This is a fictional data set

Analysis of ‘IBM HR Analytics Employee Attrition & Performance’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/uniabhi/ibm-hr-analytics-employee-attrition-performance on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Uncover the factors that lead to employee attrition and explore important questions such as ‘show me a breakdown of distance from home by job role and attrition’ or ‘compare average monthly income by education and attrition’. This is a fictional data set created by IBM data scientists.

Education 1 'Below College' 2 'College' 3 'Bachelor' 4 'Master' 5 'Doctor'

EnvironmentSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High'

JobInvolvement 1 'Low' 2 'Medium' 3 'High' 4 'Very High'

JobSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High'

PerformanceRating 1 'Low' 2 'Good' 3 'Excellent' 4 'Outstanding'

RelationshipSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High'

WorkLifeBalance 1 'Bad' 2 'Good' 3 'Better' 4 'Best'

--- Original source retains full ownership of the source dataset ---

The Synthetic Employee Attrition Dataset is a simulated dataset designed for the analysis and prediction of employee attrition. It contains detailed information about various aspects of an employee's profile, including demographics, job-related features, and personal circumstances.

The dataset comprises 74,610 samples, to facilitate model development and evaluation. Each record includes a unique Employee ID and features that influence employee attrition. The goal is to understand the factors contributing to attrition and develop predictive models to identify at-risk employees.

This dataset is ideal for HR analytics, machine learning model development, and demonstrating advanced data analysis techniques. It provides a comprehensive and realistic view of the factors affecting employee retention, making it a valuable resource for researchers and practitioners in the field of human resources and organizational development.

FEATURES:

Employee ID: A unique identifier assigned to each employee. Age: The age of the employee, ranging from 18 to 60 years. Gender: The gender of the employee Years at Company: The number of years the employee has been working at the company. Monthly Income: The monthly salary of the employee, in dollars. Job Role: The department or role the employee works in, encoded into categories such as Finance, Healthcare, Technology, Education, and Media. Work-Life Balance: The employee's perceived balance between work and personal life, (Poor, Below Average, Good, Excellent) Job Satisfaction: The employee's satisfaction with their job: (Very Low, Low, Medium, High) Performance Rating: The employee's performance rating: (Low, Below Average, Average, High) Number of Promotions: The total number of promotions the employee has received. Distance from Home: The distance between the employee's home and workplace, in miles. Education Level: The highest education level attained by the employee: (High School, Associate Degree, Bachelor’s Degree, Master’s Degree, PhD) Marital Status: The marital status of the employee: (Divorced, Married, Single) Job Level: The job level of the employee: (Entry, Mid, Senior) Company Size: The size of the company the employee works for: (Small,Medium,Large) Company Tenure: The total number of years the employee has been working in the industry. Remote Work: Whether the employee works remotely: (Yes or No) Leadership Opportunities: Whether the employee has leadership opportunities: (Yes or No) Innovation Opportunities: Whether the employee has opportunities for innovation: (Yes or No) Company Reputation: The employee's perception of the company's reputation: (Very Poor, Poor,Good, Excellent) Employee Recognition: The level of recognition the employee receives:(Very Low, Low, Medium, High) Attrition: Whether the employee has left the company, encoded as 0 (stayed) and 1 (Left).

Context

The key to success in any organization is attracting and retaining top talent. I’m an HR analyst at my company, and one of my tasks is to determine which factors keep employees at my company and which prompt others to leave. I need to know what factors I can change to prevent the loss of good people. Watson Analytics is going to help.

Content

I have data about past and current employees in a spreadsheet on my desk top. It has various data points on our employees, but I’m most interested in whether they’re still with my company or whether they’ve gone to work somewhere else. And I want to understand how this relates to workforce attrition.

Education 1 'Below College' 2 'College' 3 'Bachelor' 4 'Master' 5 'Doctor'

EnvironmentSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High'

JobInvolvement 1 'Low' 2 'Medium' 3 'High' 4 'Very High'

JobSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High'

PerformanceRating 1 'Low' 2 'Good' 3 'Excellent' 4 'Outstanding'

RelationshipSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High'

WorkLifeBalance 1 'Bad' 2 'Good' 3 'Better' 4 'Best'

Acknowledgements

https://www.ibm.com/communities/analytics/watson-analytics-blog/watson-analytics-use-case-for-hr-retaining-valuable-employees/

Inspiration

Which factors led to employee attrition?

Uncover the factors that lead to employee attrition and explore important questions such as ‘show me a breakdown of distance from home by job role and attrition’ or ‘compare average monthly income by education and attrition’. This is a fictional data set created by IBM data scientists.

Education 1 'Below College' 2 'College' 3 'Bachelor' 4 'Master' 5 'Doctor'

EnvironmentSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High'

JobInvolvement
1 'Low' 2 'Medium' 3 'High' 4 'Very High'

JobSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High'

PerformanceRating
1 'Low' 2 'Good' 3 'Excellent' 4 'Outstanding'

RelationshipSatisfaction
1 'Low' 2 'Medium' 3 'High' 4 'Very High'

WorkLifeBalance 1 'Bad' 2 'Good' 3 'Better' 4 'Best'

People HR Analytics Software Market Outlook

The global People HR Analytics Software market size was valued at approximately USD 2.5 billion in 2023 and is projected to reach USD 10.8 billion by 2032, growing at a robust CAGR of 17.5% during the forecast period. This impressive growth can be attributed to several factors, including the increasing adoption of data-driven decision-making processes within human resource departments, the integration of advanced analytics technologies, and the rising need for efficient workforce management solutions.

Key growth drivers of the People HR Analytics Software market include the escalating demand for data analytics in human resource operations, which enables organizations to effectively manage their workforce and optimize HR outcomes. The adoption of advanced analytics helps organizations to gain deeper insights into employee performance, engagement, and retention, which in turn leads to improved productivity and reduced turnover rates. Additionally, the growing emphasis on employee experience and well-being is compelling organizations to invest in sophisticated HR analytics tools that can provide actionable insights for enhancing employee satisfaction and engagement.

Another significant growth factor is the increasing prevalence of remote and hybrid work models, which has amplified the need for HR analytics solutions that can monitor and manage dispersed workforces. The COVID-19 pandemic has accelerated the adoption of remote working, highlighting the importance of digital tools for workforce management. HR analytics software provides organizations with the capabilities to track employee performance, engagement levels, and productivity, irrespective of their physical location. This shift towards remote working is expected to sustain the demand for HR analytics solutions in the long run.

Moreover, the integration of artificial intelligence (AI) and machine learning (ML) technologies into HR analytics software is driving market growth. These advanced technologies enable predictive analytics, which assists HR professionals in forecasting workforce trends, identifying potential issues, and making proactive decisions. For instance, AI can help in identifying patterns related to employee attrition, allowing organizations to take preemptive actions to retain top talent. Similarly, ML algorithms can analyze large volumes of HR data to uncover insights that can inform talent acquisition, workforce planning, and employee development strategies.

From a regional perspective, North America holds a significant share of the People HR Analytics Software market, driven by the high adoption rate of advanced technologies and the presence of major market players in the region. The region's robust economic environment and emphasis on workforce optimization further contribute to its market dominance. However, Asia Pacific is expected to emerge as the fastest-growing region during the forecast period, fueled by the increasing digital transformation initiatives and the rising adoption of HR analytics solutions by enterprises of all sizes in countries like China, India, and Japan.

Component Analysis

The People HR Analytics Software market is segmented by component into software and services. The software segment dominates the market, driven by the increasing demand for comprehensive HR analytics solutions that offer various functionalities such as talent management, performance tracking, and employee engagement. These software solutions are designed to integrate with existing HR systems, providing a seamless experience for HR professionals to manage and analyze employee data. The continuous advancements in software capabilities, such as the incorporation of AI and ML, are further enhancing the value proposition of HR analytics software.

On the other hand, the services segment, which includes implementation, consulting, and support services, is also witnessing substantial growth. As organizations adopt HR analytics software, there is a growing need for professional services to ensure successful implementation and integration with existing systems. Consulting services are particularly in demand as organizations seek expert guidance on leveraging HR analytics to achieve strategic business objectives. Support services are equally important, providing ongoing assistance to ensure the smooth operation of HR analytics solutions and addressing any technical issues that may arise.

Another critical aspect of the component analysis is the role of cloud-based solutions within the software segment. Cloud-based HR an

Analysis of ‘IBM Employee Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/rohitsahoo/employee on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Watson Analytics Sample Data

Uncover the factors that lead to employee attrition and explore important questions such as ‘show me a breakdown of distance from home by job role and attrition’ or ‘compare average monthly income by education and attrition’. This is a fictional data set created by IBM data scientists

Content

This the data about the employee on various factor influencing the attrition from the company. Predict the Attrition of an employee based on the various factor given

Acknowledgements

IBM Blog on employee attrition

Inspiration

Predict the Attrition of an employee based on the various factor given

About Dataset

Education 1. 'Below College' 2. 'College' 3. 'Bachelor' 4. 'Master' 5. 'Doctor'

EnvironmentSatisfaction 1. 'Low' 2. 'Medium' 3. 'High' 4. 'Very High'

JobInvolvement 1. 'Low' 2. 'Medium' 3. 'High' 4. 'Very High'

JobSatisfaction 1. 'Low' 2. 'Medium' 3. 'High' 4. 'Very High'

PerformanceRating 1. 'Low' 2. 'Good' 3. 'Excellent' 4. 'Outstanding'

RelationshipSatisfaction 1. 'Low' 2. 'Medium' 3. 'High' 4. 'Very High'

WorkLifeBalance 1. 'Bad' 2. 'Good' 3. 'Better' 4. 'Best'

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by Raneem Oqaily

Contents

The Human Resource Analytics (HRA) market is experiencing robust growth, driven by the increasing need for data-driven decision-making in HR departments. Companies are increasingly leveraging analytics to optimize talent acquisition, improve employee engagement, and enhance overall workforce productivity. The market's expansion is fueled by several factors, including the rising adoption of cloud-based HR solutions, the growing availability of big data and advanced analytics technologies, and the increasing focus on strategic workforce planning. The global HRA market is estimated to be valued at $20 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 15% during the forecast period (2025-2033). This substantial growth is expected to continue as organizations recognize the value of data-driven insights in addressing complex HR challenges, such as talent shortages, skill gaps, and employee attrition. Furthermore, the rise of AI and machine learning in HR processes is expected to propel market expansion. Key segments driving this growth include employee engagement and development, payroll and compensation management, and talent analytics. The BFSI, IT & Telecom, and healthcare sectors are significant adopters of HRA solutions due to their large workforces and the need for efficient talent management. Geographic growth is particularly strong in North America and Asia Pacific, reflecting the higher adoption rates of advanced technologies and the increasing digitalization of HR functions in these regions. While regulatory compliance and data security concerns pose some restraints, the overall market outlook remains positive, indicating significant potential for continued expansion over the coming years. The increasing availability of user-friendly analytics dashboards and the integration of HRA with other HR systems are further contributing to the market's growth trajectory.

Dataset

This dataset was created by Niiharikaa6

Contents

Dataset

This dataset was created by Mohini Musale

Contents

The Exit Interview Management Software market is experiencing robust growth, driven by the increasing need for organizations to gather actionable insights from departing employees. The market's expansion is fueled by several key factors. Firstly, the rising awareness of the importance of employee feedback in improving retention and organizational effectiveness is a significant driver. Secondly, the increasing adoption of cloud-based solutions simplifies data collection, analysis, and reporting, reducing the administrative burden on HR departments. Thirdly, the growing availability of advanced analytics features within these platforms allows for deeper understanding of attrition trends and their underlying causes, enabling proactive intervention strategies. The market segmentation, with a focus on large enterprises and SMEs utilizing both cloud and web-based solutions, reflects the diverse needs and technological capabilities of various organizations. While the market shows strong potential, certain restraints exist. These include the initial investment costs associated with implementing new software, resistance to change within organizations, and concerns around data security and privacy. However, the benefits of improved employee retention, reduced recruitment costs, and enhanced organizational learning are expected to outweigh these challenges. Given a projected CAGR (assume 15% based on industry average for similar SaaS solutions) and a 2025 market size (assume $500 million based on reasonable estimations for a niche SaaS market), we can anticipate substantial market expansion throughout the forecast period (2025-2033). Key players like Qualtrics, Retensa, and others are constantly innovating, adding features like advanced analytics and integration with HR systems, further propelling market growth. The regional distribution is expected to be heavily concentrated in North America and Europe initially, with Asia-Pacific showing strong growth potential in the later forecast years.

HR Employee Attrition

**Through various efforts, a significant amount of money is spent on maintaining existing staff. We want to reduce the cost of staff retention. We propose that the incentives be limited to only those employees who are in danger of leaving. We need Identify patterns in the characteristics of employees who leave the organization and use this information to predict if an employee is at risk of attrition.

Attrition is the departure of employees from the organization for any reason (voluntary or involuntary), including resignation, termination, death or retirement.

Main Objective:

To identify the different factors that drive attrition

To make a model to predict if an employee will attrite or not

Dataset: The data contains demographic details, work-related metrics and attrition flag.

EmployeeNumber - Employee Identifier Attrition - Did the employee attrite? Age - Age of the employee BusinessTravel - Travel commitments for the job DailyRate - Data description not available** Department - Employee Department DistanceFromHome - Distance from work to home (in km) Education - 1-Below College, 2-College, 3-Bachelor, 4-Master,5-Doctor EducationField - Field of Education EnvironmentSatisfaction - 1-Low, 2-Medium, 3-High, 4-Very High Gender - Employee's gender HourlyRate - Data description not available** JobInvolvement - 1-Low, 2-Medium, 3-High, 4-Very High JobLevel - Level of job (1 to 5) JobRole - Job Roles JobSatisfaction - 1-Low, 2-Medium, 3-High, 4-Very High MaritalStatus - Marital Status MonthlyIncome - Monthly Salary MonthlyRate - Data description not available** NumCompaniesWorked - Number of companies worked at Over18 - Over 18 years of age? OverTime - Overtime? PercentSalaryHike - The percentage increase in salary last year PerformanceRating - 1-Low, 2-Good, 3-Excellent, 4-Outstanding RelationshipSatisfaction - 1-Low, 2-Medium, 3-High, 4-Very High StandardHours - Standard Hours StockOptionLevel - Stock Option Level TotalWorkingYears - Total years worked TrainingTimesLastYear - Number of training attended last year WorkLifeBalance - 1-Low, 2-Good, 3-Excellent, 4-Outstanding YearsAtCompany - Years at Company YearsInCurrentRole - Years in the current role YearsSinceLastPromotion - Years since the last promotion YearsWithCurrManager - Years with the current manager

HR Technology Remains Number 1 Priority; Half of HR Leaders Plan to Increase Their Budget This Year

This dataset is designed for use in Human Resources (HR) Analytics, specifically focusing on collaboration discovery in virtual teams. It serves to help understand and analyse interactions and contributions within remote or distributed team environments, supporting the application of Artificial Intelligence (AI) in HR [1, 2]. It is part of a larger collection of datasets intended to supplement learning how AI can be applied to various HR fields [1].

Columns

The dataset sample includes identifiers for team members such as "Jeff", "Stacy", "Lisa", "Rob", and "Emma", alongside numerical values that appear to represent collaboration metrics or contribution percentages. For instance, values like 50%, 29%, 21%, 18%, 64%, 57%, 11%, 32%, 68%, 7%, and 25% are present [2]. The source indicates that a more detailed column description can be found in an accompanying notebook [2].

Distribution

The dataset is typically provided in a CSV file format [3]. Specific numbers for rows or records are not available in the current sources, but sample files are usually updated separately to the platform [3, 4]. It is structured for analysis of virtual team collaboration dynamics.

Usage

This dataset is ideal for analysing and predicting collaboration patterns within virtual teams [1, 2]. It can be applied to use cases such as: * Understanding individual and group contributions in remote work settings [2]. * Identifying key collaborators or bottlenecks in virtual team projects. * Developing AI models to optimise virtual team performance and improve communication [1]. * Researching the effectiveness of different virtual collaboration strategies.

Coverage

The dataset has a global region coverage [5]. Specific time ranges or demographic scopes for the data points are not detailed within the provided materials, but it is suitable for general application in virtual team analysis across various contexts.

License

CCO

Who Can Use It

This dataset is intended for: * HR professionals seeking to leverage AI for talent management and team optimisation [1]. * Data scientists and analysts working on HR analytics, machine learning, and natural language processing (NLP) projects [1]. * Researchers and students studying virtual team dynamics, organisational behaviour, and the application of AI in human resources [1]. * Organisations aiming to improve virtual team effectiveness and collaboration.

Dataset Name Suggestions

• Virtual Team Collaboration Insights Dataset
• HR Virtual Collaboration Analytics
• Remote Team Dynamics Data
• Team Collaboration Metrics Dataset
• AI for Virtual Teams Dataset

Attributes

Original Data Source: Original Data Source:

The HR analytics market, valued at $4.31 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 12.89% from 2025 to 2033. This surge is driven by several key factors. The increasing adoption of cloud-based HR solutions facilitates data accessibility and analysis, enabling organizations to make data-driven decisions regarding workforce planning, talent acquisition, and employee engagement. Furthermore, the growing need for improved employee experience and retention is pushing organizations to leverage HR analytics to understand employee sentiment, identify potential attrition risks, and implement targeted interventions. Automation in HR processes, coupled with advanced analytics capabilities, is streamlining operations and providing more accurate, insightful data, thereby contributing to the market's expansion. The demand for predictive analytics, enabling HR professionals to forecast future workforce needs and proactively address potential challenges, is also significantly bolstering growth. Segmentation reveals strong demand across various industries, with Telecom & IT, BFSI (Banking, Financial Services, and Insurance), and Consumer Goods & Retail sectors leading the adoption of HR analytics solutions and services. The market is witnessing a shift towards cloud-based deployment models due to their scalability, cost-effectiveness, and accessibility. The competitive landscape is characterized by a mix of established players like SAP, Oracle, and IBM, alongside specialized HR analytics vendors and emerging technology providers. These companies are continuously innovating to offer more comprehensive and integrated solutions, encompassing workforce planning, talent management, compensation and benefits analysis, performance management, and employee engagement tracking. Future growth will be fueled by increasing investment in artificial intelligence (AI) and machine learning (ML) within HR analytics, enabling advanced predictive modeling and automated insights. Regional analysis indicates strong growth across North America and Asia Pacific, driven by technological advancements and a growing awareness of the strategic value of data-driven HR decision-making. However, challenges remain, including data privacy concerns, the need for skilled HR professionals capable of interpreting complex data, and the integration of HR analytics with existing HR systems. Addressing these challenges will be key to unlocking the full potential of the HR analytics market. Recent developments include: June 2022: GainInsights, a global data and analytics firm announced it has signed an agreement with DataSwitch, an emerging AI/ML-driven Data Transformation Platform, to accelerate analytics modernization initiatives through data pipeline innovation and accelerators for migration., May 2022: Visier, people analytics and on-demand solution for people-powered businesses established a strategic agreement with Deloitte. By combining cutting-edge technology with world-class consulting, Deloitte and Visier can provide business clients with guidance and professional services support throughout their HR analytics journey.. Key drivers for this market are: Increase in Trends in Cloud-based Solutions, Increase in Workforce and Need for Reduction in Attrition Rate. Potential restraints include: Increase in Trends in Cloud-based Solutions, Increase in Workforce and Need for Reduction in Attrition Rate. Notable trends are: Telecom and IT Industry is Witnessing a Significant Share in the Market.

Overall and voluntary turnover data for State of Oklahoma classified employees beginning in fiscal year 2007.

1) Data Introduction • 'Employee dataset: Employee data' aggregates comprehensive information encompassing various aspects of employee data such as training, surveys, performance, recruitment, and attendance. This rich dataset is designed to support in-depth analysis and research in human resources.

2) Data Utilization (1) Employee dataset: Employee data has characteristics that: • The dataset includes extensive details on employee demographics, job roles, performance ratings, and other HR-related metrics. • It is an invaluable resource for modeling and predicting employee behavior and outcomes based on historical data. (2) Employee dataset: Employee data can be used to: • Human Resources Management: Assists HR professionals in making informed decisions regarding recruitment, training, and employee retention strategies. • Predictive Analysis: Enables companies to forecast trends in employee turnover and performance, aiding in proactive management and planning.

This statistic depicts the average annual employee turn over rate in the United States in 2016 and 2017, as reported by human resources (HR) professionals. During the 2017 survey, respondents reported an average annual turnover rate of ** percent.

This dataset contains a number of monthly personnel metrics such as attrition rate, vacancy rate, turnover rate, and retention rate, as well as counts like separations, active employees, hires, and vacancies.