Creating surprising insights with available and new data
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The document discusses the application of people analytics and machine learning in HR to enhance decision-making and organizational performance. It highlights the importance of understanding succession planning through data analysis and emphasizes the need to design effective HR interventions that reflect future employee value propositions. Key takeaways include the ease of implementing people analytics and the transformative potential of machine learning in HR practices.
Creating surprising insights with available and new data
- 1. Creating Surprising Insights with available and new data HR Vision Berlin 20 September , 2016
- 2. My background in HR
- 3. 4. Design the HR interventions 1. Exploration 2. Determine the most burning issues 3. Learn from available data 5. Track progress and measure effect People Analytics: 5 steps
- 4. People Analytics is using digital methods to develop a deep understanding on how people and organization relate to business performance, to drive effective, informed and fast decision making People Analytics …....
- 6. Look wide: different talent sources Internal talent pool Online Intermediary Alumni Start-ups Self employed Talent Sharing Outsourcing
- 9. 2. Determine the most burning issues
- 10. 3. Learn from available data
- 12. Example 1: Accenture Sweden, Diversity
- 13. Example 2: Succession planning • How strong and effective is the succession pipeline up-to-4 levels below the executive board? • From a risk mitigation perspective: • For how many positions are candidates listed? • From a talent development perspective: • Do top potentials have future career path?
- 15. Understanding succession plans • Data analysis shows that there is only a small unique group of employees that show up at succession plans (many names are recurring) • The graph below shows that 5% of all employees make up 60% of all succession benches 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percentageoflistedsuccessors Percentage of employees
- 16. Understanding succession plans • Question to answer: • How is it possible that a small group (5%) of all workers are mentioned on so many succession plans (60%)? • Some hypothesis: 1. Is the access to potential successors is limited ? 2. Is the ‘quality’ of the potential pool is limited ? 3. Something else?
- 17. Machine Learning helps to discover more candidates
- 18. Visualizing and predicting leadership mobility From location To location
- 19. Example 3
- 20. Example 4: Design a total reward proposition for the future Client challenge: • Building an employee value proposition that attracts the employees that are needed now and in the future, with a balance between cost to the employer and value to the employee. • And to provide predictive insights to manage change resistance.
- 21. Measuring value to the employee
- 23. Using clustering techniques to identify groups
- 24. 4. Design the HR interventions 5. Track progress and measure effect
- 25. A glimpse in the (nearby) future
- 29. Machine Learning will change HR
- 31. Joy & pride
- 36. 36
- 38. Social Credit Code China
- 39. Key take aways 1. People analytics can be easy 2. HR can increase impact with people analytics 3. Machine learning will change HR 4. Required capabilities for HR changing 5. The future is bright!
- 40. Thanks! Twitter: @tomwhaak, @hrtrendinst Website: hrtrendinstitute.com FlipBoard: The Future of HR