Big Data in Financial Services: How to Improve Performance with

Data-Driven Decisions

November 28, 2012

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.

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

Perficient brings deep solutions expertise and offers a complete set of flexible services to help clients implement business-driven IT solutions.

Our Solutions Expertise & Services

Perficient Solutions

- Enterprise Application Integration

- Business Intelligence

- Business Process Management

- Enterprise Architecture

- eCommerce

- Customer Relationship Management

- Enterprise Content Management

- Master Data Management

- Portal / Collaboration

- User Experience

- Mobile Solutions

Consulting Services• Big Data Strategy & Roadmap• Big Data Assessment• Architecture Planning & Platform

Selection• Master Data Management• Data Governance• Regulatory Compliance Assessment

BI & Analytics Capabilities• BI/Big Data Implementations• Risk and Fraud Detection • Social Analytics• Cloud Analytics• Real-time Analytics• Self-service Analytics

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.

What We Will Cover

A b o u t U sVa l u e o f B i g

D a t a

C h a l l e n g e s E f f e c t i v e S t r a t e g i e s

L e v e r a g e I T I n v e s t m e n t s Q & A

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

N e x t S t e p s

V a l u e o f B i g D a t a i n

F i n a n c i a l S e r v i c e s

What is Big Data?

Extracting insight from an immense volume, variety and velocity of data, in context, beyond what was previously possible.

What is Big Data?

2009

800,000 petabytes

as much Data & ContentOver Coming Decade

44xBusiness leaders frequently make decisions based on information they don’t trust, or don’t have

1 in 3

83%of CIOs cited “Business intelligence and analytics” as part of their visionary plansto enhance competitiveness

Business leaders say they don’t have access to the information they need to do their jobs

1 in 2

of CEOs need to do a better job capturing and understanding information rapidly in order to make swift business decisions

60%Of world’s datais unstructured

80+%

2020

35 zettabytes

Business Impacts of Big Data

Traditional Analytics

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

Value to the Enterprise

Source: CMSwire, IBM: The Business, IT Case for Big Data Investments (Oct. 31, 2012)

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

CustomersConsumers have rapidly

evolving expectations for offerings and services.

EconomyUncertain global conditions are affecting revenue and reducing

IT spending.

CapitalizationMature and emerging market segments are focus on optimizing use of capital.

RegulationRadically increased oversight is driving investment in risk management technology.

TrustRebuilding customer trust and marketplace confidence is critical to future growth.

$

Big Data Challenges

CompetitionIntensifying with mergers,

acquisitions, and non-traditional entrants.

MarketingBig data predicts the right offer for the right customer at the right time.

Relationship management

Big data considers the risk and profitability of the

entire customer relationship when pricing

new deals.

Executive leadersBig data enables more effective business decisions using accurate data across all time horizons.

Risk and financeBig data streamlines compliance and understand risk exposure across businesses and regions.

Payments Big data can detect and prevent a wire transfer incidents of fraud.Branch management

Big data interprets which branches or products are performing the

best.

Value to the Enterprise

Source: IBM Corporation

Increase flexibility and streamline operations

Create a customer- focused enterprise

Optimize enterprise risk management

Meaningful Data Drives Quality Decisions

Marketing & Solicitation

How do I retain my most profitable customers?

Who are my ideal customers and how do I attract them?

What channels are more effective to solicit customers?

Is our customer portal an effective tool for offering new

products?

Am I able to effectively identify fraud before it occurs?

Could I improve credit underwriting?

How do I deliver real-time insight at the point of impact?

How do I provide better executive visibility into

enterprise performance? Are our products competitively priced?

How do I manage the evolving risk landscape?

16

Big Data Capabilities

DATA GOVERNANCE IS CHALLENGE

Hadoop

Big Data

Distributed file system

RDBMS vs. Hadoop

Required Capabilities of Big Data:

• Processing

• Data Management

• Services

Bank’s Application Data

Financial management and budgeting

Operations and production

Strategy and business development

Sales and marketing

Customer Service

Product research & development

General management

Risk management

Customer experience management

Brand or market management

Workforce planning and allocation

1 2 3 4 5 6 7 8

Tendency to Apply AnalyticsTendency to Apply Intuition

Optimized software-only solutions like Hadoop

Scale up existing relational technologies

Cloud infrastructure or service providers

In-memory databases

Business intelligence appliances

Columnar RDBMS

Preferred Big Data Approaches

33%

32%

29%

28%

23%

37%

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

19

Growth of Social Data

Best Use Patterns

Enhanced Customer View

• Internal Customer• “Digital Persona”

Big Data Trends

Appliance Big Data Platform

Appliances provide pre-certifiedplatforms :

• Reduces time to implement• Allows the business to focus on

Analysis not set-up and configuration• Less impact to internal Network

Infrastructure

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”

B i g D a t a C h a l l e n g e s

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

25

Content

StructuredData

AnalyzeIntegrate

Govern

Data

Transactional & Collaborative Applications

Manage

StreamingInformation

Business Analytic Applications

Streams

Big Data

Data Warehouses

External Information

Sources

www

Quality

LifecycleManagement

Security &Privacy

Big Data Appliances

Master Data

Data Governance

Data Governance Applies to Big Data

E ff e c t i v e B i g D a t a S t r a t e g i e s

27

Policy

Definitio

n

Measurement

& FeedbackPolic

y

Enforc

emen

t

Comm

unication

& Education

TOOLS

STANDARDS

METADATA MANAGEMENT

DATA QUALITY & STEWARDSHIP

STRATEGY

METRICS

ORGANIZATION & PROCESS

DATA ARCHITECTURE

Master Data Governance

Data Governance Focus Areas

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)

Effective Big Data Strategies

Staffing: Skills in the operating platforms and systems to manage big data are essential

Analytics: Leverage big data patterns; incorporate big data technology and make current data analytics and storage more flexible

Align Business Needs and Prioritize Quick Wins

Network: Network layer and dedicated segments need to be optimized to work with velocity requirements for “streaming analytics”

Core values for big data success:- Find new value from existing data- Look for data from new sources - Learn to capitalize on social collaboration tools- Be customer centric - look at the data from their view- Business and technology collaboration- Exchange value with proprietary data sources- Center of excellence for analytics - Promote the capability enterprise wide

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.

Be on the Lookout for…

Leadership: Form big data steering committee with executive sponsorship to drive consensus and align business goals

Implementation: Utilize a proof concept against a small business unit with deep domain knowledge of analytics

Performance Drivers: Set obtainable goals with incremental deliverables to avoid being overwhelmed by big data

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.

ARCHITECTURE RENEWAL AND IT RENOVATION

OUTSOURCING GENERIC FUNCTIONS

BUSINESSS AND FINANCIAL REPORTING

PAYMENT CONSOLIDATION

PRODUCT INNOVATION

RISK SYSTEMS INTEGRATION

Effective Big Data Strategies

Drawing on marketplace insights and engaging customers as co-developers:

Bank location

ATM

Point of sale

Mail

Web

Phone

MICROFINANCE

POINT OF SALE AS ATM

MOBILE BANKING

SOCIAL NETWORKING

Consistent Channel

• Tailor products and services on demand• Delivers through an ever-evolving and increasingly interconnected set of channels• Ensuring consistency across any channel is crucial

32

Effective Big Data Strategies

Enriched Data Improves Management Decision Making

Effective Big Data Strategies

Focus on Generating Customer Insights

34

Improve the customer experience across channels using:• Hadoop and other tools• Unstructured feedback and social data• Online customer surveys & online chat• Click-stream data• Emails

Sentiment Analysis / Voice of Customer

Social & Text Analytics

• Increase engagement to improve acquisition

• Reduce customer service response times• Adapt marketing and sales strategies• Track customer behavior and preferences• Engage with social influencers

• Efficient fraud detection• Cross-selling of products and

services• Targeted advertising and

marketing campaigns• Customer loyalty and rewards

programs• Effective business strategies and

informed decisions

Predictive Analytics

Effective Big Data Strategies

Effective Big Data Strategies

B u s i n e s s - d r i v e n S u p p o r t f o r

Y o u r B i g D a t a S t r a t e g y

• Business Assessment

• Data Governance Assessment

• Big Data Strategy & Roadmap

• Technology Selection

• Architecture Design

• Cloud Services

• Implementation Services

• Big Data Analytics Support

• Big Data Talent

Connect with Perficient

S U B S C R I B E T O P E R F I C I E N T B L O G S O N L I N E

h t t p : / / B l o g s . P e r f i c i e n t . c o m / F i n a n c i a l S e r v i c e s /

F O L L O W P E R F I C I E N T O N T W I T T E R

w w w. Tw i t t e r . c o m / P e r f i c i e n t _ F S

B E C O M E A F A N O F P E R F I C I E N T O N F A C E B O O K

w w w. F a c e b o o k . c o m / P e r f i c i e n t

D O W N L O A D P E R F I C I E N T W H I T E P A P E R S

w w w. P e r f i c i e n t . c o m / W h i t e P a p e r s