Stay informed on HR analytics, people science, and AI
Practical perspectives on evidence-based HR from Dr. Stanley Kipsang and the Stanalytics team.
The 5 levels of HR analytics maturity
Most organisations are stuck at Level 1 or 2. Here is a practical framework for assessing where you are and what it takes to reach the next level, from descriptive reporting to predictive people science.
Read the full analysisWhy your AI HR tool is failing, and how to fix it
A common pattern: an HR team buys an AI tool, it does not deliver value, and everyone blames the technology. In most cases the problem is not the tool. It is the foundation.
Redefining quality-of-hire: A practical framework
Most organisations define quality-of-hire differently, making comparisons meaningless. This article proposes a standardised approach.
The HR data quality checklist
Poor data quality is the number one barrier to HR analytics success. This checklist covers the eight dimensions you must validate before building any model or dashboard.
How to design employee surveys that actually work
Most engagement surveys suffer from poor design, low response rates, and no follow-through. A practical guide to getting research-quality insights.
Ethical AI in HR: Beyond the policy document
Many organisations have an AI ethics policy but no practical implementation guide. Here is how to translate principles into operational safeguards.
The metrics that matter most for board reporting
Boards do not want 50 KPIs. They want six to eight that tell the story of workforce health and strategic contribution. Here is what to include.
Building a business case for HR analytics
Struggling to get budget for analytics investment? This framework helps you quantify the ROI and present a compelling case to leadership.
People analytics vs. HR reporting: Know the difference
Many teams think they are doing analytics when they are really just reporting. Here is a clear distinction and what it means for your team development.
Why employee experience data needs a research mindset
Survey results are only valuable if collected and analysed rigorously. Common methodological mistakes that undermine experience programmes.
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