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
Human-Centered Digital Transformation – Experts Panel
Covid19 crisis brought a significant portion of the workforce to work remotely, and we witnessed an acceleration of the digital transformation of work processes and particularly measures. But with the evolving practices of People Analytics, new ethical concerns emerge.
Finding Hidden Patterns in Gender Pay Gap Data
Why are we failing to see the hidden patterns in the gender pay gap? How can HR professionals work better with data scientists to spot hidden patterns? How can we generalize this case study for up-skilling and re-skilling in critical thinking and an analytical mindset?
Professional Journey and Daily Work of A People Analysts
Aspired People Analysts often ask about competencies, challenges, tasks, and tools in this profession. I was privileged to discuss these topics with a colleague who holds one of the most desired roles.
Leading With Data – Experts Panel
I was honored to participate in the experts' panel that opened the Hacking HR online event "Leading With Data" and discuss the foundational elements of data analytics and common mistakes when approaching workforce data