Working Co-operatively to Realise the Benefits of HR Analytics

Transcription

1 Working Co-operatively to Realise the Benefits of HR Analytics Jonathan Ferrar Vice President for Smarter Workforce Nigel Guenole Smarter Workforce Institute

2 Overview of our presentation 1. The Human Resources function must use more data driven insights to improve performance than ever before 2. The potential data pool for HR is vast and varied 3. Much HR data is employee related employees have very different views about privacy What s the answer? 2

3 We use HR data to solve business problems that we could not have solved otherwise - to do things with those numbers that before-hand were much more difficult to do buried in a spreadsheet. Global Talent Management Director, Engineering Company 3

4 A research-based answer to information sharing 1. Understand what workers are willing to share and are concerned about sharing 2. Identify how to promote a culture of openness and information sharing 3. Test our proposed methods with an experiment What uplift in participation can firms expect from adopting these policies? 4

5 Let s test the voting technology QUESTION: How sophisticated is your organisation s current workforce analytics programme? o Option 1 Most decisions are made based on intuition o Option 2 Historic data is used to understand the workforce (reports, dashboards, metrics) o Option 3 Existing data is occasionally used to make predictions about the future o Option 4 Predictive analytical models are used and embedded throughout HR to take business-relevant action 5

6 A vision for Workforce Analytics Smarter Workforce Enabling the workforce and talent programs to drive performance Strategic Operational Smarter HR Operations HR Value Driving efficiency and effectiveness of HR support operations Performance Planning Performance Review Talent Acquisition Compensation Management Learning & Development Global HR Payroll Succession Management Workforce Planning Scheduling & Staffing Time & Attendance 6

7 Analytics maturity 7 Value and Impact Time and Resources Benchmarking o Key Performance Indicators (KPIs) o Performance measured against best practices Basic Reporting o Standard reporting that is reasonably automated o Slice and dice data based on standard variables Data Management o Consolidation of data o Data quality and accuracy Advanced Analytics o Segmentation, predictive modeling, optimization, cognitive Analysis o Multi-dimensional analysis addresses business challenges Business Impact Effectiveness Efficiency

8 From predictive to prescriptive workforce analytics Workforce individual data Organisational climate data Analytics Tier I Metrics Reduced Turnover Performance Ratings Faster Time-to-hire Lower Cost-per-hire Process Consistency Deeper Talent Pools Tier II Metrics Sales Margins Customer Loyalty Innovation Engagement Safety 8

9 Implementation challenges in HR 0% 5% 10% 15% 20% 25% 30% 35% 40% Lack of understanding of how to use analytics Lack of management bandwith due to competing priorities lack of skills in line of business Ability to get the data Culture does not encourage information sharing Ownership issues or unclear governance Lack of executive sponsorship Concerns with data Perceived cists outweigh perceived benefits No case for change Note: Respondents were asked What are the primary obstacles to widespread adoption and use of information and analytics in your organization? Please select up to three. Source: Analytics: The New Path to Value, a joint MIT Sloan Management Review and IBM Institute for Business Value study. Copyright Massachusetts Institute of Technology

10 New types of data are required for Workforce Analytics 10

11 Tensions present challenges in accessing data sources I m worried about privacy We want to use employee data What can we do legally? How can we get workers to willingly share personal data? 11

12 How can we get workers to willingly share personal data? 1. Understand what workers are willing to share and are concerned about sharing 2. Identify how to promote a culture of openness and information sharing 3. Test our proposed methods with an experiment What uplift in participation can firms expect from adopting these policies? 12

13 Key previous study is a start point Olson, Grudin, Horvitz (2005) Asked 83 people: What sorts of information they preferred not to share o Identified 40 independent types of information content, credit cards, personal transgressions, etc. Asked 30 people: Would they share information with different sorts of people o Identified 5 types of people we share information with: Public, Co-workers, Manager, Family, Spouse 13

14 Results of Olson et al Willingness to share with co-worker and colleagues: Work and phone Preliminary findings Small sample size Most likely Mobile, home phone, age, marital status Pregnancy, health, personal preferences Credit card, personal transgressions Least likely Note: 5-point rating scale (1 unlikely to 5 very likely) 14

15 Critical improvements in the new IBM study Up-to-date categories o Categories include important new data sources relevant to workforce analytics Bigger sample size o From n=30 to n=4,043 Stronger relevance o Direct questioning about sharing for the purpose of workforce analytics Immediate results Uses Social Pulse technology Current o Conducted in September

16 Imagine yourself in the following scenario: You are an employee of firm X, and the firm wants to improve productivity by analysing employee data. Which of the following would you like to share with your employer for this analysis? 16 Current location (e.g. similar to how smartphones use GPS location) History of online instant messenger (e.g. Sametime status) Demographic data (e.g. ethnicity, marital status) Home phone number When available on a calendar Membership in online work related communities Personal job performance history All websites I have looked at during work Reasons for health related absences Personal interests (e.g. hobbies, professional bodies, religious affiliations) All of my work content) My instant messenger (e.g. Sametime) messages None of the above

17 Interpretation Many are willing to share it s worthwhile starting with these people! Many others appear sensitive about sharing even information routinely held already by HR What would encourage information sharing? 17

18 50 years of organizational research identifies four factors F O R T Feedback Opt-in Reciprocal Transparent 18

19 Next steps 1. Experimental study Vignettes examine uplift in intention to participate across FORT 2. Real world case Implement in actual workforce analytics programmes to assess participation rates 19

20 A2 Working Co-operatively to Realise the Benefits of HR Analytics

21 Thank you & questions

22 A2 Working Co-operatively to Realise the Benefits of HR Analytics