How to Prevent Bank Fraud & Monitor Risk in Real-TimeMarch 2, 2016Vassili Patrikis, Big Data Lead, AccentureMatthew Stump, Head of Product Marketing, DataStax
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ANALYTICS LANDSCAPE
IS SHIFTING
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Democratization of data and data discovery
New data sources
Complex data-driven environment with significant opportunities to create business value
Focus on advanced analytics
Big data and hybrid architectures
Changing skills requirements
Market Trends
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Issues OutcomesHarness the power of big data
Employ innovative analytical tools
Isolate the signal from the noise
Embed insights into decisions and processes
Data
Analytics
Insights
Actions
Shrinking market share
Pricing pressures
Customer defection
Fragmentation and complexity
Inefficient operations
Aged platforms and systems
Employee engagement
Fraud & non-compliance
Expanding market share
Enhanced cost andcash advantage
Customer loyalty
Speed-to-insights
Operational Excellence
Leading edge platforms
Winning the war for talent
Reduced risk and fraud
Helping our clients use data and analytics to defend, differentiate and disrupt their markets
The Accenture Analytics to High Performance
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MANAGEMENT &
MOBILIZATIONMOVEMENT CONSUMPTION
How to move data swiftly from its source to places in the organization where it is needed
How to mobilize as quickly as possible to drive data and analytic driven insights
How to enable users, machines and algorithms to leverage data and analytics to support information in circulation @ scale
Each component can address data movement, management & mobilization, and/or consumption, and each has distinctive technology features.
Challenges
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Organizations can choose from many different data technology components to build the architecture needed to support data acceleration
Cache Clusters
Ingestion
In-memory Databases
Big Data Platforms
Complex Event Processing
Appliances
Architectural Components & their technology
features
Distributed computingIn-memoryStreaming
Distributed computingIn-memoryStreaming
Distributed computingIn-memory
Distributed computingIn-memory StreamingOptimized network
In-memory
Distributed computingIn-memoryOptimized networkCustom silicon
Data Supply Chain Components
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Technology Components:
Volume, type and velocity of data determines how data management principles are applied.
Ingestion/Integration:
Big Data Platform Deployment Options Performance ClusterCommon Layer Data Management
Dat
a S
trate
gy
Service Interface Layer
Application
Big Data Core
Query Engine
In-Mem Analytics
Dat
a A
rchi
tect
ure
– D
ata
Mod
elin
g, S
truct
ures
, Etc
.
Met
adat
a M
anag
emen
t
Sec
urity
/ P
rivac
y / E
ncry
ptio
n
Mas
ter D
ata
Man
agem
ent
Mas
ter D
ata
Aut
horin
g
Con
solid
atio
nC
o-E
xist
ence
Reg
istry
Tran
sact
ion
Streaming Event Processing
Search
Dat
a C
lean
sing
/ P
rofil
ing
/ Err
or S
ensi
ng Distributed Cache In-Mem DB NoSQL
Bar
e-M
etal
Dire
ct In
stal
lV
irtua
lized
App
lianc
e
Clo
udP
ublic
Virt
ual P
rivat
eM
ulti-
tena
nt
Dat
a G
over
nanc
e
Traditional Relational
Sources
Transfer (Batch, Stream, Interactive)
Transfer Paths Added Processing Interactivity Enabled
Technology Enabled Operations @ Speed of “Now”
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• Does my data set require random access at low latency?• Does the business require high volume concurrent writes or reads from the database
layer?• Is there business impact of database downtime?• Does the data need to be replicated to more than a single database?• Does the data size have a significant growth rate over time?• Is there a business case for analytics in near real-time?
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When is DataStax the Right Solution?
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HTAPLambdaTrans-analyticsSystems of intelligenceStreaming analytics
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HTAPLambdaTrans-analyticsSystems of intelligenceStreaming analytics
REALTIMEVALU
E
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DistributedLinear scaleContinuous availabilityCommodity Hardware
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DistributedLinear scaleContinuous availabilityCommodity Hardware
EPICSCAL
E
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ScaleValue
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Cloud Applications
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Attributes of Cloud Applications
Continuously AvailableGeographically DistributedOperationally Low LatencyLinearly ScalableImmediately Decisive
Architecture of an Anti-Fraud Application
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Web App Queue Ingest Operational
Database
Architecture of an Anti-Fraud Application
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Web App Queue Ingest Operational
Database
Solr
Architecture of an Anti-Fraud Application
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Web App Queue Ingest Operational
Database
Hadoop
Solr
Queue
Hive
Search
Batch Processing
Architecture of an Anti-Fraud Application
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Web App Queue Ingest
Spark Streaming
Operational
Database
Hadoop
Solr
Queue
Hive
Search
Batch Processing
IBM Real-time Analytics Reference Architecture
Source: Explore the advanced analytics platform (http://ibm.co/1PLAneB)© 2015 DataStax, All Rights Reserved. 20
Oracle Reference Architecture
Source: Big Data in Financial Services and Banking (http://bit.ly/1GP8mmE)
Hadoop Real-time Analytics Reference Architecture
Source: Designing Fraud-Detection Architecture That Works Like Your Brain Does (http://bit.ly/1OeAnDb)© 2015 DataStax, All Rights Reserved. 22
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“70% of Hadoop deployments will not meet cost savings and
revenue generation objectives due to skills and integration
challenges.”
© 2015 DataStax, All Rights Reserved. 24 Source: Hadoop promises not yet paying off (http://tek.io/1MrHtSx)
DSE Real-time Analytics Reference Architecture
HTTP Application Message Queue
Streaming
Analytics
BatchAnalytic
s
Real-time
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DataStax Enterprise is the database for cloud applications
Gartner ODBMS MQ 2015
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DataStax Use Cases• Customer 360°• Master data management• Customer profile management• Authentication and identity management• Product personalization• Anti-fraud and money laundering• Payments and transactions• Risk reporting/capital adequacy• Market data capture/replay
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Accenture DataStax Alliance Overview
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Accenture Analytics & Big Data• Extensive capabilities – data access
and reporting• Enhanced modeling• Forecasting and sophisticated
statistical analysis
Deep industry experience
BETTER TOGETHER:
Help organizations scale and leverage their big data to become data-driven enterprises
Go to Market Focus:
• NoSQL solutions and online transaction processing
• Rapid deployment of data for analytics
• Personalization
500Global customers in 45 countries
Fast, scalable distributed database technology, delivering Apache Cassandra to innovative enterprises
DataStax is built to be agile, always-on and predictably scalable to any size
Integral part of the Accenture Analytics alliance and vendor ecosystem of big data technology providers
36,000Accenture Digital professionals
1,300+Data Scientists
23Accenture Innovation Centers including 5 Advanced Analytics Innovation Centers globally
Contacts & ResourcesAccenture:• Barbara White, Accenture Alliance Director [email protected]• Vassili Patrikis, Big Data Lead [email protected]
DataStax:• Brian Borbely, DataStax Alliance Director [email protected] • Matthew Stump, Head of Product Marketing [email protected]
Resources: • Accenture Alliance with DataStax • DataStax Alliance Partnership Press Release
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THANK YOU!
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