Utilization of Exploratory in HR Business

2019/07/29(金)に開催したExploratory データサイエンス勉強会#10の株式会社JTBコミュニケーションデザイン様のご登壇資料です。

Transcript

1. Utilization of Exploratory in HR Business OHIRA Yusuke
2. Who? • Name : OHIRA Yusuke • Age : 35 years old • Employed : JTB Communication Design Inc. • Job : Citizen Data Scientist • Domain : Human Resource Business • Used Tool : Exploratory, Microsoft Power BI • Community : Data Science Study Group, Tokyo.R Study Group, Power BI Study Group
3. Joined the Data Science Boot Camp about a Year Ago.
4. 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.
5. Analytics in the HR Domain People Analytics
6. What is People Analytics? Analyze the Data of Employee Attitudes, Behavior and Outcomes. Executives and Management make Decisions based on Objective Indicators.
7. Drive Traditional HR •Intuition •Judgement Calls •Instincts •Experience
8. People Analytics is Important 86%
9. Capability of People Analytics • Workforce Planning • Sourcing • Onboarding • Engagement • Human Development • Internal Mobility • Retention • Wellness, Health, Safety
10. Start with… Data? Business Question?
11. IMPACT Cycle for People Analytics Identify the Question Master the Data Provide the Meaning Act on the Findings Communicate Insight Track the Outcome
12. PEOPLE ANALYTICS IS JOURNEY WITHOUT TERMINAL
13. Identify the Question HR Activity & Process HR Outcome Organizational Objective
14. Four V Required For “Big Data” • Velocity • Variety • Volume • Value
15. Does the Data have Value? Assumption Hypothesis Theory
16. Guess Components Outcome Component A Component A-1 Component A-2 Component B Component B-1
17. Issue of Implement People Analytics in Japan No Database Data Siro, Data Governance Dirty Data Decision Making Culture
18. Data Preparation and Data wrangling is 90%
19. 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.
20. 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
21. Take Action! • Fear of “And then?” • Start with a frontline worker. • Actions are part of the analysis.
22. Causal Inference Based on Static Dataset Static Dataset Analytics Causal Inference
23. y = ax + b Outflow Inflow
24. Causality Problems • Omitted Variable Bias • Reverse Causality
25. 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.
26. Familiar is Paramount
27. 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