The document discusses the challenges and strategies in ensuring the success of data teams, emphasizing the need for collaboration between data scientists, engineers, and business stakeholders to generate value. It highlights that a majority of data projects fail to reach production, and stresses the importance of organizational changes alongside technical solutions. Effective management and cross-disciplinary expertise are critical for overcoming common obstacles and creating impactful data projects.

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2. Working Together as Data
Teams
What I Learned While Writing Data Teams
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3. Why Even Work Together?
How Do We Create Value?
Common Problems
What to Do?
Working Together as Data Teams
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4. 85%
Fail to get into
production
Source:
http://tiny.bdi.io/gartnerfail
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5. 15%
Successful
projects provide
incredible value
But they have to make it into
production first
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6. What Is Your Big Data
Project's Health?
4 Most Common States for Projects and the
Business' Reaction
Creating extreme value = No Way!
Creating minimal value = Meh
Stagnated/Valueless = What project?
Planning stages = ???
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7. Projects without any value are
like defunct currencies where the
cash can't be used
How long has your
project created
minimal value or
stagnated?
A huge pile of data is worthless
unless it creates value
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8. Why Even Work Together?
How Do We Create Value?
Common Problems
What to Do?
Working Together as Data Teams
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9. When the business says go hire a
bunch of data scientists
They mean "I want
a functional data
science
organization, build
it."
http://tiny.bdi.io/whynot
Data Scientist
Data Engineer
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10. The business' responsibility
doesn't end when the data
strategy is defined
Data teams must
be close to the
business problem
"Putting data scientists, data
engineers, and business
people together the right
things were happening."
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11. The C-level needs to be on board
and realizing the importance
Top-down
support of words
and actions is
critical
CEO says the core of business
"is handling data."
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12. Some companies only focus on
the technical challenges
Must include
organizational
change
Changed "where teams sat,
who they reported to, and their
responsibilities."
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13. Enable smart people and let them
create
Emergence
creates the
highest leverage
of your people
"Be a gardener and get out of
the way"
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14. Teams with every skill to create
value from beginning to end
Multi-disciplinary
create the most
value and fastest
Advanced teams only!
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15. Why Even Work Together?
How Do We Create Value?
Common Problems
What to Do?
Working Together as Data Teams
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16. Politics
"People want to protect the
old way of working. [...] This
was seen as a big project
where this was going to
happen one way or on the
other way."
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17. Friction
"There are so many things
to change that it's not just
one part of the problem that
we can attack that'll solve
everything."
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18. Value Creation
"It's much easier to
persuade the business that
[...] we've got a really good
idea that could help
contribute [...] if they can
see how it was going to help
them and benefit them."
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19. Credit For All
"But it's actually because of
everyone (that data
scientists are successful).
(Others) can make more of a
contribution, get more
recognition for what they do
as well.
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20. Right Ratios
"I did the classic thing of
hiring a bunch of data
scientists, not having a
bunch of data engineers
and then guess what, your
data scientists, are your
data engineers."
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21. Why Even Work Together?
How Do We Create Value?
Common Problems
What to Do?
Working Together as Data Teams
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22. Management is responsible for
the early success or failure
The right team
structure is
crucial
http://tiny.bdi.io/detbook
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23. Do you have 10 billion dollars of
defunct currency?
Do you a healthy
data project
generating
extreme value?
Problems don't fix themselves
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24. Thank You
bigdatainstitute.io
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