workforce Retention and Survey Insights Data
  • Employee Satisfaction and Demographic Traits Analysis
  • Organisational Health and Turnover Indicators

Attributes

Listing Stats

VIEWS

9

DOWNLOADS

1

LISTED

08/12/2025

REGION

GLOBAL

Universal Data Quality Score Logo UDQSQUALITY

5 / 5

VERSION

1.0

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Free

Download Dataset in ZIP Format