HR Metrics and Data Analytics Training

HR Metrics and Data Analytics

Master HR metrics and data analytics: predict turnover, optimize recruitment, and enhance performance. Learn Excel functions, data analysis, and strategic HR management. Develop data-driven insights for talent management and organizational change.

Please inquire for pricing   Available Online and In-class

Date Venue Duration
17 - 21 August 2026 Sandton 5 Days
07 - 11 September 2026 Sandton 5 Days
14 - 16 October 2026 Sandton 3 Days
30 November - 04 December 2026 Sandton 5 Days

Course Introduction

This HR Metrics and Data Analytics Certification Training Course addresses critical challenges faced by HR professionals, such as real-time HR analytics and predicting employee turnover and optimizing recruitment decisions. Participants will learn to leverage essential HR metrics and analytical techniques to enhance organizational performance and strategic HR management.

 

The course guides attendees through a structured process of using key HR metrics and data analytics tools to analyse, interpret, and predict various workforce dynamics. Emphasis is placed on developing data-driven insights to manage recruitment, employee engagement, productivity, retention, and performance effectively.

 

Participants will explore the primary types of HR analytics (Descriptive, Diagnostic, Predictive, and Prescriptive) to transform their HR processes from intuition-driven to evidence-based decision-making.

Course Objectives

By the end of this HR Metrics and Data Analytics Training, participants will be able to:

  • Understand the fundamentals of data analytics in the context of HR departmental operations

  • Appreciate the role of data analytics in understanding performance and behaviour

  • Define the general principles of organizational change

  • Align organizational change and HR strategy to evidence from data

  • Utilize various assessment metrics to enhance organizational input

  • Conduct detailed analytical assessments

  • Generate decisions based on empirical evidence rather than opinion

  • Use data to analyse trends in the HR industry

  • Set benchmarks to attract competent talent from the market

Who should attend?

  • HR officers and managers managing recruitment, training, and employee development

  • Data analysts providing HR insights for enhancing decision-making

  • Data scientists responsible for business problem-solving using data

  • Finance analysts monitoring financial operations and forecasting budgets in relation to performance of the human capital of the organization

  • Business analysts leveraging data to enhance operational efficiency

  • Financial auditors evaluating organizational financial data

  • Managers and supervisors overseeing organizational operations

Human Resource (HR) Courses

Training Methodology

Our diverse instructional approaches ensure effective learning:

– Lectures & Presentations: Engage with expert-driven, stimulating content.
– Course Material: Access well-crafted supporting resources.
– Group Work: Collaborate on discussions and case studies for practical insights.
– Workshops & Role-Play: Participate in immersive, scenario-based activities.
– Practical Application: Focus on applying theoretical knowledge in real situations.
– Post-Training Support: Receive extensive support after training for skill implementation.

Training Outline

Module 1: HR Metrics, Data Analytics, Strategy, and the HR Department

  • Rationale for evidence-based HR

  • Understanding data, the different levels of analytics and the overview of the analytics cycle

  • Identifying key HR metrics

  • Strategic HRM and human capital

Module 2: Data Utilized in HR Analytics

  • Internal and external data sources

  • Employee tenure, compensation, and performance appraisal

  • Metrics on competent and high-performing employees

  • Revenue per employee and training requirements

Module 3: Putting HR Metrics and Analytics into Action

  • HR planning and recruitment analytics

  • Talent management and succession planning

  • Skills and training needs analysis

  • Balanced Scorecard applications

Module 4: Driving Organizational Change

  • Principles and theories of organizational change

  • Overcoming resistance to change

  • Cultural and technological drivers of change

  • Executing strategic change

  • Data as a driver of change

Module 5: The HR Data Analysis Process

  • Aligning HR metrics with organizational objectives

  • Collating, analysing, and interpreting HR data

  • Utilizing Power BI and Databricks data analysis Tool Pak  

  • Evaluating organizational impacts of HR policies

Module 6: HR Metrics and Analytics in Action

  • Metrics application in recruitment, workforce planning, and learning & development

  • Online data driving HR decisions.

  • Analytics based on data from HR Survey Forms

  • Measuring HR effectiveness and talent management

  • Metrics for attendance, absence, and employee well-being

Module 7: Metrics for Employee Productivity and Performance I

  • Evaluating employee motivation and engagement

  • Psychological contract and empowerment.

  • Performance management methodologies

  • Training effectiveness and actionable planning

Module 8: Metrics for Employee Productivity and Performance II

  • Employee utilization rates and workforce costs

  • Productivity metrics (self-rated productivity, task completion)

  • Sales performance metrics (sales growth, revenue per representative)

Module 9: HR Analytics Cycle

  • Building analytics capability in HR teams

  • Collaborating with legal for compliance

  • Implementing HR analytics pilot projects

  • Cultivating an analytics-driven organizational culture

Module 10: Data Collection and Analysis

  • Methods for effective data collection

  • Employee self-assessment and self-report measures

  • Data security management

  • Diagnostic techniques for workforce data

  • Electronic workforce surveillance analytics

Module 11: Inferential Statistics

  • Fundamentals and types of hypothesis testing

  • Overview of hypothesis testing process

  • Statistical tests for hiring decisions (Z-test)

  • Training effectiveness evaluation (paired T-tests)

  • Productivity analysis using ANOVA

  • Salary and promotion analysis (Correlation, Covariance, ANOVA)

  • Predictive modelling using linear regression

Module 12: Customer Service Metrics

  • Tracking and interpreting customer feedback

  • Metrics for customer service efficiency

  • Complaint resolution tracking

  • Response and ticket resolution timeframes

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