The Softer Side of Data Science
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Transcript of The Softer Side of Data Science
David Quimby / Edward Chenard
8/24/16
Organizational and Cultural FactorsIn the Adoption of Big Data Tech
“The Soft Side of Data Science”
“The Soft Side of Data Science”
© 2016 STAV Data 1
"Soft Skills Are Hard to Assess... and Even Harder to Succeed Without"
"Do people underperform at your company because they lack these soft skills or do they disappoint because their technical skills aren't up to snuff?"
- Lou Adler / The Adler Group
http://www.inc.com/lou-adler/hiring-guide-soft-skills.html
“Data Science is a Team Sport”
“The Soft Side of Data Science” © 2016 STAV Data 2
“The Soft Side of Data Science”
“The Soft Side of Data Science”
“managers, leaders, and executives realize that these elements are far more complex than figures, equations, and theorems...”
- Jim Bohn, “The Mythology of Soft Skills”
https://www.linkedin.com/pulse/20140602213553-11890051-the-mythology-of-soft-skills
© 2016 STAV Data 3
Introducing big data tech without establishing an appropriate cultural foundation invites unnecessary resistance
Organizations need to solve behavioral constraints in order to optimize adoption of big data tech
The successful adoption of big data tech – like the adoption of any new technology – is both a technological innovation and an organizational / cultural / behavioral innovation
“The Soft Side of Data Science”
“The Soft Side of Data Science”
© 2016 STAV Data 4
The goal of big data in retail is improved customer experience through improved customer understanding... in real time
Designing for customer experience requires organizing for customer experience
“The Soft Side of Data Science”
“The Soft Side of Data Science”
© 2016 STAV Data 5
Designing for User Experience
“The Soft Side of Data Science”
“The Soft Side of Data Science”
© 2016 STAV Data 6
Strategy precedes technology and culture precedes strategy
But ¾ of projects in the space fail to meet expectations
Confusion is rampant – the obvious is often hard to see
“The Soft Side of Data Science” © 2016 STAV Data
“The Soft Side of Data Science”
7
© 2016 STAV Data 8“The Soft Side of Data Science”
“The Soft Side of Data Science”
The problem / solution is not technology
The problem / solution is human factors
One of the biggest reasons that data science projects fail is due to the artificiality of change.
The dressing of change without the attitude and perception of change is not change, but organizational resistance with a new wardrobe.
Organizational Resistance
9
Perception DisconnectPractice Development vs. Just Knowing Programming Languages
Many leaders think that coding is the key to success
Without domain expertise, coding is ineffective(maybe efficient – but not effective)
Second-Order Simulacra
Distinctions between representation and reality break down due to the proliferation of mass-reproducible copies of items, turning them into commodities. The commodity's ability to imitate reality threatens to replace the authority of the original version, because the copy is just as "real" as its prototype.
Third-Order Simulacra
The simulacrum precedes the original and the distinction between reality and representation vanishes. There is only the simulation, and originality becomes a totally meaningless concept.
think of the memories that you want to evokethen design for those memories
NOTwhat messages to communicate
or what media should carry those messages
intended memories / experiences
design of messages / media
design of messages / media
intended memories / experiences
NOT
© 2016 STAV Data 13
experiences
processes
inside out systemsmoments
Brandon Schauer, The (Near) Future of Managing Experiences http://bit.ly/pMumzn
as a result of
interactionswith emotional
resonance
which happen at
touchpoints
are the stories that you tell yourself
Organizing for User Experience
“The Soft Side of Data Science”
“The Soft Side of Data Science”
© 2016 STAV Data 15
“The Soft Side of Data Science”
“The Soft Side of Data Science” © 2016 STAV Data 16
culture strategy
Culture Precedes Strategy
strategy technology
Strategy Precedes Technology
“The Soft Side of Data Science”
“The Soft Side of Data Science”
Organizational Alignment / Organizational Agility
distributed architecture
organizational alignment
inter-disciplinary
teams
organizational alignment
© 2016 STAV Data 17
Is technology influencing our structure or does it emulate our structure?
Is our structure resisting our technology or does it reflect / reinforce our technology?
Can our structure learn from our technology?
© 2016 STAV Data 18“The Soft Side of Data Science”
Hierarchy vs. Distributed Architecture
Control over our environment and knowledge of how events are going to evolve is a fundamental psychological need
Most natural systems are open systems
An open system exchanges information with its environment: “organizational agility”
Command and Control vs. Distributed Leadership
© 2016 STAV Data 19
Data Centric
Silos
Specialist
Linking
Linear
Customer Centric
Collaborative
Big Picture Practitioners
Sharing
Frictionless
From To
Experiences become the key driver of our activities
Experiences are the perceptions that we have of our activities and interactions
(highly emotional based) © 2016 STAV Data 20
Distributed Architecture Means ThatOur Structure and Focus Must Change
Organizing the Organization:Network vs. Hierarchy
Anatomy of a social network:
Brokerage: A person or group that connects different clusters together.
Closure: Building trust within a cluster, the closer you are the stronger the trust.
Betweenness: Critical linking member between other nodes in the cluster.
Closeness: How easily a person can make connections
Degree: Number of connections
Developing a social aspect of personalization requires a high degree of network fluency, situational awareness, influence, compatibility and a fair amount of luck.
© 2016 STAV Data 21“The Soft Side of Data Science”
Leadership and Storytellingemotions determine memory
When we tell a story, we are sharing an experience of the story that we created – not the actual experience
Key Factor: Trust
Without trust, leadership is nothingOnce trust is lost, leadership is lost
Decisions need to be made with trust in mind
Trust is a primitive psychological variable essential to building relationships
© 2016 STAV Data 23
“The Soft Side of Data Science”
“The Soft Side of Data Science”
Organizational Alignment / Organizational Agility
distributed architecture
organizational alignment
inter-disciplinary
teams
organizational alignment
© 2016 STAV Data 24
Where Big Data Jobs Will Be In 2016
2 million jobs were created in the US during 2015 on the IT-side of big data projects- each of these new jobs is supported by 2 new jobs outside
of IT
7 big data jobs that you need to know:
http://www.talkincloud.com/cloud-computing/7-big-data-jobs-you-need-know
“Data Science is a Team Sport”
“The Soft Side of Data Science” © 2016 STAV Data 25
datascientist
dataanalyst
dataarchitect
dataengineer
statisticianbusiness analyst
databaseadministrator
“The Soft Side of Data Science”
“The Soft Side of Data Science”
Organizational Alignment / Organizational Agility
distributed architecture
organizational alignment
inter-disciplinary
teams
organizational alignment
© 2016 STAV Data 26
“The Soft Side of Data Science”
“The Soft Side of Data Science”
Organizational Alignment / Organizational Agility
high-degree organizational
alignment
organizational effectiveness
low-degree organizational
alignment
organizational resistance
© 2016 STAV Data 27
“The Soft Side of Data Science”
“The Soft Side of Data Science”
Organizational Alignment / Organizational Agility
distributed architecture
organizational alignment
inter-disciplinary
teams
organizational alignment
© 2016 STAV Data 28
“The Soft Side of Data Science”
“The Soft Side of Data Science”
Organizational Alignment / User Experience
high-degree organizational
alignment
high-fidelitycustomer
experience
low-degree organizational
alignment
low-fidelitycustomer
experience
© 2016 STAV Data 29
“The Soft Side of Data Science”
“The Soft Side of Data Science”
A Maturity Model: Four Phases of Data-Driven Culture
© 2016 STAV Data 30
non-quantitative (“intuitive”)
quantitative / static
(“statistics is not machine learning”)
quantitative / dynamic
(a culture of machine learning / experimental
design)
quantitative / dynamic with
human intelligence
(a culture of machine learning / experimental
design)