The document discusses how the Monzo data team uses Looker to enable better and faster decisions across the company. It describes how the team is structured to be embedded within business areas to provide feedback and support. Around 70% of Looker accounts are active weekly. The team aims to move from responsive analytics to self-service analytics through building wide data models, experiment dashboards, and automating tasks like training and detecting broken charts using neural networks. The overall goal is to continuously improve how the team and others can get the most value from company data.

1. Making better decisions,
faster
@neal_lathia
Looker Join London

5. We enable better
decisions, faster
→ the Monzo data team mission

6. How are we working towards
this mission?
(& how does Looker fit in?)
(1) Team foundations - values & infrastructure,
(2) Going beyond “responsive analytics,” (3)
Balance between educating & automating

7. ~70% of our Looker
accounts are active on a
weekly basis.

8. The data team

9. How do you structure a data
team?
● We are embedded in the area of
the business where we are
needed, and can receive feedback
from others with the right context.
● We can work directly with &
enable the people who benefit the
most from analytics.
● We can’t be everywhere and so
could miss supporting key areas
of Monzo.
● We are surrounded by our peers,
and can receive feedback from
others with a background in Data
Science.
● We can develop common tools,
best practices, and uniformity in
how we tackle data problems.
● We could become more distant
from the “front line” and a
bottleneck to the rest of the
company.
Centralised Distributed

10. Our current structure
Daily tasks (aim: 1-2 squads
per analyst)
Prioritisation at the squad or
collective level
Analytics peer review
Knowledge sharing
Career development
(management)
Infrastructure & Operational
health
Centralised Distributed

12. Under the hood of
analytics at Monzo

13. https://monzo.com/blog/2016/09/19/building-a-modern-bank-backend/
From hundreds of services...

14. … to a single source of truth.

16. Writing one query to answer
one question vs.
Creating data models that
enable answering many
questions.

17. Questions never come up
once
● How many new customers this week?
● How many customers got in touch with customer support
this week?
[...]
● How many new customers got in touch with customer
support this week?
We have designed wide data models that capture all of the
different fields that we care about when asking about changes to
our customer base over time (and we call it user_stats).

19. From responsive to
self-service analytics

21. Example recent experiment

22. Self-service experiment
dashboard

23. Self-service live events
explorer

24. Self-service funnels

25. Balance between
educating &
automating

26. Training

27. Automating
https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html
A recurring problem that we had was charts “breaking” because we have changed the
analytics code base. Identifying that a chart is displaying an error or showing no data
is easy to see, but difficult to quantify.
We built an image classifier using neural networks that detects Looker charts that are
broken - using a pre-trained image model.

28. Conclusions

29. Conclusions
The Monzo data team mission is to enable better decisions,
faster.
We structure our team and our tooling around the principles
of speed and enablement.
Looker has been a very powerful tool to enable others at
Monzo to get the best out of our data - from high level
company KPIs to one-off product experiments.
We are constantly keeping an eye on how we can improve
how we use our own tools and enable others.

30. Thanks!
@neal_lathia
https://monzo.com/careers/