Our framework for talentship and the new science of human capital requires: 1) a decision framework, 2) management systems integration, 3) a shared mental model, 4) data, measurement, and analysis, and 5) a focus on optimization. The learning process covers HR methodology, statistics, analytics, and removing the "business as usual" attitude to challenge common wisdom. Challenges include data not being organized for analytics, focus on efficiency over effectiveness and impact, time pressures on HR, and resistance to hard methodology. What is missing includes changing attitudes to evidence-based decision making and investing in training managers on measuring human resource contribution beyond existing HR KPIs.

1. Our framework
• Talentship, the new science of Human Capital
requires:
– a decision framework
– management systems integration
– a shared mental model
– data, measurement, and analysis
– focus on optimization
Boudreau JW & Ramstad PM, 2007, Beyond
HR: The new science of Human
Capital, Harvard Business School Press

2. From the framework to the learning
experience
Areas Content/ Learning process
A decision framework HR Methodology:
- how to define relevant issues?
- how to frame goals into meaningful research questions?
Management systems integration HR Statistics:
- how to prepare data for analysis?
- the nature of quantitative data and how to apply quantitative analysis to them?
- how to collect data and adapt them for data analysis?
A shared mental model HR Analytics and Workforce Planning:
- how to integrate HR goals with KPIs?
- how to define impact/ effectiveness/ efficiency measures?
Data, measurement, and analysis HR Statistics:
- how to perform descriptive statistics with R2?
- meaning of descriptive analysis
- Inferential statistics: data reduction (cluster, factor), ANOVA, regression analysis
Focus on optimization All program:
- break out from business as usual attitude
- challenge common wisdom

3. The model for a new HR
Identify key Define alternatives Evaluate Implement and
processes and effectiveness of verify through
talents for for supporting different constant data
sustaining strategy change alternatives analysis
- Business acumen & - Sound knowledge of - Data on - Support of data
deep knowledge of HR practices and effectiveness of analysis team
evidence based HR research results on different HR - Define reports that
- Support of data their impact practices force to provide
analysis team - Benchmarking - Support of data evidence for
- Top management - Analysis of best analysis team assumptions and
trained into HR practices - Responsibility over statements
Analytics and impact - Challenge to taken scenario analysis
analysis for granted - Define data
- Realistic preview of assumptions collection strategy
timing and success - Establish sound
of HR investments process for
- Investment in ad-hoc alternatives
management definitions (remove
information systems mere ‘intuition’ and/
and statistical or ‘experience’)
techniques

4. Challenges
• Data in existing HR ERP systems are not organized in a
way that makes HR Analytics an easy option
• Focus on existing HR KPIs captures only efficiency
measures (no impact and limited effectiveness
measures – i.e. turnover)
• HR pressured toward delivery and with scarce time to
concentrate on HR Analytics
• Individual resistance to hard methodology with an
implicit logic that “HR is soft”
• Investment required by data and measurement hard to
support in time of hard economic conditions

5. What is missing?
• Consistent with Boudreau and Ramstad, to go
beyond HR asks for a change in attitude by top/
line managers: evidence-based decision making is
not for finance and marketing alone
• Strategic HRMs need to invest in training top/
line managers in recognizing how to measure
human resources contribution
• HR KPIs are a ‘golden cage’: useful to support our
request for resources, but unable to provide
evidence of our impact