Introduction to HR Analytics Overview

Sep 23, 2024

HR Analytics Introduction

Overview

  • Session focus: HR analytics basics
  • Topics covered:
    • Definition of HR analytics
    • Types of HR analytics
    • Decision-making processes for HR managers
    • Essential knowledge for effective HR analytics

What is HR Analytics?

  • Also known as:
    • Human Resource Analytics
    • People Analytics
    • Workforce Analytics
  • Common understanding: all terms refer to the same concepts and practices.

Six Functions of HR to Remember:

  1. Recruitment
  2. Selection
  3. Learning/Training
  4. Development
  5. Performance Management
  6. Compensation

Importance of Functions

  • HR managers make decisions based on metrics related to these six functions.

Key Questions for HR Managers

  • Identify daily questions in your role:
    • Recruitment: How many candidates to attract for vacancies?
    • Learning & Development: Skills needed for employees?
    • Performance: Which departments perform best?

Types of HR Analytics

  1. Descriptive Analytics
    • What has happened?
    • Key tools: averages, standard deviation, mode.
  2. Diagnostic Analytics
    • Why did it happen?
    • Includes correlation, regression, and ANOVA.
  3. Predictive Analytics
    • What will happen in the future?
    • Uses regression and decision trees.
  4. Prescriptive Analytics
    • Optimization problems, how to achieve desired outcomes.

Focus of Current Course

  • Primarily on Descriptive Analytics.
  • Future courses will cover Diagnostic, Predictive, and Prescriptive Analytics.

Descriptive Analytics

  • Tools & Techniques:
    • Average: Understanding departmental demographics (e.g., average age).
    • Standard Deviation: Identifying outliers and variation in data.
    • Mode: Frequency distribution (most common age group).
    • Counts & Ratios: Count applications, selections, etc.

Visualization Techniques

  • Histogram: To visualize age distribution, performance metrics.
  • Pie Chart: For recruitment source proportions.
  • Bar Graph: To show employee demographics, performance levels.

Diagnostic Analytics

  • Focus on understanding relationships between variables.
  • Questions Answered:
    • Why are certain trends observed?
    • Relationship strength between metrics.
  • Tools: T-test, ANOVA, regression analysis.
  • Visualization: Scatter plots, regression plots.

Predictive Analytics

  • Tools: Linear regression, decision trees, random forests.
  • Visualization: Line charts, scattergrams.

Prescriptive Analytics

  • Requirements: High-level statistical knowledge and mathematical modeling.
  • Few organizations currently utilize this type.

Difference Between MIS and HR Analytics

  • MIS: Source of raw data.
    • Example: Employee demographics, leave records.
  • HR Analytics: Process and analyze data to improve decision-making and organizational performance.

HR Analytics Process

  1. Logic: Establish relationships between variables.
  2. Measures: Develop HR metrics.
  3. Process: Analyze and transform data.
  4. Decision-making: Use insights to inform HR strategies.

Successful Implementation of HR Analytics

  • Skills Needed:
    • Analytical skills for HR professionals.
    • IT knowledge for data management (e.g., HRIMS).
    • Basic data analytical tools and techniques knowledge.

Outcomes of HR Analytics

  • Understanding the relationship between HR processes and business outcomes.
  • Insights into employee engagement and performance metrics.

Conclusion

  • Course aims to provide foundational knowledge in HR analytics.
  • Next steps include exploring advanced analytics concepts in further courses.