The Softer Side of Data Science

David Quimby / Edward Chenard
8/24/16
Organizational and Cultural Factors
In the Adoption of Big Data Tech
“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

“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
© 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
© 2016 STAV Data 5

Designing for User Experience
© 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
7

© 2016 STAV Data 8“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 Disconnect
Practice 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 evoke
then design for those memories
NOT
what 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
interactions
with emotional
resonance
which happen at
touchpoints
are the stories that you tell yourself

Organizing for User Experience

culture strategy
Culture Precedes Strategy
strategy technology
Strategy Precedes Technology

Organizational Alignment / Organizational Agility
distributed
architecture
organizational
alignment
inter-
disciplinary
teams
organizational
alignment

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?
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

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 That
Our 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.

Leadership and Storytelling
emotions 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 nothing
Once 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

distributed
architecture
organizational
alignment
inter-
disciplinary
teams
organizational
alignment

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”
data
scientist
data
analyst
data
architect
data
engineer
statistician
business
analyst
database
administrator

distributed
architecture
organizational
alignment
inter-
disciplinary
teams
organizational
alignment

high-degree
organizational
alignment
organizational
effectiveness
low-degree
organizational
alignment
organizational
resistance

distributed
architecture
organizational
alignment
inter-
disciplinary
teams
organizational
alignment

Organizational Alignment / User Experience
high-degree
organizational
alignment
high-fidelity
customer
experience
low-degree
organizational
alignment
low-fidelity
customer
experience

A Maturity Model: Four Phases of Data-Driven Culture
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)

The Softer Side of Data Science

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

En vedette

En vedette (20)

Similaire à The Softer Side of Data Science

Similaire à The Softer Side of Data Science (20)

Plus de Edward Chenard

Plus de Edward Chenard (9)

Dernier

Dernier (20)

The Softer Side of Data Science