BigData in Human Resources - Making it Happen1. BigData in HR
Creating a New Science of Human Resources
Josh Bersin
President and CEO
October, 2012
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2. About Us
Who We Are
• Delivering the proven people strategies that
leaders and their organizations need to deliver
exceptional business results.
Practices Human
• Enterprise Learning Resources
• Human Resources
• Talent Acquisition Leadership
• Talent Management Development
• Leadership & Succession
Learning &
Services Development
• WhatWorks® Membership
• Research-based consulting services Talent
Acquisition
• IMPACT: The industry’s premiere conference
on the Business of Talent, April 22-24, 2013
• BersinInsights® Talent
• BersinBasics® Management
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3. Serving the World’s Most Successful Organizations
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4. Our Research
8 years of research into the
measurement, operations of L&D,
leadership, recruiting and HR
Education and best-practices on “how
to build an analytics function”
Detailed information on tools and
vendors
Assessment services and consulting
to help you understand where you are
and how to advance your program
Delivered through our WhatWorks®
Membership
http://www.bersin.com/hrbigdata2012
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5. Agenda
The Talent Analytics Priority
Defining BigData in HR
Guidelines for Success
The Four Stages
Preparing Your Team
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6. BigData in US Business
14 Exabytes of Data in US
6 billion photos uploaded to Corporations (1 Exabyte is
Facebook every month 40,000X the library of congress)
YouTube uploads more content
each day than all networks combined
We are at an inflexion point:
Organizations
US Businesses will be short
140,000-190,000 analytic positions
whichyears to leverage big data will far
in the next three
learn
1.5 million more data-savvy
outperform those who do needed to leverage
managers not.
big
- McKinsey data in the United States
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7. The Global Talent Imbalance
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8. 2012 Business Environment
Globalization, Innovation, Cost Containment
What Are Your Organization’s Top Business Challenges for 2012?
35% of organizations see the need to +60% Increase
accelerate growth and globalize – +10% Increase
the
highest level in four years…. yet…
51% struggle to hire the right talent,
despite high unemployment…
+70% Increase
© Bersin & Associates, Corporate TalentWatch®
Research, Senior HR and Business Executives, ■ Dec. 2011 ■ Aug. 2012
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9. #1 Risk
Loss of Customers
#2 Risk
Talent and Skill
Shortages
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10. Increasing Specialization
Expertise drives competitive
Top
advantage Management
Specialization improves Senior
quality and reduces cost Management
The
Deep skills developed Experts Middle
Management
through “deliberate practice”
and reinforcement Senior Specialists
First Line
Management
Deep skills come from a range
Functional Specialists / Front-Line Employees
of developmental experiences
We need career development Back Office, Operational, Contingent Employees
in all critical job roles
High Performing Organizations Understand This
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11. How the Workforce has Changed
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12. Young, Diverse Workforce ….
Has Created Challenges in Engagement
In 2012, 32% of employees are “planning on leaving”
their employers, vs. 19% two years ago
Only 55% of employees believe their employer is a sound
“long term” place to work vs. 65% over last three years.
People under the age of 35 are twice as likely to be looking
for new work as older workers.
- Mercer October 2011, Towers Watson July 2012
“By 2013, 47% of employees will be those born after 1977.
-- US Census Bureau
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13. Analytics is Definitely Coming to HR
The Evolution of Business Analytics in other Functions
Finance & Logistics Customer & Marketing Talent & Leadership
The Waves of
Integrated ERP Predictive Predictive
Business Analytics and Financial Customer Talent Models
Analytics Behavior - CRM HR Analytics
Integrated Web Behavior Business-driven
Supply Chain Analytics Talent analytics
1980s Customer Integrated
Financial and Segmentation Talent Management
Budget Analytics Shopping Basket Workforce Planning
Recruiting,
Logistics and Customer
Learning,
Supply Chain Analytics – CRM
Performance
analytics (Data Warehouse)
Measurement
The Industrial The Financial The Customer The Talent
Economy Economy Economy and Web Economy
Conglomerates Customer Segmentation Globalization, Demographics
Steel, Oil, Railroads
Financial Engineering Personalized Products Skills and Leadership Shortages
Early 1900s 1950s-60s 1970s-80s Today
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14. Agenda
The Talent Analytics Priority
Defining BigData in HR
Guidelines for Success
The Four Stages
Preparing Your Team
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15. What is Predictive Analytics?
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16. Typical People Decision-Making
How do baseball teams find great
players?
They look for “traditional performance
characteristics”
• Speed and athleticism
• Ability to hit, home runs
• Appearance, confidence
• Batting average (ie. GPA)
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17. Data Proves Different Factors Matter
What is the most important baseball
statistic that drives a winning team?
• Team batting average?
• Runs batted in (RBI)?
• Pitcher’s earn run average (ERA)?
On Base Percentage (OBP)
Not a Characteristic of the Player
(candidate)
It is a characteristic of how they
actually perform
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18. How do Companies Hire People?
2/3 of hiring done without any significant assessment
% of Organizations Which Regularly Use Following Assessment Practices
Background checking: 79%
2/3 use no real
Managerial interviews: 64% assessment process
Interview training: 47% at all … leaving
the process to
Behavioral assessments: 34% hiring managers
or recruiters
Reference calls: 32%
Skills-based assessments: 25%
Bersin & Associates High-Impact Talent Acquisition
Study, Fall 2010, 158 organizations responded
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19. This Science is Coming to HR
Definition of “Science”:
“Systematic knowledge of the world
gained through observation
and experimentation.”
What is Not Science
Making talent decisions on the basis of
“gut feel,” “beliefs,” or “philosophies.”
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20. Insurance
Company
Big Insurance
A $33 billion insurance company has developed a
behavioral assessment based on a set of beliefs held by
the top executives
Top sales people need college degrees from top rated
schools, they should have good grades, and they should
have experience selling high value products.
But the data proves otherwise.
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21. Insurance
Company
Results of Data Analysis
21
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22. Insurance
Company
Data Showed Six Things Matter:
Very Highly Correlated with Success
1. No typos, errors, grammatical mistakes on resume. The Belief
2. Did not quit school before obtaining some degree
3. Had experience selling real-estate or autos System
4. Demonstrated success in prior jobs
5. Ability to succeed with vague instruction Was Wrong
6. Experience planning time and managing lots of tasks
Within six months
of implementing a
What Did NOT Matter new screening
process
• Where they went to school revenues went
• What grades they had up by $4 million
• The quality of their references
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23. What is Measured Today
Source: Bersin & Associates 2012
High-Impact Learning Organization® (HILO)
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24. Business Value
Beliefs
Gut Feel
Reactive
Checks
Ad-Hoc and
Ongoing Reports
Dashboards and
Reporting
Benchmarks
Correlations
Simulations
Analytics
Predictions
Causal Modeling
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Journey into Data-Driven Decisions
Page 24
25. Agenda
The BigData Priority
Why Talent Analytics
Guidelines for Success
The Four Stages
Final Thoughts
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26. Focus on Business Problem, not Data
Business problem first, then focus on arranging and using the data
Business
Data
Problem
Why is turnover high in some areas?
What drives sales productivity?
Why is their fraud in some branches?
What sales or service processes drive account renewal?
What is the impact of training on long term productivity?
How do we assess the “right” candidates for sales?
What will our talent gaps be next year based on retirement?
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27. Measurement as a Process
Why you must build an analytics function, not a set of tools
Measurement as Process, not a Project
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28. Four Keys to Success
1. Reliable
• Data must be “true” and validated over time
• Seasonal changes, organization changes, must be handled
2. Actionable
• Reports must be detailed enough to let managers take action
• Drill, filter, group data so it is relevant and meaningful
• Goal is a “business-driven” dashboard (red/yellow/green)
3. Scalable
• The process of collecting and analyzing data must scale
• Your outputs must be useful for people at all levels
4. Understandable
• People must be able to visualize and understand what you find
• Line managers, executives, and employees must use the data
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29. But What do you Measure?
Supply, Talent Workforce Plan Open Internal
Demand Demand Plan Scenarios Positions Mobility
Recruiting Employer Time and Cost Onboarding
Onboarding Turnover
Brand, Alumni To Hire Effectiveness
Performance Ratings Managerial Development 9-box
Succession Rankings Grievances Plans Grids
Skills Succession Promotional Seniority
Readiness
Certifications Depth Readiness Skills Depth
Succession 360 and Other Successor Proficiency vs.
Leadership
Depth Assessments Readiness Leadership Comp.
Budget by Comp by Perf-Pay Compa
Compensation
Group level/perf differentials Ratios
Employee Employee Innovation Employee
Engagement
Opinion Engagement Programs Value Prop
Retirement Tenure Age, geography, Span of
Demographics
Projections Education, etc. Skill level control
HR/L&D HR/L&D Systems Satisfaction
Spending
Spending Staff Allocation Usage/$ With HR svc.
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30. Types of Measures you Need
Why the analytics team must understand all areas of talent management…
Workplace Leadership Individual
Management
Environment & Culture Characteristics
Compensation Clarity of goals Leadership Education
Benefits Coaching values Personality
Safety Job fit Clarity of mission Skills
Tools Team structure Type of leader Training
Information Span of control Style of leader Experience
Physical comfort Personal Values
Location relationship Life situation
Availability ….
Org design ….
Org “culture” ….
Org “vision” “engagement” is often a surrogate
….
for all this complexity
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31. Bersin & Associates HR Measurement Framework
HR, Recruiting, and L&D Organizational Talent & Leadership Supply Scenario Manager and
Effectiveness Readiness Workforce Planning & Future Planning Employee Dashboards
Workforce Performance
(How people impact the business)
Financial results by person and unit I Net Promoter scores I Performance and Goal attainment I Innovation/Patents | Product measures
Statistical and Data Analysis Skills, Collaboration with other Analytics Teams
External Data and Benchmarks
Measurement Process & Skills
Talent Acquisition
Workforce Measures I TM Measures I People Performance
Data Cleansing | Data Dictionary | Analytic Measures
(How well you are reaching candidate audiences)
Internal HR Measures I HR Program Effectiveness I
Employment Brand | Talent Pipeline | Time and Cost to Fill | Quality of Hire
(External Benchmarking of all HR Measures)
Integration of new sources and systems
Data governance and stewardship
Capabilities, Talent, & Leadership
(Capabilities, leadership, progression, career, talent.)
Leadership Pipeline | HiPOs I Stack Rankings I Pivotal Role Pools I Mobility | Compa Ratios I Rewards I Skill gaps I Certifications | Readiness I Turn-
over I 360’s I Technical Skill Pools I Career Progression | Development Plans | Succession Depth and Pools
Engagement & Culture
(Employee engagement, wellness, and satisfaction – including external view)
Engagement I Management Grievances I Turn-Over I Referral Rates I Exit Interviews I Development Plans | Diversity and Inclusion
HR Programs & Processes
(Status and maturity of HR Processes )
Learning Program Effectiveness I Total Rewards Effectiveness I Performance Management Effectiveness I On-Boarding Time to Productivity I Recruiting
Effectiveness and Effieicney I Candidate Pipeline I Total Rewards
Workforce Demographics
(Facts and statistics about employees, alumni, and contractors)
Payroll and Benefits | Demographics I Background I Experience I Tenure I Organization Structure I Spans of Control I
Workforce Planning Performance & Talent Learning Management Compensation Third party
Applicant Tracking |
System System System System(s) data:
Scenarios, talent supply, demand
Recruiting System Performance, development planning, Learning, certification, skills delivery, Salary, benefits, budget, bonus and assessments,
Applicant, source, recruiting data employee
org charting succession, talent pool data content, learning organization data comp related data, payroll feeds
engagement,
external brand,
HRMS’s – Payroll and Employee Demographic and System of Record Data social networks
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32. Traditional Data Architectures
• Dynamic Aggregation
• Drill up, down
• Ad Hoc Filtering
• Sorting and Subtotaling
All this complexity is now moving to • Graphing
“Cloud-Based” analytics systems. •
•
Subject Area Analytics
Very fast response
Operational Reports • Updated nightly
Dashboards and
(Crystal)
These new solutions capture data
Advanced Reports
from your systems
and deliver reporting and analysis tools
to you without significant IT support.
OLAP
Operational Data Warehouse Multi
HRMS or Or Analytics Dimensional
LMS Application Database
Turnkey ETL Process Cube
Database Server (Extract/Transform/Load) Generation OLAP Database Server
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33. Integrated Systems Now a Top Priority
BigData Projects Demand It
Are you willing to sacrifice features for an integrated HR solution?
2011 2012
Not
Not
Sure, 19%
Sure, 21%
Yes, 33% Yes, 45%
No, 36%
No, 46%
Bersin & Associates Talent and HR Systems Study, 2012 and 2011
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34. Huge Market for Solution Providers
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35. Agenda
The BigData Priority
Why Talent Analytics
Guidelines for Success
The Four Stages
Final Thoughts
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36. Talent Analytics Maturity Model®
Level 4: Predictive Analytics
Development of predictive models, scenario planning <5%
Risk analysis and mitigation, integration with strategic planning
Level 3: Strategic Analytics
Segmentation, statistical analysis, development of “people models”; <10%
Analysis of dimensions to understand cause and delivery of actionable solutions
Level 2: Proactive – Advanced Reporting
Operational reporting for benchmarking and decision making >20%
Multi-dimensional analysis and dashboards
Level 1: Reactive – Operational Reporting
Operational reporting for measurement of efficiency and compliance >60%
Data exploration and integration, Development of data dictionary
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37. How HR Data is Likely Organized
Recruiting and Performance
Comp and
Workforce Succession
Benefits
Planning Engagement
HRMS HR
Learning
Employee Operations
& Leadership
Data
Your goal is to integrate this information, over time,
into a credible, actionable, scalable, understandable
Talent Analytics function… one which delivers relevant
Information, models, and tools to line leaders and executives
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38. Key to Success: WhatWorks®
Developing Credibility
Strong Relationship with IT
Sharing Experience across analytics teams
Patience to validate data before it is shared
Multi-year analysis to experience seasonal trends
Need to present findings in an understandable way
Skills in visual design and presentation
Focus on business solutions, not HR solutions
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39. Examples of Breakthrough Solutions
Major Retailer developed integrated “people model” to correlate
relationship between engagement, rewards, leadership
capabilities, tenure, skills and revenue.
Major Payroll Provider statistically validated 30+ factors in recruiting
which led to 20%+ improvement in sales performance and completely
revamped recruiting process
Major Food Service Company identified key drivers of account
renewal and upgrade and developed statistically valid measures which
have been used to create company-wide dashboard which measure
risk on a weekly basis
Major Retail Bank correlated dozens of workforce measures against
engagement and branch financials to develop risk management
dashboard for small and large branches
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40. Agenda
The BigData Priority
Why Talent Analytics
Guidelines for Success
The Four Stages
Keys to Success
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41. Data Analysis Skills Important to HR
Top HR Skills | Capabilities Correlation
Business Partner relationship management .52
Managing and leading people .51
Coaching and Mentoring .49
Change management and communications .48
Analyzing and interpreting data .47
Knowledge Sharing and expertise directories .47
Managing your Personal Development .45
Top Organizational | Managerial Capabilities
Developing a high performance culture .77
Developing a strong learning culture .77
Developing strong talent segmentation .74
Developing high levels of career development and mobility .73
Attracting and selecting the right talent .64
Measuring business impact of HR .64
Developing high levels of engagement .54
Giving people honest feedback .49
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42. HR Organizations are Not Ready
What percentage of HR
organizations do you believe
feel have strong skills in data
analysis and interpretation?
6% Rate themselves “High”
56% - “Poor”
Bersin & Associates
High-Impact HR Organization® 2011
and 2010, n=741 organizations
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43. Large Retailer
The Evolution of Data Skills and Competencies
2007 - 2008
Solid Understanding of HR; I/O Psychology Degree;
Strong Communication & Interpersonal Skills;
Employee Research Background;
Detail Oriented ; Project Management
Qualitative Research Design & Analysis; HRIS; SPSS
2009 - 2010
Business Acumen; HR, Finance, Economics Degree; Consulting & Presentation Skills;
Quantitative Research Design & Analysis Analytical Curiosity; Problem Solving;
Passion for Data & Analytics; Strong Technical skills Collaborative; Teamwork; Networking Skills
2011 - 2012
Advanced Statistics & Social Research Acumen; Critical Thinking;
Engineering Degree; Story Telling; Data Visualization;
Customer Research Background; Statistics & Data Mining Ability to see data, and decipher insights
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44. Retailer
An Evolved Organization
VP Human Capital
Analytics
Director Director
Workforce Manager HR Brand
Org Diagnostics &
Analytics & Content
Design
Research
(2) Manager Manager Manager Business
Sr. Consultant Workforce Employee Learning Operations
ODD Analytics Research Analytics Specialist
Program Manager Consultant
(2) Analyst Employee
Learning
Sr. WFA Analyst Research
Measurement
Analyst
Learning Analytics
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45. Retailer
The Modeling Journey…
Employee Employee
2008 Engagement
Model
Segmentation
LVI
Learning
2009 Diversity &
Inclusion
Leadership
Learning & Employee
2010 Professional
Development
Lifecycle
Research
HR
Scorecards
Reactive
Analytics
Human
Company Employee Proactive &
2011 Health
Pentagram
Research
Cohorts
Capital
Executive
Dashboard
Exploratory
Analytics
Enterprise
2012 Measures of
Success
Talent
Change
Adoption
Predictive
Analytics
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46. Conclusion
BigData in HR is has become a
business imperative
Tools are not the answer, but
integrated data is needed
Analytics is a journey, it is a process
not a project
Talent analytics will integrate all the
functions within HR, and also across
the business
Expertise and patience is key, but
focus on key business problems first
http://www.bersin.com/hrbigdata2012
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Notes de l'éditeur So we’ve talked about globalization, specialization, youth, and engagement. How are these workforce changes affecting top business leaders? The answer is, they are very worried.Late last year Lloyds of London asked 500 CEO’s to rank their top 100 business risks for the coming year.The #1 risk they stated is loss of customers, which I will explain in a few minutes. But the #2 risk they state is talent and skills shortages.This means skills shortages are a larger risk than sovereign debt, currency devaluation, cyber terrorism, flood, earthquake, raw material shortages, and 90 other areas of risk. We really are in a global skills and talent crisis, and it appears to be getting worse. And the world has also become much younger. By the end of 2017, nearly half the workforce will be under the age of 35. This alone is transforming our organizations.Young people not only bring new ideas, but they also bring a whole new way of communicating. IDC now believes that Text Messaging, Facebook, and Twitter have already replaced email as the #1 way people communicate. We can expect this shift to move much faster into the future.And… young people also bring new expectations and a different set of demands from employers. They want meaningful work, and they are not afraid to switch jobs. Nearly a third of young people are planning on switching jobs once the recession is over, and only 55% think that their employers are a good long term place to work.So now we have to focus on engaging people in work, to drive both job satisfaction and what we call discretionary effort. Discretionary effort is what makes you competitive.Brenda Kowske and Charlie Goretskiwill be launching our new engagement research this week. Our research shows that there are many models for engagement, but ultimately it is one of the most important assets you have.Two sessions I want to point out: ManonKebodeaux from Baker Hughes is going to talk about the power of building diversity, and LauriKwilos from Sage, Peter Jones from Bristol Myers Squbb, and Carol Robinette from the Red Cross are going to talk about managing contingent workers. EE Engagement Model – talk about the metrics modelling linkage analysis that we startedEmployee Lifecycle Research – onboarding, engagement, exitHR Scorecards – business unit transactional metrics – one storyReactive analytics – on requestCompany Health – engagement, job sat, leadership, team, strategic direction, culture – see next slideProactive & Exploratory Analytics – turnover, reorg, employee lifecycleMoS – Engagement, Culture, Leadership – People Productivity MetricTalent – talent acquisition, quality of hire, employee readiness, succession, talent inventory (gaps vs talent pool), HiPo, etcPredictive Analytics -