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The Softer Side of Data Science

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Data science isn't all about the numbers and data. To make data science really work, you need to also excel at the soft skills.

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The Softer Side of Data Science

  1. 1. David Quimby / Edward Chenard 8/24/16 Organizational and Cultural Factors In the Adoption of Big Data Tech “The Soft Side of Data Science” “The Soft Side of Data Science” © 2016 STAV Data 1
  2. 2. "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
  3. 3. “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
  4. 4. 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
  5. 5. 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
  6. 6. Designing for User Experience “The Soft Side of Data Science” “The Soft Side of Data Science” © 2016 STAV Data 6
  7. 7. 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
  8. 8. © 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
  9. 9. 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
  10. 10. 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)
  11. 11. 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.
  12. 12. 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.
  13. 13. 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
  14. 14. 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
  15. 15. Organizing for User Experience “The Soft Side of Data Science” “The Soft Side of Data Science” © 2016 STAV Data 15
  16. 16. “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
  17. 17. “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
  18. 18. 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
  19. 19. 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
  20. 20. 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
  21. 21. 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”
  22. 22. 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
  23. 23. 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 © 2016 STAV Data 23
  24. 24. “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
  25. 25. 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 data scientist data analyst data architect data engineer statistician business analyst database administrator
  26. 26. “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
  27. 27. “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
  28. 28. “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
  29. 29. “The Soft Side of Data Science” “The Soft Side of Data Science” Organizational Alignment / User Experience high-degree organizational alignment high-fidelity customer experience low-degree organizational alignment low-fidelity customer experience © 2016 STAV Data 29
  30. 30. “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)

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