1) The document discusses how to bring data-informed decision making to a large organization with over 10,000 employees. 2) It proposes establishing a hybrid product analytics organization with central teams for tools, methods, and governance, as well as embedded product analysts to empower teams. 3) Key recommendations include developing self-service data tools, providing data education, establishing data governance policies, and setting clear expectations around experimentation.

1. DATA FOR
EVERYONE?
HOW TO BRING DATA-INFORMED
DECISION MAKING TO THE NEXT
LEVEL IN A BIG ORGANIZATION
CHRISTOPH LÜTKE SCHELHOWE
June 2017

2. 2

3. 3
Key to success:
Solve real customer
problems and move fast
(based on data)
Picture

4. 4
Example for data-informed
decision making that made
Zalando successful:
Why do we make returns as
easy and convenient as
possible for customers?
Picture

5. 5
Picture
How might we enable data-informed decision making
at scale in an organization with > 10k employees?

6. 6
Forces of gravity in
a big organization:
• Fragmented tracking
• Data silos
• Unclear data quality
• Time-consuming access
to data and tooling
• Missing analytical rigour
Picture

7. 7
Our answer:
Foundation of Product
Analytics to bring
data-informed decision
making to the next level
across Zalando

8. 8
Hybrid Product Analytics
Organization:
• Central teams for
tools, methods,
overarching governance
• Team-embedded product
analysts to empower
everyone
Picture

9. 9
Self-service tooling:
Make data access and
experimentation quick
and easy for everybody
Picture

10. 10
Data education:
• Bootcamp for expert
and multiplier level
• Self-learning videos
• Community of practice
Picture

11. 11
Data governance:
• Rules of Play
• Data event overview
• Data quality standards
and monitoring
Picture

12. 12
Picture
What are our learnings
from the last months?
Example experimentation

13. 13
Spark a fire:
Teams intrinsically get
into the flow of iterating
optimization cycles
when seeing the first
encouraging results
Picture

14. 14
Have the right balance
between small + big ideas:
Don’t only do frontend
testing, think about bigger
experimentation
use cases that involve
backend testing
Picture

15. 15
Think beyond data:
Use qualitative research to
get at the why. The value
is in the insights behind
data
Picture

16. 16
Set expectations:
• Success looks like:
Increasing number of tests
and teams
• Only a part of tests with
significant results
• Nonsignificant results
don’t mean no learning
• Avoiding downlift easier
than generating uplift
Picture

17. 17
Picture
A true data and experimentation culture helps our
team build better products and move faster...

18. 18
Picture
... ultimately becoming a competitive advantage in
today’s fast-changing world