the big data ecosystem for financial services

Martin Van Ryswyk- EVP of Engineering, DatastaxThe “Big Data” Ecosystem for Financial Services

© 2015 DataStax, All Rights Reserved.

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53% can’t distinguish between banks–

71% would rather go to the dentist–

Digital is the preferred channel–

1 in 3 willing to switch banks

Source: The Millennial Disruption Index (http://bit.ly/1gp93Q9)

© 2015 DataStax, All Rights Reserved.

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IT budgets under pressure–

Services and labor costs have exploded–

ROE at historically low 6%

Source: Managing Complexity (http://owy.mn/1UNwPPy)

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“lack of staff resources continues to be the number one barrier for financial services organizations in

2014.”

© 2015 DataStax, All Rights Reserved. Source: 2014 Payments Fraud Survey Summary of Results (http://bit.ly/1POwxkF)

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“58% of Financial Services organisations see Risk and

Operational Management as the drivers for using Big Data. 66%

see fragmentation of Systems/IT complexity as a significant hurdle

in achieving this.”© 2015 DataStax, All Rights Reserved. Source: https://datafloq.com/read/financial-services-firms-leveraging-big-data-infog/352

© 2015 DataStax, All Rights Reserved.

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The New Database MarketLegacy Post Relational

Tran

sact

iona

lAn

alyt

ical

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Architecture of a Modern Application

Web App Queue Ingest Operational

Database

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Architecture of a Modern Application

Web App Queue Ingest Operational

Database

Solr

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Architecture of a Modern Application

© 2015 DataStax, All Rights Reserved.

Web App Queue Ingest Operational

Database

Hadoop

Solr

Queue

Hive

Search

Batch Processing

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Architecture of a Modern Application

© 2015 DataStax, All Rights Reserved.

Web App Queue Ingest

Spark Streaming

Operational

Database

Hadoop

Solr

Queue

Hive

Search

Batch Processing

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IBM Real-time Analytics Reference Architecture

Source: Explore the advanced analytics platform (http://ibm.co/1PLAneB)

Oracle Reference Architecture

Source: Big Data in Financial Services and Banking (http://bit.ly/1GP8mmE)© 2015 DataStax, All Rights Reserved.

© 2015 DataStax, All Rights Reserved.

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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.

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“70% of Hadoop deployments will not meet cost savings and

revenue generation objectives due to skills and integration

challenges.”

Source: Hadoop promises not yet paying off (http://tek.io/1MrHtSx)

© 2015 DataStax, All Rights Reserved. 16

DataStax Enterprise is the database for cloud applications

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HTTP Application Message Queue

Streaming

Analytics

BatchAnalytic

s

Real-time

DSE Real-time Analytics Reference Architecture

© 2015 DataStax, All Rights Reserved.

Flying a plane is not the same as building it…

© 2015 DataStax, All Rights Reserved.

© 2015 DataStax, All Rights Reserved.

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Gartner ODBMS MQ 2015

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DataStax Use Cases• MDM: Customer 360, Product Catalog• Personalization and Recommendation• Internet of Things (IoT) and Time Series• Fraud Detection• List Management• Messaging• Inventory Management• Authentication

Customer 360° / User Profile• Consolidate data, reduce

complexity• Alleviate strain on legacy systems• Integrated data sovereignty• Eliminate downtime• Recommend higher margin

products• Customer personalization• Audit, and regulatory compliance• Full text indexing, document search© 2015 DataStax, All Rights Reserved. 21

“DataStax has allowed us to break the complexities of our legacy systems, empowering us to leverage data to innovate and make more informed decisions so we can provide a truly personalized and premium experience to our customers.”

“DataStax Enterprise gives us the power to change information in our supporting systems into actionable insights… we can not only scale but give our users a real-time, engaging customer experience.”

• Financial product recommendation• Provide the “small bank” feel through

personalization• Real-time recommendation using

transaction data, search, and browsing history.

• Measure engagement and satisfaction to prevent flight

• Website and mobile metrics

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Recommendation, Engagement, and Customer Churn Prevention

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Payments and Transactions• Transaction history tagging, search

and budgeting, cash flow prediction• Product recommendation based on

purchasing history.• Data safe against loss of node,

rack, or data center.• Shed load from legacy systems to

reduce cost and provide better user experience.

“We had reached the limits of our scalability. We wanted to remove all single points of failure. We had to be active-active. That's why we chose Cassandra.”

© 2015 DataStax, All Rights Reserved. 23

Regulatory Compliance and Capital Adequacy

“The platform enables traders to capture vast amounts of versioned market data which is then used for risk management purposes in real time.”

© 2015 DataStax, All Rights Reserved. 24

• Capture and analyze every trade, every position for every trader.

• Real-time risk analysis.• Globally replicated, 100%

uptime.• Low latency, high ingest.

Market Tick and Time Series Data• Capture high fidelity time

series information at high ingest rates, and low latencies

• 100% uptime even in the event of data center loss

© 2015 DataStax, All Rights Reserved. 25

“We primarily use Cassandra as a distributed data store for Financial Instrument and Time-Series data, where the ‘master’ data stores cannot scale globally and provide the high availability needed to support a global application with heavy user demand.”

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Mission Critical Applications

“DSE is a perfect match… easing availability challenges by being

active-active and having an always-on architecture”

Source: Exploiting Hotel Cassandra (http://bit.ly/1mf0KRE)