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 billion from 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 billion in 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 (called chief 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

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 billion from 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 billion in 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 (called chief 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?