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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.
Citation preview
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
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
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