Situation: Employee attrition and recruit-aways drive unnecessary costs and slowdowns across the business. Proactively identifying at-risk and high performers enables targeted investment opportunities. Predictive analytics informs and prioritizes these efforts for optimized effectiveness in efforts to retain, train and recruit outstanding employees. Better and more precise predictive analytics supports better and more effective efforts.

Complication: Important predictive information indicative of attrition is highly sensitive and distributed departmentally across the business. Allowing plaintext access, anonymized access, or even centralized and differentially private access all lack sufficient data privacy and confidentiality protections.

Resolution: XOR enables users to privately compute across private HR data repositories. Departmental and attribute level privacy is preserved, while facilitating and allowing for beneficial, compliant analytics to be run as required.

Outcomes:

• Additional data utilized to improved model predictive accuracy (+15%)

  • Hypothesis-proven, privacy-preserved: “more data, better models”

  • Information technology utilization and employee engagement data beneficial

  • Additional areas of 1st/2nd/3rd party data prioritized and targeted for further testing

• Proactivity and prioritization efforts informed by predictive analytics and monitored intelligence

• Individual (PII), departmental and jurisdictional attribute level privacy preserved

  • Mathematical guarantees to data privacy, residency and policy adherence

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