Published on February 27, 2014
Best Practices for Managing Customer Information
Golden Profiles Require Real-Time Knowledge of the Customer’s Context Sentiment Influence Relationships Products Location Buying Propensity 2 2
Evolution of the Customer Information Management Practice Structure Knowledge Graphs Insights Contextualized Profiles Delivery Extreme Processing 3
Knowledge Graphs Next Generation Approach 4
Knowledge Graphs: Intuitive & Agile • Rigid data models tied to RDBMS • Limited views • Adaptive data modeling enabled via Graph Data Structures • Multi-dimensional views enabled via complex relationships & hierarchy management 5
Domain Silos of Traditional Approaches Customer Hub Location/Site Hub Product Hub 6
What Traditional Approaches Don’t ‘See’ 7
Extended Network of a Customer 8
Discover Non-Obvious Relationships 9
Determine Sphere of Influence 10
Multi-dimensional views: Financial Services – Case In Point Payment Graph (e.g. Fraud Detection, Spend Graph (e.g. Org Drill thru, Credit Risk, Analysis, Chargebacks…) Product Reco’s, Mobile Payments) Asset Graph (e.g. Portfolio Master Data Graph (e.g. Enterprise Analytics, Risk Management, Market & Sentiment) Collaboration, Corporate Hierarchy, Data Governance) 11
Madoff Fraud: WSJ The Case for Data Governance 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.) Poor Data Management Blinded Chase to Madoff Fraud: WSJ 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 by Penny Crosman 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 JANhave2014 8, 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 Data locked customer in Newthe lackexample, creating fraud as well asidentifier that could link accounts were activity of a in silos and York, for of a common customer customer service to blameShortly before the financial crisis, several to identify Bernard Madoff's massive fraud, according to issues. for JPMorgan Chase's failure large banks appointed C-level data management chiefs (called chief data officers) an article in in which all accounts, transactions and had them startrelated to unified customer data Wednesday's Wall Streetand other activity creating a customer could be Journal. warehouses gathered in one place. Bank of the West recently completed such a project. (Madoff, who was arrested in 2008, stole about $18 billion from clients, sending them fake During financial crisis, these multi-year projects with an elusive ROI monthly thewith the dust reflecting fake trades, assuring customerswereto customer getting high returns statements settling, alarge,banks have been turning their attention again put were they aside. Recently, few data management. when in fact their money was gone.) But software can only do so much. The other side to this is that in Manhattan U.S. Attorney Preet Madoff Investment Securities maintained aseveral linkedignorance is described. Bharara's criminal charges against JPMorgan Chase, pattern of willful checking and brokerage accounts at Time and Chase, according to the U.S. for 22 office, the bank had strong reason to JPMorgan time again,its primary bank,Attorney's 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 12
Data Governance: In Service of the Business Process • Limited to non-existent support for roles, responsibilities, and processes between the business and IT • • • • • KPIs tied to process Monitor trends over-time Enable business stewardship Embedded workflows/exception mgmt. PII data anonymized 13
Data Visualization: Encourage Data Discovery and Exploration Entity Analytics Spatial Analytics 14
Data Visualization: Encourage Data Discovery and Exploration Pattern Views Heat Map Views Timeline Views Bar Chart Views 15
Combination of Traditional, Social Network & Spatial Analytics: Robust Context to the Knowledge Graph • 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? 16
Customer Information Management Best Practices Use Knowledge Graphs as the intuitive & agile way to organize complex customer data Establish process-centric data governance Look beyond traditional analytics to build robust contextual profiles 17
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