HR s Secret Weapon: The Power of Analytics. Thomas Daglis - Data Scientist August 2015

Transcription

1 HR s Secret Weapon: The Power of Analytics Thomas Daglis - Data Scientist August 2015

2 AGENDA What it means to be data driven The importance of good data Examples of analytics in practice 2

3 3 What if? You had the same level of business intelligence on your people as you did in other disciplines, like Finance?

4 Why Be More Data Driven in HR? It can cost up to 3x the annual salary to replace an employee Best-in-class organizations are twice as likely to have a process in place to for identifying and retaining high potential talent 91% of high performing companies rigorously assess the ROI of initiatives and programs using data 4

5 Most organizations are sitting on a wealth of workforce data as many as 16 million data points Yet only 4% of companies have successfully executed HCM programs that are driven by data 5

6 HR is trailing other initiatives 6 Source: Gatepoint Research / IBM Strategies for Integrating Analytics May 2013

7 So Why Aren t More Taking Advantage? Lack of preparedness and/or skills Technical and talent shortcomings Absence of buy-in from top leadership 7

8 Data outweighs gut feel Validate gut feel Data will never have all the facts 8

9 Let the facts drive new actions Knowledge Data Action 9

10 Start small to overcome complexity Just focus on a high ROI initiative, like RETENTION 10

11 Replacing employees is expensive Salaried Employees Nonsalaried Employees 1.5 to 3 times the annual salary $5k - $20k per employee Includes Separation, Replacement, and Training costs 11 From SHRM, NRF, J Douglas Philips, and Bersin studies

12 Garbage in garbage out The importance of good data vs bad data Understanding the difference will help you overcome skill deficiencies Question everything with curiosity! 12

13 13 An example of bad data

14 Quantifying the impact of bad data 30% Poor data quality is costing businesses at least 30% of revenues 14

15 1. Accurate 2. Current 3. Consistent 4. Complete 5. Coverage 7 ways to think about data quality 6. Unique 7. Predictive 15

16 Internal HR systems collect valuable data A single source of record provides Accuracy, Currency, Consistency, Completeness, Coverage 16

17 Externally, you have Big Data Volume (lots of data) Variety (many types) Velocity (speed of data in/out) Veracity (conformity to facts) 17

18 Inflated Expectations for Big Data 18 Source: Gartner Hype Cycle for Human Capital Management Software (July 2013)

19 90% of all the data ever collected has been collected in last two years Source: ScienceDaily 19 Data collected every 60 seconds Source: Qmee (2013)

20 Internal vs. external data More predictive Internal data Less predictive External data During Employment Preemployment Post- Employment 20

21 21 Maybe there is a fifth, most important V

22 The HCM Market Response 22 Source: Gartner Hype Cycle for Human Capital Management Software (July 2013)

23 Successful Talent Science allows for recruiting the optimal candidates classifying and categorize employees developing the appropriate leaders retaining the right employees competing strategically 23

24 Talent Science in practice 24

25 Compensation History Use the data not manager judgment Example: Predict Retention Black/White Outcome Plenty of data Predictive Analytics 25

26 Each attribute contributes differently Less important More important Education Level Org Level Changes Tenure Full or Part Time 26

27 Each employee gets their own score LOW RISK MEDIUM RISK HIGH RISK

28 How many terminate? RETAINED Measure the results from score date to today TERMINATED Historical scores HIGH RISK MEDIUM RISK LOW RISK 28

29 29 Results

30 % of Employees Retention Predictor Results 100% HIGH RISK 10% MED RISK 40% LOW RISK 50% 80% 60% 40% 20% 0% Overall Term Rate: 17.1% 49, , ,028 57,440 99,976 32, Retention Predictor Historical Score Ranges # Terminated # Retained 30

31 High Performer Predictor Results Not High Performers % High Performers 5.1% 31

32 Term Rate for High Performers is 50% Lower Overall term rate 17.7% HPI term rate 8.3% 0.0% 5.0% 10.0% 15.0% 20.0% % Terminated 32

33 By using the High Performer Indicator with the Retention Predictor, organizations can focus on saving the most valuable employees HIGH PERFORMER Employees with low Retention Predictor scores 33

34 HIGH PERFORMER Organizations can also optimize their investments in their employees, since high scoring people aren t going anywhere Employees with low Retention Predictor scores 34

35 Why do people leave? 31% don t like their boss Aberdeen Group 35% due to internal politics/turf Aberdeen Group 31% do not feel empowered Aberdeen Group 43% for lack of recognition Aberdeen Group 89% of managers believe that most employees are pulled away by better pay but 88% of voluntary resignations happen for reasons other than pay Leigh Branham, The Seven Hidden Reasons Employees Leave >60% do not feel like they get enough feedback Gallup Poll 75% of people leave because of work relationship issues Saratoga Institute 75% of people who leave voluntarily don t quit their jobs; they quit their boss Roger Herman #1 reason is lack of recognition Bersin 79% of those who quit their job cite lack of appreciation as primary reason SHRM #1 reason for millennials: not learning enough Business Insider 35

36 IT S OVERWHELMING!!! 31% don t like their boss Aberdeen Group 35% due to internal politics/turf Aberdeen Group 31% do not feel empowered Aberdeen Group 43% for lack of recognition Aberdeen Group 89% of managers believe that most employees are pulled away by better pay but 88% of voluntary resignations happen for reasons other than pay Leigh Branham, The Seven Hidden Reasons Employees Leave >60% do not feel like they get enough feedback Gallup Poll 75% of people leave because of work relationship issues Saratoga Institute 75% of people who leave voluntarily don t quit their jobs; they quit their boss Roger Herman #1 reason is lack of recognition Bersin 79% of those who quit their job cite lack of appreciation as primary reason SHRM #1 reason for millennials: not learning enough Business Insider 36

37 A change in the thought process To understand what makes people stay, you have to experiment with a population who is supposed to leave Retention scores are a great way to identify this population 37

38 Decide you will have stomach for an experiment Write down your game plan and your goals/expectations Putting it into action Execute the plan and measure the results 38

39 % of Employees Know where you are 100% 80% 60% 66% 40% 20% 0% Retention Predictor Score Ranges # Terminated # Retained 39

40 40 Identify and profile your low scoring employees

41 Set up an experiment Take Specific Action on me! Nothing special for me! Split into two groups of 50 employees 41

42 Understand the expectations Take Specific Action on me! Nothing special for me! Expectation: 33 will leave from each group 42

43 Decide how to treat & execute Money Achievement & Growth Approval & Recognition 43

44 12 months later Did the incentives work? 44

45 # of employees Track the data & measure the results saved Take Action No Action Retained Terminated 45

46 Show the results to your leadership 10 people x $50,000 x 2 = $1,000,000 Salaried Employees 1.5 to 3 times the annual salary Nonsalaried Employees $5k - $20k per employee 46 From SHRM, NRF, J Douglas Philips, and Bersin studies

47 Leadership will start paying attention 47

48 Let the facts drive new actions Knowledge Data Action 48

49 Key Points to take away Data driven HR departments are more successful Having good data quality is critically important Talent Science is here start small / don t be afraid! 49

50 50 Thomas Daglis