Littal Shemer Haim
People Analytics, HR Data Strategy, Organizational Research – Consultant, Mentor, Speaker, Influencer
People Analytics Consultant, Workforce Data Strategist
Littal Shemer Haim helps organizations improve business performance by informed decisions about people. She offers leaders guidance in the journey toward a data-driven workforce and conducts People Analytics and Organizational Research projects.
Littal explores innovation in the work-tech ecosystem. She writes and lectures about People, Data, Ethics, and the Future of Work. In addition, she leads learning programs in People Analytics and HR tech.
Littal enjoys keynote speaking at various conferences and professional events in Israel and globally. She shares her knowledge and +25 years of experience in People Analytics, Workforce Data Strategy, and Organizational Research. She is also frequently invited to speak at internal company events.
Latest Activities and Achievements
Workforce data is a mess! What can you do about it?
HR data is a mess! Nevertheless, there is so much that HR leaders can do to cope with this challenge, starting today, based on six recommendations in this article, a mixture and volume that depends on the phase in the journey to data-driven HR.
People Analytics Leader – Survive Your Onboarding!
Most case studies that we encounter represent mature stages. However, most new players in this rising profession struggle with different challenges. The onboarding of People Analytics Leaders is fascinating and worth following. Here's one example.
Effectiveness of employee evaluation process
The stakeholders and players who participate in the evaluation process, the available tools and resources, and the issues discussed in the feedback conversations, all affect the effectiveness of the process. At the end of the process, managers raise four questions regarding these sources of influence.
Data Science for HR: Critical Questions
What should HR professionals expect when working with a data scientist? What should HR professionals do for a successful data science project? How should you deal with insignificant results in the analysis? The article addresses these questions.