Analytics Driving Action - Building a Data-Driven HR Function


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Presentation I recently delivered at Tucana's 2014 People Analytics Conference in London on April 10, 2014

Published in: Business, Technology

Analytics Driving Action - Building a Data-Driven HR Function

  1. 1. Analytics Driving Action Jonathan Sidhu Executive Program Manager Business Analytics Transformation @jmsidhu Mark Tristam Lawrence Learning Intelligence Leader Global Business Services @mtlawrence #PADDHR
  2. 2. Agenda  IBM – Who we are and what we do  Workforce Analytics - Background  Analytics in Action  Getting Started
  3. 3. IBM - a virtual social community  72% of us outside Americas  64% workforce in Services business  55% workforce has less than 5 years service  36% of employees work remotely  12% from acquisitions & outsourcing deals  1% on global assignments
  4. 4. Agenda  IBM – Who we are and what we do  Workforce Analytics - Background  Analytics in Action  Getting Started
  5. 5. Source 1: 2012 IBM CEO study: Q24 ―What do you see as the key sources of sustained economic value in your organization?‖ Source 2: SHRM Human Capital Benchmarking Database, 2011 Products / services innovation Human capital Customer relationships Brand(s) Business model innovation Technology 71% 66% 52% 43% 33% 30% Human capital is the leading cited source of economic value... ...but, CEOs face significant workforce challenges. The average turnover in the U.S. is 15% per fiscal year.2 Total costs of replacement can reach 200% of an employee‘s annual salary.2 Key sources of sustained economic value1 CEO Study
  6. 6. HR Value StrategicSmarter WorkforceOperational Smarter HR Operations Succession Management Learning & DevelopmentCompensation Management Performance Review Performance Planning Talent Acquisition Workforce Planning Absence Management Payroll Benefits Time & Attendance Scheduling & Staffing Analytics are critical to HR operations and workforce effectiveness
  7. 7. We redefined IBM‘s HR Strategy Thought Leadership Develop deep expertise in business, HR, execution methods and systems thinking to enable IBM‘s ability to produce value for clients, IBMers, investors and communities Analytics Use technologies to capture, analyze and integrate data that will yield rich insights and advance the science of predicting, shaping and adapting to business trends Collaboration Use contemporary technologies and techniques within the HR community to enable seamless global communication to develop, test, refine and implement the best ideas
  8. 8. Time and Resources ValueandImpact Based on : Competing on Analytics, Davenport and Harris Data Management  Consolidation of data  Data quality and accuracy Basic Reporting  Standard reporting that is reasonably automated  ‗Slice and dice‘ data based on standard variables Benchmarking  Key Performance Indicators (KPIs)  Performance Measured against Best Practices Analysis  Multi-dimensional analysis to better understand business challenges Advanced Analytics  Segmentation, Predictive Modeling & Optimization Efficiency Effectiveness Business Impact Business Analytics Model
  9. 9. Enterprise-wideReporting PredictiveAnalytics InternalSurveys ExternalAnalytics Objectives • Integrate BI into HR as an ‗everyday‘ tool • Develop deeper analytic and predictive modelling skills • Develop key enterprise-wide reports and scorecards • Provide better insights from analytics to inform strategy • Grow analytic skills in emerging countries Purpose: To embed a culture of analytics within the HR organization SocialAnalytics IBM Workforce Analytics - a clear purpose…
  10. 10. Agenda  IBM – Who we are and what we do  Workforce Analytics - Background  Analytics in Action  Getting Started
  11. 11. Can attrition be predicted?  Questions  Can we highlight individuals who have the highest propensity to leave an organization?  Can increased compensation reduce the rate of unwanted attrition?  Method  Analysis of 5 years longitudinal data  Compensation & attrition analysis based on historical data  Visualization – use of heat maps Geography Brand Other Type of Hire Education Type Segment and sub segment of population
  12. 12. Can we see attrition risks & reduce unwanted attrition? Retention Case Selection Action Optimization—Identify retention cases and targeted actions to retain them Attrition Which employees are most likely to leave? What kind of actions, programs and investments will reduce attrition in the most effective way? How likely is each person to leave, and why? Attrition Hot Spots Identify high-attrition clusters Derive attrition ―rules‖ Estimate FUTURE attrition Understand response to incentives How well-connected are those employees most likely to leave? What actions will yield the best outcomes? Benefit($k) Cumulative Net Benefit is maximized at $9M… ... yielding an attrition reduction of 2.7 pts
  13. 13. Our results? Attrition can be predicted We can highlight those who have the highest propensity to leave the organization Proactive retention efforts can reduce rates of unwanted attrition
  14. 14. Can we measure Employment Risk?  Problem Statement  Can collective employment risk be quantified?  Can a ‗risk index‘ be built?  Methodology  Statistical analysis of 5 year longitudinal internal and external data  Tens of metrics analyzed for stability to find a suitable set of dependent metrics  Index stabilized over time  Visualization – use of heat maps, indices and maps  Built into BI tools
  15. 15. Predictive Analytics – Headlights for Employment Risk Quality of Life Index Cultural Values (e.g. Hofstede) Span of control Employee Complaints Inflation & Unemployment Surveys Headcount & Voluntary Attrition Employment Regulation Tenure, Band, Rating Political Instability Country Level Volatility Data Is Simulated
  16. 16. Our results? Employment risk can be quantified Usable risk indices can be built to:  Inform & coach  Assist leaders in managing overall enterprise risk posture  Provide insight to HR leaders
  17. 17. Smarter Learning Analytics  Problem Statement  Education performance is judged on volumes, not impact or value  Learning organisations struggle to align with rapidly changing business models  Methodology  Back to basics – why do we provide learning?  Formulation of new metrics, dependencies and relationships  Identification, acquisition and modelling of new datasets  Creation of new reports with optimal visualisation  Standardisation of formatting, branding, platform and access  Democratisation of data!
  18. 18. Smarter Learning Analytics – Evaluation Learner Reaction (Level 1) and Skill Usage (Level 3)  Resource: Free up dedicated FTE and increase self-service  Speed: Reduction of time from training to reporting  Impact: Increase in available analytics; more powerful communication
  19. 19. Smarter Learning Analytics – ROI Return on Investment (Level 4)  Automate: Reduce emails from learning organisation and enable learners to see value  Standardise: Common methodology to provide scalable solution  Impact: Draw direct line of sight between learning and revenue
  20. 20. Smarter Learning Analytics – Alignment Business Impact (Level 5*)  Delivery Excellence:  Identify and reduce factors attributed to learning or skills in troubled projects  Practitioner Utilisation:  Challenge perception that short-term time away from the client harms longer- term practitioner utilisation targets  Grow Talent:  Demonstrate the impact of learning in promotion, progression, attrition and retention
  21. 21. Smarter Learning Analytics - Findings Can we demonstrate the value of Learning? Can we align metrics with current business decisions? In practice:  Which learning activities do we prioritise; and which need revision?  Where are we effectively addressing skill gaps; and where aren‘t we?  Why should we invest in learning; and should we increase investment?
  22. 22. Can we leverage Social ‗Big Data‘ productively?  Problem Statement  Can we see what employees are saying on social media about our company?  Can we get ‗real time‘ feedback on our business from our own people?  Methodology  Consumer insight tool redesigned for employee insight  HRIS combined with social media  Highly visual design  ‗Opt in‘ consent
  23. 23. Analyzing ‗big data‘ created by social interactions → Determine unexpected affinities across multiple analytic dimensions → Discover related topics above and beyond our initial search → Glean employee sentiment across company & by segment → Perform trend analysis of sentiment over time
  24. 24. The Key Advantages of Social Analytics  Hear ‗pulse‘ of the organization  Augment surveys with better understanding of what employees think  Tailor services & programs  Ability to act in nearer real-time  Leverage existing social media footprint  Understand what competitors‘ employees are saying about their employers
  25. 25. Our results? We can see (and use) what employees are saying on social media about our company We can get ‗real time‘ feedback on our business from our own people that provides insight and allows executives to take action
  26. 26. Agenda  IBM – Who we are and what we do  Workforce Analytics - Background  Analytics in Action  Getting Started
  27. 27. Analytics: The New Path to Value: Links to the full study, a 22 minute video and presentation highlighting the key findings ( IBM Institute for Business Value +  Surveyed 3,000 executives, managers and analysts plus extensive interviews  Respondents represent more than 30 industries in 108 countries  Interviews with IBM and MIT thought leaders  Analysis by IBM and MIT teams
  28. 28. Organizational obstacles, not data or financial concerns, are holding back analytics adoption Ability to get the data Lack of management bandwidth due to competing priorities Lack of skills internally in the line of business Lack of understanding how to use analytics to improve the business Culture does not encourage sharing information Ownership of the data is unclear or governance is ineffective Lack of executive sponsorship Concerns with the data Perceived costs outweigh the projected benefits No case for change 38% 34% 28% 24% 23% 23% 22% 21% 21% 15% Primary obstacles to widespread analytics adoption Organizational Data Financial Source: Analytics: The New Path to Value;
  29. 29. Lesson Learned • Cultural shift from data extraction to business analytics • Stakeholders need to set clear priorities for data and analytics • Need a broad range of skills • Simplicity and elegance outweigh ―bells and whistles‖ • Take some risks with new tools – open people‘s minds to the opportunity • If you do what you‘ve always done, you‘ll get what you‘ve always got
  30. 30. Questions?
  31. 31. Thank you! @jmsidhu Please leave feedback: @mtlawrence
  32. 32. Additional IBM Institute for Business Value White Papers a) Business Analytics & Optimization for the Intelligent Enterprise (2009) b) Breaking away with Business Analytics & Optimization (2009) c) Analytics: The new path to value (2010) d) Analytics: The widening divide (2011) e) Analytics: The real world use of big data (2012) f) Analytics: A blueprint for value (2013)