An overview of people analytics in Japan and how to use Exploratory for analytics. ( Exploratory : R language front end tool )

1. Utilization of Exploratory
in HR Business
OHIRA Yusuke

2. Joined the Data Science Boot
Camp about a Year Ago.

3. Today’s Target
Those who want to know how to use
Exploratory in various domains.
Those who want to start HR Analytics
as a company or an individual.

4. Analytics in the HR Domain
People Analytics

5. What is
People
Analytics?
Analyze the Data of
Employee Attitudes, Behavior
and Outcomes.
Executives and Management
make Decisions based on
Objective Indicators.

6. Drive
Traditional
HR
•Intuition
•Judgement Calls
•Instincts
•Experience

7. People Analytics is Important
86%

8. Capability of
People Analytics
• Workforce Planning
• Sourcing
• Onboarding
• Engagement
• Human Development
• Internal Mobility
• Retention
• Wellness, Health, Safety

9. Start with…
Data? Business Question?

10. IMPACT
Cycle for
People
Analytics
Identify the
Question
Master the
Data
Provide the
Meaning
Act on the
Findings
Communicate
Insight
Track the
Outcome

11. PEOPLE ANALYTICS IS
JOURNEY WITHOUT TERMINAL

12. Identify the Question
HR Activity
& Process
HR Outcome Organizational
Objective

13. Four V
Required For
“Big Data”
• Velocity
• Variety
• Volume
• Value

14. Does the Data have Value?
Assumption
Hypothesis
Theory

15. Guess Components
Outcome
Component
A
Component
A-1
Component
A-2
Component
B
Component
B-1

16. Issue of Implement
People Analytics in Japan
No
Database
Data Siro,
Data
Governance
Dirty Data
Decision
Making
Culture

17. Data Preparation and
Data wrangling is
90%

18. Employee
Survey
As a Initial Process in
People Analytics
• Get the data that can be
analyzed for now.
• Increase the certainty of the
Assumption.
• Approaching a Causal
Relationship.

19. Analytics by Exploratory
Design Questions
based on Assumption.
Regression Analysis
verifies relationship
between outcome and
other components.
Explore relationships
between various
components by PCA.
Observe trends in
Free-text by NLP

20. Take Action!
• Fear of “And then?”
• Start with a frontline worker.
• Actions are part of the analysis.

21. Causal Inference Based on
Static Dataset
Static
Dataset
Analytics
Causal
Inference

22. y = ax + b
Outflow Inflow

23. Causality Problems
• Omitted Variable Bias
• Reverse Causality

24. Approaching a Causal
Relationship
• A Business dose not have just
one issue.
• The cause of change after
intervention is not always that
intervention.
• Compare the intervention
target and non-target.

25. Familiar is Paramount

26. Data
Scientist’s
work is quite
divers
Identify the
Question
Master the
Data
Provide the
Meaning
Act on the
Findings
Communicate
Insight
Track the
Outcome