The document outlines seven habits of effective data-driven companies, emphasizing the importance of proactive decision-making based on data. It provides strategic insights on creating a data strategy, understanding user needs, and fostering collaboration between business and technology teams. Furthermore, it encourages organizations to focus on long-term fundamentals and continuous improvement to successfully implement data-driven practices.

1. The 7 habits
of Effective
Data Driven
Companies
JURRIAAN BERNSON &
GIOVANNI LANZANI

2. MISTER PRODUCT MISTER DATA

3. Focus on
building
the right
things
BUT HOW DO WE
KNOW?

4. Effective
Data Driven
Companies
All decisions are based on data,
and they can be automated
It’s a journey, as you never get
there 100%. But how do you start?

6. #1 Be Proactive
Be Proactive is about taking
responsibility for your organization.
Don’t let the environment stop you,
don’t be afraid to kill a product,
learn from the mistakes through
data

7. F&B company
Ruthless 6 weeks cycle to validate idea’s
If the model doesn’t deliver the expected results in the
experimentation phase → add more data, change course, or
kill it
If it does, give it another 6 weeks for proof-of-value
If they can measure impactin 6 weeks → scale
The rest goes to the graveyard

8. #2 Begin with the end
in mind
Focus time and energy on things that can be controlled.
Therefor have a data strategy in place.
Being data driven often means changing products,
processes, people, skills. Is your organization ready for the
journey if you don’t know where you’ll go?

9. Airfrance-KLM
They wantedto start with
AI
Strategy sessions for VPs,
data discovery workshops,
and severalPoCs
They startedembedding a
data-drivenway of working
within the organization
Today data-driven from
financial forecastingto
predictive maintenance

10. Loyalty program retailer
THEY WANT TO STAY
RELEVANT FOR
CUSTOMERS
THEY NEED TO COLLECT
DATA & CONNECT IT TO
CUSTOMER PROFILES
SO THEY CAN CREATE
BETTER EXPERIENCES

11. Don’ts example: Global FI
THEY STARTED WORKING ON
PRODUCTS BECAUSE “IT WAS
COOL AND INNOVATIVE”
SEARCHING A BUSINESS
OWNER AFTER EUR 2M OF
INVESTMENT RESULTED WAS
HARD
THEY HAD BUILT A SOLUTION
WITHOUT AN UNDERLYING
PROBLEM
EPIC FAIL

12. #3 Put First
Things First
DATA IS THE ENERGY OF
YOUR TRANSITION TO
BECOME DATA DRIVEN, BUT
DON’T LOSE SIGHT OF YOUR
MISSION

13. NPO
The NPO wants to become a
personal broadcasting
organization
But transparency is their north
star
They build a personalization
algorithm that uses only what
the users want to share,
nothing more!

14. #4 Think Win-Win
Think about data products that add real-valueto you, your
customers, and the environment

15. Quby
REDUCING ENERGY WASTE BY
INTRODUCING DATA
APPLICATIONS
THE CLIENTS SAVE MONEY,
WITHOUT GIVING THEIR
PRIVACY AWAY
THE COMPANY OFFERS A
SERVICE THE COMPETITORS
DO NOT OFFER

16. Bakkersland
REDUCING BREAD WASTE BY
ACCURATE FORECAST
DECREASE WASTE, OPTIMIZE
OFFER, HAVE CLIENTS NEVER
RUN OUT OF THEIR BREAD
BETTER UTILIZATION AND
INVENTORY MANAGEMENT,
SUPERIOR PLANNING

17. #5 Understand,
Then to Be
Understood
Don’t build problems in search of a
solution/Use data to understand your
users, and then build products to delight
them

18. Suitsupply
Opening brick and mortar stores
based on data.

19. Don’ts Example:
NU.nl
Facebook reader app. Showing
your reading behavior based on
data.

20. #6 Synergize
Becoming data-driven takes the whole team
Business should be in the driver seat, no tech party
But tech has a seat at the table!

21. Analytics as a business model
ING wants to become a bank
with analytics as a business
model
They worked out with a strategy
starting with one thing in mind:
people will do things tomorrow
differently than today
They immediately started a
reskilling program for 50.000
people
Analytics sponsor and business
translators most important
personas in the org → all
projects have a clear impact!

22. Heineken: start
from the value
Heineken was able to — in a short time — work on more
than 20 project based on a value driven approach
How? They picked up all the use cases from the business
but had a strong process and core team dedicated to
make a success
Business in the driver seat

23. Don’ts Example: B2B
online platform
A company active in the B2B online market started a new
recommender project
The Analytics department did it all without ever contacting IT
At the end of the project, they asked IT to “just” implementit
IT said “we’ll put it in the calendar next year”

24. #7 Sharpen the saw
YOU’RE IN FOR THE
LONG RUN
DON’T DO QUICK
AND DIRTY
SPEND TIME ON
FUNDAMENTALS

25. KPN iTV

26. NS
They had outstanding models to
predict when trains were going to
break down
But they needed days to get data
from trains, so it was often too late
They build an analytics platform that
delivered live data from the trains
Every 5’ they can run a model that
tells them which trains will break
down within 1-3 weeks

27. Don’ts
Example:
B2B
Forecasting
A company starteddoing forecasts for
his B2B clients
They very quickly saw the model was
adding value
They used the PoC code to onboard the
first 15 customers
They modified to onboard the next 100.
And then the next 400.
They have accumulatedso much
technical debts, they need to spend half
a million to rewrite the whole system

28. How you can start
tomorrow

29. 5 things to start with
1. Start searching use cases with value & impact: without use cases, nobody will want to draft a
data strategy
Where do you want to go? Draft a clear Customer Experience that you want to create and think
about the organization & data strategy to get there!
2. Get Tech (data scientists and engineers) and Business (Product Management & Commercial)
on the same table: create a solid foundation.
3. Start with communities of practice to learn & experiment together and build the capability.
4. Stop talking about data. Start experimenting and doing.
5. Product Management needs to get real about data. (start training these capabilities)