1.
1
Connecting Kafka to Cash
K ↔ $
Lyndon Hedderly, Confluent

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22
Agenda
1. Measuring Value
2. Three Real-World Examples
3. Recap & Recommendations
1. Why?
2. Why Kafka?
3. Problems!

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33
Why Measure Value?
1. ROI

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44
Why Measure Value?
2.Stakeholder
Commitment

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55
Why Measure Value?
3.Benefits
Realization

6.
Investing $50kin 10 companies using data to
deliver Great User Experience
The UX
Fund:
4.

7.
$50k in 10 companies
in 2006 became
$306k in 2016.
A 503% gain
S&P 50%

8.
Gain as a %
$157k
>3,000% return!
Almost 900% return!
S&P Return c.50%

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99
So, why?

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1010
Innovate or be disrupted...
1. Create New
Business Models
2. Deliver new,
real-time customer
experiences
3. Deliver
massive internal
efficiencies
● Retail: Rent rather than
buy clothing
● Recommendations on
Netflix
● Seeing real-time ETA of
Uber driver
● Text alert - credit-card
fraud
● Global bank consolidating
data silos
● Regulatory reporting
● Corporate office seeing
worldwide sales in real-time
Making More
Money
Saving or Protecting
Money
New Ways of Making Money

11.
1111
Transportation
ETA
Real-time sensor
diagnostics
Driver-rider match
Banking
Fraud detection
Trading and risk
systems
Mobile applications /
customer experience
Retail
Real-time inventory
Real-time POS
reporting
Personalization
Entertainment
Real-time
recommendations
Personalized news
feed
In-app purchases

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The Problems with
measuring value

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Problems with measuring value?
1. Poor Credibility of
business cases
- Dodgy assumptions / flaky estimates

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Problems with measuring value?
2. What are we actually
measuring?

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16.
It’s not just the data that is important, it is how it is
moved around - and how timely it is
→ The ability to move the oil becomes
almost as valuable as the oil itself
→ “Data is only as valuable as your ability to quickly
act on it” - Jay Kreps, co-founder and CEO, Confluent.

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Problems with measuring value?
3. Value changes - and
what does it really mean
anyway?

19.
19Confidential
3. What does
value mean?
- The Subjective
Element of valuing
something
- Value changes...
$940 k
1999
$65 M
2018
$91 M
2019
97x in 20 yrs

20.
3. What does
value mean?
- The Subjective
Element of valuing a
(contextual) piece of
technology
- Value changes...
1. Cost: The iWatch? $83
2. Price: $350
3. With fitness information?
Medically accurate electrocardiogram?

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So, valuing is hard
1. We have to predict the future - with assumptions
and estimates
2. We have to agree on what we’re actually measuring
- the engine or the car? The oil or the pipeline?
Perhaps they’re the same thing?
3. Value is subjective and it changes - we have to be
‘situationally aware’ and be comfortable with that.

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Three Examples of measuring
Kafka’s business value

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5-step value assessment process
1.
Baseline
As-Is State
& Negative
Consequences
(3-5 yr costs)
2.
Target
State
Desired Solution
3-5 yr costs +
Project Costs
3.
Benefits
Top-line business
Benefits &
Bottom-line
Savings (TCO
comparison / ROI)
4.
Soft
Benefits
Non-Financial
Impact & Analysis:
Incl. Scenarios,
Sensitivities & Risks
5.
Proof-
Points
Metrics
(definition of
success)
Case Studies
● Below the line - Universal Pipeline
benefits of Kafka
● Above the line - Contextual Business
Applications

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ATM Disputes

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7-9,000 disputes / month
Agents = 3-5 hrs to resolve a dispute
Significant number paid off
Costs c. £4M / yr
ATM Disputes

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ATM Disputes

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Universal Event Pipeline
Data Stores Logs 3rd Party Apps Custom Apps/Microservices
from ATM’sContextual Information - Bank Accounts Analysis / Historic info - feeds from other ATM / banks
Universal event pipeline
--- high throughput, persistent,
ordered, and has low latency ---
All events and systems are
connected
ATM Disputes Benefits:
1. From batch to real-time
2. Single Enterprise wide source
of events
3. Built for scale
4. Store events while maintaining
real-time replay
5. ...

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Universal Event Pipeline
Data Stores Logs 3rd Party Apps Custom Apps/Microservices
ATM
Disputes
App
Real-Time
Fraud
Detection
Real-Time
Customer 360
Machine
Learning
Models
Real-Time
Data
Transformation
...
Contextual Event-Driven Apps
● 50% reduction in agent time
● 75% reduction in avoidable
payments
ATM Disputes

29.
ATM Disputes

30.
ATM Disputes

31.
Breakeven:
H1
ATM Disputes
Additional benefit of
improved customer
experience & loyalty.

32.
ATM Disputes

33.
A one-point improvement in CX Index
score results in (By Industry):

34.
Customer 360

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Universal Event Pipeline
Data Stores Logs 3rd Party Apps Custom Apps/Microservices
Real-Time Fraud
Detection
Real Time
Inventory
Real-Time
Customer
360
Machine
Learning
Models
Real-Time
Data
Transformation
...
Contextual Event-Driven Apps
From on-line click throughsStock / Warehouse / Inventory Info Analysis / Weather info - feeds from other data sources
● On-line; Personalized,
targeted offers: uplift
revenue
● Better manage inventory - for
offers, with dynamic pricing
Customer 360

36.
Customer 360

37.
Customer 360

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Additional benefit of
simplifying back-end
systems - not modelled here
Customer 360

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Customer 360

40.
OK Sir...
Here you go…
...but don’t you know
you can do all this
much more easily
online.
Fraud

41.
Fraud

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Universal Event Pipeline
Data Stores Logs 3rd Party Apps Custom Apps/Microservices
Real-Time
Fraud
Detection
Real Time
Inventory
Real-Time
Customer
360
Machine
Learning
Models
Real-Time
Data
Transformation
...
Contextual Event-Driven Apps
Detection &
Prevention
Mobile Banking AppsMainframe Core Banking Apps Analysis / location information, other Cyber Sec info
● Detection and prevention of
Fraud (real-time)
Fraud

43.
Fraud

44.
Fraud

45.
Fraud
Additional benefit of improved
customer experience & loyalty.
Decreased insurance premiums.

46.
Fraud

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Summary of Value
Customer 360
£10.4M
1.93x ROI Fraud Prevention
£23.275M
6.25x ROI
ATM Disputes
£6.125M
2.13x ROI
1. Create New
Business Models
2. Deliver new,
real-time customer
experiences
3. Deliver
massive internal
efficiencies

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Recap &
Recommendations

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Recap
1. Measuring Value
2. Three Real-World Examples
- ATM Disputes - save money
- Customer 360 - make money
- Fraud - save money
3. Recommendations...
1. Why?
2. Why Kafka?
3. Problems!
1. Credibility?
2. What we’re measuring?
3. Be comfortable with
subjective elements and
change

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Real-Time
Inventory
Real-Time
Fraud
Detection
Real-Time
Customer 360
Machine
Learning
Models
Real-Time
Data
Transformation
...
Contextual Event-Driven Apps

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Thank You
lyndon@confluent.io
We offer this service - contact
Sales@confluent.io