The document discusses the application of data science in human resources (HR), highlighting its role in improving decision-making in areas such as talent acquisition, employee engagement, and performance management. It outlines the challenges faced by HR departments, such as communication gaps and ineffective use of analytics, and offers data-driven solutions like natural language processing and predictive analytics. Ultimately, it emphasizes the potential of data science to enhance HR functions while acknowledging challenges related to data privacy and bias.

1. Application of Data Science in
HR Function
By Srestha Das
MA APPLIED
MA APPLIED ECONOMICS
CHRIST UNIVERSITY, BANGALORE

2. 1. Introduction 2. Challenges/Problems
3. Solutions 4. Application using data
science
Table of Contents
5. Predictive and
Descriptive Analysis 6. Conclusion

3. Human resources (HR) is responsible for managing and developing the workforce,
ensuring compliance with employment laws and regulations, and fostering a positive
work environment.
Data science has become an essential tool for HR professionals, as it enables them to
analyze workforce-related data and make informed decisions.
By using data science in areas such as talent acquisition, employee engagement,
performance management, and retention, HR professionals can improve decision-
making, reduce costs, and create a more productive workforce.
Introduction

4. CHALLENGES FACED BY HR DEPARTMENT
Challenge 1: Communication gaps among employees
Challenge 2: Inefficient use of people analytics
Challenge 3: Lack of support from leadership
Challenge 4: A prevailing negative work environment
Challenge 5: Difficulty with measuring employee engagement
.

5. Solutions
Challenge 1: Communication gaps among employees
•Natural Language Processing (NLP) can be used to analyze employee
communication, identify gaps, and suggest solutions.
•Social network analysis can be used to map out communication
patterns and highlight areas where improvements are needed.
Challenge 2: Inefficient use of people analytics
•Data science can be used to streamline and automate the process of
collecting and analyzing HR data.
•Advanced analytics and machine learning algorithms can provide
insights and recommendations that can inform HR decisions and
strategies.
Challenge 3: Lack of support from leadership
•Data science can be used to provide evidence-based insights and
recommendations that can be presented to leadership to inform
decision-making.
•Predictive analytics can identify potential issues before they arise, which
can help HR proactively address them.

6. Challenge 4: A prevailing negative work environment
•Sentiment analysis can be used to analyze employee feedback and
identify areas of dissatisfaction.
•Predictive analytics can be used to identify potential issues that can
lead to a negative work environment, such as high turnover rates, and
take proactive measures to address them.
Challenge 5: Difficulty with measuring employee engagement
•Engagement chat can be used to analyze employee feedback and
identify patterns and trends.
•Predictive analytics can be used to identify potential engagement
issues and suggest solutions to address them.

7. APPLICATION OF DATA SCIENCE IN HR
Talent analytics maturity model - The talent analytics maturity model uses data science to
assess the organization's current talent analytics capabilities and provide a roadmap to
advance it. This helps HR teams to optimize their talent management strategies and improve
employee engagement and retention.
Recruitment - Data science can optimize the recruitment process by identifying the best
candidates for open positions. Recruitment data analytics can help HR teams to predict which
candidates are most likely to succeed in similar roles based on historical data and statistical
models.
Performance management - Data science can track and evaluate employee performance by
analyzing employee data, such as performance metrics, feedback, and ratings. This helps HR
teams to identify areas for improvement, provide targeted training and development
programs, and increase employee engagement and productivity.
Engagement chat - Engagement chat uses natural language processing and sentiment
analysis to understand employee engagement levels and identify potential issues. This helps
HR teams to take action to improve employee engagement and retention, such as creating
.

8. PREDICTIVE ANALYSIS BY DATA SCIENCE
1.Candidate sourcing and filtering: Using data analysis to identify the most
suitable candidates for a role.
2.Head-hunting: Using predictive analytics to identify potential candidates who
may not have actively applied for a job.
3.Social profile analysis: Analyzing social media profiles to assess candidates'
suitability and predict job performance.
4.Effective communication: Using analytics to identify communication patterns
that are most effective in recruiting and managing employees.
5.Job candidates' facial analysis: Using facial recognition to identify personality
traits that may be relevant to a role.
6.Meeting analysis: Analyzing meeting data to identify areas where employee
productivity could be improved.
7.Employee monitoring: Monitoring employee behavior and performance to
identify areas where training or support may be required.

9. DESCRIPTIVE ANALYSIS BY DATA SCIENCE
1.Employee survey data analysis: Discovering trends and patterns in employee
feedback to inform HR policies and practices.
2.Employee demographics analysis: Identifying workforce trends and
disparities to develop strategies for addressing issues.
3.Employee communication analysis: Detecting bottlenecks & improve
collaboration within the organization.
4.Employee turnover analysis: Identifying trends and patterns in turnover to
develop retention strategies.
5.Employee performance analysis: Evaluating top-performing
employees/teams & areas for improvement to develop performance improvement
plans.
6.Employee benefits analysis: Assessing high utilization or low ROI areas &
improve employee satisfaction/engagement with benefits.
7.Employee training and development analysis: Identifying trends and
knowledge gaps to develop targeted training programs.

10. Conclusion
•Data science can provide HR professionals with valuable insights into
their workforce, including recruitment, retention, and performance
management.
•By using data science, HR professionals can make data-driven
decisions that improve productivity, efficiency, and overall business
performance.
•However, there are challenges associated with data science in HR,
including data privacy concerns, the potential for bias, and ethical
considerations.
•The application of data science in the HR function represents a
significant opportunity for organizations to improve their talent