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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:
Recruitment
Selection
Learning/Training
Development
Performance Management
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
Descriptive Analytics
What has happened?
Key tools: averages, standard deviation, mode.
Diagnostic Analytics
Why did it happen?
Includes correlation, regression, and ANOVA.
Predictive Analytics
What will happen in the future?
Uses regression and decision trees.
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
Logic:
Establish relationships between variables.
Measures:
Develop HR metrics.
Process:
Analyze and transform data.
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.
📄
Full transcript