Lecture on People Analytics

Introduction

• Overview: History and evolution of people analytics, current trends, challenges, best practices, advancements in AI, and ethical considerations.
• Exciting Time for HR: The potential of AI, big data, machine learning, and robotics in HR.

Defining People Analytics

• Terminology: Also known as HR analytics, human capital analytics, or workforce analytics.
• Four Domains: HR, IT systems, statistics, and science.
  • HR & Systems Interface: HR Information Systems (e.g., Workday, SuccessFactors).
  • Systems & Statistics Interface: Role of computer scientists.
  • Science & Statistics Interface: Behavioral economists.
  • Science & HR Interface: Organizational psychologists.
• Data Science Sweet Spot: Intersection of science, statistics, and systems.
• Purpose: Improve people-related decision making and HR strategy.

Maturity in People Analytics

• HR's Immaturity: Compared to other business areas like customer service and marketing.
• Challenges: Moving from reactionary, operational measures to predictive and prescriptive analytics.
• Example: Predicting employee attrition using machine learning vs. retrospective metrics like regrettable turnover.
• Maturity Models: Moving from descriptive to predictive analytics.
  • Josh Bersin’s Model: Highlights operational to advanced analytics percentages.

Drivers of People Analytics Maturity

• Data Governance: Importance of a data council.
• Business Partnerships: Strong relationships with CHRO, HR business partners, and other business units.
• Organizational Culture: Promoting data-driven decision making and data literacy programs.

Breaking the Wall in Analytics

• First Wall: From descriptive to predictive analytics.
• Second Wall: From ad-hoc projects to scalable solutions.
• Insight 222: Characteristics of leading companies in people analytics.
• Guidance for Impact: Focus on practical solutions and business value rather than complex models.

Practical Examples

• Strategic Layer: Executive level metrics and integrating HR data with business KPIs.
• Operational Layer: Providing real-time, actionable insights to line managers.
  • Branch Manager Example: Using real-time data to manage operational challenges.

Harnessing AI in HR

• Fourth Industrial Revolution: Fusion of technology, AI, robotics, IoT, and blockchain.
• Gartner Hype Cycle: HR digital transformation predictions for the next 5-10 years.
• Definition of AI: Building smart machines capable of performing tasks requiring human intelligence.
• Potential and Challenges: Balancing the potential with early-stage challenges and ethical considerations.

Ethical and Legal Considerations

• AI Bias: Examples of bias in hiring tools and legal implications.
• Transparency: Need for transparency in AI models and algorithms.
• Unilever Example: Clear policies and governance around AI and ethics.

Practical AI Applications

• Employee Attitudes: Openness to AI in non-personal tasks vs. resistance in personal tasks like performance appraisals.
• Purple People Concept: Blend of technical expertise and business acumen for effective people analytics.
• Emotional Intelligence: Importance of EQ in leveraging AI insights effectively.

Conclusion and Demonstration

• Virtual Agent Example: Using AI to provide real-time, contextual HR insights.
• Importance of Data Foundations: Ensuring accurate and up-to-date data before implementing advanced technologies.