Josh Bersin - Datafication of HR


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  • 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.
  • 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.
  • 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.
  • 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.
  • 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?
  • Let me start with a little history, to try to explain why the topic of data science is so fundamental to HR itself.
  • 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.
  • 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
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 togetherBringing 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?
  • Josh Bersin - Datafication of HR

    1. 1. The Datafication of HR Josh Bersin Principal, Deloitte Consulting LLP October, 2013 1
    2. 2. Datafication (Noun) Re-defining a business process to focus on data. Turning a business into a “data business.” 2
    3. 3. Technical Capabilities The “Overwhelmed” employee War for Talent Localization Retention and Engagement Globalization Millenials BigData Analytics Workforce Planning Employment Brand Social Everything The world has gotten complicated Leadership Pipeline Global Payroll HR as Decision Science Social Recruiting Global Recruitment Waning Capabilities of HR Disruption of the CHRO MOOCs Cloud HR Systems 400 LMS and TM vendors Social Everything 3
    4. 4. 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? We are shifting our business to a services business. How do I transform the workforce? How can I retain and engage my top talent? The skills of our HR business partners and specialists need improvement. Our company has capability gaps in new technology areas across the organization. ? How can we globalize our employment brand and talent programs? Our training organization is too expensive and not driving enough value. How do we drive greater innovation into the organization? 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 increase women and diversity in leadership? We need to restructure HR to build common systems and reduce costs. We are still having trouble attracting millenials and younger workers. How do we optimize our global mobility program? ? Our mid-level and entry leadership gaps are still huge Our performance and comp process is obsolete and not engaging people. We need better data and analytics in HR. How do we create more collaboration and knowledge sharing across the company? Questions HR Leaders Struggle With 4
    5. 5. Can HR really become Data-Driven? 5
    6. 6. 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 2X 3X as likely to believe they are excellent at selecting the right candidates as likely to believe they are delivering a strong leadership pipeline as likely to believe they are efficiently operating HR Generating 30% higher stock returns than the S&P 500 over the last three years 6
    7. 7. HR Has Been Evolving Big Data Solutions for Years A History of Data Science in HR 7
    8. 8. 8
    9. 9. “Worker Selection” 9
    10. 10. WW1: Testing Goes Mainstream 10
    11. 11. 1930s: Hawthorne Studies Employee Engagement Matters 11
    12. 12. 1950s: Testing Expands Assessment Centers and Statistics 12
    13. 13. 1960-70s: Mainframes Store HR Data 13
    14. 14. 1980s: Applicant Tracking Systems and the Parsing Engine 14
    15. 15. 2000s: Talent Management Systems 15
    16. 16. Today: HR Analytics is Hot 16
    17. 17. And.. Datafication of HR is Inevitable Finance & ERP Consumer & CRM ERP & Supply Chain Financial & Budgeting Predictive Models For Talent/HR Web Buying Behavior Customer Segmentation Market Basket Customer Analytics (Data Warehouse) Logistics & Purchasing Talent, Leadership, HR Talent Mgt Workforce Planning Recruiting Learning Performance The Industrial Economy The Financial Economy The Customer Economy and Web The Talent Economy Steel, Oil, Railroads Conglomerates Financial Engineering Customer Segmentation Personalized Products Globalization, Demographics Skills and Leadership Shortages Early 1900s 1950s-60s 1970s-80s Today 17
    18. 18. Our Research So Where Are We? 18
    19. 19. Short Answer: Not There Yet 19
    20. 20. The Goal of Talent Analytics: Bring HR & Business Data Together Recruiting and Workforce Planning Comp and Benefits Learning & Leadership HRMS Employee Data Sales Revenue Productivity Performance Succession Engagement Engagement & Assessment + Accidents Errors Fraud Customer Retention Product Mix Quality Downtime Losses + Data management, analytics, IT, and business consulting expertise = Groundbreaking New Insights & Tools for Managers to Make Better Decisions 20
    21. 21. 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 Level 3: Advanced Analytics Segmentation, statistical analysis, development of “people models”; Analysis of dimensions to understand cause and delivery of actionable solutions Level 2: Proactive – Advanced Reporting Operational reporting for benchmarking and decision making Multi-dimensional analysis and dashboards Level 1: Reactive – Operational Reporting Ad-Hoc Operational Reporting Reactive to business demands, data in isolation and difficult to analyze 4% 10% 30% 56% 21
    22. 22. Advancing Takes Effort Level of Value Level of Effort Choke Point for Most Organizations Level 1 Operational Reporting Level 2 Strategic Reporting Level 3 Advanced Analytics Level 4 Predictive Analytics 22
    23. 23. The Ugly Part of The Story Predictive Models Advanced Analytics Visual Dashboards The Ugly Side: Data Management Data Integration Data Dictionary Data Governance Ownership Disparate Systems Stats and Data Skills Reporting Tools Data Quality Time and Seasonality Big Data Tools Visual Skills 23
    24. 24. It Takes A Multi-Disciplinary Team Know the business well Connected To IT Strong data management Connected to Executives Consultant with business Process oriented World Class Analytics Team Visual storytelling Connected to Finance and Operations Statistical rigor Good with numbers Connected to External Data Curious, learning nature 24
    25. 25. Retailer A New Organization within HR Talent Analytics is a Function VP Human Capital Analytics Director Workforce Analytics & Research Director Org Diagnostics & Design Sr. Consultant ODD Program Manager Manager Workforce Analytics Sr. WFA Analyst Manager Employee Research Analyst Employee Research Manager HR Brand Content Manager Learning Analytics Business Operations Specialist Consultant Learning Measurement Analyst Learning Analytics 25
    26. 26. What it Takes to Succeed Ability to Aggregate and Collect High Quality Data Ability to Connect to IT, Sales, Operations, Finance Ability to Analyze and Make Sense of the Data and Relationships Ability to Consult, Visualiz e, Tell Stories and Drive Change 26
    27. 27. How Will Talent Analytics Change Your Thinking? 27
    28. 28. Applying Science to People Decisions What is “Not Science” Definition of Science Making talent decisions on the basis of “gut feel,” “beliefs,” or “Systematic knowledge of the world gained through observation “philosophies.” and experimentation.” 28
    29. 29. 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 29
    30. 30. Who Will Be Top Sales Performers? 30
    31. 31. What Will Reduce Fraud? 31
    32. 32. How Do We Improve Client Retention 32
    33. 33. Will Increasing Pay Drive Retention? 33
    34. 34. How Do We Develop Leaders in China? 34
    35. 35. The Real Question: Will Your Organization Change the Way it Makes Decisions? 35
    36. 36. Yes. And you will have no choice. 36
    37. 37. END 37
    38. 38. 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 40
    39. 39. Driving Hyper Performance Normal Distribution of Performance “Power Law” Distribution How traditional labor-driven organizations think about talent. How IP, innovation, and service-driven organizations think about talent. 41
    40. 40. History of Data Science in HR (Early Years) Time and Motion Studies Frederick Taylor publishes Principles of Scientific Management (1911) – 1900 “Father” of Industrial Psychology Hugo Munsterberg publishes Psychology and Industrial Efficiency (1913) Worker Selection – The Trolly Car Drivers Qualitative Observations Interview Notes World War I – Large Scale Selection Testing American Psychological Association Army Alpha Army Beta World War II – Army General Classification Test Benchmarking Large Scale Testing Myers Briggs MBTI Popularizes Personality Testing 1920 Carl Jung and Elton Mayo Social Intelligence “Belonging” matters, leadership matters, non-monetary factors, Social Intelligence Quantitative Job Observations 1950s Research in Work Motivation “Hawthorne Studies” at Western Electric and Harvard Engagement Matters Quantitative Test Scores Human Capital Consultancies Walter Bingham, James Cattell found the Psychological Corporation Science applied to bottom line Large “Flat” Databases of Test Scores 42
    41. 41. History of Science in HR (WWII to Present) Mainframe Computers Enable rapid analysis of HR data, statistics, and beginnings of HR databases and testing data 1960s 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 Survey and Personnel Databases HRMS and Payroll Online Integral, Tesseract, PeopleSoft put HR Data Online 1970s Pre Hire Assessment Applicant Tracking Systems, Expansion of testing for leadership and succession “Resume Scoring” Statistics and Psychology Mix Uniform Guidelines Established 1978 Selection Practices Cannot Discriminate against Groups 1980 Cloud Computing Integrated HR technology (Workday, SuccessFactors, Oracle) offer analytics embedded 1990 Talent Management Authoria and McKinsey pioneer the term, a $5 billion industry of integrated systems is created SaaS Client/Server Databases 2000 Moneyball Popularizes Use of Data in Selection Present BigData and Data Scientists Yahoo spins off Hadoop, Google, Facebook, Twitter, Spawn Data Scientists BigData Principles Big Data Solutions 43