1 Copyright © 2011, Oracle and/or its affiliates. All rights
reserved.Confidential – Oracle Restricted
2 Copyright © 2011, Oracle and/or its affiliates. All rights
reserved.Confidential – Oracle Restricted
Universal Approach to Data Quality
with Oracle Enterprise Data Quality
Martin Boyd, Senior Director, Enterprise Data Quality
Mala Narasimharajan, Senior Product Marketing Manager Session ID: 19400
3 Copyright © 2011, Oracle and/or its affiliates. All rights
reserved.
The following is intended to outline our general product
direction. It is intended for information purposes only, and may
not be incorporated into any contract. It is not a commitment to
deliver any material, code, or functionality, and should not be
relied upon in making purchasing decisions.
The development, release, and timing of any features or
functionality described for Oracle‟s products remains at the sole
discretion of Oracle.
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reserved.
Agenda
• Why Bother With Data Quality?
• Data Quality Domains – Does One Size Fit All?
• “Fit for Purpose” – Measuring & Assuring High DQ
• Enterprise Data Quality – Product Overview
• Data Quality Application: Watchlist Screening
5 Copyright © 2011, Oracle and/or its affiliates. All rights
reserved.
© 2009 Oracle Corporation© 2009 Oracle Corporation
Your Data is Growing
It is estimated that 4 exabytes (4.0X1018)
of unique information will be generated this year.
That is more than the previous 5000 years.
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Companies
Your Data is Changing
• 240 businesses will change addresses
• 150 business telephone numbers will change or be disconnected
• 112 directorship (CEO, CFO, etc.) changes will occur
• 20 corporations will fail
• 12 new businesses will open their doors
• 4 companies will change their name
Source: D&B, US Census Bureau, US Department of Health and Human Services, Administrative Office of the US Courts,
Bureau of Labor Statistics, Gartner, A.T Kearney, GMA Invoice Accuracy Study
• 5,769 individuals in the US will change jobs
• 2,748 individuals will change address
• 515 individuals will get married
• 263 individuals will get divorced
• 186 individuals will declare a personal bankruptcy
Individuals
Master data changes at rate of 2% per month
Products
• On average 20% duplicates in product data
• 90% product introductions fail
• Retailers lost 40 billion or 3.5% of total sales lost each year due to item info inefficiencies
• 60% error rate for all invoices generated
• Global Data Sync will realize 30% lower IT costs
In one hour… In one hour… In one year…
Compounded, 2% monthly change is 27% per year, 61% in two years, 104% in three years!!!
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Your Data Contains Errors and Inconsistencies
Variation or Error
ExampleVariation or
ErrorExample
Sequence errors • Mark Douglas or Douglas MarkTranscription
mistakes• Hannah, Hamah
Involuntary corrections
• Browne – BrownMissing or extra
tokens• George W Smith, George Smith, Smith
Concatenated names
• Mary Anne, MaryanneForeign sourced
data
• Khader AL Ghamdi, Khadir A.
AlGamdey
Nicknames and aliases
• Chris – Christine, Christopher, TinaUnpredictable
use of initials• John Alan Smith, J A Smith
Noise• Full stops, dashes, slashes, titles,
apostrophesTransposed
characters• Johnson, Jhonson
Abbreviations• Wlm/William, Mfg/Manufacturing Localization • Stanislav Milosovich – Stan Milo
Truncations • Credit Suisse First Bost Inaccurate dates• 12/10/1915, 21/10/1951, 10121951,
00001951
Prefix/suffix errors
• MacDonald/McDonald/DonaldTransliteration
differences• Gang, Kang, Kwang
Spelling & typing
errors• P0rter, Beht Phonetic errors • Graeme – Graham
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With More Variations Than You Can Imagine
10hp motor 115V Yoke mount
mtr, ac(115) 10 horsepower 115volts
MOT-10,115V, 48YZ,YOKE
This 10hp yoke mounted motor is rated for
115V with a 5 year warranty
10 Caballos, Motor, 115 Voltios
TEAO HP = 10.0 1725RPM 115V 48YZ YOKE MTR
Motor, TEAO, 1725 RPM, 48YZ, 15 Voltios,
Montaje de Yugo, hp = 10
Item Motor
Classification 26101600
Power 10 horsepower
Voltage 115
Mounting Yoke
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Including Duplicates and Missing Information
Product IDManufa-
cturerDescription Product Type Power Voltage Mount
ABC123 AA Inc. 10hp motor 115V Yoke mount Motor, AC/DC 10hp 115V Yoke
abc-123 A.A. mtr, ac(115) 10 horsepower 115volts AC/DC Motor 10 115 AC.
ABC/123/Q AA/Craft 10 Caballos, Motor, 115 Voltios Mot-AC 10 H-pow 115
QA-ST5 Craft TEAO HP = 10.0 1725RPM 115V 48YZ YOKE MTR 26101604
Z99 Z99 MOT-10,115V,48YZ,YOKE Z99
Motor powered pulley, 3/4" Motor
Inconsistent names
(representing same business)
(Often) Rich information, but
mostly non-standard
Attributes non-standard,
missing or invalid
Mis-classified item
(not a motor)
Companies struggle with the basics of Product Data
• 80% companies are not confident in the quality of their product data
• 73% find it “difficult” or “impractical” to standardize product data „PIM Business & Technology
Trends - Survey‟, Sept 2007
Inconsistent formats (extra
characters often added)
Inconsistent classifications &
misclassifications
Widespread
duplication
(often hard
to spot)
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Business Impact of Data Quality
With Bad Data With Good Data
• Reduced ROI
• Increased project risk, time and cost
• Expensive downstream consequences
– wrong shipment, wrong invoices,
incorrect parts…
• Increased ROI on existing systems
• Increased agility
• Increased efficiency
• Increased customer satisfaction
• Increased scalability
“Only 30% of BI/DW
implementations fully succeed.
The top two reasons for failure?
Budget constraints and data
quality.”
“Data integration and data quality are
fundamental prerequisites for the
successful implementation of enterprise
applications, such as CRM, SCM, and
ERP.” ”
“#1 reason CRM projects fail:
Data Quality”
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Data Quality Delivers Value Across All Industries• Single view of high quality customer data drives accurate customer insight and
improved marketing effectiveness
• Supports compliance and reporting KYC requirements
• Single view of citizen for better internal information sharing, service delivery, licensing, provision of child care, and fraud detection
• Reduce costs through system rationalisation
Financial Services
• Harmonizes customer data from multiple channels to improve sales and marketing effectiveness
• Enhance online product search for ECommerceRetail
• Improves customer insight for revenue optimization and targeted customer retention
• Effective compliance and risk mitigation for next generation servicesTelco
Energy & Utilities• Expands understanding of network assets and customer delivery points
• Improves management of regulatory compliance and reporting requirementsUtilities
• Delivers a comprehensive view of patient for care and billing
• Manages patient, epidemiology, diagnosis and treatment data quality across systems and organizations
Healthcare
Government
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Agenda
• Why Bother With Data Quality?
• Data Quality Domains – Does One Size Fit All?
• “Fit for Purpose” – Measuring & Assuring High DQ
• Enterprise Data Quality – Product Overview
• Data Quality Application: Watchlist Screening
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Many Domains, Many DQ Problems
Party
Data
Product
Data
All
Data
Customer
Employee
Citizen
Patient
Operational
Supplier
Locations
Financial
Sales
Forecast
Data Warehouse
AssetsItems for
Sale
MRO SKUs
ItemProcurement Items
Core Requirements:
• Name and address
verification, standardization,
match
• Multiple languages & locales
• Locale-specific processes
Core Requirements:
• Multi-category architecture
• Semantic recognition and
learning
• Attribute extraction and
standardization
• Classification
• Translation
Core Requirements:
• Easy to configure based on data
• Transparent/tunable rules
• Share and reuse custom processes
• Immediate feedback based on data
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Customer Data Quality vs. Product Data QualityDifferent problems, different approaches
Party Data Product (part, SKU, item, asset etc.)
Over 30,000 different product categoriesVariations by
country/locale
Require:
• Pre-built processes
• Pattern-based matching
Name & Address Data
• Relatively fixed syntax
• Mis-spellings and name equivalents
• Verify against postal files
Require:
• Semantic understanding
• Ability to learn new domains quickly
Product Data
• No fixed syntax – few standards
• Infinite variability – format, content, syntax
• Different rules for each product category
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Agenda
• Why Bother With Data Quality?
• Data Quality Domains – Does One Size Fit All?
• “Fit for Purpose” – Measuring & Assuring High DQ
• Enterprise Data Quality – Product Overview
• Data Quality Application: Watchlist Screening
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• How do you know?
• What is the business impact?
• What should you do about it?
Data Quality – Is Your Data “Fit for Purpose”?
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Health Check – Is Your Data “Fit for Purpose”?
• Understand current data „fitness for purpose‟
• Estimate DQ impacts & ROI
• Identify critical issues & quick wins
Understand
Improve
Protect
GovernYour
Data
Your Experts
Current
issues,
gaps,
errors
Business &
data
standards
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Improve Data, Improve App Performance
• Improve ROI and performance of existing applications
• Engage users and executives
• Bring data to a known, baseline quality – ready to roll-
out new applications and initiatives
Understand
Improve
Protect
Govern
Metrics,
KPIs
Fit for
purpose
data
Parse/
extract
Stand-
ardize
Match/
merge
Verify
Enrich
‘Gold’
data
Apply data standards
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„DQ Firewall‟ – Continuous Protection for Information Assets
• Continuous, consistent enforcement of standards
• High quality data drives ROI
• No more DQ projects!
Understand
Improve
Protect
Govern
Hub
Apply data standards/validate
External
sources/
feeds
Data Integration/ETLNon-DQ/MDM-
aware Apps
DQ/MDM-
aware AppsWeb
service
call
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DQ Governance – Continuous Process Improvement
• Monitor ongoing effectiveness
• Track and resolve issues
• Improve overall effectiveness
Understand
Improve
Protect
Govern
DQ/MDM-
aware Apps
Target
system DQ
metrics
‘Gold’
data
Apply data standards
Source
system DQ
metrics
DQ
process
metrics
22 Copyright © 2011, Oracle and/or its affiliates. All rights
reserved.
Agenda
• Why Bother With Data Quality?
• Data Quality Domains – Does One Size Fit All?
• “Fit for Purpose” – Measuring & Assuring High DQ
• Enterprise Data Quality – Product Overview
• Data Quality Application: Watchlist Screening
23 Copyright © 2011, Oracle and/or its affiliates. All rights
reserved.
Introducing Enterprise Data Quality
DQ-Based Solutions
Domain Knowledge
Business Solutions• Customer-delivered
• Partner-delivered
• Oracle-deliveredApplication Connectors
Data Quality Platform
• Complete range of DQ capabilities
• Best-of-breed capabilities for party and product
data
• Easy to use, intuitive
• Open, tunable, flexible
Pre-Built Solutions
• Any scope – components to end-to-end solutions
• Any pre-built/reusable item– Processes, methods
– Knowledge, reference data
– Application integration
Enterprise Data Quality
Dashboards
Party Data
Extensions
Match/Merge
Governance
Product Data
Extensions
Standardization
Profile and Audit
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Introducing Enterprise Data Quality
DQ-Based Solutions
Domain Knowledge
Business Solutions• Customer-delivered
• Partner-delivered
• Oracle-deliveredApplication Connectors
Benefits
• High quality data boosts ROI of any application
or technology („bad‟ data limits ROI)
• Complete solution with best of breed
capabilities for both Party and Product entities
• Essential component in any Data Integration or
Master Data Management (MDM) strategy
• Avoid doing business with sanctioned or risky
individuals and entities
Enterprise Data Quality
Dashboards
Party Data
Extensions
Match/Merge
Governance
Product Data
Extensions
Standardization
Profile and Audit
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Enterprise Data Quality – for Party Data
DQ-Based Solutions
Domain Knowledge
Business Solutions• Customer-delivered
• Partner-delivered
• Oracle-deliveredApplication Connectors
Party Data Capabilities
– Pre-built processors – to handle most
common name & address problems
• Address parsing & standardization
• Address verification & geocoding*
• Match/merge – individuals, households, businesses
• Country, language & locale-specific functions
– International Reference Data – for global
deployment
– Integration with Oracle Customer Hub
(UCM) – for rapid time to value
* Available soon
Enterprise Data Quality
Dashboards
Party Data
Extensions
Match/Merge
Governance
Product Data
Extensions
Standardization
Profile and Audit
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Enterprise Data Quality – for Product Data
Product Data Capabilities
– Semantic recognition – to deal with extreme
variability of product information
– Category-specific architecture – to deal with
large number of different product categories
– Pre-built knowledge – for common product
data attributes
– Integration with Oracle Product Hub – for
rapid time to value
DQ-Based Solutions
Domain Knowledge
Business Solutions• Customer-delivered
• Partner-delivered
• Oracle-deliveredApplication Connectors
Enterprise Data Quality
Dashboards
Party Data
Extensions
Match/Merge
Governance
Product Data
Extensions
Standardization
Profile and Audit
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DQ Spans MDM and Data Integration
Custom
Applications
Watchlist
Screening
Oracle
Applications
3rd Party
Applications
Oracle
BI/EPM
Content ManagementServices
Business IntelligenceServices
CollaborationServicesData Services
Oracle
MDMOracleData
Integration
Data Federation Replication
Customer Hub Product Hub
Information Management
OLTPSystem
Data Warehouse/Data Mart
OLAP Cube
Supplier Hub Financial Hub
Storage
Transformation SynchronizationETL/E-LT
Site Hub
Web and Event Services, SOA
Transaction ProcessingServices
Enterprise Data QualityProfiling Standardization Match/Merge
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Su
pp
lie
r
Sit
e
Fin
an
cia
l
Enterprise-Grade MDM from OraclePurpose-Built MDM Applications Approach
CRM
Other Sources
ERPC
usto
me
rCRM
EPM/BI
Legacy…
Pro
du
ct
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EDQ With Oracle Data Integrator: Use CasesSources
Target(s)
E.g. Data
Warehouse
such as Exadata
Oracle Data
Integrator
One-off ProfilingUnderstand data to build
ODI transformation
and mapping processes.
Automated ProcessesDe-duplication, complex
transformation and parsing
called during ODI data flow.
Measure Ongoing DQAssess quality of data
in target system. How well
is ETL working?
Enterprise Data
Quality
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EDQ and ODI: Complimentary Features
Oracle Data Integrator Enterprise Data QualityExtracts, Transforms, Loads Extracts, Transforms, Loads
'Majors' on extraction and loading Basic extraction and loading capabilities, but merely a means to an end
May also be used to perform ‘simple’ transformations:• Concatenate • Simple parsing • Remove invalid values
Strong matching, transformation and parsing capabilities:• Parse & standardize address fields• Parse & standardize product description• Extract & standardize key attributes• Locale-specific rules• Categrory-specific rules etc.
Use strong profiling capabilities to design ODI processes.
E t L e T l
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Main EDQ Console, Focused on the User
ProjectBrowser
Main Canvas
Results Browser
Tool Palette
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Immediate drill-down to examine real data
Field-level completeness, uniqueness, validity etc.
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Immediate drill-down to examine real data
Drill-down to see actual data values and determine
required rules, standards etc.
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Immediate drill-down to examine real data
Drill-down to see actual data values and determine
required rules, standards etc.
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Build-out Full DQ Process
Process can be „simple‟ or „complex‟
Process thumbnail assists in navigation
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Key Feature: Pre-built processors
• Comprehensive DQ Functionality with a Single User Interface and Repository
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Key Feature: Language, Region and Country-Specific Capabilities
• Country and region-specific processors
• Fully unicode compliant
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Transformation – Data Improvement & Cleansing
Use profiling results to create your own data
improvement rules
Use provided processors for common tasks
such as address standardization
• Fully configurable data transformation rules
• Operates in both Batch and Real-Time
• Full control over data updates
• Original data always preserved (and all steps in between)
• Source data may either be staged and processed or „streamed‟ through the process
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„Cleansing‟ for Party Data
39
• Parse, standardize, transform
• Split names and name elements
• Identify individuals and businesses
• Derive additional attributes
Name: Dr Ellen Van Der Heijde
Title: Dr
First: Ellen
Last: Van Der Heijde
Gender: Female
Name: Jalila Abdul-Alim (Do Not Call)
First: Jalila
Last: Abdul-Alim
Gender: Female
Note: Do Not Call
Title: Mr
First: R
Middle: J
Last: MacDonald
Gender: Male
Name: Mr RJ & Mrs FB MacDonald
Title: Mrs
First: F
Middle: B
Last: MacDonald
Gender: Female
Title: Ms
First: April
Last: James
Gender: Female
Name: Ms April James DBA AJ Designs
Company: AJ Designs
Not just names but any data such as addresses, dates
& phone numbers etc.
40 Copyright © 2011, Oracle and/or its affiliates. All rights
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„Cleansing‟ for Product Data
Category
Piping & plumbing
Fasteners
Headgear
Capacitors
Wine
Extracted Attributes
Diameter = 4 inch
Length = 4 inch
Quantity = 4
Capacitance = 4ESU = 4.45 picofarad
Volume = 750ml; varietal = Merlot
Standardized Description
End cap, 4 inch
Hex cap screw, 4 inch
Baseball cap, 4 per box
Capacitor, 4.45pf (4 ESU)
Wine: Merlot, 750ml, screw cap
Missing information
Material; Thread
Thread; Diameter; Material
Color
Manufacturer; Mount type; Operating characteristics
Input Description
4” end cap
4 in cap screw
4 in box b-ball cap
4 ESU cap
750ml Merlot, screw cap
1. Semantic recognition uses whole context to determine meaning and category
2. Information extraction uses category-specific semantic models to identify and extract target information
3. Category-specific semantic models flag missing information for potential remediation
4. Data is converted, transformed and re-assembled according to requirements of target system
Semantic Rec.
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Matching – Duplicate Identification and Prevention• Designed for business users
• Flexible matching engine for any data with many comparison algorithms
• Provided template match processors for individual, entity and address matching
• Easy reuse of configured match processors
• Fully configurable outputs (Links, Groups, Master and Slaves, Best Record)
• Operates in both Batch and Real-Time
• See Match Essentials deck for more information on Matching
Pre-built rules can be
switched on and off and/or
customized
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Matching – Party Match Scenario
42
Title: Mr
First: Robert
Last: Fulmar
Gender: Male
DoB: 12/05/1978
Phone: 555-120-1329
Address:
9405 Main St
Fairfax
Virginia
22030
First: Bob
Last: Fulmar
Gender: Male
Email: [email protected] Title: Dr
First: R
Last: Fulmer
DoB: 01/01/1978
Email: [email protected]
Address:
9407 Main Street
Fairfax
VA
22031-4001
Title: Dr
First: Robert
Last: Fulmar
Gender: Male
DoB: 12/05/1978
Email: [email protected]
Phone: 555-120-1329
Address: 9407 Main St, Fairfax, VA
22031-4001
Match & Merge data from
disparate sources
Create the „best‟ record
Match Individuals
Match Households
Match Businesses
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Matching – Product Match Scenario
All Items
Needles
Biopsy Needles
Manufacturer MEDICAL DEVICE
TECHNOLOGIES, INC.
UOM BX
Item NEEDLE
Needle use BIOPSY
Needle type BONE MARROW
Diameter 11GA
Needle length 4IN
Point type
Needle style
Handle type
Reusability DISPOSABLE
MEDICAL DEVICE
TECHNOLOGIES, INC.
MEDICAL DEVICE
TECHNOLOGIES, INC.
MEDICAL DEVICE
TECHNOLOGIES, INC.
BX BX BX
NEEDLE NEEDLE NEEDLE
BIOPSY BIOPSY BIOPSY
BONE MARROW BONE MARROW BONE MARROW
11GA 11GA 11GA
4IN 4IN 4IN
DOUBLE DIAMOND DOUBLE DIAMOND
JAMSHIDI JAMSHIDI HARVEST
ERGONOMIC TWIST-LOCK ERGONOMIC TWIST-LOCK
DISPOSABLE
85% 70% 70%
Match #1 Match #2 Match #3
MEDICAL DEVICE
TECHNOLOGIES, INC.
MEDICAL DEVICE
TECHNOLOGIES, INC.
MEDICAL DEVICE
TECHNOLOGIES, INC.
BX BX BX
NEEDLE BIOPSY BONE
MARROW JAMSHIDI 11GA
4INL DISPOSABLE
NEEDLE BIOPSY BONE
MARROW JAMSHIDI
DOUBLE DIAMOND TIP
11GA 4INL ERGONOMIC
TWIST-LOCK HANDLE
NEEDLE BIOPSY BONE
MARROW HARVEST
DOUBLE DIAMOND TIP
11GA 4INL ERGONOMIC
TWIST-LOCK HANDLE
Manufacturer MD TECH
UOM BOX
Description NDL BONE MARROW
11G X 4IN THRWAWY
Potential PIM category matches
Required for
match
Raw
data
from
PIM
Optional -
weighted
Match score
Domain definitionsRecognition, validation,
vocabulary, relationships
etc.
Item from Hospital
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Dashboards – Snapshot & trend analysis
44
Validate data against business rules -
publish results to data quality dashboard
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Enterprise Data Quality Differentiators
Integrated DQSolution
Domain-specific capabilities
Ease of use
• Integration of all core DQ capabilities – profiling, cleansing,
classification, repurposing, matching & reporting
• Engineered for business users
• Easy to configure and integrate „DQ Services‟
• Integrated team collaboration and management
• Specialized capabilities for major domains – Party and Product
• Pre-built processes and knowledge
• Use „the right tools for the job‟ depending on the task
• Extensible for any domain, task, process
• Agnostic of data-domain, vertical market & application• Brings DQ out of the back-office• Users can monitor what matters to them
–Personalised dashboard
–Select content and define order
• Business context to gain understanding and consensus
45
46 Copyright © 2011, Oracle and/or its affiliates. All rights
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Agenda
• Why Bother With Data Quality?
• Data Quality Domains – Does One Size Fit All?
• “Fit for Purpose” – Measuring & Assuring High DQ
• Enterprise Data Quality – Product Overview
• Data Quality Application: Watchlist Screening
47 Copyright © 2011, Oracle and/or its affiliates. All rights
reserved.
Oracle Watchlist Screening
Oracle Watchlist Screening
MatchingData
Preparation
Case
Management
Enterprise Data Quality
Dashboards
Party Data
Extensions
Match/Merge
Governance
Product Data
Extensions
Standardization
Profile and Audit
Broad leverage of EDQ platform* enhances Watchlist
Screening capabilities
Source data preparation
• Profile source systems
• Parse, standardize, transform data prior to matching
• Enrich and remediate as required
Matching
• Highly efficient, scalable match engine
• 500+ pre-built rules can be switched on and off
• Any custom rules can be built as required
• Fully internationalized data handling with – Name recognition
– Name and nickname standardization
– Cross-script transliteration, transcription for matching
Case Management
• Work queue management
• Alerts, reporting, audit trail
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Watchlist Screening• What it is:
– A business process for identifying a potential match between a customer record and anyone (customer, supplier, business partner, employee) that may appear a „watch list‟ (public or private)
• Why it matters:
– International and regional regulations
• Anti-Money Laundering (AML)
• Counter Terrorist Financing (CTF) legislation
– Determine the potential for exposure to money laundering, bribery & corruption and/or terrorist financing
• Pressure for a „next generation‟ solution:
– Improved results
• Better matching
• Reduced false positives
• Reduced operating costs
• Better scalability
48
“Know Your Customer” (KYC) and Enhanced Due Diligence (EDD) obligations
• Bank Secrecy Act 1970
• Foreign Corrupt Practices Act 1977
• Money Laundering Control Act of 1986
• Terrorism Act 2000
• Financial Services & Markets Act 2000
• USA Patriot Act 2001
• Proceeds of Crime Act 2002
• EU 3rd Money Laundering Directive 2007
• Money Laundering Regulations 2007
• UK Bribery Act 2010
• ...
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What Happens if it Goes Wrong?
• Substantial fines and
penalties against• Companies
• Individuals within companies
• Lloyds $350m
• CSFB $536m
• Aon £5.25m
• ANZ $7m
• ABN $500m
• UBS $100m
• Wachovia etc…
• Brand damage and loss of
reputation
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reserved.
Office of Foreign Assets Control (OFAC) List
Approx 300,000
names just on
US OFAC list...
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Watchlist Screening Process
Customer lists• Multiple systems• Inconsistent information
& standards
Watch lists• Public lists (HMT, OFAC,
EU, UN, etc.)• Commercial Lists• Private lists
Understand
Structure
Standardize
Enrich
Translation/ transliteration
Aliases/name equivalents
Match & risk scoring
500+ match rules
Prepare & Optimize Match & Score Case Management
Prioritized alerts
Customizable workflow
Reporting & metrics
Audit trail
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Oracle Watchlist Screening
Minimize Risk Exposure
Most FlexibleSolution
Lowest Total Cost of Ownership
• Low false positives due to underlying DQ technology
• Configurable risk & match scoring
• Designed for business user
• Complete audit trail of review process
• Optimizes all data for screening
• Integrated case management & reporting tools
• Agnostic to data format, language and structure
• 500+ match rules with option to customize
• Avoids unnecessary or repetitive investigations
• Screens all risk profiles
• Pre-built workflows enables rapid integration and deployment
• Low maintenance overheads
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Watchlist Screening Customer Proof Points
FirstRand*2
Barclays*3
Leading Investment Company
• 1m customer records screened
• False-positive rate = 5% of industry average*
• 10m customer records screened
• False-positive rate = 3.5% of industry average*
• 100m records screened daily (name matching only)
• False-positive rate = 10% of industry average*
Large Financial Services Company
Tier 1 Global Financial Services Company
*Using data sourced from UK FSA report into Financial services firms‟ approach to UK sanctions - April 2009
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