Framework hr analytics
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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.
Framework hr analytics
- 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