People Analytics: Data to Decisions

Description

This book is an exploration of the people analytics possibility, bringing out both theoretical frameworks and detailed practical case studies from the author's experience in industry and business across both sides of the table, with an understanding of data science models and SMAC (Social, Mobile & Cloud) technologies underpinning it. It further explores and lays out a business case for why organizations need to invest behind this space and why HR functions and businesses need to embrace and adopt it. The book examines how people analytics makes a difference to business, describes stages of adoption and maturity models for its effective deployment in organizations and explores the journey from employee master data management and conversion to reporting and visualizations to dash-boarding and descriptive analytics, operational analytics to finally predictive modelling.   The book provides insights on the impact of big data and social networks on HR and talent frameworks and the opportunity for HR to mine these networks with a view to culling out predictive insights for the business. It also describes in great detail the specific applications of people and talent analytics through case examples. The book discusses and makes the case for HR to be metric driven focused on business outcomes. It enumerates upon “lead” and “lag” indicators and the need to leverage relevant measurement systems. It provides an understanding of relevant statistical tools that could be deployed to mine key insights from the data to enable robust decision-making, and examines the power of “visual intelligence” and data representation that goes beyond traditional tools like Excel. This book is for HR practitioners who seek to challenge the status quo. It does so by helping them leverage a data and evidence based approach; asking the right questions and building new capabilities with a view towards leading change and driving transformation both in their domain, the wider business and the larger organization. The book is also useful for HRM students to gain a deep understanding of “people analytics” as a critical sub-domain within HR.   “HR is not just about people but now also about Tech, Data and Analytics. Upgrading numerical/analytics skills in order to have greater impact on the business, is the new wave of HR, which Rahul helps address via his own rich experience.” -       Gurprriet Siingh, Managing Director, Russell Reynolds Associates, Mumbai, India.   “This book would help HR & Leadership Teams find a way of discarding perceptions and uncovering truth by embracing data patterns as opposed to just continuing with incremental changes to how it has always been. This is particularly so of successful organizations.” -       Vikas Gupta, Divisional Chief Executive Officer, Education and Stationery Products Business, ITC Limited, Gurugram, India. Preface Acknowledgements Contents About the Author 1 People Analytics—Making a Difference to Business 1.1 Context 1.2 People Analytics and a Single Employee View 1.3 Difference to Business 1.4 Tangible Business Impact 1.5 People Analytics Enables Agile and Robust Decision-Making Through 1.6 Transforming Mindsets 1.7 Real-World Case Study Examples 1.8 FLM Work and Time Utilization Trends Deploying Analytics 1.9 Analytics Insight Programme Framework 1.10 Summing up 2 Operational Analytics and Predictive Modelling 2.1 Context 2.2 Predictive Modelling in Human Capital Management 2.3 Predictive Models 2.4 Predictive Analytics—Competitive Advantage 2.5 People Analytics Through Cost Modelling: Understanding the Business Impact 2.6 Cost Modelling Project 2.7 Optimizing Cost Through Operational Analytics—Some Examples and Tools/Visualizations 2.8 Key Outcomes Untitled 3 All Things Talent and Organization Networks 3.1 Context 3.2 Critical Challenges in Talent Management 3.3 People Analytics—Pivotal Role 3.4 People Analytics in Talent Management—Some Frameworks 3.5 Data-Driven Recruiting 3.6 Applying Talent Analytics 3.7 Organizational Networks—Uncovering “Hidden and Passive Internal Talent Pools” 3.8 Organization Networks—Impacting Talent Outcomes 3.9 Illustration: Employee Churn, Prediction and Retention 4 Deploy and Embed Analytics—Employee Lifecycle 4.1 Context 4.2 Staged Approach 4.3 Employee Lifecycle Management 4.4 Capabilities and Skill Sets for People Analytics Deployment 4.5 Critical Competencies 4.6 Where Do Companies Begin? 4.7 A Case Well Documented is Around—How a Large Technology Company Developed Its Renowned Workforce Analytics Team 4.8 People Analytics: Integrated into HR Service Delivery Model 4.9 Pitfalls to Avoid 4.10 Summing-up 5 Data and Social, Mobile, Analytics, Cloud (SMAC) 5.1 Context 5.2 The Future of People Management Will Be Grounded in Data 5.3 People Data and Business KPI Data Integration 5.4 Challenges and Opportunities for Data Flow Management 5.5 Evolved HR Technologies: SMAC—Social, Mobile, Analytics, Cloud 5.6 Leverage the Cloud 5.7 SMAC Tool—ORGSENS 5.7.1 Why ORGSENS? 5.8 Future of Work—Post-COVID-19 5.9 Social, Mobile, Analytics, Cloud (SMAC) 5.10 Data Quality 5.11 Situation and Context 5.12 Key Benefits and Outcomes 5.13 Key Benefits and Outcomes: Innovation Features 5.14 Situation and Context 5.15 Analytics Objectives 5.16 Key Outcomes 5.17 Master Data Management—People Directory with a Single Version of the Truth 5.18 Situation and Context 5.19 Analytics Objectives 5.20 Key Outcomes 5.21 Conclusion 6 HR Risk Analytics—Identification, Management and Mitigation 6.1 Context 6.2 Human Capital Risk Matrix—Operational/Reputational/Talent 6.3 HR Audit Capabilities 6.4 HR Risk/Audit Analytics Framework 6.5 Assessing Risk 6.6 Managing Risk 6.7 Situation and Context 6.8 Analytics Objectives 6.9 Key Benefits 6.10 Key Risks Identified 7 Shape Culture and Drive Engagement—Real-time Actionable Insights 7.1 Context 7.2 Defining Corporate Culture 7.3 Questions to Answer 7.4 Culture and Engagement—Organization Actions 7.5 Employee Engagement—Shifting Your Corporate Culture 7.6 Voice of Employees 7.7 Leveraging the Voice of the Employee (VoE) 7.8 You Cannot Afford to Lose Your Talent 7.9 Engagement and the Data Challenge 7.10 Contemporary Tools to Capture VoE—Text Analytics 7.11 Leverage Artificial Intelligence for Actionable Insights 7.12 Contemporary Tools to Capture VoE—Chatbot 7.13 Real-time Feedback 7.14 Situation and Context 7.15 Scope 7.16 Analytics Insight Objectives 7.17 Analytics Insight Programme Framework 7.18 Key Benefits 7.19 Situation and Context 7.20 Sustaining Engagement and Employee Morale—A Diversified Manufacturing Company 7.21 Steps Taken 7.22 Situation and Context 7.23 The Service Profit Chain 7.24 Hypotheses 7.25 Bank Used the Following Research Methodology 7.26 The Team then Delivered the Following Insights from the Analytics Programme: Behaviours Driven by the Organization Culture 7.27 A Well-documented Case Study in the Matter of Employee Engagement and Customer Loyalty is that of a Retail Company 8 People Analytics in Mergers and Acquisitions 8.1 Context 8.2 M&A Deal Continuum 8.3 Key Risks in M&A Integrations 8.4 HR Data Structures—Due Diligence 8.5 Summing up 9 People Analytics Enablement Through Systems Thinking 9.1 Context 9.2 Five-Step Systems Thinking 9.3 People Analytics Deployment 9.4 People Analytics Journey and Enablement 10 Organization Design, Rewards and HR Value Chain 10.1 Context 10.2 HR Challenge #1 10.3 HR Challenge #2 10.4 HR Challenge #3 10.5 Illustration: Incentive Design Considerations 10.6 Rewards Modelling—Some Examples 11 Metrics, Measurement, Scorecards and Power of Visual Intelligence 11.1 Context 11.2 Design Measures 11.3 Define Measures—What is Involved 11.4 Implement and Evolve Measures 11.5 Types of Measures 11.6 Frameworks and Scorecards: Asking the Right Questions 11.7 HR Scorecards: Types of Measures that Could Be Tracked Regularly 11.8 Visual Intelligence and the Power of Visualization 12 Role and Deployment of Statistics and Data Science in People Analytics 12.1 Context 12.2 Statistical Learning 12.3 Overview of Tools in Power BI 12.4 Power BI in People Analytics 12.5 Excel Dashboards 12.6 Tableau Data Visualization 12.7 Data Science 12.8 What is R? 12.9 Python and Python IDE 12.10 Case Study: Usage of Statistical Tools for Predicting Employee Turnover 13 People Analytics Industry Landscape—Has its Time Come? 13.1 Context 13.2 Social Networks 13.3 Leveraging Artificial Intelligence 13.4 Recruitment 13.5 Augmented Writing 13.6 Sourcing 13.7 Assessment and Selection 13.8 Chatbots 13.9 Market Opportunity 13.10 Current Adoption and Challenges 13.11 People Analytics Third-Party Support Options 13.12 Outsourcing of People Analytics 13.13 Illustration—Creation of Insight Dashboards 13.14 Illustration—Predictive Analytics Models 13.15 Summing-up Case Studies—Summaries and Business Outcomes Bibliography