The document provides an introduction to HR analytics including frameworks like LAMP and scorecards. It discusses understanding and analyzing both qualitative and quantitative HR data to provide insights. The document outlines how HR analytics can be applied across the HR value chain from descriptive to prescriptive analytics to support evidence-based HR decision making.

1. I n t r o d u c t i o n t o H R A n a l y t i c s
L A M P F r a m e w o r k & H R S c o r e c a r d
U n d e r s t a n d i n g D a t a
Q u a l i t a t i v e A n a l y s i s of D a t a
H R A n a l y t i c s Va l u e C h a i n i n H R D e c i s i o n M a k i n g
H R A N A LY T I C S I N
P E R S P E C T I V E S
Session 1-5
Module – I

2. Course Learning Outcomes (CLOs)
Sl. No. Components Weightage %
1 02 Pre-announced Quizzes of 10 marks each (both inclusive) 20
2 02 Individual Assignments (In Class using MS-Excel & Word) 20
3 Team Assignment (Report Submission) 20
4 End-Term Examination (Moodle/Online) 40
Assessment Components
This course will help you to:
1. Illustrate the use of analytics in HR.
2. Analyze the issues in HR functions using HR analytics framework.
3. Recommend strategic solutions to HR problems using statistical tools.

3. HR/Workforce Analytics as HR’s JOURNEY
FROM ATTENDANCE SHEET TO BALANCE SHEET

4. HR Analytics
HR Analytics, is also known as
Workforce/People Analytics
Quantitative & Qualitative assessment
of:
Human Resources
HR Functions & Processes
# Reference: Jack Fitz-enz (1978…) & Davidson at
Saratoga Institute/PwC & ASPA, 1980… (American
Society of Personnel Administration), 2002 & later

5. HR Analytics Tools…
HR / Workforce Analytics uses the following:
 Descriptive Analytics (Dashboard Reporting Results of Past/Historical
Using Basic Statistics & HR Metrics for measuring HR Costs & Efficiency
 Predictive Analytics (Predicting & Forecasting future with past/current
Using numbers, applying statistical tools such as SPSS/MS-Excel, BIs etc.
Trend Analysis, Markov Analysis, Datamining (EFA & CFA, Regression)
 Prescriptive Analytics (Applying Business T&Cs for Optimizing Solutions)
Using numbers, applying statistical tools such as SPSS/MS-Excel/BIs etc.
LP, Optimization Techniques (MS-Solver), Multi-level Decision Criteria
(AHP)
 Benchmarking (HR Metrics results with Industry Benchmark)
 Operational Experiments (Simulation of Current Operational Changes)
 Workforce Modeling (Simulation of Future changes w.r.t. PEST)

6. Importance of HR Analytics w.r.t. HR Functions
Hindsight Insight Foresight
Gather HR data
by reporting
Make sense of
HR data by
analysis
Predict &
Prescribe as HR
decisions

7. What should/could be measured and analyzed…?
HRM
Recruitment
Retention
Performance &
Career
Management
Training
Comp. & Benefits
Workforce
Organizational
Effectiveness

8. Measure &
Manage
Return on
Investment
Linkage of
Business
Objectives
& People
Strategies
Performanc
e
Improveme
nt
Why HR Analytics?
“What gets measured,
gets managed; what
gets managed, gets
executed”
- Peter Drucker
“The business demand
on HR are increasingly
going to be on analysis
just because people are
so expensive”
- David Foster
“To clearly demonstrate
the business objectives
and workforce strategies
to determine a full
picture of likely
outcomes ”
- HR Dashboard SAP
“Global organizations with
workforce analytics and
workforce planning
outperform all other
organizations by 30%
more sales per employee”
- CedarCrestone

9. SITUATION…
• You are an HR Rep. in a company. A last chance is given to you about
turning the table of fortune into HR Dept.’s favour. You are going to make
it or break it…
• What do you envisage as bigger picture of HR from HRM to SHRM using
HR Analytics?
• What changes would you like to make in your current HRM functions and
processes to stay alive against the HR Dept. being scrapped-off by the
CEO?
• Share your Blueprint for the following HR Functions & Process
 TA
 I&O (Onboarding)
 L&D
 PMS
 C&B

10. HR Analytics Value Chain in HR Decision Making
Reports/Metrics
Descriptive/
Benchmarking
Regression/
Causal Analysis
Predictive
Analytics
Cognitive
Analytics
Prescriptive
Analytics
What happened?
How, what happened looks, compared to
Standards?
What factors were responsible/caused
‘what happened’?
For a hypothesis – what pattern we see
from the data?
What multiple patterns we see for a hypothesis or
an alternate hypothesis from same dataset?
What actions can be taken based on the
patterns, for future?
B
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S
V
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D
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G
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O
F
M
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Y
Low
High

11. Understanding Data
 Data is integral and essential pre-requisite for data analysis
 Quality of analysis depends on the data fed to the system
Types of Data
Structured Data: Categorized info. into rows and columns with defined
characteristics
 E.g., MS-Access/Excel database with a defined format/layout, having
numbers and characteristics
Semi-structured Data: Information describing “semantic elements of
information” in coded forms
 E.g., HTML/XML data, coded as <main>, <caption>, <figure>, <footer>,
<header>
 Unstructured Data: Data does not have any defined format., carrying
qualitative information

12. Data Preparation
 Data Reliability
(Source/Consistency)
 Data Validity (Accuracy)
“What is valid may be reliable but not vice
versa”
 Data Adequacy (No./Vol./Sample
Size)
 Data Variation (Range, Max., Min.,
Mean)
 Data Applicability
(Measure/Criteria)
 Data Cleansing (Usability)
Data Cleansing
 Removing missing values
Sources of Data
 Primary Data Sources
 HR Functions
 HR Processes
 Organization Surveys
 Secondary Data Sources
 Historical in nature
 From outside
organization
 Websites
 Newspapers
 Consulting
Firms/Agencies

13. Activity / Exercise

14. Qualitative Data Analysis Methods
 Content Analysis
 Frequency & Patterns of words/phrases/images)
 Narrative Analysis
 Stories of people and interpreting their mindsets
 Discourse Analysis
 Evaluating debates/speeches/conversations
 Thematic Analysis
 Experiences, Views & Opinions via Focused Groups
 Grounded Theory (GT)
 Building new theory with new data/experiences
 Interpretive Phenomenological Analysis (IPA)
 Subject-centered, people’s personal experiences, say
Data Collection Method
 Interview transcripts
 Documents
 Open-ended Surveys
 Discussion/Forums
 Images
 Audios
 Videos
Qualitative Data Analysis
 A type of data analysis that focuses on words, descriptions, concepts or ideas.
 It investigates the “softer side” of things to explore and describe, the hidden and potential
meaning of a phenomenon, problem or view.
 Its complex in nature as its findings may mean different things for different people
 Consumes longer time to arrive at a logical conclusion.
 However, it could be more powerful and meaningful than what a number based analysis
might explain
Softwares for QDA
 NVivo
 ATLAS.ti
 Quirkos etc.

15. Activity: Glassdoor Review Analysis
 Visit Glassdoor or a similar platform and
 Search for employee feedback on a few companies (these could
be on companies that come for placements or different from
them depending on your decision).
 Derive dominant themes and peripheral themes from the
available feedback
 Analyze the feedback from a Qualitative feedback angle
 Suggest which HR/People/Workforce Analytics framework can be
applied to track the issues for employer
 Which of those qualitative feedbacks, can be pursued as
Quantitative Data Analysis?
https://www.glassdoor.co.in/Reviews/index.htm