2013 03-07-culture of analytics-enabled agility
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See blog for slides: http://blog.strimgroup.com/?p=385
Predictive Analytics in HR –Creating a culture of
Geneva, 7th March 2013
analytics-enabled agilityGeneva, 7 March 2013
I t d tiIntroductionThe STRIM group of companies
The STRIM management sphere system with its sevenmanagement principles – grouped into three clusters – is thelogical answer to often occuring implementation problems in practice.
STRIMacademy guides and provides support to employeesSTRIMacademy 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 if required adjusting the strategyand, if required, adjusting the strategy.
STRIMconsult translates strategies in actions and aligns the organization as well as leadership- and incentive systems with the strategy
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the strategy.
I t d tiIntroductionThe Conference Board
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CEO t iti l h ll i 2013
Top 5 Strategies to meet the Top 3 Challenges
CEOs most critical challenges in 2013Human capital and Operational excellence are the top challenges
p g p g
4Source: The Conference Board: CEO Challenge Report 2013
Human Capital being involved
5Source: Predictions for 2013. Corporate Talent, Leadership and HR – Nexus of Global Forces Drives New Models for Talent.Josh Bersin, January 2013
Vi HRView on HRThe core characteristics of the HR function serve to perpetuate many of today s current challenges
Low strategic licenseNot having the roles or the bandwidth to be strategic
Few „home-grown“ strategic capabilities, difficulty attractinghigh-caliber talent, and ineffective tools to assess them
Ultimate ownershiptricky
Lack of ownership orpeer mindset and
Convincing businesscase & burningplatform elusive
Limited use of forward-………………….pacceptance/willingnessto plod along vs. leapfrog
Lack of a budget to
Limited use of forwardlooking metrics that wouldreduce reactive measures
Difficulty in communica-ting convincingly – in
innovateg g y
terms expected of a strategic business partner
6Source: McKinsey & The Conference Board Research, 2012
Vi HRView on HRC-Level and BU leaders view HR as lagging in strategic performance
3,8 / 6,
4 / 6
7Source: McKinsey & The Conference Board Research, 2012Legend: Rating on scale of 1-6, 1 = “needs improvement”, 6 = “best practice”
Vi HRView on HRA minority of CEOs get comprehensive reports on their workforce
P t f CEOPercentage of CEOs
100%Percentage of CEOs whobelieve the relevant information is important orvery important
80%
60%
Information Gap:CEOs believeinformation isimportant butdon´t receive
Don´t receive information
y
40%
don t receivecomprehensive
reports
Not adequate
20%
0%
Adequate but would likeit now
Information received iscomprehensive
0%
Costs ofemployeeturnover
Return oninvestmenton human
capital
Assessmentsof internal
advancement
Labourcosts
Employeesviews and
needs
Staffproductivity
8Source: PwC Saratoga: Key trends in human capital 2012. A global perspective
P di ti A l ti i HR
Stages Ki d f I f tiF
Predictive Analytics in HRStages of development
Stages Kind of InformationFocus
oriented towardsthe past
quantitative & qualitative master data:headcount, FTE, pay and other cost
categories, competencies, recordingHRReporting
actualconnection of deliverables with corporate
goals such as quality, innovation, relating
the past g , p ,engagement, retention, etc.
explicitly & detailed(descriptive
relationships among data without givingmeaning to the patterns (exploratory);comparing &
productivity, risk, etc.
related to thefuture
comparisons between what happenedyesterday to what will probably happen
analytics, benchmarking)
trends from the past (risky!); tie HRmetrics to the business
understanding
HR Analytics
future(prescriptive
analytics)
tomorrow, meaning to the patternsobserved in descriptive analysis;
focus on (a few) leading indicators
predictingBusiness Intelligence
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P di ti A l ti i HRPredictive Analytics in HRMap of causalities (learning and growth perspective)
Managerial Retention of Key
0,506gLeadership
Training HumanC it l
RelationalC it l
Human Capital
of Key People
0,442
0,530 0,326 0,360
R2=68,2%Failure and Availability RiskAlignment
Risk
Training Capital Capital
StructuralC it l
Capital Effectiven.
R2=28,5%0,7510,358
0,307
0,475 -0,337OccupationalSkill RiskCapital
Business Perfor-mance
KnowledgeGeneration
EmployeeEngage-
ment
EmployeeSatisfaction
R2 44 1%
0,4910,734 0,327
0,543 0,439
Skill Risk
EmployeeMotivation
StrategyExecution*
KnowledgeIntegration
R2=44,1%0,456 0,429
0,394
0 430 0 262
-0,372IntegrityRisk
Resignation
Value Alignment
KnowledgeSharing
Human Capital
Depletion
R2 28 5%
0,430
0,285 -0,233
0,262
Motivation Risk
ResignationRisk
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R2=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
P di ti A l ti i HRPredictive Analytics in HRHC RoI measurement
Levels of measurement RevenueRevenue by business unitRevenue by country/regionRevenue by product line
Levels of measurement
Non-wage costsMaterial costs1 HC RoI = 1,28 2 HC RoI = 1,22
y p
Facilities and overhead costsCosts of outsourced activities…
Revenue Non Wage Costs
5%5%
Average remunerationSalary and wage levelsPerformance-related pay
HC RoI = = 1,13Revenue
Av. Remuneration
Non-Wage Costs
Number of FTEs
-
x
5%
Full-time equivalentsFull-time v part-time
…5%
3 HC RoI = 1,19
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pTemps and casualsContract workers…
Remark: Anonymised project example of an international transport and logistics company
P di ti A l ti i HR T St t iPredictive Analytics in HR – Top StrategiesEffective Leaders
M t M t 2006 2011Eight core practices
Ask, „What needs to bedone?“
MeasurementLevels
MeasurementCategory
2006Status
2011Status
0 Inputs/Indicators1 Reaction2 Learning
100%92%48%
100%89%59% done?
Ask, „What is right for theenterprise?“
Develop action plans
3 Application4 Impact5 RoI
11%8%2%
34%21%11%
Take responsibility fordecisions
Take responsibility forcommunicating
Are focused on opportunities rather thanon problems
Run productive meetings
Think and say „we“ ratherthan „I.“
12Source: Jack Phillips, Patricia Pulliam Phillips, Rebecca L. Ray: Measuring Leadership Development. Quanify YourProgram´s Impact and RoI on Organizational Performance, 2012.
P di ti A l ti i HR T St t iPredictive Analytics in HR – Top StrategiesRaising Engagement (1/2)
Six successful traitsDrivers of Engagement
Trust and integrity
Six successful traits
Nature of the job
Line of sight betweenindividual performance andcompany performance
Career growth opportunity
Pride about the company
Coworkers/team members
E l d lEmployee development
Personal relationships withone s manager
Pay fairness
Personal influence
Well-being
13Source: 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.
P di ti A l ti i HR T St t iPredictive Analytics in HR – Top StrategiesRaising Engagement (2/2)
Effect of motivation and hygiene factors on engagementCulture of Engagement
Set the tone at the top; alignwords and actions
Communicate the importance
Effect of motivation and hygiene factors on engagementand contribution
Communicate the importanceof teamwork and collaboration
Increase company events tomix groups together
La nch peer coaching/Launch peer coaching/ mentoring programs
Financial rewards/peerrecognition
L h f i l kLaunch cross functional taskforces to break down silos
Use technology to enablesharing/access of information
Adapt performancemanagement systems to hold managers and employeesaccountable for success
14Source: Scarlett Surveys International, 2008.The Conference Board. Employee Engagement in a VUCA* World, 2011. (* Volatile, Uncertain, Complex, Ambiguous)
E id B d M tEvidence-Based ManagementConnect scientific coherences with company-specific procedures
Capital E“ and small e“ Capital „E“external evidence
sound scientific evidence
generalizable cause effect
Identification of general causal relations (theories)
Capital „E and small „e
Identification of specific practices (instruments)
generalizable cause-effectrelationships
Small „e“
Science Practice
internal evidence
organization-specificevidence
data that are systematically
Meta-analyses
Casestudy data that are systematically
collected in a particular organization and situation to enable local evidence-based decisions
Controlledlaboratory/fieldexperiments
Systematicreviews
Systematicevaluation
Expertsurvey
Comprehensivecorrelation studies
SystematicFollow-up the interaction creates a
collective intelligence
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P di ti A l ti i HRPredictive Analytics in HRHC RoI analysis
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Cl i R kClosing RemarksOur approach in Predictive Analytics in HR
Scan Focus on leading indicators, corresponding benchmarks
Plan
Transparency on the data collectionprocess, what formulas are being used,whythe data matter to the operationHuman capital facts: quantitative
ScanCommitment on target corridors and timelinesCapability planning and gap-analysis – associated withworkforce categories in terms of valued capabilitiesSuccession planning, scenario planning, forecasts, etc.
Human-capital facts: quantitative and qualitative informationInternal analyses: segmen-tation of HR data, corecompetencies, etc.
Execution of measuresidentified new methods and
Produce
p ,External analyses: targetgroups, PESTEL, etc. HC RoI Analysis: Capital „E“ and small „e“;
identified, new methods andguidelines as well as IT systems and warehousesProcess optimization appliedto hiring, compensation,
impact on bus. performance development or retentionHR service delivery: serviceintegration, outsourcing andoffshoringHR meas rement foc sed on
PredictHR measurement focused on value-adding resultsTalent value model: „Why do employees choose to stay?“
Rethink leading indicators; employee engagement, knowledgemanagement, turnover, executive reward, etc.Reassess the suitability of leadership development programmesand overall effectiveness of learning and development
17Remark: 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.
Recommendations for policy adjustments
C t t P > F lid i it htt //bl t i /? 385Contact Person ==> For slides, visit: http://blog.strimgroup.com/?p=385
President and CEO at STRIMgroup AG in Zurichin Zurich
Senior Fellow Human Capital atThe Conference Board in New York
Member in several corporate andMember 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, Deutsche Bank AG and
845 Third AvenueNew York NY 10022-6600
Gütschstrasse 22CH-8122 Binz (Zürich)Deutsche Bank AG, and
Senior Manager hrs atPricewaterhouseCoopers AG.
New York, NY 10022-6600
Telefon: +1 410 433 660 558volker.mayer@conferenceboard.org
CH-8122 Binz (Zürich)
Telefon: +41 (0)43 366 05 58volker.mayer@strimgroup.com
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