BigData in HR How to build a world-class Talent Analytics function Josh Bersin Principal, Deloitte Consulting LLP May, 2013 1
Research & BigData Working Group 8 years of research into the measurement, operations of L&D, leadership, recruiting and HR 20 leading practitioner organizations advising us on strategy Our goal: education and bestpractices on how to build an analytics function Develop assessment services and tools to help you understand how to advance your program Continue to study state of the market and the best-practice solutions http://www.bersin.com/hrbigdata2012 2
Agenda Why BigData in HR is Needed Defining BigData in HR What we have learned The Four Stages Case Studies Where we are 3
Today s Global Talent Challenges We have entered a global economy where talent and skills shortages challenge world economic and business growth around the world. - Klaus Schwab, Chairman, World Economic Forum Despite the high unemployment rates in many countries, more than 65% of global leaders cite talent and leadership shortages as their #1 business challenge. - Bersin & Associates TalentTrends, Fall 2012 4
2013: A Nexus of Change nexus (Noun) A connection or series of connections linking two or more things. A connected group or series: "a nexus of ideas". 5
A Nexus of Talent Challenges Competition for Talent Social Sourcing & Recruiting 6 Business Speed and Scale Disruptive Competition Agile Management & Leadership Models A New Generation of HR Practices and New Type of HR Organization 1 Shift toward Emerging Markets 2 21 st Century Models of Leadership 5 New Technology Social Tools, Analytics Need for Improved HR skills and capabilities. Specialization Contingent Work New Job & Career Models 4 Borderless Workplace Team Model of Work 3 6
How the Workforce has Changed From The Shift Index by Deloitte 7
Has Created Challenges in Engagement Young, Diverse Workforce. 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 8
Increasing Work Specialization Expertise drives competitive advantage Specialization improves quality and reduces cost Deep skills developed through deliberate practice and reinforcement Deep skills come from a range of developmental experiences Intelligent leadership paths, career paths, training, work assignments, understanding high-performing competencies are all drives of success. The Experts Senior Specialists Top Management Senior Management Middle Management First Line Management Functional Specialists / Front-Line Employees Back Office, Operational, Contingent Employees 9
Agenda Why BigData in HR is Needed Defining BigData in HR What we have learned The Four Stages Case Studies Where we are 10
Do YOU know. What characteristics drive high performing sales people? What work assignments will lead to strong leadership? What attributes of a job candidate will lead to perfect fit? Why retention is low in certain locations and jobs? What is the real result of poor on the job safety? Why some of your top people leave for competitors What compensation and rewards will drive most value?. 11
BigData in HR Defined Big data is a collection of data so large and complex that it is difficult to process using traditional data processing applications. - Wikipedia The typical HR system has more than 400 data elements about your own employees, and this data is being updated nearly every day. We all have BigData opportunities within our own HR, training, recruiting, and talent organizations. 12
Analytics is Definitely Coming to HR The Evolution of Business Analytics in other Functions The Waves of Business Analytics Finance & Logistics Integrated ERP and Financial Analytics Customer & Marketing Predictive Customer Behavior - CRM Talent & Leadership Predictive Talent Models HR Analytics Integrated Supply Chain Web Behavior Analytics Business-driven Talent analytics Logistics and Supply Chain analytics 1980s Financial and Budget Analytics Customer Analytics CRM (Data Warehouse) Customer Segmentation Shopping Basket Integrated Talent Management Workforce Planning Recruiting, Learning, Performance Measurement 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 13
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. 14
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% Managerial interviews: 64% Interview training: 47% Behavioral assessments: 34% Reference calls: 32% Skills-based assessments: 25% 2/3 use no real assessment process at all leaving the process to hiring managers or recruiters Bersin & Associates High-Impact Talent Acquisition Study, Fall 2011, 158 organizations responded 15
Big Insurance Insurance Company 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. 16
Results of Data Analysis Insurance Company 17 17
Data Showed Six Things Matter: Insurance Company Very Highly Correlated with Success 1. No typos, errors, grammatical mistakes on resume. 2. Did not quit school before obtaining some degree 3. Had experience selling real-estate or autos 4. Demonstrated success in prior jobs 5. Ability to succeed with vague instruction 6. Experience planning time and managing lots of tasks What Did NOT Matter Where they went to school What grades they had The quality of their references The Belief System Was Wrong Within six months of implementing a new screening process revenues went up by $4 million 18
Moving to Predictive Analytics 19
Agenda Why BigData in HR is Needed Defining BigData in HR What we have learned The Four Stages Case Studies Where we are 21
The Big Aha! In HR, talent, leadership, and capabilities you already have most of the information you need to deliver breakthrough new solutions for your organization. What most organizations do not have is the organization structure, skills, leadership, and tribal knowledge to use this information yet. Many of the skills are likely available in your organization. This marketplace is rapidly evolving and over the next two years companies who do not implement a BigData in HR strategy will fall behind those that do. 22
We Don t Measure the Right Things Source: Bersin & Associates 2012 High-Impact Learning Organization (HILO) 23
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Focus on The Problem, not the Data Business problem first, then focus on arranging and using the data Business Problem Data 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? 25
The Yahoo Question Are the people working from home getting enough work done? 26
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Treat Measurement as a Process Why you must build an analytics function, not a set of tools Measurement as Process, not a Project 28
The 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 29
The Rich Sea of Data Opportunities Supply, Demand Recruiting Onboarding Talent Demand Plan Employer Brand, Alumni Workforce Plan Scenarios Time and Cost To Hire Open Positions Onboarding Effectiveness Internal Mobility Turnover Performance Succession Ratings Rankings Managerial Grievances Development Plans 9-box Grids Readiness Skills Certifications Succession Depth Promotional Readiness Seniority Skills Depth Leadership Succession Depth 360 and Other Assessments Successor Readiness Proficiency vs. Leadership Comp. Compensation Budget by Group Comp by level/perf Perf-Pay differentials Compa Ratios Engagement Employee Opinion Employee Engagement Innovation Programs Employee Value Prop Demographics Retirement Projections Tenure Education, etc. Age, geography, Skill level Span of control Spending HR/L&D Spending HR/L&D Staff Allocation Systems Usage/$ Satisfaction With HR svc. 30
Understanding HR measures Hundreds of HR measures Many easy to find Many not easy to find Need for data dictionary Basic principles for success 31
Bersin HR Measurement Framework HR, Recruiting, and L&D Effectiveness Organizational Readiness Talent & Leadership Supply Workforce Planning Scenario & Future Planning Manager and Employee Dashboards Measurement Process & Skills Data Cleansing Data Dictionary Analytic Measures Statistical and Data Analysis Skills, Collaboration with other Analytics Teams Integration of new sources and systems Data governance and stewardship 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 Talent Acquisition, Brand, Sourcing (How well you are reaching candidate audiences) Employment Brand Talent Pipeline Time and Cost to Fill Quality of Hire 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 Turnover 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 External Data and Benchmarks (External Benchmarking of all HR Measures) Internal HR Measures I HR Program Effectiveness I Workforce Measures I TM Measures I People Performance Workforce Planning System Scenarios, talent supply, demand org charting Applicant Tracking Recruiting System Applicant, source, recruiting data Performance & Talent System Performance, development planning, succession, talent pool data Learning Management System Learning, certification, skills delivery, content, learning organization data HRMS s Payroll and Employee Demographic and System of Record Data Compensation System(s) Salary, benefits, budget, bonus and comp related data, payroll feeds Third party data: assessments, employee engagement, external brand, social networks 32
How HR Data is Typically Organized Recruiting and Workforce Planning Comp and Benefits Performance Succession Engagement Learning & Leadership HRMS Employee Data HR Operations 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 33
Finding the Data is Work HR Manages a Plethora of HCM Products 87% have more than two systems 20% have more than 6 systems 7% have more than10 solutions 18% of large companies report use of more than ten HCM different systems 57% plan to procure new software within the next 18 months 61% will both replace and procure new solutions 23% will solely replace existing solutions;16% will solely add new products. 33% will replace standalone TM applications 22% with an integrated suite. 10% will replace their existing suites. 34
The Problem with the Systems Market 35
Why You Need a Data Dictionary 36
Standards Remain Elusive You cannot wait.. you have to develop your own Other Standards Out There: Bersin by Deloitte Factbooks Turnover Metrics (SHRM ) Diversity & Inclusion (SHRM ) SAP Book of Data TDR Reporting (Yay!) Engagement Standards (coming) SASB Sustainability Many more 37
Tools Alone are Not the Solution 38
Agenda The BigData Priority Why Talent Analytics Guidelines for Success The Four Stages Final Thoughts 39
Talent Analytics Maturity Model Level 4: Predictive Analytics Development of predictive models, scenario planning Risk analysis and mitigation, integration with strategic planning Level 3: Strategic 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 Operational reporting for measurement of efficiency and compliance Data exploration and integration, Development of data dictionary <5% <10% >20% >60% 40
Level 1: Reactive Operational Reporting 1 Goals: - Implement a scalable, accurate, easy to use reporting environment - Understand all the data and systems you have to work with Tasks: - Understand and collect data you have - Build a Data Dictionary - Work with IT to implement standard reporting tools Key Skills - Patience and database interest - Great relationship with IT - Ability to write, document, and manage projects Expected Outcome - Standard tools and reports - Ease in responding to any report request - Tools to help managers find their own data 41
Level 2: Proactive - Advanced Reporting 2 Goals: Develop skills and tools to implement proactive reporting and tools for line managers Look at trends, benchmarks, and results against plan Develop actionable business dashboards Tasks: Understand all the dimensions of your data (how it will be drilled and filtered) Audience analysis who are your audiences and what decisions do they make? Performance consulting start focusing on one or two major problems Purchase or select benchmarking data Key Skills Understanding of multi-dimensional reporting Business acumen and relationship with finance organization Strong business alignment and partnership with business leader (or leader) Ability to influence what IT does Expected Outcome Dashboards used by the business A business unit success 42
Level 3: Strategic Analytics 3 Goals: Developing causal models or people models which identify cause and effect Segmenting people into groups which can be analyzed in detail Integrating data with recruiting, performance, compensation, leadership, etc. Tasks: Building strong relationships with all areas of HR Selecting a key problem to start analytic study Implement analytics project, iterate, and demonstrate results Key Skills Analytics and statistics skills Information visualization and compelling presentation skills Excellent performance consulting and ability to understand work environment Partnership with line executives and ability to focus on key problems Skills in development of tools across many areas of HR and Talent Management Expected Outcome A success project which delivers some breakthrough findings Direct change or decision-making tools in the hands of the business 43
Level 4: Predictive Analytics 4 Goals: Putting place models which can predict future scenarios Integrate your work with workforce planning and business planning functions Tasks: Expand your analytics skills and expertise Directly connect with business planning, finance, and recruiting teams Expand relationship with 3 rd party data, engagement, and consulting firms Key Skills Modeling and deeper statistics skills Finance and business planning Senior experience in organization design Strong relationship with or deep experience in workforce planning and acquisition Expected Outcome A workforce planning model which describes how performance can be improved Repeatable models which can be extended into new domains Credibility with Finance An integrated, strategic analytics function 44
Key to Success 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 45
Agenda Why BigData in HR is Needed Defining BigData in HR What we have learned The Four Stages Case Studies Where we are 46
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 47
Energy Company Why is our China Leadership Pipeline Weak? College Degree College Major Job Level Job Type Tenure Hire Date Org Unit Home Geography Hipo Level Work Geography Promotion Type Perform. Tier Work Country Trainings Completed Date Promoted 1. Examine Historic Data & Outcomes 2. Build A Predictive Model MBA vs. Engineering Degree Lack of US Experience Different criteria for success Position Held Position Level Date Since Training 49
The Evolution of Data Skills and Competencies Large Retailer 2007-2008 Solid Understanding of HR; I/O Psychology Degree; Employee Research Background; Qualitative Research Design & Analysis; HRIS; SPSS Strong Communication & Interpersonal Skills; Detail Oriented ; Project Management 2009-2010 Business Acumen; HR, Finance, Economics Degree; Quantitative Research Design & Analysis Passion for Data & Analytics; Strong Technical skills Consulting & Presentation Skills; Analytical Curiosity; Problem Solving; Collaborative; Teamwork; Networking Skills 2011-2012 Advanced Statistics & Social Research Acumen; Engineering Degree; Customer Research Background; Statistics & Data Mining Critical Thinking; Story Telling; Data Visualization; Ability to see data, and decipher insights 50
The Modeling Journey Retailer 2008 Employee Engagement Model Employee Segmentation LVI Learning 2009 Diversity & Inclusion Leadership 2010 Learning & Professional Development Employee Lifecycle Research HR Scorecards Reactive Analytics 2011 Company Health Pentagram Employee Research Cohorts Human Capital Executive Dashboard Proactive & Exploratory Analytics 2012 Enterprise Measures of Success Talent Change Adoption Predictive Analytics 51
Retailer An Evolved Organization VP Human Capital Analytics Director Org Diagnostics & Design Director Workforce Analytics & Research Manager HR Brand Content (2) Sr. Consultant ODD Manager Workforce Analytics Manager Employee Research Manager Learning Analytics Business Operations Specialist Program Manager (2) Sr. WFA Analyst Analyst Employee Research Consultant Learning Measurement Analyst Learning Analytics 52
Purpose of Department Organizational Research, Analysis, and Planning Department Manufacturer Help to achieve a competitive advantage through providing strategic HR analysis focused on talent Build a culture of analytics and planning within the Global Human Resources function Provide HR Intelligence through the highest quality; most valid and reliable analytical products and services 53
Evolution of Human Capital Analytics Team Manufacturer Explore Strategize Execute Operate Implement Impress Oct. 2006 2007 2008 2009 2010 2011 Start of department One person Focus on Advanced HR Studies & Corporate HR Scorecard Audience equal CHRO Report to Director of HR Functional Excellence Receive goals from CHRO Sourced two part-time I/O Psychology interns Added focus on Global HR Scorecard, Workforce Forecasting, Performance Management, and Training Effectiveness Report to VP of Talent Acquisition & HR Functional Excellence Audience equal mostly HR; but also business and Functional leaders Receive goals from CHRO & Staff; some Functional/Business leaders Eight person department Aligned department by business and region Added focus on predictions, scenario planning, succession planning, HR processes, on-site root-cause analyses & OD Audience equal HR and non-hr leaders down to the plant/facility level Report to VP of HR Functional Excellence Receive goals from CHRO, Business Leaders, and Functional Officers A great deal of hiring Two person department (Hired Ph.D. I/O Psychologist) Added focus on Job Analysis, Competency Assessment, & Organizational Culture Report to VP of Talent Acquisition & HR Functional Excellence Audience equal CHRO & Staff Goals from CHRO Downsized to one person Added focus on Talent Acquisition Same reporting structure Audience equal HR, functions, & business leaders Receive goals from CHRO, Business Leaders, and Functional Officers Hired two people in India (operations research & BI) Fourteen person department Added focus on organizational structure, potential countries to do business, and labor cost forecasting Report to VP of Talent Management & Organization Effectiveness Goals provided by HR and non -HR leaders Audience is the same Implement HR BI with Oracle (OBIEE), Reporting Service Center 54
Key Deliverables Roadmap Manufacturer DESIGN IMPLEMENT Identify Solution or Problem Formulate Study Plan Collect Data Convert Data to Actionable Information Present Results and Potential Solutions Collaboratively Identify Solutions 2007 Key Deliverables & Time Allocation 2012 40% Centralized HR Reporting, Analysis, and Benchmarking 20% 40% Advanced HR/Business Studies 25% 10% Building a Culture of Analytics Through Training & Development 20% 5% HR Measurement On-Site Consulting and/or Client Engagements 15% 5% HR Planning through Data Analysis 20% 55
Building A Culture of Analytics Manufacturer Accessing the data Interpreting the data Presenting the data Executing with the data ACCOUNTABILITY Know what data is in the system and how to access it. Understand what data is captured in the system and what it represents Understand how to run reports and create ad hoc reports Analyze and interpret data and metrics Know what data is in the system and how to access it. Analyze data and evaluate trends Drill down in order to ask the question behind the question Understand the why behind the what Conduct root cause analysis Understand analytics and present data to tell a story Analyze and interpret data and metrics Know what data is in the system and how to access it. Translate, analyze and present data to various audiences Identify business issues that are being impacted Create actionable HR plans the positively impact the business Establish a plan and execute a plan with the data Understand analytics and present data to tell a story Analyze and interpret data and metrics Know what data is in the system and how to access it. Design solutions to support specific business strategies Be anticipatory & participate in what s next decision making Proactively initiate actions to improve organizationwide performance & avoid incoming issues Understand leading vs. lagging indicators Re-evaluate using quantitative analyses Sustain best practices and eliminate waste COMPETENCY 56
The Skills Issue 57
The WhatWorks Approach Talent Analytics Fits into our High-Impact HR Framework http://www.bersin.com/hrbigdata2012 58
Conclusion BigData in HR is has become a business imperative Integration of analytics teams and building capability are key, not tools and technology Analytics is a journey which will change the way you think Talent analytics will extend to the other business analytics groups in your organization Expertise and patience is key, but focus on key business problems first http://www.bersin.com/hrbigdata2012 59