[Verse 1] Numbers tell the story that whispers can't convey Turnover voluntary, involuntary, which walked away Time-to-fill those vacant seats, time-to-start anew Cost-per-hire climbing high, acceptance rates fall through Track retention at ninety days, then circle back at one full year Engagement scores reveal the truth that exit surveys never hear [Chorus] Count the metrics, read the patterns Absence rates and training matters HR ratio, revenue per soul Compensation gaps that take their toll Data mining human stories Numbers hiding workplace glories But remember what the models miss Ethics guard the analytics [Verse 2] Internal mobility mapping paths from desk to desk Training hours logged and learned, completion rates assess Compa-ratio calculations, pay equity demands Labor costs as revenue percentage, budget in your hands Network analysis reveals who collaborates with whom Sentiment from surveys paints the culture's hidden bloom [Chorus] Count the metrics, read the patterns Absence rates and training matters HR ratio, revenue per soul Compensation gaps that take their toll Data mining human stories Numbers hiding workplace glories But remember what the models miss Ethics guard the analytics [Bridge] Predictive turnover algorithms forecast who might flee Workforce planning scenarios, headcount strategy Diversity pipeline tracking representation at each stage But beware the crystal ball that puts bias in a cage Statistical significance isn't human relevance Models built on yesterday might steal tomorrow's chance [Verse 3] Flight risk calculations could enable darker games Retention practices discriminate behind data's claims Understand the mathematics, correlation isn't cause Question every recommendation, challenge what it draws People analytics powerful, dangerous when misused Let wisdom guide the metrics, let ethics not be bruised [Outro] Measure what matters, question what you find Data serves humanity, not the other way in kind
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