What is this meeting about?
Building upon the calculation tool, originally created by Badal Inani, we have continued the journey with fellow Rumbold members to get the tool more dynamic, and provide the output necessary to inform the leaders of a company, and provide insights for reward professionals where the pay gaps might be.
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Key take-aways:
The R2PT – Calculation Engine Continued session shows how the Rumbold community is strengthening its shared, practical approach to pay equity, data literacy, and transparent analytics. The Excel-based engine is evolving into a powerful, explainable tool that helps reward professionals test hypotheses, understand data foundations, and navigate EU pay transparency demands.
At its core, the engine reinforces a central message: sound job architecture, good data, and transparent models are the foundation of credible pay equity work. Visualisations, correlations, and regressions help reward teams move from assumptions to evidence, distinguishing structural pay drivers from genuine inequities. And by sharing datasets, experiences, and use cases, the Rumbold community accelerates collective learning on the Road to Pay Transparency.
- Use the engine as a learning and diagnostic tool
The calculation engine is built for quick pay equity scans, scenario testing, and strengthening internal understanding—not for formal legal reporting. Treat it as a safe environment to explore data patterns, validate assumptions, and prepare conversations with HR, finance, and leadership. - Data quality and job architecture determine analytical credibility
Clear job levels, consistent ranges, and solid data foundations are essential. The engine helps reveal outliers, missing values, and inconsistencies early—critical steps before drawing any conclusions about pay equity or running regressions. - Combine visuals, correlations, and regressions for a complete story
Scatter plots show patterns at a glance, correlations test direct relationships, and regression quantifies what is explained vs. unexplained. Together, they provide a structured narrative that distinguishes structural drivers from potential bias. - Unexplained pay gaps require targeted follow‑up
When regression leaves a meaningful unexplained gap, treat it as a priority signal for deeper analysis and potential action—not as a definitive conclusion. This helps organisations identify where structural fixes, policy reviews, or pay adjustments may be needed. - Lean on the Rumbold community as an extension of your team
With ongoing sessions, expert deep dives, and shared datasets, the Rumbold ecosystem offers guidance and sparring on complex pay transparency topics. Leveraging this network accelerates capability-building and strengthens collective readiness.
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