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1
The Datafication
of HR
Josh Bersin
Principal, Deloitte Consulting LLP
October, 2013
22
Datafication (Noun)
Re-defining a business process
to focus on data.
Turning a business into
a “data business.”
33
The world has
gotten complicated
Technical
Capabilities
War for Talent
Globalization
Localization
Leadership
Pipeline
W...
44
We need to accelerate
hiring of senior and mid
leadership in Asia and
Middle east.
How do we more rapidly
move talent f...
55
Can HR really
become
Data-Driven?
66
The Answer is Yes
Today, 14% of HR ORGANIZATIONS believe they
“regularly use data to make talent and HR strategy
decisi...
77
HR Has Been Evolving Big
Data Solutions for Years
A History of
Data Science in HR
88
99
“Worker Selection”
1010
WW1: Testing Goes Mainstream
1111
1930s: Hawthorne Studies
Employee Engagement Matters
1212
1950s: Testing Expands
Assessment Centers and Statistics
1313
1960-70s: Mainframes Store HR Data
1414
1980s: Applicant Tracking Systems
and the Parsing Engine
1515
2000s: Talent Management Systems
1616
Today: HR Analytics is Hot
1717
Logistics &
Purchasing
Financial &
Budgeting
ERP
& Supply
Chain
Finance & ERP
Customer
Analytics
(Data
Warehouse)
Cus...
1818
Our Research
So Where Are We?
1919
Short Answer: Not There Yet
2020
What is Talent Analytics:
Bring HR & Business Data Together
Recruiting and
Workforce
Planning
Comp and
Benefits
Perfo...
2121
What Our Research Discovered
Bersin by Deloitte Talent Analytics Maturity Model®
Level 4: Predictive Analytics
Develo...
2222
Advancing Takes Effort
Level 2
Strategic Reporting
Level 3
Advanced Analytics
Level 4
Predictive Analytics
Level 1
Op...
2323
The Ugly Part of The Story
Visual
Dashboards
Advanced
Analytics
Predictive
Models
Data
Integration
Data
Dictionary
Da...
2424
It Takes A Multi-Disciplinary Team
Connected
To IT
Connected
to Finance and
Operations
Connected
to Executives
Connec...
25
A New Organization within HR
Talent Analytics is a Function
VP Human Capital
Analytics
Director
Org Diagnostics &
Desig...
2626
What it Takes to Succeed
Ability to
Aggregate and
Collect High
Quality Data
Ability to Analyze
and Make Sense
of the ...
2727
How Will Talent Analytics
Change Your Thinking?
2828
Applying Science to People Decisions
Definition of Science
“Systematic knowledge of the world gained through observat...
2929
Debunking Five Workforce Myths
1. People from top universities with good grades
are high performers
2. Training and e...
3030
Who Will Be Top Sales Performers?
3131
What Will Reduce Fraud?
3232
How Do We Improve Client Retention
3333
Will Increasing Pay Drive Retention?
3434
How Do We Develop Leaders in China?
3535
The Real Question:
Will Your Organization Change
the Way it Makes Decisions?
3636
Yes.
And you will have no choice.
3737
END
4040
And the Payoff is Enormous
 Stock performance outperformed the
S&P 500 by 30% over the last three
years
 Companies ...
4141
Normal Distribution of
Performance
How traditional labor-driven
organizations think about talent.
“Power Law” Distrib...
4242
History of Data Science in HR (Early Years)
1900 1920
Time and Motion Studies
Frederick Taylor publishes
Principles o...
4343
History of Science in HR (WWII to Present)
1960s 1970s 1990 2000 Present1980
Organizational
Development and
Engagemen...
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The Datafication of HR: People Science is Here

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The history and evolution of data science and People Analytics in HR. How People Science has become a core discipline of HR and business.

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The Datafication of HR: People Science is Here

  1. 1 The Datafication of HR Josh Bersin Principal, Deloitte Consulting LLP October, 2013
  2. 22 Datafication (Noun) Re-defining a business process to focus on data. Turning a business into a “data business.”
  3. 33 The world has gotten complicated Technical Capabilities War for Talent Globalization Localization Leadership Pipeline Waning Capabilities of HR Cloud HR Systems MOOCs BigData Analytics Disruption of the CHRO 400 LMS and TM vendors Social Recruiting Employment Brand Retention and Engagement Millenials The “Overwhelmed” employee Workforce Planning Global Recruitment HR as Decision Science Social Everything Social Everything Global Payroll
  4. 44 We need to accelerate hiring of senior and mid leadership in Asia and Middle east. How do we more rapidly move talent from early leadership to senior leadership? The skills of our HR business partners and specialists need improvement. Our company has capability gaps in new technology areas across the organization. We are shifting our business to a services business. How do I transform the workforce? How do we create more collaboration and knowledge sharing across the company? How do we increase women and diversity in leadership? ? How can I retain and engage my top talent? Our training organization is too expensive and not driving enough value. We need to restructure HR to build common systems and reduce costs. Our mid-level and entry leadership gaps are still huge ? How do we drive greater innovation into the organization? We are still having trouble attracting millenials and younger workers. Our performance and comp process is obsolete and not engaging people. How can we globalize our employment brand and talent programs? We compete for engineers with some of the most successful Silicon Valley companies? How can I attract and retain the brightest in our company? How do we optimize our global mobility program? We need better data and analytics in HR. Questions HR Leaders Struggle With
  5. 55 Can HR really become Data-Driven?
  6. 66 The Answer is Yes Today, 14% of HR ORGANIZATIONS believe they “regularly use data to make talent and HR strategy decisions”… …and these organizations, are… 2X as likely to believe they are excellent at selecting the right candidates 2X as likely to believe they are delivering a strong leadership pipeline Generating 30% higher stock returns than the S&P 500 over the last three years 3X as likely to believe they are efficiently operating HR
  7. 77 HR Has Been Evolving Big Data Solutions for Years A History of Data Science in HR
  8. 88
  9. 99 “Worker Selection”
  10. 1010 WW1: Testing Goes Mainstream
  11. 1111 1930s: Hawthorne Studies Employee Engagement Matters
  12. 1212 1950s: Testing Expands Assessment Centers and Statistics
  13. 1313 1960-70s: Mainframes Store HR Data
  14. 1414 1980s: Applicant Tracking Systems and the Parsing Engine
  15. 1515 2000s: Talent Management Systems
  16. 1616 Today: HR Analytics is Hot
  17. 1717 Logistics & Purchasing Financial & Budgeting ERP & Supply Chain Finance & ERP Customer Analytics (Data Warehouse) Customer Segmentation Market Basket Web Buying Behavior Consumer & CRM Recruiting Learning Performance Talent Mgt Workforce Planning Predictive Models For Talent/HR Talent, Leadership, HR And.. Datafication of HR is Inevitable The Industrial Economy The Financial Economy The Customer Economy and Web The Talent Economy Early 1900s 1950s-60s 1970s-80s Today Steel, Oil, Railroads Conglomerates Financial Engineering Customer Segmentation Personalized Products Globalization, Demographics Skills and Leadership Shortages
  18. 1818 Our Research So Where Are We?
  19. 1919 Short Answer: Not There Yet
  20. 2020 What is Talent Analytics: Bring HR & Business Data Together Recruiting and Workforce Planning Comp and Benefits Performance Succession Engagement Learning & Leadership HRMS Employee Data Engagement & Assessment + Sales Revenue Productivity Customer Retention Product Mix Accidents Errors Fraud Quality Downtime Losses Groundbreaking New Insights & Tools for Managers to Make Better Decisions =Data management, analytics, IT, and business consulting expertise +
  21. 2121 What Our Research Discovered Bersin by Deloitte Talent Analytics Maturity Model® Level 4: Predictive Analytics Development of predictive models, scenario planning Risk analysis and mitigation, integration with strategic planning 4% Level 3: Advanced Analytics Segmentation, statistical analysis, development of “people models”; Analysis of dimensions to understand cause and delivery of actionable solutions 10% Level 2: Proactive – Advanced Reporting Operational reporting for benchmarking and decision making Multi-dimensional analysis and dashboards 30% Level 1: Reactive – Operational Reporting Ad-Hoc Operational Reporting Reactive to business demands, data in isolation and difficult to analyze 56%
  22. 2222 Advancing Takes Effort Level 2 Strategic Reporting Level 3 Advanced Analytics Level 4 Predictive Analytics Level 1 Operational Reporting Level of Value Level of Effort Choke Point for Most Organizations
  23. 2323 The Ugly Part of The Story Visual Dashboards Advanced Analytics Predictive Models Data Integration Data Dictionary Data Quality Time and Seasonality Big Data Tools Data Governance Ownership Reporting Tools Disparate Systems Visual Skills Stats and Data Skills The Ugly Side: Data Management
  24. 2424 It Takes A Multi-Disciplinary Team Connected To IT Connected to Finance and Operations Connected to Executives Connected to External Data Know the business well Consultant with business Statistical rigor Good with numbers Curious, learning nature Visual storytelling Strong data management Process oriented World Class Analytics Team
  25. 25 A New Organization within HR Talent Analytics is a Function VP Human Capital Analytics Director Org Diagnostics & Design Sr. Consultant ODD Program Manager Director Workforce Analytics & Research Manager Workforce Analytics Sr. WFAAnalyst Manager Employee Research Analyst Employee Research Manager Learning Analytics Consultant Learning Measurement Analyst Learning Analytics Business Operations Specialist Manager HR Brand Content Retailer
  26. 2626 What it Takes to Succeed Ability to Aggregate and Collect High Quality Data Ability to Analyze and Make Sense of the Data and Relationships Ability to Connect to IT, Sales, Operations, Finance Ability to Consult, Visualize, Tell Stories and Drive Change
  27. 2727 How Will Talent Analytics Change Your Thinking?
  28. 2828 Applying Science to People Decisions 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.”
  29. 2929 Debunking Five Workforce Myths 1. People from top universities with good grades are high performers 2. Training and education reduces loss and fraud 3. Customer service will increase client retention 4. People will leave their jobs if we don’t pay them enough 5. Our leadership development process will work around the world
  30. 3030 Who Will Be Top Sales Performers?
  31. 3131 What Will Reduce Fraud?
  32. 3232 How Do We Improve Client Retention
  33. 3333 Will Increasing Pay Drive Retention?
  34. 3434 How Do We Develop Leaders in China?
  35. 3535 The Real Question: Will Your Organization Change the Way it Makes Decisions?
  36. 3636 Yes. And you will have no choice.
  37. 3737 END
  38. 4040 And the Payoff is Enormous  Stock performance outperformed the S&P 500 by 30% over the last three years  Companies have 4X the “credibility” and “ability to make data driven decisions” than average  Companies have 5-20 people on the team and have been focused on analytics for 4 years or more  Companies are well known brands that have enduring customer recognition over many years Level 3 and Level 4 Organizations
  39. 4141 Normal Distribution of Performance How traditional labor-driven organizations think about talent. “Power Law” Distribution How IP, innovation, and service-driven organizations think about talent. Driving Hyper Performance
  40. 4242 History of Data Science in HR (Early Years) 1900 1920 Time and Motion Studies Frederick Taylor publishes Principles of Scientific Management (1911) – “Father” of Industrial Psychology Hugo Munsterberg publishes Psychology and Industrial Efficiency (1913) Worker Selection – The Trolly Car Drivers World War I – Large Scale Selection Testing American Psychological Association Army Alpha Army Beta Human Capital Consultancies Walter Bingham, James Cattell found the Psychological Corporation Science applied to bottom line Research in Work Motivation “Hawthorne Studies” at Western Electric and Harvard Engagement Matters Qualitative Observations Interview Notes Quantitative Test Scores Large “Flat” Databases of Test Scores Quantitative Job Observations 1950s World War II – Army General Classification Test Benchmarking Large Scale Testing Carl Jung and Elton Mayo Social Intelligence “Belonging” matters, leadership matters, non-monetary factors, Social Intelligence Myers Briggs MBTI Popularizes Personality Testing
  41. 4343 History of Science in HR (WWII to Present) 1960s 1970s 1990 2000 Present1980 Organizational Development and Engagement Industry Researchers at NTL and elsewhere (e.g., Lewin, Likert) begin exploring the use of data and survey feedback to change organizational behavior Uniform Guidelines Established 1978 Selection Practices Cannot Discriminate against Groups Talent Management Authoria and McKinsey pioneer the term, a $5 billion industry of integrated systems is created Cloud Computing Integrated HR technology (Workday, SuccessFactors, Oracle) offer analytics embedded Survey and Personnel Databases BigData Principles Big Data Solutions Statistics and Psychology Mix SaaS Client/Server Databases Mainframe Computers Enable rapid analysis of HR data, statistics, and beginnings of HR databases and testing data Pre Hire Assessment Applicant Tracking Systems, Expansion of testing for leadership and succession “Resume Scoring” BigData and Data Scientists Yahoo spins off Hadoop, Google, Facebook, Twitter, Spawn Data Scientists HRMS and Payroll Online Integral, Tesseract, PeopleSoft put HR Data Online Moneyball Popularizes Use of Data in Selection

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