Data Coaching: A Cure for HR Data Analysis Paralysis? Theresa M. Welbourne, Ph.D. Denise R. Avink Research Professor Human Resources Center for Effective Organizations Northrop Grumman University of Southern California Aerospace Systems President and CEO, eepulse, Inc. Editor in Chief, HRM, the Journal Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 1
AGENDA Introduction to Data Coaching The Case for Data Coaching The Art and Science Measurement Map Story at Northrop Grumman Join us for Data Coaching December 8 10 th, LA Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 2
CEO Story ROI stories vs. ROI numbers Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 3
ROI Money Significant (in hundreds of millions) But he talked about the stories (samples below) ACTION ROI STORY Run credit workshop. Set up recycling and reduce or revise scheme on our team. Replace handset prompt p bullet point. Reimplement buddy Ops groups. Discuss at Buzz meeting to gain everyone's understanding of how buddies are allocated. Will save the company money Better team work, Engaged workforce. Consider how we manage checking system. Meeting to review whole process. Be bolder with timeframes. Increased efficiency and time spent on value add exercises Senior VP to come into team meeting. We will We will all know what own our own research and report back what we are working we learn. We will prepare a teams news towards, which can bulletin only be good for the business as a whole. Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 4
HR Data Dashboard and Reports Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 5
HRM Measurement Question Often Asked What are the magic numbers that t we should collect in our company? Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations
HRM Measurement Question Often Asked What are the magic numbers that t we should collect in our company? What we need are magic stories that come from the right HR data not just more HR data Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations
Better Numbers vs. More Numbers Mallan (1997) showed that stories differ from othernarratives (arguments, scientific reports, articles and we add pie charts and bar charts) in that theyorient our feelings and attitudes about the story content This emotional engagement is why info presented in the structure of a story is more easily remembered Emotions trigger activation i in the brain From Story Proof by Hendall Haven Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 8
What is a Story? A detailed character based narrative of a character s struggles to overcome obstacles and reach an important goal Blend the art of story telling with the science of data analysis and research = Power Story Telling Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 9
Story Arc COMPLEX SIMPLE Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 10
Art and Science of Data Coaching Blend Traditional Analytics with the Narrative Predictor Variables OutcomeVariables Moderator Variables Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 11
Art and Science of Data Coaching Blend Traditional Analytics with the Narrative Predictor Variables OutcomeVariables The protagonist hero Moderator Variables Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 12
Art and Science of Data Coaching Blend Traditional Analytics with the Narrative Predictor Variables OutcomeVariables The protagonist hero Target of the quest Moderator Variables Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 13
Art and Science of Data Coaching Blend Traditional Analytics with the Narrative Predictor Variables OutcomeVariables The protagonist hero Target of the quest Moderator Variables Obstacles to overcome Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 14
Art and Science of Data Coaching Example #1 Predictor Variables OutcomeVariables Moderator Variables Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 15
Art and Science of Data Coaching Example #1 Predictor Variables OutcomeVariables Employees are satisfied Moderator Variables Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 16
Art and Science of Data Coaching Example #1 Predictor Variables OutcomeVariables Employees are satisfied Employees are more likely to stay (retention) Moderator Variables Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 17
Art and Science of Data Coaching Example #1 Predictor Variables OutcomeVariables Employees are satisfied Employees are more likely to stay (retention) Moderator Variables What obstacles may get in the way? What are the conditions under which this simple linkage may not hold up? Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 18
Art and Science of Data Coaching Example #2 Predictor Variables OutcomeVariables Moderator Variables Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 19
Art and Science of Data Coaching Example #2 Predictor Variables OutcomeVariables Leaders learn new leadership skills (emotional intelligence, for example) Moderator Variables Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 20
Art and Science of Data Coaching Example #2 Predictor Variables OutcomeVariables Leaders learn new leadership skills (emotional intelligence, for example) Moderator Variables The company performs better; stock price goes up; sales improve; profitability increases Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 21
Art and Science of Data Coaching Example #2 Predictor Variables OutcomeVariables Leaders learn new leadership skills (emotional intelligence, for example) Moderator Variables The company performs better; stock price goes up; sales improve; profitability increases Obstacles to overcome???????? Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 22
Art and Science of Data Coaching Blend Traditional Analytics with the Narrative Predictor Variables Outcome Variables The protagonist hero Target of the quest Moderator Variables Obstacles to overcome The story lens improves our story telling or our research. Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 23
How do we find good HR data stories in our organizations? Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 24
Conduct Data Audits and Align Data with Business Goals MEASUREMENT MAP Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 25
Data Key Components of Measurement Map Dialogue Action Results As data becomes more complex, story telling, dialogue and persuasion are MORE important, and as both become more rigorous, action and results are stronger. Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 26
Two Data Dimensions Type of data Objective vs. subjective Time of data collection One point in time Multiple points in time (e.g. longitudinal data) Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 27
Two Data Dimensions Type of data Objective vs. subjective Time of data collection One point in time Multiple points in time (e.g. longitudinal data) Subjective data (often primary source data) = you need to do the work to obtain these data dt (e.g. focus groups, surveys, interviews), often attitude or opinion based dd data, supplied by the subject. Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 28
Two Data Dimensions Type of data Objective vs. subjective Time of data collection One point in time Multiple points in time (e.g. longitudinal data) Subjective data (often primary source data) = you need to do the work to obtain these data dt (e.g. focus groups, surveys, interviews), often attitude or opinion based dd data, supplied by the subject. Objective data (often secondary source data) = convenience data, data that you may not have to collect on your own; archival data (from an HRIS system, such as turnover, absenteeism). Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 29
Measurement Map Part 1 Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 30
Measurement Map Part 1 Number of people who worked Monday Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 31
Measurement Map Part 1 Number of people who worked Monday through Friday Number of people who worked Monday Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 32
Measurement Map Part 1 Satisfaction of people who came in Monday Number of people who worked Monday through Friday Number of people who worked Monday Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 33
Measurement Map Part 1 Daily satisfaction scores of people who came in Monday through Friday Satisfaction of people who came in Monday Number of people who worked Monday through Friday Number of people who worked Monday Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 34
Measurement Map Part 1 Satisfaction scores and tenure of people who came in Monday Daily satisfaction scores of people who came in Monday through Friday Satisfaction of people who came in Monday Number of people who worked Monday through Friday Number of people who worked Monday Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 35
Measurement Map Part 1 Changes in satisfaction per tenure group from Monday to Friday Satisfaction scores and tenure of people who came in Monday Daily satisfaction scores of people who came in Monday through Friday Satisfaction of people who came in Monday Number of people who worked Monday through Friday Number of people who worked Monday Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 36
Measurement Map Part 1 Changes in satisfaction per tenure group from Monday to Friday Satisfaction scores and tenure of people who came in Monday Daily satisfaction scores of people who came in Monday through Friday Changes in satisfaction affects weekly production and quality BUT ONLY FOR new hires. Tenure group is a moderator or obstacle. Satisfaction of people who came in Monday Number of people who worked Monday through Friday Number of people who worked Monday Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 37
Part 2: Audit process conducted at each level Action Results Dialogue Data Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 38
Part 2: Audit process conducted at each level Action Results Data Dialogue Learn what s working and what s not working. Keep the success stories; record what data, dialogue and action are needed to drive different results. Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 39
Match People to Data Levels Good stories come from all levels of data Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 40
Match People to Data Levels DATA PEOPLE Good stories come from all levels of data Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 41
Match People to Data Levels DATA PEOPLE DIALOGUE PEOPLE Good stories come from all levels of data Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 42
Learning from the Case Study Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 43
Denise Avink s Experience as a Data Coach Introduction to Denise and Northrop Grumman
Northrop Grumman Corporation & Aerospace Systems Overview USC CEO Data Coaching Webinar October 27, 2010 Denise Avink Human Resources
Northrop Grumman Today $32 billion sales in 2009 $69 billion total backlog 120,000 people, 50 states, 25 countries Second largest U.S. defense contractor Leading capabilities Systems integration C4ISR and battle management Information technology and networks Defense electronics Naval shipbuilding Space systems Missile defense
Northrop Grumman Corporation Five Operating Sectors Aerospace Systems Electronic Systems Information Systems Shipbuilding Technical Services Large Scale Systems Integration Unmanned Systems Environmental & Space Science Satellite Systems C 4 ISR Airborne Ground Surveillance / C2 Naval BMC2 Global / Theater Strike Systems Electronic Combat Operations ISR Satellite Systems Missile il Defense Satellite Systems MILSATCOM Systems Directed Energy Systems Strategic Space Systems Radar Systems C 4 ISR Electronic Warfare Naval & Marine Systems Navigation & Guidance Military Space Government Systems Command & Control Systems Network Communications Intelligence, Surveillance & Reconnaissance Systems Enterprise Systems and Security IT/Network Outsourcing Intelligence Federal, State/Local & Commercial Homeland Security & Health Naval Systems Integrator Surface Combatants t Expeditionary Warfare Ships Auxiliary Ships Marine Composite Technology Coast Guard Cutters Commercial Ships Nuclear Aircraft Carriers Nuclear Submarines Fleet Maintenance Aircraft Carrier Overhaul & Refueling Systems Support Base and Infrastructure Support Range Operations Maintenance Support Training and Simulations Technical and Operational Support Live, Virtual and Constructive Domains Life Cycle Optimization Performance Based Logistics Modifications, Repair and Overhaul (MRO) Supply Chain Management Lead Support Integrator (LSI)
Aerospace Systems $10B Business 23,000 Employees Prime contractor/major partner on large platform programs Manned and unmanned aircraft Space systems Missile systems Differentiated by technology leadership Large development programs Long production cycles and substantial cash returns Significant large new competitive opportunities World-class workforce 48
Aerospace Systems Organization FUNCTIONS President Gary Ervin SERVICES Business Development Bill Schaefer Engineering Gene Fraser Life Cycle Logistics & Support Jim Zortman Strategy Development & Planning Bruce Gerding Space Systems Dave DiCarlo Battle Management & Engagement Systems Production Pat McMahon Operations Tommy Tomlinson DIVISIONS Strike and Surveillance Systems Duke Dufresne Advanced Programs & Technology Paul Meyer Human Resources & Administration Bob McNulty Information Technology Solutions & CIO AtL Art Lofton Sector Counsel Georgetta Wolff CFO & Business Management Chuck Wands Global Supply Chain Lisa Kohl Communications Cynthia Curiel Quality, Safety & Mission Assurance Christopher Cool Internal Audit Wayne Watanabe Strength, Experience, Innovation
Aerospace Systems Space Systems Division Global Hawk STSS Restricted Missile Systems NPOESS Fire Scout Strike and Surveillance Systems Division F-35 JWST AEHF Targets F/A-18 Restricted B-2 Battle Management and Engagement Systems Division EA-6B Broad Area Maritime Surveillance (BAMS) Restricted Advanced Programs and Technology Division ABL LCROSS EA-18G E-8C JSTARS UCAS Restricted Maritime Laser Demo
Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations
How does data coaching supplement the skills already in place in your HR team? Why something new? WHY DATA COACHING? Copyright 2010 Dr. Theresa M. Welbourne Copyright and Center 2010 for Effective Northrop Organizations Grumman 52
Premise New CEO, Wes Bush, holds leadership conference with focus on increasing committed leadership and building a culture of performance Each vice president in attendance given action to share with Wes how they will support culture of performance Vice president for ~3,000 person organization where I worked, System Engineering, Integration Test & Launch (SEIT), focused on committed dleadership Committed leadership: Owns the vision Communicates the plan Engages g the team Acts with urgency Executes and performs Copyright 2010 Dr. Theresa M. Copyright Welbourne and 2010 Center Northrop for Effective Grumman Organizations 53
Art and Science of Data Coaching Our Story Predictor Variables OutcomeVariables Moderator Variables Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 54
Art and Science of Data Coaching Our Story Predictor Variables OutcomeVariables Committed leadership Moderator Variables Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 55
Art and Science of Data Coaching Our Story Predictor Variables OutcomeVariables Committed leadership Success Moderator Variables Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 56
Art and Science of Data Coaching Our Story Predictor Variables OutcomeVariables Committed leadership Success Moderator Variables Obstacles to overcome Step 1: What are the obstacles? Step 2: Do something about them Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 57
Putting it into Action Did not have a sense of where leadership team was on its understanding and commitment to success of committed leadership theme Had some discussions as a group, but no data to back up discussions After Data Coaching course, decided to employ a short survey with leadership team Survey results showed that there was confusion amongst the leadership team on what committed leadership meant As follow up, I utilized a leadership offsite to dialogue on the topic of committed leadership and instill a foundation of storytelling Note that the leadership group is comprised of engineers, a.k.a. rocket scientists Copyright 2010 Dr. Theresa M. Copyright Welbourne and 2010 Center Northrop for Effective Grumman Organizations 58
Offsite Introduction Exercise Today we ll discuss a lot of goals/objectives based on data, but then merely communicating the goals/objectives within our organizations is not enough. Instead, in order to truly engage our employees, the SEIT top team needs to craftand and communicate a story about whoweare we and our overall purpose so that our employees both remember and better understand the goals. The objective was to demonstrate that a story is more memorable than a mere list of facts Copyright 2010 Dr. Theresa M. Copyright Welbourne and 2010 Center Northrop for Effective Grumman Organizations 59
Offsite Application of the Measurement Map What results did we want to achieve? Committed leadership attributes and characteristics that will establish and sustain the Culture of Performance. Own the vision Communicate the plan Engage the team Acts with Urgency Executes & Performs What actions need to be taken, by whom, and by when? What dialogue needs to take place to influence action? What data will help the story? Copyright 2010 Dr. Theresa M. Copyright Welbourne and 2010 Center Northrop for Effective Grumman Organizations 60
What happened next Some members of the leadership team cascaded the survey in their organizations In addition to the survey, some organizations held discussion meetings to ensure common understanding of committed leadership concept Individual organizations held action planning meetings, which spawned grass roots initiatives at all levels Had we not done any of this work, we would have continued discussing committed leadership in our staff or round table meetings. Further, we would have assumed the leaders were of similar understanding and as a result, organizations could have gone in disparate directions! Copyright 2010 Dr. Theresa M. Copyright Welbourne and 2010 Center Northrop for Effective Grumman Organizations 61
Lessons Learned How did story change the process? How can you leverage that experience in the future? How do we train leaders to be data coaches? If it works for rocket scientists then what does that mean for people in other jobs? Copyright 2010 Dr. Theresa M. Copyright Welbourne and 2010 Center Northrop for Effective Grumman Organizations 62
Summary HR Data Analysis Paralysis Dashboards; Data Books; Huge Slide Decks Slicing and dicing Manager denial syndrome Why is HR data so much more controversial than other data? Why do we collect and report HR data less frequently compared to what s done in other areas of the business? Our conclusions Few good stories, no or not many results More data Better data Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 63
Questions? Join us in the next data coaching public program. Los Angeles December 8-10, 2010, Theresa M. Welbourne and Lacey Leone McLaughlin Call if interested in custom data audit and data coaching engagements. Resources: http://ceo.usc.edu/ www.leadershippulse.com www.energizeengage.com www.hrmthejournal.com Theresa M. Welbourne, Ph.D. theresa@eepulse.com www.eepulse.com +1-734-429-4400 Beth Mills, Executive Assistant http://www.linkedin.com/in/theresawelbourne Theresa s blog: http://blog.eepulse.com/ Copyright 2010 Dr. Theresa M. Welbourne and Center for Effective Organizations 64