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”


© 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

http://www.inc.com/lou-adler/hiring-guide-soft-skills.html



“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 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






culture strategy

Culture Precedes Strategy

strategy technology

Strategy Precedes Technology



Organizational Alignment / Organizational Agility

distributed architecture

organizational alignment

inter-disciplinary

teams



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 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.


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







inter-disciplinary

teams



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”


datascientist

dataanalyst

dataarchitect

dataengineer

statisticianbusiness analyst

databaseadministrator

http://www.talkincloud.com/cloud-computing/7-big-data-jobs-you-need-know






inter-disciplinary

teams






high-degree organizational

alignment

organizational effectiveness

low-degree organizational

alignment

organizational resistance







inter-disciplinary

teams





Organizational Alignment / User Experience

high-degree organizational

alignment

high-fidelitycustomer

experience

low-degree organizational

alignment

low-fidelitycustomer

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)

Date post:	16-Apr-2017
Category:	Business
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The Softer Side of Data Science

Business