Explore the Possibilities

Transcription

1 Explore the Possibilities 2013 HR Service Delivery Forum Got Predictive Analytics? 2013 Towers Watson. All rights reserved.

2 Reporting and analytics progress continuum current state of market Late Bloomers Starting Majority Advancing Majority Leaders Analytic Geeks Report only current-state statistics on an ad hoc basis Lack documented reporting protocols Inconsistent or nonexistent definitions used for data entry No BI tool or equivalent to support reporting Report basic statistics on current state and trends Have documented reporting protocols Have definitions for data entry (but no built-in quality checks) Requires significant user intervention to get results Report statistics versus targets or benchmarks Have built-in reporting protocols Have built-in definitions for data entry with basic quality checks Requires moderate user intervention to produce results Report past, present and future metrics Have Business Intelligence tool covering most markets for reporting Minimize data manipulation Make significant up-front investment to get data and reporting methods right Have bifurcated expertise for standard reporting vs. custom analytics Have true global data warehouse Have automatic feeds from all systems Take advantage of advanced research methods 1

3 Quiz #1: Are your customers ready? Thinking about your organization, where would you classify yourself in the analytics continuum? a. Late bloomer b. Starting majority c. Advancing majority d. Leader e. Analytic geek 2

4 Some framing questions to test needs and capability Are business leaders asking for workforce analytics and planning? If not, what would you need to have to build the business case? What workforce analytics are you doing today? How are they being used by the business? Do you already know the key metrics that deliver the highest yield? If not, what steps could you take to identify potential high-yield metrics? Do you have a replicable process and supporting technology to deliver key metrics, analytics and forecasts? If not, have you looked at whether you can leverage an existing BI capability or reviewed other available tools to support workforce analytics and planning? Does someone own this process? If not, is there someone you can assign who can work to build this capability? 3

5 Reaching for the stars: Analytic methods have varying impact Predictive Analytics Complex How do we drive performance? How do we predict performance? Projections/ Future-State Modeling Causal Analysis/ Controlled Evaluation Analytical Sophistication How are we performing over time? How are we performing relative to benchmarks? Benchmarking Trending How are we performing today? Dashboard/ Metrics Reporting Foundational How do we access data to monitor performance? Global Data Warehouse Data Management Internal Tracking External Reference Point Directional Insight Future Risk Management Outcome Optimization Return on Investment 4

6 Quiz #2: Is HR ready? What offering provides clear answers to the question How do we drive performance? a. Projections b. Trending c. Causal analysis d. Benchmarking 5

7 Global data warehouse provides more than just metric consistency; segmentation can tell the story 6

8 SAMPLE OUTPUT Dashboard/Metrics reporting Actives By Grade Turnover Rates Time in Position I I I H G F E Years H G F E H G F E D D D 2 C C C 1 B B B 0 A A A % 5% 10% 15% 20% 0% 50% 100% Employees in higher grades are more likely to be in their position longer, which could suggest a bottleneck for career advancement This finding is further strengthened by examining the turnover rates by grade, where we see an increase in turnover rates in Grade E, as employees may perceive potential career advancement issues (given the long time in position of those employees in Grade F and above) 7

9 SAMPLE OUTPUT Benchmarking 8

10 SAMPLE OUTPUT Trending (format options vary) Four-Quadrant Dashboard Pure Numeric Reporting Blended Dashboard Heat Map Format 9

11 SAMPLE CONTENT Projections/Future-state modeling Goals of workforce projections: Provide insights into the potential future state of the workforce and labor cost based on historical trends Show implications for key workforce segments of alternative staffing models (e.g., temporary vs. full- time associates, buy vs. build strategy for different roles) Be as simple or complex as needed to support strategic planning 10

12 Causal analysis/controlled evaluation Internal Data External Data Engagement Data Business Data Employee Location Job Manager Rewards Performance Market Conditions Survey data Customer satisfaction scores Revenue 11

13 All else equal, which factors drive key workforce, engagement and business outcomes? 12

14 Predictive modeling is a unique undertaking Set project goals and review background Gather and prepare data Build predictive models Incorporate constraints Analyze impacts Wrap up and implement 1. Plan Project: Establish scope, objectives and requirements 2. Gather and Prepare Data: Gather data and create necessary model files. This is usually the most time-consuming part of the project; it can be up to 80% of the work 3. Build Models: Use historical data to build and validate models 4. Incorporate Constraints: Convert modeled pure data into indications after consideration of internal and external constraints 5. Analyze Impacts: Analyze the results and finalize decisions 6. Wrap Up: Document decisions and communicate results 13

15 And requires specialized skills Set project goals and review background Gather and prepare data Build predictive models Incorporate constraints Analyze impacts Wrap up and implement Gathering and preparing data Database management Understanding how raw data is structured and stored Understanding how data needs to be structured for analyses Data manipulation and scrubbing SAS SPSS SQL programming Build predictive models Understand methods Regression Neural networks Decision trees Machine learning GLM theory The art of modeling Other data-mining techniques 14

16 SAMPLE CONTENT Using advanced research methods Engagement drivers example Likelihood of Lower Engagement Scores 0% 10% 20% 30% Manager < 1 yr in position Last quarter bonus in bottom quartile Avg. work team turnover > 20% 1 year lower tenure on work team 10% lower base pay increase Bottom performance decile Being in Location J Tier 1 education More than 2 prior jobs Manager span > 8 15

17 SAMPLE CONTENT Using advanced research methods Taking action on factors identified as having lower engagement scores Opportunity Timing Significant Factors Associated with Lower Engagement scores Supervision Immediate Managers < 1 year in position Manager span > 8 Rewards Short-term Last quarter bonus in bottom quartile 10% lower base pay increase Talent Management Interventions Develop on-the-job training program for new managers to shadow current managers before taking on supervisory responsibilities (to reduce potential transition issues) Consider a policy to restrict management spans of control to no more than seven Review rewards mix to ensure incentive alignment (e.g., recognizing engagement impact) Review guidelines for merit pay adjustments to set minimum floor by performance level, given link between engagement and customer service Retention Long-term Average work team turnover >20% Review work teams with more than 20% turnover to determine ways to mitigate risk (e.g., manager assessment, enhanced training opportunities, rewards review, career development initiatives) 16

18 Whose technology is promising (and actually delivering) predictive capability? User activity in the BI and analytics platform market is from organizations that are trying to mature from descriptive to diagnostic analytics; very few organizations are tackling predictive modeling in the HR space Gartner analysis has been expanded to include Analytics in scope for these solutions: SAS remains the pure analysts choice although it is difficult to set up and maintain IBM s SPSS is a close second and can be offered alongside a broad suite of solutions Open source R is embraced by Oracle, SAP and many niche players such as Jaspersoft Pentaho is a full open-source solution with strong predictive capability Tibco gets high marks for predictive capability but not for integration Birst is a cloud-based solution whose customers are using predictive functionality 17

19 In closing, here are some things NOT to do 1. Wait to do ANYTHING until they have perfect data on EVERYTHING 2. Fail to recognize the QUALITY of the data you are already using 3. Focus on DATA rather than INSIGHTS 4. Rely too much on what OTHERS DO 5. Think a TOOL is the SOLUTION 6. See the first results and RUSH to judgment 7. Look for the SILVER BULLET 8. Treat HCM as a ONE-OFF EVENT 9. Believe the organization can TAKE CARE OF ITSELF 10. Make measurement NO ONE S JOB 18

20 Questions 19

21 Today s presenters Helen M. Friedman Workforce Analytics and Planning, Global Practice Leader helen.friedman@ Dave Young Consultant dave.young@