Most banking and financial services organizations have only scratched the surface of leveraging customer data to transform their business, realize new revenue opportunities, manage risk and address customer loyalty. Yet a business’s digital footprint continues to evolve as automated payments, location-based purchases, and unstructured customer communications continue to influence the technology landscape for financial services.

1. Big Data in Financial Services:
How to Improve Performance with
Data-Driven Decisions
November 28, 2012

2. About Perficient
Perficient is a leading information technology consulting firm serving clients
throughout North America.
We help clients implement business-driven technology solutions that integrate
business processes, improve worker productivity, increase customer loyalty and create
a more agile enterprise to better respond to new business opportunities.

3. Perficient Profile
 Founded in 1997
 Public, NASDAQ: PRFT
 2012 Projected Revenue of $320 Million
 Major market locations throughout North America
— Atlanta, Austin, Charlotte, Chicago, Cincinnati, Cleveland,
Columbus, Dallas, Denver, Detroit, Fairfax, Houston,
Indianapolis, Los Angeles, Minneapolis, New Orleans,
Philadelphia, San Francisco, San Jose, Southern California,
St. Louis and Toronto
 Global delivery centers in China, Europe and India
 2,000+ colleagues
 Dedicated solution practices
 87% repeat business rate
 Alliance partnerships with major technology vendors
 Multiple vendor/industry technology and growth awards

4. Our Solutions Expertise & Services
Consulting Services Perficient Solutions
• Big Data Strategy & Roadmap - Enterprise Application Integration
• Big Data Assessment - Business Intelligence
• Architecture Planning & Platform - Business Process Management
Selection - Enterprise Architecture
• Master Data Management - eCommerce
• Data Governance - Customer Relationship Management
• Regulatory Compliance Assessment - Enterprise Content Management
- Master Data Management
BI & Analytics Capabilities - Portal / Collaboration
• BI/Big Data Implementations - User Experience
• Risk and Fraud Detection - Mobile Solutions
• Social Analytics
• Cloud Analytics
• Real-time Analytics
• Self-service Analytics Perficient brings deep solutions expertise and
offers a complete set of flexible services to help
clients implement business-driven IT solutions.

5. Our Speakers
Mike Panzarella, Director, Financial Services Practice
With 20 years of experience with Big Four consulting and
commercial banking, Mike has expertise in BI platform architectures
for Fortune 100 financial service firms with a focus on social media
and mobile convergence. Mike has extensive experience in
designing and implementing Big Data solutions for Fortune 100
companies.
Jeff Fisher, Director, FS Practice Operations &
Advisory Services
With over 20 years of experience as a technology leader with global
enterprise organizations, Jeff has a proven track record of success
leading technology teams in financial services organizations.

6. What We Will Cover
Va l u e o f B i g Big Data
About Us Data Tr e n d s
Challenges Effective Strategies
Leverage IT
Next Steps Investments Q & A

7. Value of Big Data in
Financial Services

8. What is Big Data?

9. What is Big Data?
Extracting insight from an immense volume, variety and velocity
of data, in context, beyond what was previously possible.

10. Business Impacts of Big Data
44x
as much Data & Content
2020
35 zettabytes
Business leaders frequently
Over Coming Decade 1 in 3 make decisions based on
information they don’t trust, or
don’t have
1 in 2 Business leaders say they don’t
have access to the information
they need to do their jobs
of CIOs cited “Business
2009
800,000 petabytes
80+% 83% intelligence and analytics” as
part of their visionary plans
to enhance competitiveness
Of world’s data
is unstructured of CEOs need to do a better job
60% capturing and understanding
information rapidly in order to
make swift business decisions

11. Traditional Analytics
• Managed schema
• Data in many siloes
• Customer view not always federated across the enterprise
• Slowly changing facts and dimensions

12. Value to the Enterprise
Customer-centric Outcomes
• Retail mobile offers based on
preferences or buying patterns
• Model targeting done with online
banking offers based on
Functional Outcomes
• Collect KPI and metrics for Enterprise
Performance Management (EPM)
• Compliance checks and audits
Source: CMSwire, IBM: The Business, IT Case
for Big Data Investments (Oct. 31, 2012)

13. Big Data Challenges
Economy Trust
Uncertain global conditions are Rebuilding customer trust
affecting revenue and reducing and marketplace
IT spending. confidence is critical to
future growth.
Competition Regulation
Intensifying with Radically increased
mergers, oversight is driving
acquisitions, and non- investment in risk
traditional entrants. management
technology.
Customers
Consumers have rapidly
evolving expectations for
$ Capitalization
Mature and emerging market
segments are focus on
offerings and services. optimizing use of capital.

14. Value to the Enterprise
Payments
Big data can detect and prevent a wire
Branch management transfer incidents of fraud.
Big data interprets which branches
or products are performing the
best. Executive leaders
Big data enables more effective
business decisions using accurate
data across all time horizons.
Relationship
management
Big data considers the risk
and profitability of the
entire customer
Risk and finance
relationship when pricing
Big data streamlines
new deals.
compliance and
Marketing understand risk exposure
Big data predicts the right across businesses and
offer for the right customer at regions.
the right time.
Source: IBM Corporation

15. Meaningful Data Drives Quality Decisions
Marketing & Solicitation Increase flexibility and
What channels are more
streamline operations
effective to solicit customers?
How do I deliver real-time
Is our customer portal an insight at the point of impact?
effective tool for offering new
products? How do I provide better
executive visibility into
Are our products enterprise performance?
competitively priced?
How do I manage the
evolving risk landscape?
Optimize enterprise Create a customer-
risk management focused enterprise
Who are my ideal customers
Am I able to effectively identify and how do I attract them?
fraud before it occurs?
Could I improve credit How do I retain my most
underwriting? profitable customers?

16. Big Data Capabilities
Required Capabilities of Big Data:
• Processing
• Data Management
Big Data
• Services
Hadoop
Distributed file system
DATA
GOVERNANCE IS
CHALLENGE RDBMS vs. Hadoop
16

17. Bank’s Application Data
Tendency to Apply Intuition Tendency to Apply Analytics
Financial management and budgeting
Operations and production
Preferred Big Data Approaches
Strategy and business development
Optimized software-only solutions like Hadoop 37% Sales and marketing
Scale up existing relational technologies 33% Customer Service
Product research & development
Cloud infrastructure or service providers 32%
General management
In-memory databases 29%
Risk management
Business intelligence appliances 28% Customer experience management
Brand or market management
Columnar RDBMS 23%
Workforce planning and allocation
1 2 3 4 5 6 7 8

18. B i g D a t a Tr e n d s

19. Growth of Social Data
19

20. Best Use Patterns
Enhanced Customer View
• Internal Customer
• “Digital Persona”

21. Big Data Trends
Appliance Big Data Platform
Appliances provide pre-certified
platforms :
• Reduces time to implement
• Allows the business to focus on
Analysis not set-up and configuration
• Less impact to internal Network
Infrastructure

22. Tailored Cloud Services
Cloud-based Infrastructures
• Low-cost, low-risk solution
• Scalable without impact to internal
networks and infrastructure
• Great first step to “test the water”

23. Big Data Challenges

24. Big Data Challenges
• Hard to
quantify
value to the
enterprise
• Data
Scientists
roles are
difficult to fill
• Difficult to
design
effective
visualization
and reporting
of new data
sets
24

25. Data Governance
Data Governance Applies to Big Data
Transactional &
Collaborative Business Analytic
Applications Applications
Integrate Analyze
Master Big Data
Data www
Data
Warehouses Structured
Manage Data
Streams
External
Information Big Data
Data
Sources Appliances
Content
Streaming
Information
Security &
Govern Lifecycle Privacy
Quality Management
25

26. Effective Big Data Strategies

27. Data Governance Focus Areas
TOOLS
DATA ARCHITECTURE STANDARDS
METADATA
ORGANIZATION & Master Data MANAGEMENT
PROCESS
Governance
DATA QUALITY &
METRICS
STEWARDSHIP
STRATEGY
27

28. Effective Big Data Strategies
Dispelling the Skepticism
• Integration with existing infrastructure can be loosely
or deeply integrated based on value and need
• Leverage service providers and don’t be afraid to use
existing talent to fill “Data Scientist” roles
• Very real value for clickstream analysis, log file
analysis and voice of customer (VOC) are quick wins
(internal & external)

29. Effective Big Data Strategies
Align Business Needs and Prioritize Quick Wins
Core values for big data success: Leadership: Form big data Staffing: Skills in the operating
- Find new value from existing data steering committee with executive platforms and systems to manage
- Look for data from new sources sponsorship to drive consensus big data are essential
- Learn to capitalize on social and align business goals
collaboration tools
- Be customer centric - look at the Analytics: Leverage big data Implementation: Utilize a
data from their view patterns; incorporate big data proof concept against a small
- Business and technology technology and make current data business unit with deep domain
collaboration analytics and storage more flexible knowledge of analytics
- Exchange value with proprietary
data sources
- Center of excellence for analytics Network: Network layer and Performance Drivers: Set
- Promote the capability enterprise dedicated segments need to be obtainable goals with incremental
wide optimized to work with velocity deliverables to avoid being
requirements for “streaming overwhelmed by big data
analytics”
Be on the Lookout for…
Distribution Maturity: Commercial distributions of Hadoop include
Cloudera, MapR, Hortonworks, InfoSphere, BigInsights, EMC Greenplum HD, and others. Expect the Hadoop
framework to be expanded and leveraged by many more technology vendors. Evolving NoSQL solutions such as
Cassandra and Neo4j offer additional big data options.

30. Effective Big Data Strategies
Built around an optimized and integrated back office—one that
leverages advancements in technology, global integration
opportunities and a continuous flow of data to cut costs, drive
speed and further innovation.
OUTSOURCING
GENERIC FUNCTIONS
PRODUCT
INNOVATION
ARCHITECTURE RENEWAL
AND IT RENOVATION
PAYMENT
CONSOLIDATION
BUSINESSS AND
FINANCIAL REPORTING
RISK SYSTEMS
INTEGRATION

31. Consistent Channel
Drawing on marketplace insights and engaging customers as co-developers:
• Tailor products and services on demand
• Delivers through an ever-evolving and increasingly interconnected set of channels
• Ensuring consistency across any channel is crucial
Bank location
MOBILE BANKING MICROFINANCE
Phone ATM
SOCIAL POINT OF SALE AS ATM
NETWORKING
Point
Web
of sale
Mail

32. Effective Big Data Strategies
Enriched Data Improves Management Decision Making
32

33. Effective Big Data Strategies
Focus on Generating Customer Insights

34. Effective Big Data Strategies
Sentiment Analysis /
Voice of Customer
Improve the customer experience across Predictive
channels using:
• Hadoop and other tools
Analytics
• Unstructured feedback and social data • Efficient fraud detection
• Online customer surveys & online chat • Cross-selling of products and
• Click-stream data services
• Emails • Targeted advertising and
marketing campaigns
Social & Text • Customer loyalty and rewards
programs
Analytics • Effective business strategies and
• Increase engagement to improve informed decisions
acquisition
• Reduce customer service response times
• Adapt marketing and sales strategies
• Track customer behavior and preferences
• Engage with social influencers
34

35. Effective Big Data Strategies
Business-driven Support for
Your Big Data Strategy
• Business Assessment
• Data Governance Assessment
• Big Data Strategy & Roadmap
• Technology Selection
• Architecture Design
• Cloud Services
• Implementation Services
• Big Data Analytics Support
• Big Data Talent

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