In this session, we will explain the benefits of implementing a Cloud Native approach for running closed loop analytics. This talk will show how you can achieve business value from analytics quickly and without downtime on a sophisticated platform such as Pivotal Cloud Foundry. We will also demo this combined approach to running Data Microservices for a use case like Fraud Analytics and Detection. Speaker: Malik Bilal & Vijay Rajagopal, Pivotal

2. LIVE DEMO:
Cloud Native Closed Loop Analytics
Malik Bilal & Vijay Rajagopal
Pivotal
#PivotalForum #Dubai #DigitalTransformation

3. Why do we want to analyse event data
•Tell customers about what they want to know
–Break the assumption that we only communicate with customers to sell
–Push info to customers rather than having them pull it
•Encourage “good” behaviours
–Reduced risk
–Help customers avoid penalties and fees
•Manage Expectations

4. “We’ve found that when a
host selects a price that’s
within 5% of [our
recommendation], they’re
nearly 4 times more likely
to get booked”

5. Why do we want to analyse event data
•Reduce cost to serve
–Pull channels often more expensive than push
–Better informed customers are less likely to complain
•Learn more about what customers really want to know
–Observed preference is a better guide than reported
preference

6. Great software companies leverage Big Data
to fundamentally change the customer
experience and pioneer entirely new business
models

7. Traditional Enterprise Analytics Process

8. Better…

9. Catch people or things in
the act of doing something
and affect the outcome

10. Closed Loop Analytics
IMDG
Stream
Processing
Traditional
Systems
Traditional
Systems
IMDG
Big Data
Time
Value of
Informatio
n
µs ms s hour day month year yr+

11. What can we say about a data point?
How much $ is this worth? What can we say about its
unknown dimensions?
Can I predict its behaviour?
How do I optimize my
business with this?

12. Can we find other points like it?

13. How can we separate these points?
Like this?
Or this way?

14. What is it going to do next?
?

15. Anatomy of a typical Data Pipeline
Source
Processing
Step
Processing
Step
Processing
Step
Processing
Step
Data Data
Monolith
Destination

17. Data Pipeline
Source Destination
Processing
Step
Processing
Step
Processing
Step
Processing
Step
Contract
µService
Contract
µService
Contract
µService
Contract
µService
Contract
µService
Contract
µService

19. IT’S ABOUT BUILDING A
GREAT CULTURE

20. Picture of Dev Environment of the past. Fills screen.
Your teams can’t look like this…

21. They need to look like this.

22. PIVOTAL LABS IS THE ULTIMATE IMMERSIVE EXPERIENCE
Ensuring companies have the modern
methodologies and technical capabilities to
continuously innovate after the project is over
Areas of Guidance and Coaching
1. Software Engineering
2. Product Management & Design
3. Data Science
Tandem Training
Working hands-on with a personal
coach to learn new tools and best
practices
Strategic Scoping
Working with a transformation lead
to identify barriers and implement
solutions
Lunch & Learn Workshop
Building shared understanding
with people throughout the
organization
Knowledge Sharing Toolkit
Supporting organizational
alignment executive retreats,
playbooks, and scorecards
Management Coaching
Creating tools for managers and
teams to support and propagate
new ways of working

24. Data Pipeline
Source Destination
Processing
Step
Processing
Step
Processing
Step
Processing
Step

26. Scale
Re-deploy
Migrate
Distribute
Upgrade Update
Pivotal Cloud Foundry

27. Backing Services
Transport Options
Pivotal Cloud Foundry
Auto Scaling
Auto Healing
Aggregated
Logging
Integrated
Metrics
Transport
Transparency
Infrastructure
Transparency
Data Pipeline
Visual Design
Integrated
Monitoring

28. IMDG
IMDG
Stream
Processing
Traditional
Systems
Traditional
Systems
Big Data
What are we demonstrating
Time
Value of
Informatio
n
µs ms s hour day month year yr+

29. What are we demonstrating
IMDG
Stream
Processing
Big Data

30. Big Data
What are we demonstrating
IMDG
Stream
Processing

31. Big Data
What are we demonstrating
IMDG
Stream
Processing

32. Big Data
What are we demonstrating
In Memory Data Grid
Stream
Processing

33. Big Data
What are we demonstrating
In Memory Data Grid
Stream
Processing

34. Big Data
and
Analytics
What are we demonstrating
In Memory Data Grid
Stream
Processing

35. Big Data
and
Analytics
What are we demonstrating
In Memory Data Grid
Stream Processing
Spring Cloud
Data Flow

36. JSON
Filter TransformEnrich
Custom
HTTP
Sample pipeline
Deploy
Pivotal Cloud
Spring Cloud
Data Flow
f{}

37. Big Data
and
Analytics
What are we demonstrating
In Memory Data Grid
Stream Processing
Spring Cloud
Data Flow

38. Catch people or things in
the act of doing something
and affect the outcome

39. BECOMING A
DATA-DRIVEN COMPANY…
IS NOT Just about deploying Hadoop
OR How many Data Scientists you have

40. IT’S ALL ABOUT
HOW YOU
OPERATIONALISE
YOUR INSIGHTS