Comp and Coffee 112: Give your salary data strategy a glow up
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Apr 29, 2025 In this discussion, Sarah Hillenmayer, a lead in AI and data insights at Payscale with over 15 years in HR tech, delves into refining compensation data strategies. She highlights that having multiple data sources isn't always beneficial unless used strategically. The conversation covers the necessity of data transparency and monitoring biases. Additionally, Sarah explores the transformative role of AI in compensation practices and provides insights on crafting effective pay ranges, especially for budget-conscious organizations.
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Choosing Salary Data Sources
- Ensure your data source covers at least 80% of the jobs you need to price with 70% job match accuracy.
- Confirm repeatability, explainability, data recency, potential biases, and accessibility of the data source.
Aggregating & Using Market Data
- Aggregate multiple data sources considering weighting, aging, and methodology consistency across job and talent groups.
- Align market data with existing internal pay structures and decide how ranges are used and communicated, especially under pay transparency laws.
Pay Transparency Spectrum & Impact
- Pay transparency spans from full secrecy to openly publishing salaries, with most firms sharing salary ranges internally.
- Sharing broader salary ranges for roles beyond the current one supports meaningful career and pay progression conversations.