As an analyst, the start of a problem is a wonderful place. All your time is focussed on discovering what drives the business you work in, or what causes it the most pain. Then you build something that helps to fix the problem. That’s when your life becomes less exciting. Every problem you solve comes with a reduction in your capacity for the new and exciting, and your time is filled with monitoring, reporting and tweaking. We want to talk to you about how you can build a team, tools and a culture to allow your analysts the opportunity to focus on the things they do best and enjoy the most – resulting in them being able to fix more problems (and spend some time trying to work out what AI actually is).

1. Make Analysis Great Again!

2. Ed Thewlis
CTO
Doesn’t do much work anymore. Can be found
at the coffee bar or laid on the sofa.
Tony Jozefek
Head of Analytics
Hates dogs, balloons and people. Loves Data
and Statistics

3. Once upon a time, analytics was fun…

4. Businessvaluecreation
Analytical maturity
Chaos
Beginner
Developing
Performing
Transformational
AI
DESCRIPTIVE PREDICTIVE PRESCRIPTIVE

5. Fix the less ‘technically’ challenging questions

6. Who are my
customers?
How should I
talk to my
customers?
What does
revenue look
like in 12
months?
Will my
customers
repay their
loans?
Which
customers
might want to
buy my new
product?

7. Who are my
customers?
How should I
talk to my
customers?
What does
revenue look
like in 12
months?
Will my
customers
repay their
loans?
Which
customers
might want to
buy my new
product?
Clustering
(describe) or
Decision Trees
(value)
Logistic
Regression
(Channel
preference/
propensity)
Time Series
Forecasting
Logistic
Regression,
Survival/Hazard
Model
Logistic
Regression,
Matchbox
recommender

8. The journey should be fun…

9. 0
1
2
3
1 2 3 4 5 6 7 8 9 10
Bodies
The Utopia…
Model 1
Model 2

10. 0
1
2
3
4
5
6
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Bodies
Where does all our time go?!
Monitoring
Model 4
Model 3
Model 2
Model 1

11. Now you’re stuck in drudgery...

12. Insight ≠ Action
Changes to capacity don’t lead to incremental
insights
Monitoring > Thinking
KPI Paralysis
Effort > Incrementality

13. MAKE ANALYSIS GREAT AGAIN!

14. Step one: Automate all of the things

15. Build
Experiment
Publish
Analytical Pipeline
Evaluate Monitor
High Value
Human
Interaction
Low-to-Zero
Value Human
Interaction

16. Build
Hypothesis/Exp
eriment Publish
Analytical Pipeline
Evaluate Monitor
DevelopSpike/Design Release
Engineering Process
Test Monitor

17. ...But which tools should I use?!

18. The Analyst’s Best Friends
Monitoring & Alerting
Reporting
Framework
Alerting Tool
Analytics Workspace
Notebooks
Git Repositories
CI/CD Tool
Ticketing System
Data Stores
“Data Lake”
Reporting
Repository
Self-Service ETL
Tool

19. Monitoring & Alerting
Power BI
Logic Apps
Analytics Workspace
Azure Notebooks
Azure DevOps
Azure DevOps
Azure DevOps
Data Stores
Blob Storage/Data
Lake
Azure SQL
Logic Apps/Data
Factory

20. Monitoring & Alerting
AWS Quick Sight
AWS Lambda
Analytics Workspace
AWS SageMaker
BitBucket
BitBucket Pipelines
JIRA
Data Stores
S3 RDS Glue
Monitoring & Alerting
AWS Quick Sight
AWS Lambda
Analytics Workspace
AWS SageMaker
BitBucket
BitBucket Pipelines
JIRA
Data Stores
S3 RDS Glue

21. Step one: Automate all of the things
Why? So you can free up your time and focus on the
exciting and valuable work

22. Step two: Measure yourselves.

23. Time to Insight
How efficiently can you
generate the insight to a
key decision point
Time to Action
How efficiently can the
business act on your
insight
Time to Value
Over what horizon are
you able to measure
your impact

24. Understand where you can make the biggest difference

25. 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
0. Gut
Feel
1. Report 2. SegmentedReport 3. Target Segments 4. Scorecard 5. Neural Network
CumulativeValue
Analysis Type/Effort
Incrementality of Analytical Effort
Simple Levers Feature Overload

26. Backlog
3
12
50
Unsuccessful
1
3
6
Delivered
1
Average:
85 days
2
Average:
22 days
12
Average:
5 Days
Large
Medium
Small
£1.5mil
incremental
margin
£300k
incremental
margin
£200k
incremental
margin
£2mil incremental
margin

27. Step two: Measure yourselves
Why? So you can easily explain why focussing effort in
certain places is potentially hugely valuable

28. Step three: Collaborate Effectively

29. Technology
AnalysisOperations

30. Collaborate!
Analytics -> Technology
• Demonstrate your adoption of their best practices
• Show them what analytics can do for them
• Demonstrate your firm plan for automation (they’ll love
it!)
Analytics -> Operations
• Learn their jobs
• Sit with them daily
• Understand their problems, and make sure they make it
onto your backlog
• Celebrate your successes with them!

31. So... what should I do tomorrow?

32. What should I do
tomorrow?
Collaborate
Start collaborating effectively
•Learn the right language to allow you to influence strategic thinking
•Ensure you are at the forefront of ‘What if’ questions
Measure
Measure yourselves
•Do you what you do best – but to yourselves
•Let the business case for growth write itself
•Celebrate your successes publicly
Automate
Automate all of the things
•Free yourself of drudgery
•Build a more efficient team
•Steal the best of the engineering teams
Understand
Know the levers of your value-chain
•Where can you influence decisions?
•What is the size of the opportunity?
•What is the type of problem?
•How will you measure your influence?

33. Analytics is awesome. Stop making it boring.
MAKE IT GREAT AGAIN!!

34. Any questions?