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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
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
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comp process is obsolete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
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
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
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)
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
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
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
+
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%
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
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
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
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
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
2727
How Will Talent Analytics
Change Your Thinking?
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.”
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
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 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
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
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
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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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
  • 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
  • 12. 1212 1950s: Testing Expands Assessment Centers and Statistics
  • 14. 1414 1980s: Applicant Tracking Systems and the Parsing Engine
  • 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
  • 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?
  • 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.
  • 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

Editor's Notes

  1. (c) Bersin and Associates
  2. First, what does this word mean? The word was first coined by Andrew Waitman, the ceo of a data management firm called Pythian, in early 2013. His argument was that in the late 1800s and early 1900s as electricity became readily available, companies “electrified” their plants and businesses and we have the “electrification of business.” No business can survive without electricity, and we have a robust and ever-present infrastructure of electricity to draw upon. Similarly, today no business can operate without its data infrastructure and datafication. We now have a global network of data services and data tools which have now made data as necessary and important to business as electricity. And just like electricity obsoleted coal and let factories stay open all night, so data is transforming the businesses we run by giving us brand new ways to deliver value. I think if you look at Google, Facebook, LinkedIn, Twitter, Glassdoor, and most other fast growing companies today (even GE), you find that data has become more and more central to their entire business. We used to have to hire companies like IBM to manage our data – today we can do it ourselves, and it’s time for Human Resources to catch up.
  3. There are many decisions and challenges we face in HR. These are just a few – the world has become quite complicated and most CEOs now tell us that talent is their biggest challenge. The Conference Board, a global research firm in the US, surveyed 780 CEO’s earlier this year and found that their #1 issue is now human capital capabilities. This was rated 30% higher than “operational execution” in their list of priorities for success. And as this chart shows, the questions HR executives face are new and different from only a few years ago. Not only have global talent markets changed dramatically, but the structure of our organizations, the technology we use, the role of the manager, and the way people are educated is rapidly changing – giving you as HR leaders a more daunting set of challenges than ever before.
  4. When I go out and talk with CHROs I hear many business-critical challenges. Companies have to make very important decisions about how to source people, how to develop people, how to manage and pay people, how to assess and build leadership, and of course how to run HR. Our most recent study shows that only 28% of HR organizations feel they are well aligned with their own companies, so CHROs are concerned not only about decision making for talent but also how to skill, reorganize, and redesign HR. When you buy new technology it doesn’t change anything – rather it forces you to rethink how you run HR and how you make decisions. Which leads me back to my point: nearly every CHRO I now meet with tells me they want better data and analytics from which to make decisions. But they just arent’ there yet.
  5. So I ask the question: can HR, after 30+ years of technology, really become data-driven? My premise is yes – now is the time, because we have an amazing confluence of factors which have brought together what I call the “datafication” of HR.
  6. We just completed two years of research in this area and found that yes, a small number of HR organizations (14% in fact) are well ahead of the curve and have effectively “datafied” their operation. These unique companies are seeing tremendous improvements in business performance: they are two times better at recruiting, twice as capable of building the right leadership pipeline, their HR organizations are typically 3X more efficient, and their stock prices rose 30% higher than the average of the S&P 500 over the last three years. The question one has to ask is who are these companies and how did they get here?
  7. Let me start with a little history, to try to explain why the topic of data science is so fundamental to HR itself.
  8. In the last 1800s a mechanical engineer by the name of Fredrick Taylor started the datafication of HR. He applied scientific principles to the business of making steel, and performed what are now called “time and motion studies.” By carefully measuring what workers did, he found that laborers who carried typical pigiron billets which were 75 pounds each were less productive overall than those who carried billets which were 45 pounds. Why? Because the ones carrying the heavier loads had to rest more and ultimately produced less work. His book, which you can download and read for free, is a fascinating scientific look at the data behind work. Not only did Taylor unlock many secrets to labor productivity, but he also started to understand that people don’t only work for money, but also for psychological reasons – giving early birth to the industry now known as Industrial and Organizational Psychology. So the origins of using data in HR started more than 110 years ago.
  9. In the early 1900’s when Sigmund Freud was unlocking the secrets of our ego and id, a brilliant psychologist by the name of Carl Jung, who was a friend of Freud, figured out that psychology had a big impact on work. Jung, who is now credited with the original thinking behind many of the tools we use to assess people, came up with the idea of “social intelligence” – the fact that we actually are all a little different and the way we interact with others can be measured and used to help improve the workplace. Jung created the concept of “types” of people, which was later turned into the MBTI or Myers Briggs assesment in the 1940s – now the most widely used personality assessment in the world. Which is basically a data driven decision-making tool. At the same time a psychologist by the name of Hugo Munsterburg, who is credited with starting the field of I/O psychology, studied the performance of trolly car operators in Philadelphia and actually created a simulated trolly car to study performance. He learned that selection of people was among the most important things testing could do – and set in place an enormous industry of data collection using testing and assessment. Hugo Munsterberg publishes Psychology and Industrial Efficiency (1913) Worker Selection – The Trolly Car Drivers
  10. The real explosion of testing started in world war 1, when the US army came up with a test called the “alpha test.” The idea of the “alpha” was to see whether a typical rural young soldier, who may not have been taught to read or write or use math, could learn to shoot a gun. The alpha test was a simple IQ test and was used to help decide who made it into battle. More than a million young men were tested and this set off a huge data collection process which then moved into business.
  11. Of course this approach to testing then made it into the busienss world and starting in the 1950s companies started many types of testing and data collection about people. The old “in box test,” which may wife actually took when she got promoted at Pacific Bell, was intended to see how well you could handle the workload of executive or management life. These tests were numerically coded and statistics were used to figure out how well you could manage as a leader. In the early days these tests and the pre hire testing data was stored in notebooks and analyzed by analysts. But then in the 1960s something big happened: computers.
  12. The birth of the mainframe computer set off a new era of data science in HR. In fact the first application of the mainframe was the US census, which in some ways is an HR data science project. Within ten years of the birth of mainframes, companies like IBM, ADP, Tesseract, Integral, and later many others started to process payroll records and then store employee data in the computer. Which soon led to huge databases about people. Nobody was using the data very strategically at that time (except for the Army and a few leading companies) but soon the testing industry, which was evolving on its own, started to develop the concepts of a “pre-hire assessment.” In 1978 when I graduated from Cornell I interviewed at Procter and Gamble as well as at the US Navy Nuclear Program and took a battery of such tests. In both cases I felt like I had been through a very rigorous evaluation. So the data collection process in HR got even more advanced and then in the early 1980s another innovation occurred: the emergence of the applicant tracking system.
  13. Originally applicant tracking systems were used to store the deluge of resumes which appeared on fax machines, but soon these software companies realized they could scan the data and actually score candidates. SO here again, data about people become even more valuable. We had no idea that these simple systems would later become as powerful as they are today – and this evolution, which started only 25 years or so ago, created the enormous market for pre-hire assessment data, leadership assessment data, testing data, and other forms of people-related data. The academic world exploded with research and during the 1970s and 1980s and beyond we built an entire industry of psychologists trained in advanced testing and statistics.
  14. But despite all this data and statistics, analysis of people in businesses was still difficult because the data was not in one place. And as you all know, HRMS data is not very useful without data about each individual’s background, job history, performance ratings, education and training, etc. So the next big thing that pushed data science in HR forward is the beginning of the integrated talent management systems market. This market was pioneered by a small company called Authoria in the early 2000s and today nearly every major vendor sells end to end HR software that tries to capture data on very step in an employee’s life at work. All this data is being stored in these systems and while we still don’t have a single vendor who does everything, we’re getting closer every years. The integrated talent management systems market gave birth to the new market of ERP-integrated talent and HR systems which is where we are headed today. And by the way we had a few recessions along the way, leading companies to make a lot of difficult decisions about who to lay off, who to hire, who to promote, and who to give more money to. These decisions became mission-critical during the last ten years, driving executives to demand that HR use more science in their decision-making.
  15. And now poor HR has to catch up. The book Moneyball certainly educated us that data can make a big difference in selection, and people like Malcolm Gladwell and many others have popularized the idea of using data to make more scientific decisions about people. I seem to read an article in the New York Times every week about this topic now, and more and more stories about how companies are using data to better select people keep appearing. Finally of course the big software players (SAP, Oracle, and Workday) have now turned their massive resources in this direction and are starting to buy up and build integrated data platforms to help you make talent analytics easier. Unfortunately today tools are not the answer, as I will show you in a minute, but the “dataifaction of HR” has a long history and now it has become hot.
  16. One final point. There is an unrefutable history that every business process goes through a 10-15 years maturity of becoming “datafied” once it becomes strategic to the business. IN the 1970s and 1980s supply chain and integrated financials become strategic and companies spent billions of dollars building ERP systems and financial and manufacturing analytics systems which are now widely used around the world. In the 1980s and 1990s a similar transition took place in marketing, customer relationship management, and now advertising. These business functions now are very data-driven and we have tools and service providers (and an industry of experts) that know how to collect, manage, and analyze data about customers, buyers, and advertising. Today, with talent as the #1 priority on the minds of CEO’s, the same pressure is being applied to HR. So in my mind the trend is inevitable.
  17. So why have only 14% of companies done this and why does this space seem so slow to adapt? Let me explain what our research found.
  18. It turns out that while the pressure is on, our organizations are just not quite there yet. Most HR leaders know they need to implement analytics in a more complete and business oriented way, but they simply have not invested in this area yet.
  19. Our research showed that the hardest part of this process is levels 1 and 2: aggregating, cleaning, and rationalizing data so we have business and HR data in one place.
  20. Of course the really big thing that exploded analytics was the growth of social networking companies who proved that we can make a lot of money on data. And the big players on the left developed tools and data infrastructure which we all now use and let new companies like those on the right emerge. These companies on the right essentially use bigdata sourced from testing and other systems to help us select, assess, and engage people better. They are all companies filled with world-class data scientists applying their work to people problems.
  21. Fredrick Taylor theory of measurement, requires observation and data. WL Bryan skills of morse code. Walter scott “work psychologist” – psychology of advertising, how to increase human efficiency at work. Taylor principles of scientifically designed work, selection based on understanding of that work, and worker management collaboration. Increase pig iron from 12.5 to 47 pig iron. Political backlash by workers and politicians (demonization). He believed increased productivity would be good for economy. Hugo Munsterburg, father of IO psychology. Selection, job analysis. Deconstructed the trolly car and created in-house trolly cars. Beginning of rigorous data analysis. “Assessment center” – simulated work environment. National Training Laboratories took this and turned it into work simulations and inbox tests. World War 1 – Alpha test, “deselection” test. Was push from APA – sees WW1 as great opportunity to legitimize work psychology, lobbied for this test. Conscripting mass people from immigrants and rural areas, no way to know if they could succeed. Created world of Human Capital Consultancies. Predominantly testing. Job analysis, do a selection test, scored. Hawthorne Study: took individual employee performance and looked at it as “influence of social norms” or “culture” on performance. Studies sophisticated (Harvard, statistics) large flat databases. Discovered the concept of “engagement.” People who feel valued do better. (Later turned into engagement surveys by Edgar Schein) “Engagement” comes out of theory that “satisfaction increases productivity” – until Organ found that it wasn’t their “internal” but “external” – above and beyond work. Not shovel more pig iron, but rather help the guy next to you. Inside – staying late, etc. Outside – helping others. WW2 – Army General Classification Test – profile where he or she would be best suited for war effort. Big Data sets.
  22. Human relations theory: people arent just out to increase their pay. “hygiene factors vs. motivators” – we should engage people and assume they want to do the right thing (Theory Y vs. Theory X – people are lazy) HRIS now had employee name and how much they were paid. Human relations theory says people are motivated by other than pay, so we should measure some of thee other things. Civil rights act – employers liable if selection tests or hiring principles are proven to discriminate. HR has to track selection, promotion, and compensation practices. (More Data). Now validation of selection tests is a legal requirement. Put burden on organizations to hire or contract scientists. Myers Briggs personality testing, economies now built on the use of other factors. You can’t eliminate adverse impact, so the only way to really address “pro-social” agendas is to insert non-skills assessment. 1997 “War for Talent” was written: Ulrich and McKinsey. Testing – Statistics – Technology coming together Bringing Data Science to HR – we wont have to “guess” we can look at real data. Then find causes. HR still doesn’t do its job well: just proliferate job codes. Maybe ‘competency management” goes away because we can see what competencies are needed? Maybe “fairness in PM” goes away because we can pay for real performance? Everyone’s performance management data is “bi modal” We can redefine performance on a more granular basis. Get rid of the “human rater” and overlay the real differentiate for us. Do we need competency management? What is the real distribution of performance? Why do we have to be equitable?