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HR / Talent Analytics orientation given as a guest lecture at Management Institute for Leadership and Excellence (MILE), Pune. This presentation covers aspects like: ...

HR / Talent Analytics orientation given as a guest lecture at Management Institute for Leadership and Excellence (MILE), Pune. This presentation covers aspects like:

1. Core concepts, terminologies & buzzwords
- Business Intelligence, Analytics
- Big Data, Cloud, SaaS

2. Analytics
- Types, Domains, Tools…

3. HR Analytics
- Why? What is measured?
- How? Predictive possibilities…

4. Case studies

5. HR Analytics org structure & delivery model

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HR / Talent Analytics HR / Talent Analytics Presentation Transcript

  • HR Analytics - Akshay Raje
  • Agenda 1. Core concepts, terminologies & buzzwords Business Intelligence, Analytics Big Data, Cloud, SaaS 2. Analytics Types, Domains, Tools… 3. HR Analytics Why? What is measured? How? Predictive possibilities… 4. Case studies 5. HR Analytics org structure & delivery model
  • Core concepts and terminologies Decision Analytics =Business Intelligence
  • Core concepts and terminologies “Business intelligence (BI) is a set of theories, methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information for business purposes. “Business analytics (BA) refers to the skills, technologies, applications and practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. 1. Business Intelligence Success Factors: Tools for Aligning Your Business in the Global Economy. Hoboken, N.J: Wiley & Sons. ISBN 978-0-470-39240-9. 2. Beller, Michael J.; Alan Barnett (2009-06-18). "Next Generation Business Analytics". Lightship Partners LLC
  • History of BI and Analytics • Decision support systems (DSS) began in the 1960s as computer-aided models created to assist with decision making and planning. • From DSS, data warehouses, Executive Information Systems, OLAP… • …and finally Business Intelligence came into focus beginning in the late 80s. • Because of the rigidness of enterprise level BI tools, analytics started gaining traction in mid 00’ Logistics & Supply Chain Analytics 1980’s Financial & Budget Analytics Integrated Supply Chain Integrated ERP & Financial Analytics Customer Analytics, CRM & Data Warehousing Customer Segmentation and Shopping Basket Web Behavior Analytics Predictive Customer Behavior Recruiting, Learning, Performance Management Integrated Talent Workforce Planning Business-driven Talent Analytics Predictive Talent Models – HR Analytics Early 1900s 1950s-60s 1970s-80s Today Industrial Economy Financial Economy Consumer & Web Economy Talent Economy Steel, Oil, Railroads Conglomerates Segmentation & Personalization Globalization, Diversity, Skill & Leadership shortages Source:Bersin&Associates
  • Buzzwords #BigData #Cloud #SaaS
  • Agenda 1. Core concepts, terminologies & buzzwords Business Intelligence, Analytics Big Data, Cloud, SaaS 2. Analytics Stages, Types, Domains, Tools… 3. HR Analytics Why? What is measured? How? Predictive possibilities… 4. Case studies 5. HR Analytics org structure & delivery model
  • Stages of Analytics Reporting What happened? Analysis & Monitoring Why did it happen? What is happening now? Predictive Analytics What can happen? Complexity Businessvalue
  • Types of Analytical Models Reporting Analysis & Monitoring Predictive Analytics Past Data Current Data Future PREDICTS PREDICTS PREDICTS Drawing Conclusions or Inferences Representation of Data and Summarizing INFERENTIAL ANALYTICS DESCRIPTIVE ANALYTICS REPORT PREDICTIVE ANALYTICS
  • Analytics Domains Retail Sales Analytics Financial Services Analytics Risk & Credit Analytics Talent / HR Analytics Marketing Analytics Consumer Behavior & Cohort Analytics Collections Analytics Fraud Analytics Pricing Analytics Telecom Analytics Supply Chain Analytics Web Analytics
  • Tools, Matrices, Software Reporting Metric Types Description Rate Proportion of one or more parts to a whole of 100% Ratio One number relative to another, often expressed as a reduced fraction Composition Breakdown of a whole into its parts, showing the number or percentage allocated to each Index Weighted combination of disparate data into one number relative to a scale or anchor Volume Number of people or units with a characteristic, or occurrences of an event Cost Organizational expenses, revenues, profits, or value Time Process cycle time, volume of time invested, or timeliness of events Quality Performance of people, processes, or systems Satisfaction Participants’ subjective perceptions of a process, program, or experience Typical tools / software: • Microsoft Excel (max used) • BI reporting tools • ERP reporting tools, dashboards
  • Tools, Matrices, Software Analysis & Monitoring Typical tools / software: • Microsoft Excel (limited usage) • BI reporting tools • Statistical software like SAS, R etc Representation of Data: • Frequency Distributions: Relative and Percent Frequency • Graphs: Bar, Pie, Dot Plot, Histogram, Ogive • Cumulative Distributions Measures of central tendency: • Mathematical: Arithmetic / Geometric / Harmonic Mean • Positional: Median, Mode Measures of dispersion: character of variability in data • Absolute: Range, Quartile / Mean / Standard Deviation • Relative: Coefficient of Range / QD / MD / variation Correlation: degree or extent to which two or more variables fluctuate with reference to one another • Pearson Correlation: Correlation for Continuous data • Spearman Correlation: Correlation for Ordinal Data DESCRIPTIVE ANALYTICS
  • Tools, Matrices, Software Analysis & Monitoring Typical tools / software: • Statistical software like SAS, R etc • Survey tools Sampling Types: • Random • Systematic Sampling • Stratified • Cluster Sampling Statistical inference: Inference about a population from a random sample drawn Confidence Intervals: Using standard error (SE) for applying confidence intervals to estimates Hypothesis Testing: Assertion regarding the statistical distribution of the population INFERENTIAL ANALYTICS
  • Tools, Matrices, Software Predictive Analytics Typical tools / software: • Statistical software like SAS, R etc Regression: • Linear Regression • Non Linear Regression Factor Analysis Cluster Analysis
  • Over to Dilbert 
  • Agenda 1. Core concepts, terminologies & buzzwords Business Intelligence, Analytics Big Data, Cloud, SaaS 2. Analytics Stages, Types, Domains, Tools… 3. HR Analytics Why? What is measured? What can be measured? Predictive possibilities… 4. Case studies 5. HR Analytics org structure & delivery model
  • Why HR Analytics? Measure & Manage Return on Investment Linkage of Business Objectives and People Strategies Performance Improvement “What gets measured, gets managed; What gets managed, gets executed” - Peter Drucker “The business demands on HR are increasingly going to be on analysis just because people are so expensive“ - David Foster “ To clearly demonstrate the interaction of business objectives and workforce strategies to determine a full picture of likely outcomes” HR Dashboards - SAP “Global organizations with workforce analytics and workforce planning outperform all other organizations by 30% more sales per employee.” - CedarCrestone
  • Steps in HR Analytics Hindsight Insight Foresight Gather data by Reporting Make sense of data by Analysis and Monitoring Develop predictive models
  • What is generally measured/tracked today? 63% 52% 48% 45% 37% 31% 30% 27% 27% Employee Engagement Performance Ratings Retention / Turnover HIPOs & HIPO pipeline % employees with dev plans Readiness for job Internal hire %age Diversity of workforce Level of expertise / competance Source: Bersin & Associates 2012 – US research
  • What should/could be measured? HR Matrices Recruitment Retention Performance & Career Management Training Comp & Benefits Workforce Organization effectiveness
  • Recruitment Recruitment Internal Movement Staffing Effectiveness 1. Employment Brand Strength 2. External Hire Rate 3. Net Hire Ratio 4. New Position Recruitment Rate 5. New Position Recruitment Ratio 6. Recruitment Source Breakdown 7. Recruitment Source Ratio 8. Rehire Rate 1. Career Path Ratio 2. Cross-Function Mobility 3. Internal Hire Rate 4. Internal Placement Rate 5. Lateral Mobility 6. Promotion Rate 7. Promotion Speed Ratio 8. Transfer Rate 9. Upward Mobility 1. Applicant Interview Rate 2. Applicant Ratio 3. Average Interviews per Hire 4. Average Sign-On Bonus Expense 5. Average Time to Fill 6. Average Time to Start 7. Interviewee Offer Rate 8. Interviewee Ratio 9. New Hire Failure Factor 10. New Hire Performance Satisfaction 11. New Hire Satisfaction Offer Acceptance Rate 12. On-Time Talent Delivery Factor 13. Recruitment Cost per Hire 14. Recruitment Expense Breakdown 15. Referral Conversion Rate 16. Referral Rate 17. Sign-On Bonus Rate
  • Retention Turnover Employee Engagement Cost of Turnover 1. Involuntary Termination Rate 2. New Hire Turnover Contribution 3. Retention Rate 4. Termination Breakdown by Performance Rating 5. Termination Reason Breakdown 6. Voluntary Termination Rate 1. Employee Commitment Index 2. Employee Engagement Index 3. Employee Retention Index 4. Market Opportunity Index 5. Offer Fit Index 1. Average Termination Value 2. Average Voluntary Termination Value 3. Termination Value per FTE 4. Turnover Cost Rate—< 1-Year Tenure
  • Performance & Career Management Performance Management Career Management 1. Average Performance Appraisal Rating 2. Employee Turnaround Rate 3. Employee Upgrade Rate 4. High Performer Growth Rate 5. Peer Review Rate 6. Performance Appraisal Participation Rate 7. Performance Rating Distribution 8. Performance-Based Pay Differential 9. Performance Contingent Pay Prevalence 10. Self Review Rate 11. Upward Review Rate 1. Cross-Function Mobility— Managers 2. Employee Satisfaction with Leadership 3. LDP Prevalence Rate 4. Manager Instability Rate 5. Manager Quality Index 6. Positions Without Ready Candidates Rate 7. Successor Pool Coverage 8. Successor Pool Growth Rate
  • Training & Development Training Education & Development 1. Average Training Class Size 2. E-Learning Abandonment Rate 3. Employee Satisfaction with Training 4. Training Channel Delivery Mix 5. Training Course Content Breakdown 6. Training Expense per Employee 7. Training Hours per FTE 8. Training Hours per Occurrence 9. Training Penetration Rate 10. Training Quality 11. Training Staff Ratio 12. Training Total Compensation 13. Expense Rate 1. Development Program Penetration Rate 2. Educational Attainment Breakdown 3. Staffing Rate—Graduate Degree 4. Staffing Rate—High Potential 5. Tuition Reimbursement Request Rate
  • Compensation & Benefits Compensation Benefits Equity 1. Average Cost Rate of Contractors 2. Average Hourly Rate 3. Bonus Actual to Potential Rate 4. Bonus Compensation Rate 5. Bonus Eligibility Rate 6. Bonus Receipt Rate 7. Compensation Satisfaction Index 8. Direct Comp Operating Expense Rate 9. Direct Compensation Breakdown 10. Direct Compensation Expense 11. per FTE 12. Market Compensation Ratio 13. Overtime Expense per FTE 14. Overtime Rate 15. Total Compensation Expense per FTE 16. Upward Salary Change Rate 1. Benefits Expense per FTE 2. Benefits Expense Type Breakdown 3. Benefits Operating Expense Rate 4. Benefits Satisfaction Index 5. Benefits Total Compensation Rate 1. Average Number of Options per Employee 2. Equity Incentive Value per Employee 3. Net Proceeds of Options per Employee Exercising 4. Number of Options Exercised per Employee 5. Stock Incentive Eligibility Rate
  • Workforce Demographic Structural Tenure 1. Age Staffing Breakdown 2. Average Workforce Age 3. Ethnic Background Staffing Breakdown 4. Gender Staffing Breakdown 5. Staffing Rate—Disability 6. Staffing Rate—Female 7. Staffing Rate—Minority 8. Staffing Rate—Multilingual 1. Average Span of Control 2. Customer-Facing Time Rate 3. Employee Ownership Rate 4. Employment Level Staffing Breakdown 5. Function Staffing Breakdown 6. Staffing Rate—Corporate 7. Staffing Rate—Customer Facing 8. Staffing Rate—Managerial 9. Staffing Rate—Part Time 10. Staffing Rate—Revenue Generating 11. Staffing Rate—Temporary 12. Staffing Rate—Union Employees 1. Average Workforce Tenure 2. Organization Tenure Staffing 3. Breakdown Staffing Rate—< 1- Year Tenure
  • Organizational Effectiveness Productivity Structural Innovation 1. Human Investment Ratio 2. Operating Expense per FTE 3. Operating Profit per FTE 4. Operating Revenue per FTE 5. Other Labor Rate 6. Return on Human Investment Ratio 7. Work Units per FTE 1. Corporate Expense Rate 2. Employee Stock Ownership Percentage 3. Intangible Asset Value per FTE 4. Market Capitalization per FTE 1. New Products & Services 2. Revenue per FTE R&D Expense Rate
  • Critical areas for HR Predictive analytics 1. Turnover modeling. Predicting future turnover in business units in specific functions, geographies by looking at factors such as commute time, time since last role change, and performance over time. 2. Targeted retention. Find out high risk of churn in the future and focus retention activities on critical few people 3. Risk Management. Profiling of candidates with higher risk of leaving prematurely or those performing below standard. 4. Talent Forecasting. To predict which new hires, based on their profile, are likely to be high fliers and then moving them in to fast track programs
  • Advanced Analysis & Predictive examples Problem statement: An Indian MNC has a linear growth model. It wants to identify relationship between % revenue growth and % headcount growth. They have revenue and headcount details for past 10 years. Solution approach: • Identify the correlation coefficient based on the type of data and plot a scatter plot. • Given that revenue growth is estimated at X% for the next year, we can predict headcount growth 1 Problem statement: An HR manager identify 20 variables such as educational qualification, college, age, gender, nationality etc. that predicts the hiring effectiveness. He wants to identify mutually exclusive variables which affect hiring effectiveness. Solution approach: • Using factor analysis , mutually exclusive factors can be identified 2
  • Advanced Analysis & Predictive examples Problem statement: Campus hiring team is interested in how variables, such as entrance test score conducted by company, GPA (grade point average) and prestige of the institution, effect selection . The response variable, selected/not selected, is a binary variable Solution approach: • Selection data is collected for past 5 years for the above parameters indicated. • Here dependent variable is selected/not selected( Selected =1, Not Selected= 0) and independent variables are Test Score, GPA, Prestige of the institute. • Using logistic regression a equation can be developed 3 Problem statement: A company conducted a employee engagement survey using a questionnaire developed by internal HR team. The questionnaire had 15 questions and responses were collected from 50 employees. As a HR manager, we want to identify mutually exclusive factors Solution approach: • Using factor analysis , mutually exclusive factors can be identified 4
  • Over to Dilbert 
  • Agenda 1. Core concepts, terminologies & buzzwords Business Intelligence, Analytics Big Data, Cloud, SaaS 2. Analytics Stages, Types, Domains, Tools… 3. HR Analytics Why? What is measured? What can be measured? Predictive possibilities… 4. Case studies 5. HR Analytics org structure & delivery model
  • Real world case studies Starbucks, Limited Brands, and Best Buy—can precisely identify the value of a 0.1% increase in employee engagement among employees at a particular store. At Best Buy, for example, that value is more than $100,000 in the store’s annual operating income. Many companies favor job candidates with stellar academic records from prestigious schools—but AT&T and Google have established through quantitative analysis that a demonstrated ability to take initiative is a far better predictor of high performance on the job. Employee attrition can be less of a problem when managers see it coming. Sprint has identified the factors that best foretell which employees will leave after a relatively short time. In 3 weeks Oracle was able to predict which top performers were predicted to leave the organization and why - this information is now driving global policy changes in retaining key performers and has provided the approved business case to expand the scope to predicting high performer flight .
  • Real world case studies Dow Chemical has evolved its workforce planning over the past decade, mining historical data on its 40,000 employees to forecasts promotion rates, internal transfers, and overall labor availability. Dow uses a custom modeling tool to segment the workforce and calculates future head count by segment and level for each business unit. These detailed predictions are aggregated to yield a workforce projection for the entire company. Dow can engage in “what if” scenario planning, altering assumptions on internal variables such as staff promotions or external variables such as political and legal considerations.
  • Agenda 1. Core concepts, terminologies & buzzwords Business Intelligence, Analytics Big Data, Cloud, SaaS 2. Analytics Stages, Types, Domains, Tools… 3. HR Analytics Why? What is measured? What can be measured? Predictive possibilities… 4. Case studies 5. HR Analytics org structure & delivery model
  • Importance of HR Analytics as a function 20% Analysis 80% Data Capture 80% Analysis 20% Data Capture Go out and measure & analyze the reasons for turnover of my sales people Build a dashboard that continuously correlates retention with engagement, competency scores and other measures Measurement as a Project Measurement as a Process
  • How does Analytics fit in HR delivery model Shared Services / Process administration Center of Excellence HR Business Partnering HR Head Zonal HR Head Location 1 HR Location 2 HR Business Unit HR BU 1 HR BU 2 HR Compensation & Benefits Recruitment & Selection Learning & Development HR Analytics Shared Services
  • Common mistakes to avoid 1. Keeping a metric live even when it has no clear business reason for being 2. Relying on just a few metrics to evaluate employee performance, so smart employees can game the system 3. Insisting on 100% accurate data before an analysis is accepted— which amounts to never making a decision 4. Assessing employees only on simple measures such as grades and test scores, which often fail to accurately predict success 5. Using analytics to hire lower-level people but not when assessing senior management 6. Analyzing HR efficiency metrics only, while failing to address the impact of talent management on business performance
  • Key to success in HR Analytics 1. Transparency of business and workforce information 2. Analytics as a journey, not an end 3. Develop culture of data-driven decision-making 4. Empower line leaders, not just HR and L&D Build an HR Data Warehouse Deliver Actionable Business Information
  • Thanks! Q&A time… For one last time  LinkedIn: in.linkedin.com/in/akshayraje/ Twitter: @akshayraje Email: akshay.raje@gmail.com