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2013 03-07-culture of analytics-enabled agility


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Presentation given during Oracle Solutions Summit Geneva. Theme: Predictive Analytics in HR - creating a culture of analytics-enabled agility.

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  • 1. See blog for slides: Analytics in HR –Creating a culture ofanalytics-enabled agilityGeneva, 7th March 2013
  • 2. I t d tiIntroductionThe STRIM group of companies The STRIM management sphere system with its seven management principles – grouped into three clusters – is the logical answer to often occuring implementation problems in practice. STRIMacademy guides and provides support to employees during the implementation of strategies and measures. Obtaining acceptance and addressing hesitation are both addressed through effective communication. g STRIMservices aims to have companies focussing on value added activities. Analyses lay the groundwork for monitoring and, required, strategy. and if required adjusting the strategy STRIMconsult translates strategies in actions and aligns the organization as well as leadership- and incentive systems with the strategy strategy. 2
  • 3. IntroductionI t d tiThe Conference Board 3
  • 4. CEO most critical challenges i 2013 CEOs t iti l h ll in Human capital and Operational excellence are the top challenges p g p g Top 5 Strategies to meet the Top 3 ChallengesSource: The Conference Board: CEO Challenge Report 2013 Human Capital being involved 4
  • 5. Source: Predictions for 2013. Corporate Talent, Leadership and HR – Nexus of Global Forces Drives New Models for Talent.Josh Bersin, January 2013 5
  • 6. Vi View on HR The core characteristics of the HR function serve to perpetuate many of today s current challenges Low strategic license Not having the roles or the bandwidth to be strategic Few „home-grown“ strategic capabilities, difficulty attracting high-caliber talent, and ineffective tools to assess them Ultimate ownership Convincing business tricky case & burning Lack of ownership or platform elusive peer mindset and p Limited use of forward- forward …………………. acceptance/willingness looking metrics that would to plod along vs. leap reduce reactive measures frog Difficulty in communica- Lack of a budget to ting convincingly – in g gy innovate terms expected of a strategic business partnerSource: McKinsey & The Conference Board Research, 2012 6
  • 7. Vi View on HR C-Level and BU leaders view HR as lagging in strategic performance 3,8 / 6 , 4/6Source: McKinsey & The Conference Board Research, 2012Legend: Rating on scale of 1-6, 1 = “needs improvement”, 6 = “best practice” 7
  • 8. Vi View on HR A minority of CEOs get comprehensive reports on their workforce Percentage of CEO P t f CEOs Percentage of CEOs who 100% believe the relevant information is important or very important y 80% Information Gap: CEOs believe 60% information is Don´t receive information important but don t don´t receive comprehensive reports 40% Not adequate 20% Adequate but would like it now 0% Costs of Return on Assessments Labour Employees Staff employee turnover investment on human of internal advancement costs views and needs productivity Information received is capital comprehensiveSource: PwC Saratoga: Key trends in human capital 2012. A global perspective 8
  • 9. Predictive Analytics inP di ti A l ti i HRStages of development Stages Focus F Kind f Information Ki d of I f ti HR quantitative & qualitative master data: Reporting oriented towards headcount, FTE, pay and other cost recording the past categories, competencies, g , p , engagement, retention, etc. connection of deliverables with corporate relating actual goals such as quality, innovation, productivity, risk, etc. explicitly & detailed relationships among data without giving comparing & (descriptive meaning to the patterns (exploratory); understanding analytics, trends from the past (risky!); tie HR benchmarking) metrics to the business HR Analytics comparisons between what happened related to the yesterday to what will probably happen future predicting tomorrow, meaning to the patterns (prescriptive Business observed in descriptive analysis; analytics) Intelligence focus on (a few) leading indicators 9
  • 10. P di ti A l ti i HR Predictive Analytics in Map of causalities (learning and growth perspective) Managerial g 0,506 Retention of Key Leadership People Alignment 0,442 Failure and Availability Risk Risk R2=68,2% 0,530 0,326 0,360 Human Human Relational Training Capital Capital C it l Capital C it l Effectiven. 0,751 0,307 R2=28,5% 0,358 -0,337 0,475 Structural Occupational Capital C it l Skill Risk 0,734 Employee 0,491 0,327 Business Employee Knowledge Engage- Perfor- Satisfaction Generation ment mance 0,543 0,439 R2=44,1% 44 1% 0,456 0,429 Strategy 0,394 Knowledge Integrity Execution* Integration -0,372 Risk Employee Motivation 0,430 0 430 0,262 0 262 Resignation Risk Motivation Risk 0,285 -0,233 Human Value Knowledge Capital Alignment Sharing Depletion R2=28,5% 28 5%Remark: Referring to Nick Bontis and Jac Fitz-Enz: Intellectual Capital ROI, 2010* for further remarks: Mark A. Huselid, Brian E. Becker, Richard W. Beatty: The Workforce Scorecard, 2005 10
  • 11. P di ti A l ti i HR Predictive Analytics in HC RoI measurement Levels of measurement Revenue Revenue by business unit Revenue by country/region Revenue by product line yp Non-wage costs 1 HC RoI = 1,28 2 HC RoI = 1,22 Material costs Facilities and overhead costs 5% 5% Costs of outsourced activities … Revenue - Non Wage Costs Non-Wage HC RoI = = 1,13 Average remuneration Av. Remuneration x Number of FTEs Salary and wage levels Performance-related pay 5% … 3 HC RoI = 1,19 Full-time equivalents Full-time v part-time p Temps and casualsRemark: Anonymised project example of an international transport and logistics company Contract workers … 11
  • 12. P di ti A l ti i HR – T St t i Predictive Analytics in Top Strategies Effective Leaders Measurement M t Measurement M t 2006 2011 Levels Category Status Status Eight core practices 0 Inputs/Indicators 100% 100% 1 Reaction 92% 89% Ask, „What needs to be 2 Learning 48% 59% 3 Application 11% 34% done? done?“ 4 Impact 8% 21% Ask, „What is right for the 5 RoI 2% 11% enterprise?“ Develop action plans Take responsibility for decisions Take responsibility for communicating Are focused on opportunities rather than on problems Run productive meetings Think and say „we“ rather than „I.“Source: Jack Phillips, Patricia Pulliam Phillips, Rebecca L. Ray: Measuring Leadership Development. Quanify YourProgram´s Impact and RoI on Organizational Performance, 2012. 12
  • 13. P di ti A l ti i HR – T St t i Predictive Analytics in Top Strategies Raising Engagement (1/2) Six successful traits Drivers of Engagement Trust and integrity Nature of the job Line of sight between individual performance and company performance Career growth opportunity Pride about the company Coworkers/team members E l Employee d l development Personal relationships with one s manager Pay fairness Personal influence Well-beingSource: PwC Saratoga. Managing people in a changing world. Key trends in human capital, a global perspective, 2010.The Conference Board. Linkage of Engagement Drivers to Common Challenges, 2013. 13
  • 14. P di ti A l ti i HR – T St t i Predictive Analytics in Top Strategies Raising Engagement (2/2) Effect of motivation and hygiene factors on engagement and contribution Culture of Engagement Set the tone at the top; align words and actions Communicate the importance of teamwork and collaboration Increase company events to mix groups together Launch La nch peer coaching/ mentoring programs Financial rewards/peer recognition L Launch cross f h i l k functional task forces to break down silos Use technology to enable sharing/access of information Adapt performance management systems to hold managers and employees accountable for successSource: Scarlett Surveys International, 2008.The Conference Board. Employee Engagement in a VUCA* World, 2011. (* Volatile, Uncertain, Complex, Ambiguous) 14
  • 15. E idEvidence-Based M B d Management tConnect scientific coherences with company-specific procedures Capital „E and small „e E“ e“ Capital „E“ external evidence Identification of general Identification of specific causal relations (theories) practices (instruments) sound scientific evidence generalizable cause effect cause-effect relationships Science Practice Small „e“ internal evidence organization-specific Meta- Case evidence analyses study data that are systematically collected in a particular Controlled Systematic Systematic Expert organization and situation laboratory/field reviews evaluation survey to enable local evidence- experiments based decisions Comprehensive Systematic correlation studies Follow-up the interaction creates a collective intelligence 15
  • 16. Predictive Analytics inP di ti A l ti i HRHC RoI analysis 16
  • 17. Cl i R k Closing Remarks Our approach in Predictive Analytics in HR Plan Focus on leading indicators, corresponding benchmarks Scan Commitment on target corridors and timelines Transparency on the data collection Capability planning and gap-analysis – associated with process, what formulas are being used,why workforce categories in terms of valued capabilities the data matter to the operation Succession planning, scenario planning, forecasts, etc. Human-capital Human capital facts: quantitative and qualitative information Produce Internal analyses: segmen- tation of HR data, core Execution of measures competencies, etc. p , identified, identified new methods and External analyses: target guidelines as well as IT groups, PESTEL, etc. systems and warehouses HC RoI Analysis: Process optimization applied Capital „E“ and small „e“; to hiring, compensation, impact on bus. performance development or retention HR service delivery: service integration, outsourcing and Predict offshoring HR meas rement foc sed on measurement focused Rethink leading indicators; employee engagement, knowledge value-adding results management, turnover, executive reward, etc. Talent value model: „Why do Reassess the suitability of leadership development programmes employees choose to stay?“ and overall effectiveness of learning and development Recommendations for policy adjustmentsRemark: Referring to Fitz-Enz, J.: The New HR Analytics: Predicting the Economic Value of Your Company´s Human Capital Investment,2010 ( HCM:21) and to Davenport, T.; Harris, J.; Shapiro, J.: Competing on Talent Analytics, HBR October 2010. 17
  • 18. C t tPContact Person ==> F slides, visit: htt //bl > For lid i it ti /? 385 President and CEO at STRIMgroup AG in Zurich Senior Fellow Human Capital at The Conference Board in New York Member in several corporate and educational networks Lecturer in the Master degree course Human Capital Management at the Constance University of Appl. Sciences Author and publicist to strategy and human resources issues Selected professional positions: Head of Global HR Analytics, Gütschstrasse 22 845 Third Avenue Deutsche Bank AG and AG, CH-8122 Binz (Zürich) New York, NY 10022-6600 York Senior Manager hrs at Telefon: +41 (0)43 366 05 58 Telefon: +1 410 433 660 558 PricewaterhouseCoopers AG. 18