Talent Acquisition Analytics - Nirma University 12072024.pptx

This session was taken by Rishi Jain for Nirma University students on Talent Acquisition Analytics as part of their HR program. Rishi Jain has been leader in People Analytics / HR Analytics and was leading People Analytics for Adani Group in India.

1. Rishi Jain Head – Head of Analytics, Adani Cement (ACC & Ambuja ) Previously – Head of People Analytics, Adani Group Nirma University 12 th July 2024 Talent Acquisition Analytics

2. Recruiter

3. Candidate

4. Technology evolved faster or talent acquisition needs?

5. The Talent Acquisition Process (laterals)

6. KPI Dashboard for HR Recruitment Process

7. Personas

8. Data Analytics for Talent Acquisition

9. Talent Acquisition – Balancing Act Time to Hire Time to fill Infant attrition Quality of Hire

10. Efficiency - Accessing historical data about recruitment process to predict future

11. Identifying bottlenecks in hiring process 21

12. Effectiveness – Understanding Hires by different recruitment channels Infant Attrition by Country * Reference LinkedIn Data Science using People Analytics Performance Rating

13. T e chn ol og y Stack at high level Databases Datasets Data Warehouse Data Lake Data Cloud DATA PLATFORM PURPOSE Reporting Analytics Purpose Built Customizable Demo

14. Tips for Effective Talent Acquisition Analytics Forecast – Conduct Workforce Planning and Budgeting exercise Boost your brand (Freshers, etc.) Involve more people across the organization in hiring e.g. Offer, Onboarding Leverage Recruiting Technology -Optimize career websites -Optimize Job Postings -Optimize Application process for Mobile users -Data entered once available everywhere Develop an effective Onboarding Program Talent Marketplace tools (Internal repository and Job matching) Use analytics for Efficiency and Effectiveness

15. Thank You ! https://www.linkedin.com/in/rishijaiin/ [email protected] Rishi Jain – 7411301697 15

16. Frameworks

17. Apple Confidential–Internal Use Only People Analytics Pillars Embedded Business Economics Custom Solutions Advisory What does the business need? Strategic Operational Finance Integration Planning How do we get the right people in the right jobs at the right time? Census Milestone Pulse Sensing What are employees thinking, feeling and doing? Research Models Intelligence Analytics What happened? Why did it happen? What might happen next? Data Reporting Visualization Technology How do we democratize talent insights? 17

18. Appendix

19. Design Principles Focus – Different approaches to solve for different personas Agile Approach – Have a Proof of Concept (Rapid Prototyping) team to discover fast either fail or pass Scalable platform – Data Lake with data from different systems in a single place – to support future deployments Single source of truth - Have robust training mechanism, so all use the same system Decentralized Org structure – Build Platform, Data Science, Visualization teams at the Centre with BU Analysts in the BUs supporting the businesses Robust Data quality and Governance mechanism – Data Quality team to ensure good data quality across systems, interfaces. Same definition is used across BUs. Data driven insights – Mature towards data and insights democratization solutions (AI enabled) Superior User Experience – Insights in minimal clicks, focused solutions. Data and Analytics – Have capacity, capability building with Infrastructure support Feedback and usability – Collect user feedback and product utilization rate