The document discusses HR analytics, emphasizing the application of statistics and modeling to analyze employee-related factors for better business outcomes. It highlights the importance of data in improving decision-making regarding recruitment, retention, and employee engagement while addressing the challenges organizations face in utilizing high-quality HR data. Additionally, it outlines various types of analytics, such as descriptive and predictive analytics, to guide HR strategies and enhance performance.

1. HR Analytics
Application of statistics, modelling, and analysis of
employee-related factors to improve business
outcomes.
Presented By:
Chandan Ray (D/15/EE/001)
Biswajit Changkakoty (D/15/EE/106)
Abhishek Kumar (D/15/EE/108)
Amit Jyoti Deka (D/16/EE/202)

2. HR Analytics?
Human Resource analytics is about analysing
an organizations’ people problems using data.
Datas are being used to answer questions
like: How High is your employee annual
turnover?
How much of your employee turnover
consists of regretted loss?
Do you know which employees will be the most
likely to leave your company within a year?

3. Need of HR Analytics
 Many organizations have high quality HR data (residing
with a multitude of systems, such as the HRMS,
performance management, learning, compensation,
survey, etc.) but still struggle to use it effectively to
predict workforce trends, minimize risks and maximize
returns.
 The costs of attrition, poor hiring, sub-optimal
compensation, keeping below par employees, bad
training & learning strategies are just too high
 Data-driven insights to make decisions are always better
than judgmental (subjective) HRpractices in
terms of
 how to recruit
 whom to hire
 how to onboard and train employees
 how they keep employees informed and engaged
through their tenure with the organization

4. Why HR Analytics?
Performance
Improvement
“Global Organizations with
workforce analytics and
workforce planning
outperform all other
organizations by 30%more
sells per employee.”
-CedarCrestone
Return Of
Invesstment
The business demands on HR
are increasinglygoing to be onanalysis
just becausepeople are so expensive.
Manage and
Measure“What gets measured, gets
managed;What gets
managed, gets executed”
- PeterDrucker
Linkage of Business
Objectives and
people strategies
“ Toclearly demonstrate the
interaction of business
objectives and workforce
strategies to determine a full
picture of likely
outcomes”
HR DashBoard - SAP

5. Terminologies:
Business Intelligence
Business Analytics
Business Intelligence:
Technology-driven process for analyzing data
and presenting actionable information to help
executives, managers and other corporate
end users make informed business decisions.
Business analytics:
the skills, technologies, applications and practices
for continuous iterative exploration and
investigationof pastbusiness performanceto gain
insightanddrivebusinessplanning.
Analytics
Decision
Business Intelligence=
Core
Terminologie
s

6. Objectives of HR
Analytics
Drive skills
training
Track changes over timeBecoming Team
Focused
Tracking Company
Profitability

7. LEVELS of HR
Analytics
Descriptive Analytics : Insight into the past
Descriptive Analytics : Insight into the past
 Predictive Analytics : Understanding the future
Prescriptive Analytics : Advise on possible outcomes

8. Descriptive Analytics

9. Diagnostic Analytics

10. Predictive Analytics
What could happen in the future ?
Regression equation for region 2
Sales = 5.6 +1.2* Advertisement Exp + 0.5*
Discount

11. Prescriptive Analytics
What should we do ?
1. Increase the Advertising Expense by 10%
2. Give a 5% discount for two months

12. Critical areas for
HR Predictive
analytics
Turnover modeling. Predicting future turnover in business units
in specific functions, geographies by looking at factors.
Targeted retention. Find out high risk of churn in the future and
focus retention activities on critical few people
Risk Management: profiling of candidates with higher risk of
leaving prematurely or those performing below standard.
Risk Management: profiling of candidates with higher risk of
leaving prematurely or those performing below standard.

13. HR
Analytics
Enhance
Employee
Morale
Anticipate
performance
Reduce
Attrition Benefits of
HR Analytics
Reduce Attrition
Anticipate Performance
Enhance employee morale

14. Finding the right talent to
run HR analytics.
A lack of confidence Currying executive support. Data quality.
Challenges to make Analytics Work
1 2 3 4 5
Data Deluge.

15. Conclusion
The model gives vision to HR leader to make right decision.
Understand the relationship between turnover and compensation.
Helps the organization to reach their goals.

16. Thank You
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