Overview

Data literacy for HR and people teams means reading attrition, absence and engagement data with the rigour it deserves. This practical course, a role-based companion to Data Literacy for Business 101, covers survey literacy, fair cross-team comparisons and responsible use of sensitive people data. Four short units, real scenarios, no hype.

Target Audience

HR and People teams at any level. No technical background required.

Learning Objectives

Upon completion, learners will gain a full and clear understanding of:

• How to define and calculate the core people metrics precisely, and spot the traps that make them easy to misread
• How to read an engagement or exit survey report sceptically, understanding response bias and anonymity thresholds specific to people data
• How to compare attrition, absence and engagement fairly across different departments, sites or functions
• How to apply an evidence-based approach to a people policy or initiative, and question a people-data claim of cause and effect
• How to handle protected-characteristic and health-related people data with the extra care it demands
• How to use people data responsibly in high-stakes decisions, and be transparent with employees about what is collected and why

Course Contents

Unit 1: People Metrics and Their Traps

• The core people metrics, attrition and turnover, absence, engagement and time-to-hire, defined precisely
• The definitional traps built into each one, starting with voluntary versus involuntary attrition
• Why small headcounts and seasonal hiring patterns make these numbers unusually easy to misread
• Benchmarking your own figures against a genuine sector norm, not an arbitrary internal target

Unit 2: Survey Literacy for People Data

• Who tends to respond to an engagement or exit survey, and who tends not to
• Why exit survey responses can be especially unrepresentative of everyone who actually left
• What counts as a large enough sample at company level, and at team level
• Anonymity thresholds, the real convention behind why some breakdowns are withheld, and why that is right

Unit 3: Fair Comparisons Across Teams and Evidence-Based HR

• Why comparing whole departments, sites or functions fairly is an HR-analytics problem in its own right
• The structural baseline trap: comparing a naturally high-turnover function against a naturally low-turnover one unfairly
• Adjusting for role type, tenure mix and sector norms before comparing teams at all
• Treating a people policy or initiative as a decision worth properly evaluating

Unit 4: Using People Data Responsibly

• Why protected-characteristic and health-related data need a further layer of care beyond general anonymisation
• Using people data fairly and lawfully in recruitment, promotion and restructuring decisions
• How a dataset built from past decisions can quietly repeat past unfairness
• Being transparent with employees about what people data is collected, and why