WEAVING the class - Society for Human Resource Management
Transcript of WEAVING the class - Society for Human Resource Management
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Copyright @2017, Theresa M. Welbourne, PhD1
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Making Sense, Weaving Stories and Taking Action with DataTheresa M. Welbourne, PhD
Executive Director, Alabama Entrepreneurship Institute, Culverhouse College of Commerce, University of Alabama
President and CEO, eePulse, Inc.
Affiliated Research Professor, University of Southern California, Center for Effective Organizations
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History: WEAVING the class
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Data Dialogue Action Results
Story
The Art and Science: DDAR Model
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Myth #1More complex data are better data
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Learning
Green sticky notes = high impact
Yellow sticky notes = medium impact
Pink sticky notes = no impact
LEARNING FROM YOUR PEERS
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Why Simple Can Win
• The Science, Part 1
• Sense-making research
– “explicit and implicit mental processes to render a view of how things get done” (Woodside,
2001)
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Challenge is to know HOW to simplify to drive dialogue, action and results
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Example: The Employee Retention StoryTurnover
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Storytelling Version of the Same Data
Stacking Work Syndrome is making
employees leave.
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Myth #2Our job is to present raw data and work with manager to decide what it means
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Choice Theory
More Choices?
Choice =
freedom
liberation
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*The Paradox of Choice - Why More Is
Less (2004), Barry Schwartz.
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More Choices = More Paralysis
Plus, it’s your JOB to choose
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Myth #3
It is unethical to interpret the data because it’s easy to lie with statistics
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Neuroscience
When large volumes of data are experienced:
“It’s like hundreds of new actors jumping on the stage briefly and then running off” … you don’t remember the information..
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The Story of Ben
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Myth #4
We don’t need measurable business results
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What’s Not a Myth?
• Your job is to be a
Data-Driven Story Teller
• ONLY IF you want your data to drive dialogue, action and RESULTS
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Let me tell you a story about putting the art and science of DDAR together
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Case #1 – placeholder for
company story / data
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Data Dialogue Action Results
They were
STUCK
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Used data-driven storytelling techniques
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Being innovative
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Our Discussion
• Go back to basics
– What assumptions did they have?
– What assumptions might audience have?
– Alternative scenarios to explain the data?
• Review goals again
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Flipped the Model
Results Action Dialogue Data
What story should
the data tell?
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A Story About Opportunity
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Data Dialogue Action Results
They got Unstuck
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Science of the DDAR Model
• Neuroscience– What drives action? – Emotional response needed
• Dialogue involves two people – The role of common sense and sense making– Help build mental maps that drive action
• Choice theory – There aren’t as many choices as you may think– Avoid analysis paralysis
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Better to NOT be the Bozo30
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The Scenario
• Three years of tough business
• First employee survey
• Vendor presentation – Mental maps of leaders not taken into
account – Focused was on what vendor believed was
right
• Bozo status declared
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HR Leaders
• Survey was their idea
• Believed there was a way to salvage project
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Used Leader Lens33
What Employees Give Value Employees Get
QUESTIONS INSERTED QUESTIONS INSERTED
Employee
Value
Exchange
Proposition
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What employees give to the company
Low Medium High
What they
get from
the
company
High
Medium
Low
GETTING
MORE
THAN
GIVING
GIVING
MORE
THAN
GETTING
RISK
OF
ENTITLEMENTALIGNED NEGLECTED
WARRIORS
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Told Stories
• Risk of entitlement group
• Aligned employees
• Neglected warriors
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Changed the narrative
It was THEIR story – not a
benchmarking story
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Link to Science
Labels inspired emotional connection
Example: Neglected warriors
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WEAVING
Data ContextGoalsHistoryIndividual differences
Bring together to weave a dialogue that will drive action
That leads to measurable business results
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The ART
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Creatively analyze data
Deliver a compelling story
Use pictures and titles that have meaning
Influence and get action takers on the same page
Measure and share results
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Questions?
Resources:
www.eepulse.com
@theresawelbourn
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