In this second installment of our Executive Webinar Series on Fast Data Strategy you will learn how innovative companies in Mobile/Telco, Financial Services, Media & Entertainment, and Internet of Things (IoT) have successfully tapped into the fast data opportunity. You will learn key metrics and evaluation criteria for three case studies that delivered superior value, profitability and growth. Niall Norton, CEO of Openet will outline how leveraging a disruptive database technology dramatically improved competitive differentiation and business operations. To view the webinar recording, click here: http://learn.voltdb.com/WRExecSeries2.html

1. page
HOW FIRST TO VALUE BEATS FIRST
TO MARKET: CASE STUDIES OF
FAST DATA SUCCESS
Executive Webinar
Series on Fast Data

2. page© 2016 VoltDB
EXECUTIVE WEBINAR SERIES: FAST DATA STRATEGY
1. Fast Data for Competitive Advantage: 4 Steps to
Expand your Opportunity
2. How First to Value Beats First to Market: Case
Studies of Fast Data Success
3. Fast Data Choices: Strategies for Evaluating
Alternative Business and Technology Options
2

3. page© 2016 VoltDB
OUR SPEAKERS
3
Peter Vescuso
CMO
VoltDB
Niall Norton
CEO
Openet

4. page© 2016 VoltDB
DATA IS TRANSFORMING BUSINESS
4
Broad content targeting to generic viewers Smarter, more individualized customer experiences
AUDIENCES
Content Metrics
INDIVIDUALS
Consumer Centric
From: AUDIENCES To: INDIVIDUALS

5. page
Big Data
“Perishable insights can have exponentially more value than
after-the-fact traditional historical analytics.”
Mike Gual2eri, Principal Analyst, Forrester Research
Fast Data
DATA IS TRANSFORMING BUSINESS

6. page
FAST = ADVANTAGE
6© 2016 VoltDB

7. page© 2016 VoltDB
• Forrester’s findings:
• Businesses can’t get the data they need fast enough
• Data volume and variety are crushing business systems
• The mobile mind shift hinges on data, e.g., metadata that data needs to be
classified, linked, and exposed to create “mobile moments”.
7

8. Niall
Norton,
CEO,
Openet

9. 9
©
Copyright
2016
Openet
–
Company
Confiden=al
For
Use
Under
Non-­‐Disclosure
Only
• Openet
has
always
been
about
real-­‐=me
• Background
is
large
scale
transac=on
processing,
control
and
mone=za=on
of
data
for
communica=on
service
providers
• Wanted
to
take
it
to
the
next
level
to
enable
smarter
engagement
for
our
customers
• This
helps
communica=on
service
providers
grow
to
be
able
to
work
and
beOer
compete
with
OTT
and
content
providers
(Google,
Facebook,
Amazon,
Skype,
NeTlix,
Skype,
Spo=fy,
etc)
• Enables
communica=on
companies
transform
to
be
Digital
Service
Providers
Openet
–
Why
Fast
and
Smart
Data
is
Crucial

10. 10
©
Copyright
2016
Openet
–
Company
Confiden=al
For
Use
Under
Non-­‐Disclosure
Only
Openet
–
Mee=ng
the
Needs
of
The
Digital
Service
Provider

11. 11
©
Copyright
2016
Openet
–
Company
Confiden=al
For
Use
Under
Non-­‐Disclosure
Only
• Advanced
PCC
-­‐
The
world's
most
advanced
Policy
and
Charging
suite
• Big
Data
Prepara2on
-­‐
Turn
big
data
into
smart
data
that
delivers
real
business
benefits
• NFV
-­‐
Openet’s
solu=ons
are
all
fully
virtualized
providing
the
founda=on
for
faster
=me
to
market,
reduced
implementa=on
and
upgrade
=me
• CEM
-­‐
Having
smart
data
available
to
provide
a
holis=c
view
of
all
customers
as
well
as
understanding
customer
context
in
real-­‐
=me
enables
personalized
marke=ng
offers
• Network
Op2miza2on
-­‐
Improve
quality
of
experience,
reduce
cost
and
maximize
revenue
through
efficient
and
proac=ve
management
of
network
resources
Openet
Exper=se

12. 12
©
Copyright
2016
Openet
–
Company
Confiden=al
For
Use
Under
Non-­‐Disclosure
Only
Openet
Enables
Smarter
Engagement

13. 13
©
Copyright
2016
Openet
–
Company
Confiden=al
For
Use
Under
Non-­‐Disclosure
Only
• A
higher
performance,
in-­‐memory
database
that
could
combine
the
capabili=es
of
an
opera=onal
database,
real-­‐=me
analy=cs,
and
stream
processing
in
one
easy-­‐to-­‐use
plaTorm.
• An
in-­‐memory
database
that
could
handle
fast
data
• Database
technology
that
would
be
complimentary
to
our
innova=ve
soaware
solu=ons
and
suitable
for
virtualized
deployments.
• A
database
that
was
elas=cally
scalable
and
could
grow
and
contract
as
needed.
• The
result
–
Openet
is
now
rolling
enabling
smarter
engagement
at
many
of
the
most
innova=ve
service
providers
in
world.
To
Deliver
Smarter
Engagement
Openet
Worked
with
VoltDB
to
Deliver:

14. 14
©
Copyright
2016
Openet
–
Company
Confiden=al
For
Use
Under
Non-­‐Disclosure
Only
• Smarter
Engagement
with
Customers
–
use
smart
data
and
enable
a
beOer
customer
experience
and
enable
service
providers
to
compete
for
a
bigger
share
of
customers’
digital
spend.
Smarter
Engagement
with
Customers
How
do
you
become
more
relevant
to
your
customers?

15. 15
©
Copyright
2016
Openet
–
Company
Confiden=al
For
Use
Under
Non-­‐Disclosure
Only
• Smarter
Engagement
with
Real-­‐
2me
Data
–
understand
customer
context
in
real-­‐=me.
Use
this
to
push
personalized,
contextually
aware
offers.
Smarter
Engagement
with
Fast
Data

16. 16
©
Copyright
2016
Openet
–
Company
Confiden=al
For
Use
Under
Non-­‐Disclosure
Only
• Smarter
Engagement
with
Technology
–
using
NFV
to
run
smarter
systems,
including
real-­‐=me
charging
and
policy
Smarter
Engagement
with
Technology

17. 17
©
Copyright
2016
Openet
–
Company
Confiden=al
For
Use
Under
Non-­‐Disclosure
Only
• Smarter
Engagement
with
Exis2ng
Systems
-­‐
reconfigure
legacy/diverse
networks
and
systems
Smarter
Engagement
with
Exis=ng
Systems
Be
Digital
Ready
-­‐
‘Best
of
Breed
‘
adjunct
approach
enables
fast
track
system
transforma2on

18. 18
©
Copyright
2016
Openet
–
Company
Confiden=al
For
Use
Under
Non-­‐Disclosure
Only
Sample
Use
Cases:
Used
by
Many
of
the
World’s
Most
Innova=ve
Service
Providers
Shared
Data
-­‐
Enterprise
Video
Op=miza=on
Access
type
Policy
IN
Replacement
Audience
Measurement
Tradi=onal
Media=on
Time
of
Day
Pricing
Conges=on
Management
VoLTE
Service
Enablement
Spend
No=fica=ons
and
Bill
Shock
Control
Device
Type
Policy
Bandwidth
on
Demand
Fair
Usage
Service
Tiers
Time-­‐based
Service
Pass
Parental
Controls
Dual
Persona
(BYOD)
Data
Volume
/
Speed
Tiers
Data
Roaming
Service
Pass
Data
Roaming
No=fica=ons
Content
Bundles
with
OTT
Services
Applica=on
Service
Pass
Fast
Device
/
Service
Rollout
Device
Tethering
Real-­‐Time
Contextual
Offers
Shared
Data
-­‐
Mul=
Device
Network
Selec=on
Intelligence
Shared
Data
-­‐
Mul=
User
Account
Data
Giaing
Sponsored
Data

19. 19
©
Copyright
2016
Openet
–
Company
Confiden=al
For
Use
Under
Non-­‐Disclosure
Only
Chosen
by
Leading
Service
Providers

20. 20
©
Copyright
2016
Openet
–
Company
Confiden=al
For
Use
Under
Non-­‐Disclosure
Only
Openet
Best of breed
Cross functional for
growth & innovation
“Big
stack”
guys
Quality
Applicability
Flexibility
Compatibility
Expansibility
Performance
Quickly redesign services
for a dynamic market
Gets along well with other
systems
Cloud ready, hardware
agnostic
Industry leading
Afterthought or offloading
altogether (e.g. NSN)
Closed silo designed for
yesterday
Submit change request.
Cross fingers.
Vendor lock in
Proprietary
Demand Overwhelms
Why
We’re
Different

21. 21
©
Copyright
2016
Openet
–
Company
Confiden=al
For
Use
Under
Non-­‐Disclosure
Only
• Telecoms
is
transforming
• Everyone
had
a
strategy
but
need
the
flexibility
to
adapt
in
=mes
of
change
• Those
who
don’t
best
adapt
to
change
will
be
lea
behind
• Legacy
way
of
doing
business
and
systems
will
soon
be
obsolete
• Not
just
about
big
data.
It’s
using
data
in
a
fast
and
smart
way
to
drive
change
and
open
new
revenue
streams
• It’s
about
enabling
change
Summing
Up
–
Openet
and
VoltDB

22. page© 2016 VoltDB 22
From Development to Release
First to Market First to Value
From Development to hitting
Sales and Profitability Goals
When First to Market doesn’t lead to First to Value, it’s due to either
the wrong solu7on or the wrong technology pla;orm.
versus

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FIRST TO VALUE WITH FAST DATA – THE
CHALLENGES
• Fast data applications have different technology
requirements
• Early adoption of technology doesn’t guarantee success
• Many technology options
• Need to pick the business and technology strategy that’s
right for you
23

24. page© 2016 VoltDB
Batch/Iterative
Analytics+
Big DataFast Data
Rapid Data Ingestion
and
Transformation
Streaming
Analytics
Operational
Interaction/
Transactions
COMPARISON OF FAST AND BIG

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COMPETITIVE STRATEGY DRIVES TECHNOLOGY
AND DATA MANAGEMENT REQUIREMENTS
25
Hyper Personalization
Real-Time Resource Management
Real-time Policy Enforcement
IoT & Sensor Data

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WHAT’S YOUR CORE COMPETENCY?
- CUSTOMERS AND APPLICATIONS
- DISTRIBUTED SYSTEM INFRASTRUCTURE
26© 2016 VoltDB

27. page© 2016 VoltDB
EVALUATION CRITERIA
Criteria
Considera2ons
Data
Volume
&
Velocity
Capacity
to
ingest,
process
and
export
at
speed
of
data
Response
speed,
Performance
Need
for
interac=ve,
real-­‐=me
Personaliza=on
Batch
vs
con=nuous
event
processing
Accuracy,
Data
Consistency
Is
data
high
value,
cri=cal?
Scalability
Accommodate
rapid
growth.
Cloud-­‐ready
Standards
SQL
for
data
abstrac=on
vs
Applica=on
heroics
Skill
Set
Specialty
open
source
skills,
e.g.,
Cassandra
27

28. page
SOME TECHNOLOGY OPTIONS…
28© 2016 VoltDB

29. page© 2016 VoltDB
THE “DIY” DATA INFRASTRUCTURE
29
Glue
Code
Glue
Code
Community Supplied You write this
Zookeeper

30. page© 2016 VoltDB
THE “DIY” DATA INFRASTRUCTURE
30
Glue
Code
Glue
Code
Community Supplied You write this
Zookeeper
Implications
- Need a specialized skill set
- Development: more work to write glue code, test and QA system for potential failure modes
- Support: test and maintain “glue” code with each component release
Bottom line:
- More $ invested in developing data infrastructure
- Longer time to value

31. page© 2016 VoltDB
THE “DIY” DATA INFRASTRUCTURE VS VOLTDB
• Rigorous testing and QA
• 1/4th of the components
• Simpler, Faster
• SQL and Java
• Easier to test, maintain applications
Glue
Code
Glue
Code
Zookeeper

32. page© 2016 VoltDB
BATCH PROCESSING VERSUS CONTINUOUS EVENT
PROCESSING
• Batch processing is an efficient way of
processing large volumes of data
• Collect – Process – Report
• Fast data processing involves a continuous
process; each event is treated individually
• Ingest - Analyze - Act
32

33. page
BATCH PROCESSING
33
Event Occurs
Analyze,
Gain Insight
Take Action
Collect Data Process Data Act on the Data
TimeNow Later

34. page
CONTINUOUS EVENT PROCESSING
Analyze,
Gain Insight
Take ActionEvent Occurs
34
TimeNow Later

35. page© 2016 VoltDB
SQL VERSUS NOSQL
35
• SQL (structured query language) is for relational databases
• Powerful query language
• Standard and widely adopted
• Flexibility - abstracts application from the data
• ACID transactions – ensures immediate data consistency, reliability
• NoSQL
• Analytics are difficult/painful due to ridged data model
• Non-standard programming interface (each product is different)
• Lack of SQL and ACID transaction guarantees drives complexity to the Application
Ø Data integrity becomes the job of the Application developer

36. page© 2016 VoltDB page
CASE STUDIES
36

37. page© 2016 VoltDB
Personalized trade
recommendations
Business challenges:
- “Interactive” speed
- Personalized offers
- Data accuracy,
integrity (compliance)
- Multiple data sources
CASE STUDY: FINANCIAL SERVICES

38. page© 2016 VoltDB
CASE STUDY: FINANCIAL SERVICES
38
Data Sources
Rules
Engine
In-Memory Grid
AppApp App
• Event data from multiple sources
• Each application database replicates to
Cassandra and Hadoop
• In-memory grid used to maintain logic and
publish ‘state’ back and forth
• Rules engine with fast access to Cassandra
• MySQL used for slow-changing data

39. page© 2016 VoltDB
BEFORE
Data Sources
Rules
Engine
In-Memory Grid
AppApp App

40. page© 2016 VoltDB
BEFORE
40
Data Sources
Rules
Engine
In-Memory Grid
AppApp App App App App
AFTER
Data Sources

41. page© 2016 VoltDB
CASE STUDY: FINANCIAL SERVICES
Results
ü Simplified system architecture
ü Immediate data consistency
ü Real-time recommendations
ü Faster time to value
41

42. page© 2016 VoltDB
CASE STUDY: MEDIA AND ENTERTAINMENT
Content Delivery Network Service
Provider
Business challenges:
- Real-time analytics for customers
- Data accuracy: over/under billing
- Scalability
42

43. page© 2016 VoltDB
CASE STUDY: MEDIA AND ENTERTAINMENT
43

44. page© 2016 VoltDB
CASE STUDY: MEDIA AND ENTERTAINMENT
44
Results
ü Simplified system architecture
ü 1/10th the compute resources
ü 100% budget accuracy,
eliminated $$$ under/over
spending
ü Faster time to value
“We chose to go with VoltDB over other streaming aggregate solu2ons (like Trident) for its SQL
interface, real-­‐2me Ad-­‐Hoc queries over our raw data, and simpler overall design”
Behzad Pirvali, Architect, MaxCDN

45. page© 2016 VoltDB
CASE STUDY: INTERNET OF THINGS
IoT Device Manufacturer
Platform
- Smart devices, appliances
Business challenges:
- High volume and velocity of data from
smart devices
- Complexity (multiple ingest points, apps,
databases)
- Performance – need to automate action on
inbound data at the velocity of the feeds

46. page© 2016 VoltDB
CASE STUDY: INTERNET OF THINGS
46
Device Data
Rules
Engine
In-Memory Grid
AppApp App
• Device data flows from cloud from multiple
devices, appliances
• Each application database replicates to
Cassandra and Hadoop
• In-memory grid used to maintain logic and
publish ‘state’ back and forth
• Rules engine for intra-day data to trigger
actions (e.g., ‘turn lights on’)
• PostgreSQL used for dimension data

47. page© 2016 VoltDB
BEFORE
Device Data
Rules
Engine
In-Memory Grid
AppApp App

48. page© 2016 VoltDB
BEFORE
48
App App App
AFTER
Data SourcesDevice Data
Rules
Engine
In-Memory Grid
AppApp App

49. page© 2016 VoltDB
CASE STUDY: INTERNET OF THINGS
Results
ü Simplified system
architecture
ü Single ingest point for high-
velocity feeds of inbound
data
ü Faster time to value
49

50. page© 2016 VoltDB
WHY VOLTDB?
Faster
Smarter Simpler
Our customers realize exceptional business value

51. page© 2016 VoltDB
QUESTIONS?
• Use the chat window to type in your questions
• Try VoltDB yourself:
Ø Free trial of the Enterprise Edition:
• www.voltdb.com/Download
• Email us at: info@voltdb.com
51