There is more to building a great data team and products. Learn some of the concepts that help make a team successful.

1. The Team Ecosystem of Data
Products
Data Science as a Team Sport
Edward Chenard

2. Agenda for Today
Why are Team Ecosystems so Important
The Abstract Aspect of Team Ecosystems
Nuts and Bolts of Building Your Dream Team Ecosystem
1
2
3
The Ecosystem of data product teams has rapidly changed over a short amount of time and few teams have kept up.

3. A little history of the Data Ecosystems
2011
• Hadoop goes mainstream
• Big data teams start to form
2012
• Data Science starts to form around stats and coding
2014
• Data Science goes mainstream
• Spark starts to take off
2015
• Machine Learning and Deep Learning go mainstream
2016
• Data Strategy starts to take off
Despite all these advancements, failure and frustration still runs rampant.

4. Why is an understanding of data products important?
95% of new products fail
98% of email products fail to convert
80% fail to improve brand life
Over 80% of data products fail to achieve
financial success
The current methods just aren’t working
You can’t win at the speed of beuarcacy
Why the Failure? New environment:
Tight coupling: When one part of the team is impacted it
impacts other parts
Speed: Things change faster than they used to, our elegant
solutions no longer are able to keep up.

5. Team or Group?
• Group example: yoga class is a “group”?
• Groups of people who play hockey as a “team”?
• A collection of people are not necessarily a group and a group
is not necessarily a team

6. How a group becomes a team
• Evolutionary process
• Teams are constantly changing and developing
• Groups go through four stages of development (Tuckman,
1965)
• Vary in duration and sequence for different groups

7. Five stages of Team Development
Forming
•Team engages in social comparisons, assessing each others strengths and weaknesses
• Members try to determine if they are a fit with the team
Storming
• Resistance to leadership may occur as positioning begins to take place
• Internal infighting can occur
Norming
•Conflicts are resolved
•Focus on a common goal and cohesion starts to form
Cohesion
•“The total field of forces which act on members to remain in the group” (Festinger et al, 1950)
•Social and Task Cohesion becomes an important part of team success, where team really forms
Performing
• Team focuses on success with mutual respect of skills (respect ≠ liking someone)
• Team energies focused on problem solving

8. Cohesion
A team is not the sum of its parts. Trust and purpose is what separates good teams
and great teams. Builds a special bond that gets things done even when the
environment is changing. The effectiveness of the team is all about the bonds the
team forms with one another, also known as cohesion.
In theory this is all nice and can be done, but do people have the will to have it be
done?
Getting a perfectly efficient system is elusive in the new environment. People often carry the baggage of doing
things right. If you follow the process often you won’t be criticized even if you fail. But the current processes will
give you failure, which is unsustainable.

9. Social Loafing
• Ringelmann (1913,
1927) observed that men
pulling on a rope
attached to a
dynamometer exerted
less force in proportion
to the number of people
in the group:
The Ringelmann effect
0
10
20
30
40
50
60
70
1 2 3 4 5 6 7 8
Group size (persons)Forceperperson(kg)
Expected performance
Actual performance
BASIC PRINCIPLE
The larger the number of individuals whose work is combined on a group task, the smaller is each individual’s
contribution.

10. Elements of our Various Selves
The Basic Selves
Each person brings several aspects to the table of any team, of themselves. An understanding of the
various selves helps leaders understand how to engage teams
Our Various Selves that Playout
• Combine the strengths of Google
and Facebooks methods with
psychograph techniques.
• Listen, Adapt, Respond
• Services co-created with
customers and are interpedently
with wider service networks.
Psychograph
Self
Facebook Self
Google Self
Clash between
Today and Future
Aspirational
You
Present You
1-1

11. Various Aspects of you
Google Edward
(Public online self)

12. Various Aspects of you
Facebook Edward
(Aspirational Self)

13. Various Aspects of you
Psychograph Edward (Offline Self -
External)

14. Various Aspects of you
Psychograph Edward (Offline Self - Internal)

15. Winning or Not Losing
Are you Interested in Winning or Not Losing?
Teams fail when there is not good alignment. Not losing is the not the same
as winning.
Not losing personalities can be toxic in a team environment.
Not losing is often shown when we overvalue what we have
(the endowment effect).
The positivity ratio is the # of positive statements to the # of negative
statements. High positive ratios help teams focus on winning, not on not
losing
High performing teams average 6:1; low performing teams average 1:1

16. The Distortion Problem
• The virtual equivalent of smoking addiction
• Technology and group think can create a dependency that distorts one’s
world view and actually encourages the dependency with false facts

17. Filter Bubbles
• A filter bubble is the restriction of a user’s
perspective that can be created by
personalized search technologies. (Haughn
2015)
• Information pluralism in the media refers to
the fair and diverse representation of and
expression by various political and ideological
groups, including minorities, in the media.
(Leuven et al. 2009, p. 12)

18. Open Vs Closed Systems
Most natural systems are open systems. An open system is a system that
exchanges information with its environment.
Most processes that are customer facing are closed systems, with limited
exchanges.
A resilient team ecosystem by it’s nature needs to be an open system, sharing
information with customers or any data that a customer/team member wishes
to bring into the system.
Team Ecosystems needs to not only be adaptive, but are often complex
systems that are open. These are the models that survive, the more closed the
model, the less use it will have by customers. (Think city vs corp systems)
Three criteria of a good system: Distributed control, strong identity, resilient
(not robust).

19. The Red Queen Effect
Innovation Theatre
Robust vs resilient. Robust systems are efficient. Resilient system can handle many unexpected challenges and be affective

20. What Makes Data Science Successful
Data science is a hybrid of business, technology and science. All three areas must be supported for success.
All human activities can be described by five high-level components: Measure, Evaluate, Decide, Improve, Extend, Promote.
This model helps data science be successful in all five.

21. Aspects of Data Strategy/Science
Measure: Metrics dashboards
Evaluate: Data Driven Testing
Decide: Ad hoc Data Insights
Improve: API’s for all Models
Extend: New product Features
Promote: PR via data
It is not a delivery team, nor pure R&D, it takes parts but is not entirely one
or the other. Moderate success at best will be achieves if one dominates
the other. If balanced, great success will be achieved

22. Transformation Strategy
Focus on
Productivity
Focus on Customers
Enhancement
Focus on a
Platform
HowtoPlay
HowtoWin
Capabilities and
Operating Model
Innovation Business
Model
Talent and Culture
Partner Ecosystem
Model
Data and Connected
Infrastructure
Change the Game Harness the Platform Go Together, Go Far Building Data,
Insights, Action
into our DNA
All About Outcomes

23. Wisdom
Collective application
of knowledge into
action
Knowledge
Experience, values, context
applied to a message
Information
A message meant to change receiver’s
perception
Data
Discrete, objective facts about an event
Experience
Grounded Truth
Complexity
Judgement
Heuristics
Values & Beliefs
Quantitative
Contextual
Evaluative
Qualitative
Intuitive
Informative
Quantitative
Connectivity
Transactions
Informative
Usefulness
Quantitative
Cost, Speed
Capacity
Timeliness
Relevance,
Clarity
Adding Value:
Action-oriented
Measurable efficiency
Wiser decisions
Adding Value:
Contextualized
Categorized
Calculated
Corrected
Condensed
Adding Value:
Comparison
Consequence
Connections
Conversations
Transitioning to emerging technologies
+
Human/Machine
=
Transformation
Establish a culture that allows the team to drive from data to wisdom. A combination of both machine and
human wisdom is needed to out perform competitors

24. Data Ethics – Ethical Analytics
▶With more granular insights comes greater
responsibility
▶Just because you can, doesn’t mean you
should
▶A culture of “ethical” analytics
Data science is a field filled with legal and compliance pitfalls. A culture of ethical analytics must be
instilled in the team to ensure we don’t run foul of any legal, ethical or social norms with our data
collecting or insights.

25. Give the team a purpose
Break Even
Break Through
Break Away
Efficient or Effective, you can’t be effective if you are more
interested in the efficiency of the process than on the goals of
why the process is there in the first place. To be effective,
adjust processes to adapt to the rapid changes

26. A Hybrid Structure
Hybrid Team
Model
Business
Data
Science
Developers
Designers
Social
Science
Engineers
Increase adaptability
Most teams are still operating like the 20th century
For now

27. Hub and Spoke Embedded Model
aka Dandelion Model
• Pod teams are embedded in various delivery and business teams to work on projects for a portion
of their time. When not working those teams, they are in a hub and spoke model, in a more
centralized area working as a larger group to work on enterprise wide problems.
• This model is becoming the most common approach to data science teams.
• Decentralization means let people be doers and thinkers, not just push responsibility down the
down the chain.
• Team players need to be more intrapreneurs than just an employee

28. New team models are supported by:
Decentralization, networked self-
organization
Open Collaboration, hyper-
competition, crowd
input/delegation, resource
sharing
DIY, maker culture, passion driven
Radical transparency,
transdisciplinary, community-
directed, network oversight
Responding to opportunity,
value-centric, let network do the
work
Examples:
Stakeholder councils
Hackathons
Open APIs
Open Source
Ambassador programs
Tech incubators
P&L focus
Design thinking
Communication means providing wisdom

29. Maturing of Data Science and Big Data
Present Needs
Future Value
Alignment
As these disciplines grow from our current start up
phase, they will need to change how they are
structured and managed in order to meeting their
ability to create value in the near future.
Data Science is not IT or Business but a hybrid role
and a way of running and building these disciplines
needs to also address this. Making Services and
Solutions that address the needs of customers to
apply big data and data science in a more tangible
form is what will get the most future value.
A future value focus: Technological revolutions tend
to involve some important activity becoming cheaper,
like the cost of communication or finding information.
Machine intelligence is, in its essence, a prediction
technology, so the economic shift will center around
a drop in the cost of prediction. Our future value
must focus on machine intelligence i.e. machine
learning

30. Creating an Insight and Action Driven Team
Invest in the Foundations
Culture: Create a culture which expects decisions are informed by data and experience.
e.g. Determining strategy, goal setting, impact estimates of initiatives
Process: Consciously map how you use data and arrive at insights and actions
e.g. product strategy reviews, design discussions, testing and documentation but not
just templating someone’methods
Tools: Invest in the data ecosystem
e.g. Specialized skills, data quality, pipeline, access tools.
Culture
Process
Tools

31. Data scientist as a partner, not a service – give context and communicate
constantly
Strong bias to actionable impactful insights, speed of iteration & feedback
• Data Foundations: governed datasets, consistent shared datasets and metrics
• Data Democratization: self serve data exploration platform
• Enable innovation: environment supports speedy ad-hoc analysis
Culture
Process
Tools
Actionable insights lead
to product feature
Success Metric
Definition
Tracking
Instrumentation
spec
Experimentation
Setup and Analysis
Post-Launch
Analysis
Build Measure Track Ship Tweak

32. Design of Experiments (DoE) Otherwise known as Lean
Innovation
Learn
Compare
CompleteShare
Frame
Empathize
Hypothesize
Dollarize
Document
Build
Design
ImplementDeploy
Measure
Collect
AnalyzeOrganize

33. http://upload.wikimedia.org/wikipedia/commons/thumb/6/66/Einstein_1921_by_F_Schmutzer.jpg

34. Rumsfeld Analytics
Things We
Don’t
know
Facts – could be wrong
We don’t
know
Intuition – quantify to
improve
Know
We don’t
know
Questions – do reporting
Exploration – unfair advantages
We know
We know
Innovation should be
focused on exploring the
unknown unknowns to give
us an unfair advantage

35. DATA IS THE COMMODITY,
ACTION BASED ON
WISDOM IS THE SCARCE
RESOURCE
Data and “facts” don’t mean anything without
context. What is happening on the street needs
to be part of the learning process, not just data.

36. ACTION THAT
CREATES CHANGE
IS THE SCARCEST
RESOURCE

37. How to Define Where Data Innovation Provides Value
Data
Provide
Insights
New
Features
Increase
Revenue
Open
Markets
Improve
Operations
Innovation is hard, if you haven’t been fired or threatened with being fired, you probably weren’t doing innovation

38. Going Forward, The Human Frontier
• The most important frontier today is our understanding of
being human
– Neuroscience
– Positive Psychology
– Behavioral Economics

39. Thank you