INSTITUTE –University School of Business

DEPARTMENT -Management
M.B.A
HR Analytics- 20BAC725
Faculty Name : Dr. Jasmine Kaur
Topic: Process of HR Analytics

UNIT-1 : HR Analytics in Perspective DISCOVER . LEARN . EMPOWER

1
 Course Objective

COB No. Title

The objective of this paper is to acquaint students about

workforce analytics, or talent analytics, analyzing people
COB1
problems using data to answer critical questions about
organizations.
 Course Outcomes
CO No. Title Level
Understand/
To understand the concepts and practical relevance of HR analytics in an Remember
CO1
organizational set-up.
 

To apply appropriate research methods and methodologies to solve HR

CO2  Apply
problems in business organizations

CO3 To analyze and interpret the statistical results in context to the problems of
Analyze
human resources being faced by organizations

CO4
To select best insights for typical HR issues for people management Evaluate

CO5 To develop and validate predictive models based on HR challenges to drive

Design/Create
teams effectively
 Points to Ponder

• Metrics Vs Analytics
• Process of HR Analytics
• Statistical Models Used for Business Analytics
• Software Tools used for HR Analytics

4
 Metrics and Analytics

• Metrics are focused on • Analytics is about using

tracking past or available past or available data to
data. generate predictions or
making decision.

• How many employees left in • Why do my employees

last few years? keep leaving?
 How does HR Analytics work?

• HR Analytics is made up of several components that feed into each other.

• To gain the problem-solving insights that HR Analytics promises, data must first
be collected.
• The data then needs to be monitored and measured against other data, such as historical
information, norms or averages.
• This helps identify trends or patterns. It is at this point that the results can be analyzed at
the analytical stage.
• The final step is to apply insight to organizational decisions.

6
 Process of HR Analytics

Collection of Measuremen
Analysis Application
Data t

https://www.valamis.com/hub/hr-analytics

7
 Collecting Data

• Big data refers to the large quantity of information that is collected and aggregated by HR for the purpose
of analyzing and evaluating key HR practices, including recruitment, talent management, training, and
performance
• The system that collects the data also needs to be able to aggregate it, meaning that it should offer the
ability to sort and organize the data for future analysis.
• What kind of data is collected?
• Employee profiles on-boarding
• Training
• Engagement
• Retention
• Turnover
• Absenteeism

8
 Measurement

• At the measurement stage, the data begins a process of continuous measurement and
comparison, also known as HR metrics.
• HR analytics compares collected data against historical norms and organizational
standards. The process cannot rely on a single snapshot of data, but instead requires a
continuous feed of data over time.

9
 Measurement- Examples

There are some examples of specific metrics that can be measured by HR:
• Time to hire - The number of days that it takes to post jobs and finalize the hiring of
candidates. This metric is monitored over time and is compared to the desired
organizational rate.
• Recruitment cost to hire - The total cost involved with recruiting and hiring candidates.
This metric is monitored over time to track the typical costs involved with recruiting
specific types of candidates.
• Turnover - The rate at which employees quit their jobs after a given year of employment
within the organization. This metric is monitored over time and is compared to the
organization’s acceptable rate or goal.
• Absenteeism - The number of days and frequency that employees are away from their
jobs. This metric is monitored over time and is compared to the organization’s acceptable
rate or goal.
• Engagement rating - The measurement of employee productivity and employee
satisfaction to gauge the level of engagement employees have in their job. This can be
measured through surveys, performance assessments or productivity measures.
10
 HR Metrics
Process

Source: SHRM
HR Metrics

Source: AIHR

12
 Analysis

• The analytical stage reviews the results from metric reporting to identify trends and
patterns that may have an organizational impact.

• There are different analytical methods used, depending on the outcome desired.

• These include: descriptive analytics, prescriptive analytics, and predictive analytics.

13
Statistical Models Used for HR Analytics
 Correlation Models

 Regression Models

 Forecasting Models

 Time Series Modeling

 Chi-Square Analysis
Statistical Models Used for HR Analytics
 ANOVA Analysis

 Simulation Models

 Multicollinearity Models

 Bayesian Models

 Structural Equation Modelling

 Application

• Once metrics are analyzed, the findings are used as actionable insight for organizational
decision-making.
• Examples of how to apply HR analytics insights:
• Here are some examples of how to apply the analysis gained from HR analytics to decision-
making:
• Time to hire - If findings determine that the time to hire is taking too long and the job
application itself is discovered to be the barrier, organizations can make an informed
decision about how to improve the effectiveness and accessibility of the job application
procedure.
• Turnover - Understanding why employees leave the organization means that decisions can
be made to prevent or reduce turnover from happening in the first place. If lack of training
support was identified as a contributing factor, then initiatives to improve on-going training
can be put together.
• Absenteeism - Understanding the reasons for employee long-term absence enables
organizations to develop strategies to improve the factors in the work environment
impacting employee engagement
16
Software Tools used for HR Analytics

 MS Excel  Python

 SPSS  COGNOS

 SAS  Hadoop

 R  SAP HANA

 Smart PLS  Tableau

 Visier
References
Books Moore,D., McCabe, G., Duckworth, W., and Alwan, L. 2008. The Practice of Business Statistics: Using Data for
Decisions, 2nd Ed., New York: W.H. Freeman. ISBN: 9781429221504

Fitz, J., Mattox II, J. 2014. Predictive Analytics for Human Resources. Wiley Publication. ISBN: 9781118893678
R1 Becker, B., Huselid, M., Ulrich, D. 2001. The HR Scorecard: Linking People, Strategy, and
Performance. Harvard Business Press. ISBN: 9781578511365

Fitz, J., 
The New HR Analytics- Predicting the Economic Value of Your Company’s Human Capital Requirements. Harper C
ollins Publishers.
ISBN: 9781118893678

Fitz, J., Mattox II, J. 2014. Human Capital Analytics-. How to Harness the Potential of Your Organization's Greatest
Asset, Wiley Publication, ISBN- 9781118466766,
Fitz, J., Mattox II, J. 2014. Human Capital Analytics-. How to Harness the Potential of Your Organization's Greatest
Asset, Wiley Publication, ISBN- 9781118466766

E-Book • http://www.free-management-ebooks.com/news/leadership-skills/
Websites • https://www.toolbox.com/hr/hr-analytics/articles/what-is-hr-analytics/
• https://www.valamis.com/hub/hr-analytics
• https://www.logility.com/blog/descriptive-predictive-and-prescriptive-analytics-explained/
• https://www.keka.com/hr-analytics
Video Links • https://www.youtube.com/watch?v=lTzWGaFpimk
• https://www.youtube.com/watch?v=JL1-G2Pi6hQ&t=290s

• https://www.youtube.com/watch?v=X2LstLBOVzw&t=1086s
18
 Summary
• HR Analytics is the application of Statistical Models and Technology Tools to
analyse HR Functions

• The six HR Analytics Components are HR Functions, HR Metrics, Analytics

Process, Analytics Models, Statistical Models and Technology Tools

• HR Analytics can lead to higher Employee Value Proposition(EVP) and rise in

Productivity

• People Analytics

19
 Assessment Pattern

Components HT-1 HT-2 Assignment Surprise Business GD Forum Attendance Scaled

Test Quiz Marks

Max. Marks 10 10 6 4 4 4 2 40

20
 Thank you !!
Please write back to me in case of any queries
Jasmine.e13020@cumail.in

21