This session by The Economist Group, Cloud BI Ltd and Looker explores the challenges of data-driven decision making and how powerful the approach can be. Hear how the solution was implemented quickly and evolved in the cloud and the benefits of being able to see and understand customer preferences through a 360-degree view.

1. PROOF OF CONCEPT IN WEEKS,
DATA SOLUTION IN MONTHS
Improving data-driven decision making
Bobby
Gill
Pete
Grant
Sebastien
Fabri

2. Where it started

3. Objectives
1. Provide a platform that allows future scalability, flexibility and agility to
consume new customer data inputs.
2. Progressively consolidate customer data silos into a single customer
view repository based on value.
3. Provide a platform that will allow more sophisticated analysis (e.g.
predictive modelling).
4. Provide the business with self-service capability to key metrics and
dashboards through the use of a data analytics and visualisation tool.

4. Platform Size

5. Implementation Approach
1.Available platform and software services over
development and apparent control
• Time is the most scarce resource
• We’re not set up to COMPETE with AWS…

6. Services Without Commitment
More effective to use
some of this…

7. Services Without Commitment
…than to make our own
from scratch

8. What We Actually Did Part 1
• We used S3, Data Pipeline, EMR, Redshift -
the solution is AWS specific
• We used Lambda which is AWS specific in that
it calls AWS services
• We used CodeCommit
• We used Jenkins as a scheduler

9. Implementation Approach
1.Available platform and software services over
development and apparent control
2.Creative assembly of services over product
customisation
• Any one product might not satisfy all requirements
• Customising products might not be the best option

10. Custom Assembly
Custom is better
Customising the product might work

11. But the gap isn’t usually just a paint job

15. What We Actually Did Part 2

16. Implementation Approach
1.Available platform and software services over
development and apparent control
2.Creative assembly of services over product
customisation
3.Empowering the end user over charging for
effort
• Avoid writing and reading specs
• Cut out the middle men

17. What We Actually Did Part 3

19. GOVERNANC
E
DATA
UI & VIZ
TRIGGER
EMAIL
STREAM
API
Where Do We Start?

20. UI & VIZ
TRIGGER
EMAIL
STREAM
API
MODELING
LAYER
(LookML)MPP
How Do We Deliver It?

21. REDSHIFT DATA TEAM DATA PLATFORM END USERSINNOVATION
THE
ECONOMIST
LOOKE
R
The Economist Implementation

22. REDUCED DATA
PREPARATION
AGILE DEV TRUST THE DATA
EMPOWER USERS
NEW INSIGHTSINNOVATION
THE
ECONOMIST
LOOKE
R
The Economist Value

23. Time Lines
PoC.
Month 1 Month 2 Month 3 Month 4 Month 5-6
CEO requests for
combined Data Vision
Internal reviews and
requirements
gatherings
PoC Built and tested!
AWS Environment and
Looker Dashboards
Business Review
Road show
Building
Business Case
Production
Month 8-9 Month 10 Month 11 Month 12
Production System
Build - AWS
Internal Agile
workshops - Identify
the top 5 deliverables
Looker Dashboard build
-Stick Rate reporting
-Customer Journey
Phase 1 sign off
and Phase 2
planning

24. Feedback
1. Our project lets us tie data together, leveraging data across the whole
group, in order to spot practical, actionable insights. This information is
crucial to our business.
2. The potential to drive real insight becomes suddenly considerably
easier.
3. We are now armed with facts and can set out priorities, i.e. to develop
features to our app and site to spur usage.
4. Cross-platform analysis, for the first time, we can begin to understand
the impact of registration and digital usage on conversion and retention

25. Questions?