With access to rich, real-time information, today’s consumers expect businesses to understand their unique needs in the context of their current situation. CIO’s, CDO’s and architects, however, face several challenges inhibiting them from utilizing their information assets to meet this expectation. In this session, we will share the latest practices and case studies from leading companies on their customer information management strategy helping them standout in the marketplace and get ahead of the competition.

1. New Best Practices for
Managing Customer Information
Navin Sharma, VP of Product Management, Pitney Bowes

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The New Age of the Smart Consumer

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What Makes Consumers Smart?
“The Nexus of Forces”
1.Ubiquitous Mobile,
Cloud, Social Platforms
2.Access to timely &
relevant information
3.Ability to share “bad
experiences” quickly
thru personal networks

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What Makes A Business Smart?
Business Agility
The Capacity to Identify and
Capture Opportunities More
Quickly Than Rivals

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Internal Barriers Stall Business Agility
The main obstacles to improved business
responsiveness are slow decision-making,
conflicting departmental goals and priorities risk-
averse cultures and silo-based information.

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Adverse Impact of Information Silos
Sales
• Who are my top clients?
• Where else do they have a relationship within the enterprise?
• What is their current status – service requests, VOC surveys?
Support
• What is the value of the customer calling in across
the enterprise?
• What products do they own across the portfolio?
• What was the feedback from recent VOC surveys?
Marketing
• What’s our current share-of-wallet across portfolios?
• What opportunities exist for cross-sell/up-sell?
• Which of my prospects are actual customers?
Partner
• Who are my top performing partners across the enterprise?
• What’s a profile of an ideal partner selling a particular product?
• How do we leverage their relationships and make them more
effective in understanding what leverage we have with clients?

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Knowledge Graphs Power
Smart Consumers
Next Generation Approach

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Knowledge Graphs Should Power
Smart Businesses

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Information Management Best Practice
Model to the
business outcome
Source with
trusted data &
insights
Consume
Search
Integrate
Visualize

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• Rigid data models tied to
RDBMS lose agility
• Limited views
• Business-outcome drive, white-
boarding approach to modeling
• Multi-dimensional views enabled
via complex relationships &
hierarchy management
Knowledge Graphs: Intuitive & Agile

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STEP 1: Model to the Business Outcome

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STEP 2: Source Trusted Data

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• Who is a high spender?
• What is their propensity
to buy?
• Is the customer within my
pre-defined Geo-fence?
• How does it influence my
marketing offers?
• Who is both influential in their community
& a high spender?
• Which products would customers prefer that
others “like” them have purchased?
STEP 2: And Combine it with Insights

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STEP 3: Visualize the Knowledge Graph

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STEP 3: Search the Knowledge Graph

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STEP 3: Integrate the Knowledge Graph

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Retail – Case In Point
Information Silos of Traditional Approach
Location/Site Hub Product Hub
Customer
Hub

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What Traditional Approaches Don’t ‘See’

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Extended Network of a Customer

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Discover Non-Obvious Relationships

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Determine Sphere of Influence

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Financial Services – Case In Point
Payment Graph (e.g. Fraud Detection,
Credit Risk, Analysis, Chargebacks…)
Spend Graph (e.g. Org Drillthru,
Product Recommendations,
Mobile Payments, Etc.)
Asset Graph (e.g. Portfolio Analytics,
Risk Management, Market & Sentiment,
etc.)
Master Data Graph (e.g. Enterprise
Collaboration, Corporate Hierarchy,
Data Governance, etc.):

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Poor Data Management Blinded Chase to
Madoff Fraud: WSJ
by Penny Crosman
JAN 8, 2014
Data locked in silos and the lack of a common customer identifier that could link accounts were
to blame for JPMorgan Chase's failure to identify Bernard Madoff's massive fraud, according to
an article in Wednesday's Wall Street Journal.
(Madoff, who was arrested in 2008, stole about $18 billionfrom clients, sending them fake
monthly statements reflecting fake trades, assuring customers they were getting high returns
when in fact their money was gone.)
Madoff Investment Securities maintained several linked checking and brokerage accounts at
JPMorgan Chase, its primary bank, for 22 years. The bank structured and sold investment
vehicles tied to the firm's purported returns. The bank has agreed to pay $2.7 billionin fines to
the federal government for failing to report warning signs of Madoff's scheme.
"Despite recognizing suspicious activity in its U.K. unit in 2008 — and notifying U.K. regulators
that Mr. Madoff's returns were 'too good to be true' — the bank didn't notify its own U.S.-based
AML staff or American authorities. AML experts say that JPMorgan's anti-fraud systems should
have automatically flagged Madoff accounts across the company," the paper reports. In one of
the terms of the bank's settlement, JPMorgan has agreed to continue reforms of its Bank Secrecy
Act/Anti-Money Laundering compliance program.
Customer data that's strewn across a company and not linked has been a problem that has
plagued large banks for many years. A London division of a bank could have no idea of the
Madoff Fraud: WSJ
by Penny Crosman
JAN 8, 2014
Data locked in silos and the lack of a common customer identifier that could link accounts were
to blame for JPMorgan Chase's failure to identify Bernard Madoff's massive fraud, according to
an article in Wednesday's Wall Street Journal.
(Madoff, who was arrested in 2008, stole about $18 billionfrom clients, sending them fake
monthly statements reflecting fake trades, assuring customers they were getting high returns
when in fact their money was gone.)
Madoff Investment Securities maintained several linked checking and brokerage accounts at
JPMorgan Chase, its primary bank, for 22 years. The bank structured and sold investment
vehicles tied to the firm's purported returns. The bank has agreed to pay $2.7 billionin fines to
the federal government for failing to report warning signs of Madoff's scheme.
"Despite recognizing suspicious activity in its U.K. unit in 2008 — and notifying U.K. regulators
that Mr. Madoff's returns were 'too good to be true' — the bank didn't notify its own U.S.-based
AML staff or American authorities. AML experts say that JPMorgan's anti-fraud systems should
have automatically flagged Madoff accounts across the company," the paper reports. In one of
the terms of the bank's settlement, JPMorgan has agreed to continue reforms of its Bank Secrecy
Act/Anti-Money Laundering compliance program.
Customer data that's strewn across a company and not linked has been a problem that has
plagued large banks for many years. A London division of a bank could have no idea of the
activity of a customer in New York, for example, creating fraud as well as customer service
issues. Shortly before the financial crisis,several large banks appointed C-level data
management chiefs (calledchief data officers) and had them start creating unified customer data
warehouses in which all accounts, transactions and other activity related to a customer could be
gathered in one place. Bank of the West recently completed such a project.
During the financial crisis,these large, multi-year projects with an elusive ROI were put aside.
Recently, with the dust settling, a few banks have been turning their attention again to customer
data management.
But software can only do so much. The other side to this is that in Manhattan U.S. Attorney Preet
Bharara's criminal charges against JPMorgan Chase, a pattern of willful ignorance is described.
Time and time again, according to the U.S. Attorney's office, the bank had strong reason to
The Case for Data Governance

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• Limited to non-existent support
for roles, responsibilities, and
processes between the business
and IT
• KPIs tied to process
• Monitor for trends over-time
• Enable business stewardship
• Embedded workflows &
exception management
• PII data anonymized
Data Governance:
In Service of the Business Process

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Information Management for
Smart Businesses
Knowledge Graphs are Intuitive & Agile
Establish Process-centric Data Governance
Businesses Can Get Smarter Just Like Consumers

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Questions?