Using Big Data to Generate New Insights about Your Workforce Sirota Science Webinar Series

Transcription

1 August 2013 Using Big Data to Generate New Insights about Your Workforce Sirota Science Webinar Series

2 Welcome to Our 2013 Sirota Science Webinar Series SirotaScience Webinar Series Regular review of current workforce trends Purpose: explore topics, share insights, and exchange ideas Discuss data, academic research, and your observations from the field Format 30 to 45 minute presentation followed by open discussion Please hold questions until open discussion Looking ahead to September Mindfulness at Work 2

3 Today s Agenda Big Data Putting Things In Context Big Data Methodology Applications What Can We Learn? About Leadership About Employees About Performance Recommendations Building a Big Data Practice in Your Organization 3

4 We re Living in The Digital Age We Have Enough Data to Reach the Moon And We re Producing Data at a Faster Pace than Ever According to IBM, 90% Of the world s data has been created in the past two years 45 billion photos stored 55.7 billion blogs 1m customer transactions per hour In 2000, only 25% of the world s stored information was digital. Today, less than 2% of all stored information is non-digital (Cukier & Mayer-Schoenberger, 2013). 4

5 This Explosion in Data has Created an Analytic Opportunity In a 2001 white paper for Meta Group, Doug Laney identifies three emerging data management challenges Data Velocity Data Volume Data Variety Since then over 25 working definitions have emerged PC Magazine Big Data refers to the massive amounts of data that collect over time that are difficult to analyze and handle using common database management tools. Big Data includes business transactions, messages, photos, surveillance videos and activity logs. Gartner "Big data are high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization." Rick Smolan Big data is the process of helping the planet grow a nervous system 5

6 Methodological Mindset Shifts Cukier & Mayer-Schoenberger (2013) From Some To All Clean Messy Causation Correlation Using big data will sometimes mean forgoing the quest for why in return for knowing what 6

7 Two Real World Examples Google Tracks Flu Outbreaks using search terms Google handles more than a billion searches in US per day They took 50 million most commonly searched terms between 2003 and 2008 and compared them against influenza data from CDC Allowed algorithms to determine if any search terms correlate with geographic specific flu outbreaks After running half a billion calculations, 45 words (e.g, headache, runny nose) emerged that had a strong correlation with CDC outbreaks NYC Uses Big Data to Fight Fires Illegally subdivided buildings are fire hazards NYC gets 250,000 complaints a year about overcrowded/subdivided buildings, but only has 200 inspectors Small team of analysts assembled database of 900,000 buildings in the city and added in data collected from 19 city agencies Amongst other things, they found that buildings obtaining permits for exterior brick work were at a lower risk for fires (upkeep = less likely to be overcrowded) Based on data, Building inspectors increased their vacate order effectiveness from 13% to 70% See Cukier & Mayer-Schoenberger (2013) for additional details on both examples 7

8 Promises & Pitfalls of Big Data Promises Increased Clarity & Transparency Segmentation & Customization More Data based Decision Making Innovations & Insights Pitfalls Skills & Resources Data Integration Analysis Paralysis & Data Overload Ethical Data Management & Governance We need a roadmap for conducting Employee Focused Big Data Analysis 8

9 What Can We Learn About Using Big Data Techniques? Employee Experience Leader Impact Workforce Performance Cross-Database Linkage Analyses 9

10 A Big Data Analysis of Leader Impact Research Question: Does Manager Engagement impact Team Level Engagement and Turnover? Manager Engagement +5 Manager Effectiveness +3 12% Team Engagement Team Member Retention +5 Fairness Manager Engagement Data Team Level Engagement Data HRIS Turnover Data Insight: Reducing turnover starts with ensuring managers are engaged 10

11 A Big Data Analysis of the Employee Experience Research Question: Do various HR policies and programs, including training and formal recognition programs, impact employee engagement? Engagement Scores for 310 Employees Stable Start Despite high Performance Ratings and Training Hours, only 26% received Recognition Reward and only 30% received Promotion Significant increase in Training Hours; Over 50% were Promoted & Formally Rewarded Insight: If we re going to train employees, we better recognize & reward them 11

12 A Big Data Analysis of Workforce Performance Research Question: What do call center employees need to deliver exceptional customer service? Leadership + Encouragement + r My leader gives me a clear picture of the direction in which the company is headed.37 My leader values diverse ideas and perspectives.31 On my team, my ideas are listened to and often implemented.39 I feel encouraged to come up with new and better ways of doing things.33 Resources I have the tools I need to provide excellent customer service.33 Insight: Our call center employees want to be involved in improving service 12

13 Big Data Can Help Us Understand How Critical Factors Are Related Efficiency Productivity Innovation Profit Business Performance Personality Leadership & Management Practices Behaviors Competencies Development Programs EVP Retention Complaints Customer Experience People Policies & Procedures Strategy Structure Advocacy Satisfaction Retention Employee Experience Engagement Training Where are the relationships? Systems Individual Differences 13

14 Two Big Data Studies We re Currently Pursuing Exploring the Relationship Between Personality, Behavior, Climate, and Engagement Manager Personality Manager Behavior Team Climate Team Engagement Conscientiousness Innovation (r =.35*); Engaging (r =.39*) Emotional Stability Customer Service (r =.24*) Identifying Distant Early Warning Signs of Turnover Recent Exit Survey Findings Approximately 4 out of 10 exiting employees started considering leaving at least 6 months before they left Stagnant Disconnected Unsupported Disenchanted 14

15 Pulling it all Together: A Big Data Research Framework Recruiting & Onboarding Organizational Culture HR Programs & Offerings Training Business Environment EVP/Deal Career Pathing Climate Factors My Manager My Job My Career My Team Individual Differences Motivation Fit Engagement Commitment Performance Business Performance Turnover Service Efficiency Productivity Profit Stock Performance Demographics & KSA Career Stage Mindset Aspirations 15

16 Developing a Big Data Practice in Your Organization Five Recommendations

17 Step 1: Conduct a Data Inventory Assessment What data do you have in house? How can we leverage our data more by linking databases together? Leadership HR Programs & Policies Employees Customers Performance Personality Assessments EVP/Deal Demographics & KSAs Satisfaction KPIs Selection Data Training Programs Aspirations Advocacy Productivity /Output 360 Results Rewards & Incentives Engagement Complaints Defects Leadership Development Cohorts & Unique Populations Retention Retention Revenue/Profit Oftentimes, we have more data than we realize. 17

18 Step 2. Brainstorm Possible Research Directions Leadership & Management Practices Strategic Practical People Policies & Procedures Inductive Deductive Employee Experience General Specific Customer Experience Exploratory Confirmatory Business Performance Quantitative Qualitative 18

19 Recent Client Example: Exploring Turnover 2010 Engagement Database 2012 Engagement Database 2012 Exit Survey Database HRIS Information 1. What are the attitudinal antecedents of turnover at XYZ? Which items from the 2010 and 2012 Employee Engagement surveys predict attrition in 2012? 2. What longitudinal trends and patterns do we see amongst exiting employees? What is the relationship between 2010 and 2012 attitudes and 2012 Exit attitudes? 3. What is the relationship between manager attitudes and employee turnover? Do engaged managers do a better job retaining employees than disengaged managers? 4. What impact does team climate have on turnover? Are exiters more likely to leave positive, neutral, or negative environments? 5. Do written comments provide additional insight into why employees leave XYZ? 19

20 Step 3. Integrate & Explore the Data 360 Database Engagement Database Customer Satisfaction Database Big Data Database Statistical Analysis Sirota Advanced SORT for Practitioners Descriptives Correlations Regressions Relative Weights ANOVAs MANOVAs Logistic Regression SEM Integrate Data Trends Patterns Cohorts 20

21 Step 4: Turn Insights Into Action Hospitality Methodology Employee survey conducted Customer survey Integrated Major Outcomes: Significantly improved attitudes toward supervision No decline in customer attitudes despite 15% increase in customer volume Prior experience was that increases in customer volume almost invariably led to significant declines in customer satisfaction Development of Human Capital Decision Tool Actions focused on supervision Selection and placement Training and coaching Emphases: Respect, recognition, listening, coaching 21

22 Step 5: Build a Big Data Analytics Program Establish a Clear Mission We produce regular, data-based, strategic insights about our workforce that improve individual and organizational effectiveness and performance Develop a Collaborative Process Involve key strategic stakeholders throughout the organization, including senior leaders, manager, function heads Feed the Big Data Machine Regular integration of HRIS data, assessment data, survey data, performance data Deliver Compelling Results Quarterly report to key stakeholders Recommendations and tools for action 22

23 In Sum Big Data is the next big thing in HR By integrating data from various sources and searching for expected and unexpected relationships, we can generate new insights about our leaders, our workforce, and performance Business Performance Leadership & Management Practices With a clear roadmap, organizations can develop an effective Big Data research program Customer Experience Employee Experience People Policies & Procedures Effective programs will generate two things New Insights New Action 23

24 Thank You For additional information, please contact: Patrick Hyland, PhD Director of R&D 24