how big data is reducing workforce turnovermedia.computer.org/pdfs/rosenbaum.pdf · cost...
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How Big Data is Reducing
Workforce Turnover Michael Rosenbaum
CEO, Catalyst IT Services
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Applying Data to Hiring and Team Assembly
•The Subjectivity of Hiring
- One of the most inefficient areas of economic
activity (bias, perception, information problem)
• The Industry
- Early entrants of the Oakland A’s to today
• Outcomes
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Approaches
• Platforms - Pegged Software, Evolv on Demand
• Internal Use - Google, Catalyst IT Services
• Generalized talent predictions - Gild, Codecademy
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Technologies and Methodologies
• More sophisticated analysis of existing data
(resumes, other application information
including code, text)
• Public data
• Metadata
DEFINING THE DEPENDENT VARIABLE
FOR WHAT OUTCOME ARE YOU OPTIMIZING
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Outcomes - Turnover
Pre-platform deployment compared to Post-platform deployment
Across 3M job applications/yr and 119 healthcare facilities
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Outcomes – Turnover (Demonstrating Causation)
Turnover improvement in Platform-deployed job categories
compared to control groups—either rest of the hospital or
same job categories in other hospitals in the same system
over the same time period
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Outcomes—Quality Improvement (Healthcare)
Using HCAHP likelihood to recommend
scores as outcomes
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Applying Data to Assembling Engineering Teams
Examples of Enterprises Using Model of Applying
Data to Software Engineering Team Assembly
Fortune 500 Sports Apparel Company
Fortune 50 Technology Company
eBay/StubHub
Red Hat
Starwood
Aetna
Cambia Health
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Improvements – Software Engineering
Productivity and Quality Comparisons: Software
Engineering Using Data to Assemble Teams vs Conventional
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Cost Improvements – Software Engineering
• Combined teams
• Data is on no
fewer than 40
software
engineers per
partner
• 3 Tier 1 offshore,
one traditional
onshore, one using
data
Cost Comparisons: Software Engineering Using Data to
Assemble Teams vs Conventional
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Call Centers
• At Xerox, Evolv found almost no correlation between
relevant prior experience and performance
• Initial 6 month trial showed 20% improvements in attrition
pre-post
• At Novo 1, pre-post comparisons showed 39%
improvements in attrition
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Adoption: Outcomes as the Key
• Limitations for HR – budget not tied to P&L
• Limitations in sophistication
• Regulatory environment
• Individual resistance – comparison to online dating
industry
OUTCOMES DRIVE ADOPTION