Partner Webcast – Oracle Enterprise Data Quality for ODI: The importance of Quality in Data Integration - 30 May 2013

Copyright © 2012, Oracle and/or its affiliates. All rights reserved. #OracleDataIntegration 1

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“This slide format serves to call attention to a quote from a prominent customer, executive, or thought leader in regards to a particular topic.”

Name

Title, Company Name

blogs.oracle.com/IMC



Oracle Data Integration

Moving Data

to Transform

Business

Oracle Data Integration Platform

Enterprise Data Quality Webcast 30th of May 2013

Ugo Pollio Business Development Oracle EMEA


Agenda

Why Data Quality

Why Oracle

Competition

Use cases

Conversation with customers


Why Data Quality?


New Requirements in Data Integration

Real-time

Analytics

Any Data,

Any Source

Zero Downtime,

High Availability


Data Deluge What Analysts are saying about Growing Data Volumes & Complexity

“External data sources are proliferating - On average, organizations are integrating 14 external data sources, up from 11 a year ago.

- Aberdeen Group

“New data stored by enterprises exceeded 7 exabytes of data globally in 2010 and new data stored by consumers exceeded an additional 6 exabytes..”

- McKinsey Global Institute

“As data growth and complexity accelerates, companies should focus on quality assured data exchange (ensure data consistency and accuracy from the point of entry.”

- Aberdeen Group

“40% projected growth in global data generated per year vs 5% growth in global IT spending.”

- McKinsey Global Institute

7


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!!!


Data Quality market outlook

Gartner says (6-SEPT-2012):

– Demand for data quality software is rising fast as more

organizations seek to support data governance initiatives,

application modernization projects and master data

management (…).

– Organizations are also using data quality tools to support a

much wider range of use cases than in previous years,

said Ted Friedman, vice president at the Stamford, Conn.-

based analyst firm and author of the report.

http://searchdatamanagement.techtarget.com/news/2240162796/New-Gartner-Magic-Quadrant-finds-demand-rising-for-data-quality-tools

Growing market

727 800

950

145,4 160 190 0

200

400

600

800

1000

2009 2010 2011

DQ market worldwide (USD mil)

DQ market (USD mil)

DQ in EMEA (20% estim.)

12 16

18

0

5

10

15

20

2009 2010 2011

CAGR % 5yrs

CAGR % 5yrs























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”


Your Data Contains Errors and Inconsistencies

Variation or Error

Example Variation or

Error Example

Sequence errors • Mark Douglas or Douglas Mark Transcription

mistakes • Hannah, Hamah

Involuntary corrections

• Browne – Brown Missing or extra

tokens • George W Smith, George Smith, Smith

Concatenated names

• Mary Anne, Maryanne Foreign sourced

data

• Khader AL Ghamdi, Khadir A.

AlGamdey

Nicknames and aliases

• Chris – Christine, Christopher, Tina Unpredictable

use of initials • John Alan Smith, J A Smith

Noise • Full stops, dashes, slashes, titles,

apostrophes Transposed

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/Donald Transliteration

differences • Gang, Kang, Kwang

Spelling & typing

errors • P0rter, Beht Phonetic errors • Graeme – Graham


An example of a different use case AttributeName.1 OC_Name

AttributeValue.1 at_ns:.oc.ERoss3g1

AttributeName.2 Identifier

AttributeValue.2 1546863

AttributeName.3 Target_Entity

AttributeValue.3

RCROOT at_ns:.oss.3g1RCROOT SNW NISTE05

RNC NISTE05

AttributeName.4 Event_Type

AttributeValue.4 QualityofServiceAlarm

AttributeName.5 Managed_Object

AttributeValue.5

RCROOT at_ns:.oss.3g1RCROOT SNW NISTE05

RNC NISTE05

AttributeName.6 Probable_Cause

AttributeValue.6 ThresholdCrossed

AttributeName.7 Severity

AttributeValue.7 Warning

AttributeName.8 Event_Time

AttributeValue.8 18/12/2012 19:10:16

AttributeName.9 State

AttributeValue.9 Outstanding

AttributeName.11 Notification_ID

AttributeValue.11 3589640175"

Value.NmsTags.AlarmId 38444174\n

Value.NmsTags.ProposedRepair \n

Value.NmsTags.ManagedObject

kalkan,SubNetwork=ONRM_ROOT_MO,SubNetwo

rk=NISTE05,MeContext=NISTE05,ManagedEleme

nt=1,RncFunction=1,UtranCell=WIS04296\n

Value.NmsTags.SpecificProblem UtranCell_RrcEarlyReject\n

Value.NmsTags.Class RCROOT\n

{"OC_Name": "at_ns:.oc.ERoss3g1","Identifier": "1546863","Target_Entity": "RCROOT

at_ns:.oss.3g1RCROOT SNW NISTE05 RNC NISTE05","Event_Type":

"QualityofServiceAlarm","Managed_Object": "RCROOT at_ns:.oss.3g1RCROOT SNW

NISTE05 RNC NISTE05","Probable_Cause": "ThresholdCrossed","Severity":

"Warning","Event_Time": "18/12/2012 19:10:16","State":

"Outstanding","Additional_Text":

"UtranCell_RrcEarlyReject\n\nstart_nms_tags\n@AlarmId=38444174\n@ManagedObj

ect=kalkan,SubNetwork=ONRM_ROOT_MO,SubNetwork=NISTE05,MeContext=NIST

E05,ManagedElement=1,RncFunction=1,UtranCell=WIS04296\n@SpecificProblem=Ut

ranCell_RrcEarlyReject\n@ProposedRepairAction=\n@Class=RCROOT\nend_nms_ta

gs \n\nSource:OSSRC_FM","Notification_ID": "3589640175"}

Parse & classify complex unstructured,

semi-structured data

Transform in structured data


Why Oracle? A product overview


Very easy to use

Intuitive

Modular

Great productivity

Robust

Flexible

Most common users’ comments

Data Quality demistified


What do you need to know about EDQ? Integrated Solution for All Data Quality Problems

Broadest DQ offering

Intuitive GUI and easy to use tool

Profiling, standardization, advanced parsing, matching, case

management, remediation, governance

Most usable DQ offering Completely integrated offering – designed to work together

Designed for business and technical users

Transparent operation and results – no black boxes

Leverages best practices, high productivity, solution

packaging for full reusability

Pervasive operation for enterprise data quality

governance Scalable and flexible platform, java based

Within legacy systems and MDM Hubs

As part of migration/system load

As part of data movement/transfer

Profile

Standardize

Match

Govern

Quickly understand data content

Drive conformance to standards

Identify & merge duplicates

Monitor effectiveness & resolve problems

Com

mon A

ccess/U

I


Introducing Enterprise Data Quality

DQ-Based Solutions

Domain Knowledge

Business Solutions • Customer-delivered

• Partner-delivered

• Oracle-delivered Application 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


Collaboration Across User Communities

Data Analysts

Business Analysts

Executives & Stakeholders

Director Users

Director Data Stewards

Director Executives

Director Reviewers


Build-out Full DQ Process


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


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


EDQ Address Verification

EDQ Address Verification Server

• Verify – Get the address correct

• Worldwide address cleansing – over 240 countries – all populated countries on earth

• The most advanced error-tolerant parsing algorithms

• Geocode – Attach a location to a correct address

• Generates a latitude/longitude coordinate for any address worldwide

• Leverages the most comprehensive multi-source geographical reference data

Global Knowledge Repository Data Packs

• Parse

• Transliterate

• Validate

• Format

Verify

Add

latitude/longitude

coordinates

Geocode

EDQ Parse and Standardize

EDQ Profile and Audit

EDQ Match and Merge


The Oracle Information Platform Vision

Simplify the delivery of Trusted

Business Information

Collaborate across different

areas of the business

Reduce the risk of deploying

separate components

Best of Breed and Integrated

Accelerate time to value

A scalable platform from small

business to the enterprise

A Complete, Open and Integrated Information Platform

Enterprise Data Quality

Oracle Data Integration Oracle Business

Intelligence

Oracle

EPM

Oracle MDM

Oracle RDBMS

Oracle Master Data Management

Visualise Deliver Transform Clean Understand Discover


EDQ Competitive Product Analysis Capabilities, Features Oracle Enterprise Data Quality

Product Environment, GUI, Look & Feel • EDQ GUI extremely intuitive and easy to use.

• Collaborative environment for different roles and users.

• EDQ is codeless. No need of code implementation. For instance even Regular Expressions, can

be directly written without any function usage or interpretation.

Profiling • EDQ has seamless profiling capabilities. For instance, you can create a matching process that

includes profiling capabilities in order to profile matched/unmatched data and derive further

insights.

Auditing • Flexible rules management from Basic to Complex.

• No specific language (sql or others) required

• Tens of rules provides equivalence to hundreds of rules in other products

Matching • EDQ matching and parsing capabilities are quite more flexible and configurable than Infa and

IBM, where you can’t easily extend the rule set for matching and parsing

• Multiple clustering capabilities in a single pass

• Graphical summary of matching rules, gives at a glance clear understanding of all matching

criteria

Address Validation

• Frequent “240 countries” statement: the number of countries in not that meaningful. Most of

our competitors claim it, but more importantly, we cover all populated countries on Earth with a

greater level of detail than our nearest competitors

• Provide Geocode data for 240 populated countries, much more compared to our competitors.

• Provide out of the box statistical capabilities for the address validation process, useful for

classifying validated address and summarize results.

Architecture • EDQ is java based, you can install it almost everywhere

• EDQ doesn’t require client installation. Just an URL where download a WebStart App.

• EDQ relies on Oracle Technology (DB, Weblogic), competitors don’t.


DIY Versus Pre-Integrated – It’s Your Choice...

Engineered to Work Together

Models Data

Quality ETL MDM

Data

Warehouse BI

+ + + + +

+ + + + +

– You invest in building integration between each component

OR

– Oracle invests in integration between each component

http://www.teradata.com/


Investment in Innovation and Integration

MORE THAN

$24B IN R&D

SINCE 2004 $1.3B $1.5B

$1.9B $2.2B $2.7B $2.8B

$3.3B

$4.5B $4.5B

FY04 FY05 FY06 FY07 FY08 FY09 FY10 FY11 FY12

Figures in GAAP


Use Cases


Data Quality Use Cases: Cross - Industry • 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

• Harmonizes customer data from multiple channels to improve sales and marketing effectiveness

• Enhance online product search for ECommerce

Retail

• Improves customer insight for revenue optimization and targeted customer retention

• Effective compliance and risk mitigation for next generation services

Telco

• Expands understanding of network assets and customer delivery points

• Improves management of regulatory compliance and reporting requirements Utilities

Healthcare

Government

Financial Services

• Delivers a comprehensive view of patient for care and billing

• Manages patient, epidemiology, diagnosis and treatment data quality across systems and organizations


• Reduce ODI Implementation Time and Risk – 50% of data warehouse/BI projects have limited acceptance or are outright failures as

a result of lack of attention to data quality issues

– ETL mappings should not be solely developed based on specifications

– Data Profiling helps uncover defects, patterns, formats early in the ETL development process

– Use EDQ Profiling to analyze and understand your data and required mappings

• Populate a Data Warehouse with High Quality Data – Avoid making poor decisions based on poor data (avoid garbage-in, garbage-out)

– Platform for Data Governance/Data Stewardship and ongoing quality improvement

– Engage business users in defining and implementing appropriate business rules

– Use EDQ Batch Processing to deliver accurate, consistent and complete data

Core Use Cases with ODI


High reusability, Faster ROI, thanks to ability to

deploy common solutions and best practices,

enforce Data Monitoring

Better scalability, thanks to high re-usage of best

practices, standardized developments, better

readability of deployed solutions

Improved collaboration between IT and Business,

using a collaborative platform

Core functionalities (matching, standardization)

significantly improved thanks to data remediation,

matching review process initiative

Benefits

Data Consolidation, Data Migration Exploit the potential of your data asset

EDQ ODI

Real-Time


Data Consolidation How to spot

•Customers who urge to have a consolidated view of their customers, products, reference data.

•Business impacts

•Useless or ineffective, not in time reporting

•Applications integration challenges

•poor customer services

•ineffective marketing campaigns

•Symptoms

•Many sources

•IT projects slow down or take time to start

•Evident inconsistencies and gaps of information across LoBs

Why Oracle

•Position ODI+EDQ as best tool for heterogeneous environment

•EDQ ease of use, GUI VERY friendly

•ODI best in ETL/ETL for scalability and productivity

•EDQ, ODI great reusability (ODI KMs, EDQ Processors and packaging)

•EDQ, ODI modern platform java based

•EDQ, ODI leverage end 2 end solutions, Engineered Sytems, Oracle Database, DB Options, Weblogic

Benefits

•Fast ROI: as soon as data are consolidated and cleaned there’s a direct positive effect on insisting applications, reporting, etc.

•IT projects are faster and risk, contingency are under control


Increased data accuracy and consistency, sharper

vision of the business

No more costs for manual data cleansing

Lowering risks in production due to stops and

issues

Faster time to value and go live when using data

from DWH for new marketing, sales initiatives, IT

projects

Complement Reporting and Dashboards with DQ

metrics, trends, KPIs. Entrust your insights,

discover new ones

Solution & Benefits

Trustable DWH and BI Get consistent measures to your business and decisions

EDQ ODI

ODI

EDQ

EMP

D

E

PT

DIM

FACT

DIM

DIM DIM

ODS

Schema

DW

Schema

Goldengate

ODI


Next Gen Data Warehouse Change data into valuable Information

The Business Issue

– BI Reports are not trustable, because of the

state of source data

Reduce risks

– Improve data quality by integrating cleansing

as part of the process

– Eliminate data redundancies

Improve Business Insights

– Improved business insight with improved

data quality

– Better profiling of data to eliminate gaps in

insight

Profiling

• Investigate, Analyze, Audit

Cleasning

• Standardize, Enrich, Deduplicate

Control • Govern over time

Do not trust this

information!


Datawarehouse and Business Intelligence

How to spot

•Business users don’t trust IT because of issues with BI reporting

• IT and/or business claims BI platform isn’t valuable or useful

•Business impacts

•Useless or ineffective reporting

• Inconsistent views on forecast, sales, supply chains, etc.

•Lack of insights, business modernization

•Symptoms

• IT struggles to respect SLAs

• IT spend a lot of time in planning rollbacks because of bad data, ending up sometime with a backup restore (huge impacts)

• IT projects slow down or take time to start

•Evident inconsistencies and gaps of information across LoBs

Why Oracle

•Position ODI+EDQ as best tool for heterogeneous environment and for Datawareouses (Oracle is leader in DWH)

•ODI and EDQ as strategic tools for Oracle, embedded in Oracle ecosystem

•EDQ ease of use, low learning curve, easy to be adopted by Business Users

•Doesn’t require a client installation

•EDQ provide stats, KPIs, metrics taht can be embedded in any BI platform

•EDQ, ODI leverage end 2 end solutions Engineered Sytems, Oracle Database, DB Options, Weblogic

Benefits

•Business side

•Better decisions

•Time to value

•Faster start-up new marketing/sales initiatives

•Faster response to business events

•Ensure Company good reputation

•Cost savings

•Cross sell – Up sell

• IT Dept side

•Ensure SLA

•Faster start-up for IT projects

•System Reliability, then confidence in IT

•Resource utilization effectiveness

•Meet Business User’s expectations


Bad data awareness

• Customer is not completely aware about bad data impacts within the Organization. Let show them how to answer these 3 question: How do I know I have bad data? What is the business impact? What should I do about it?

How to spot

• Profiling day

• Prove EDQ by analyzing customer data with the help of a business user

• Show them all findings and relate them with business issues

Why Oracle

• Falls down into previous use case

Benefits


Q&A


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Title, Company Name

twitter.com/oracleimc


EDQ Live demo

38 Copyright © 2012, Oracle and/or its affiliates. All rights

reserved. Confidential – Oracle Restricted

EDQ With Oracle Data Integrator: Use Cases Sources

Target(s)

E.g. Data

Warehouse

such as Exadata

Oracle Data

Integrator

Data Profiling Analyze and understand data

to build ODI mappings

Automated Processes De-duplication, complex

cleansing and parsing

invoked in ODI workflow

Measure Ongoing Data Quality Assess quality of data

in target system. How well

is ETL working?

Enterprise Data

Quality



EDQ and ODI: Complimentary Features

• EDQ complements ODI in the following areas

– Data Profiling

– Semantic/Contextual Data Parsing and Standardization

– Complex Matching and Merging of various entities: individuals, households,

products etc.

– Data Deduplication

– Address Validation & Geolocation



EDQ and ODI: Comprehensive Data Quality

Process

Sources

Oracle Enterprise Data Quality

Parsing Standardization Cleansing Matching Merging

Targets

Oracle Data Integrator

E-LT/ETL Process

- Continuous Quality Monitoring - Quality Alerts

4

Create new Data Quality Rules

2

- Add Data Quality to E-LT/ETL Flow

3

Profile Data 1

41

Desktop

Repositories

Information Management infrastructure Shared Infrastructure for ODI & EDQ

ODI Studio

Operator

Designer Topology

Security

Sources and Targets

Legacy Applications

ERP/CRM/PLM/SCM

Files / XML DBMS DW / BI / EPM

JVM

Java EE Application

ODI SDK

WebLogic 11g / Application Server

Data Sources Connection Pool

Web Service Container

ODI Public WS

Data Services

FMW Console ODI Plug-in

Servlet Container

ODI Console Java EE

Application

ODI SDK

Runtime WS

Java EE Agent

JVM

Runtime WS

Standalone Agent

EDQ Repository EDQ Result Schema

EDQ Engine EDQ WS

EDQ Match Review

EDQ Case Mgmt

Service Bus

EDQ Launchpad

Director

Administration

Console

Match Review

ODI Master Repository

ODI Work Repository #n

ODI Work Repository #1

Case Mgmt

…

ODI Server Mgmt EM Monitoring EDQ Server Mgmt


EDQ system sizing

The key is to understand the use case, or purpose, of the system and the tasks it is to perform. If you

can answer the following questions, PM can give a ballpark answer to processor sizing:

What is the basic use case?

– Business purpose?

– Technical functions – profile, parse, standardize, transform, match, merge, real-time or batch operation etc.?

– How will the results be used?

How many tables & columns are involved?

How many rows?

Is there a time window for the operation(s)?

Is there a requirement that there will be multiple environments for production, development, QA etc.

43

How ODI and EDQ work together

Example: Loading a Slowly Changing Dimension

Staging Sources Target

Customers Prospects

Stg_Customers

DIM_Customers

ODI

extracts

Stg_Valid_Customers ODI

loads

1

2

3

E-LT

EDQ

Standardize, Parse

& Match w reference data

2

44

SRC_PROSPECTS

NAME ADDRESS CITY STATE ZIP PHONE COUNTRY LEAD_CREATED

Mr Norm G Desmond 1052 Ala Moana Blvd Honolulu HI 96814 808 555-1127 USA 2000-12-15

Timothy Johnson 1020 NW 63rd St Oklahoma City OK 73116 405 555-1175 USA 2000-12-15

Dr Phillip O Oxenberg

1710 287 Business W

Suite 150 Waxahachie TX 75165 972 555-2877 USA 2001-10-20

Dr Sheila T Bergin 103 N 50th Road Omaha NE 68132 402 555-3141 USA 2002-05-06

Maxx Zaphrey 3828 South First St Austin TX 78704 512 443-1311 USA 2007-12-20

Lawrence Getty

825 E. Rundberg Lane

Ste B1 Austin TX 78753 512 836-5472 USA 2007-12-20

Step 1

ODI extracts from source & stage

STG_CUSTOMERS

Name Address City Postcode Country Account_num Acct_rep Territory

Dr Simon

Brennan 11 Abotsford Street London N153BT UK UK02306 1-DDE UK-LON

Miss Kylie

Brennan 11 Abottsford Street Londn N153BT UK UK02307 1-FMM UK-LON

Simon and

Karen Brennan 11 Abottsfurd Ave London N153BT UK UK02308 1-FMM UK-LON

... ... ... ... ... ... ... ...

Extracts data from heterogenoeus sources

CUSTOMERS.XLS

Total_Orders Name Phone Address1 City Postcode Country Account_N Acct_rep Territory

5500.36

Dr Simon

Brennan 01249 442878

11 Abotsford

Street London N153BT UK UK02306 1-DDE UK-LON

5500.36

Karen K

Brennan 01249 442879

11 Abotsford

Avenue London N153BT UK UK02307 1-GEB UK-LON

5500.36

Miss Kylie

Brennan 01249 442879

11 Abottsfurd

Street Londn N153BT UK UK02307 1-FMM UK-LON

5500.36

Dr Simon

Brennan 01249 442878 11 Abottsford St Lodnon N153BT UK UK02306 1-GEB UK-LON

5500.36

Simon and

Karen Brennan 01249 442873

11 Abottsfurd

Ave London N153BT UK UK02308 1-FMM UK-LON

ODI

45

Step 2

EDQ Cleanse Staged Data

DIM_CUSTOMERS

Name Address City Postcode Country Account_num Acct_rep Acct_Status Territory Clrecid

Dr Simon

Brennan

11 Abottsfurd

Avenue London N153BT UK UK02306 1-DDE ACTIVE UK-LON U21015

STG_CUSTOMERS


Dr Simon

Brennan 11 Abotsford Street London N153BT UK UK02306 1-DDE UK

Miss Kylie

Brennan 11 Abottsford Street Londn N153BT UK UK02307 1-FMM UK-Lon

Simon and

Karen Brennan 11 Abottsfurd Ave London N153BT GB UK02308 1-FMM

UK

london

STG_VALID_CUSTOMERS


Dr Simon

Brennan 11 Abottsford Ave London N153BT UK UK02306 1-DDE UK-LON

Miss Kylie

Brennan 11 Abottsford Ave London N153BT UK UK02307 1-FMM UK-LON

Simon


Karen Brennan 11 Abottsford Ave London N153BT UK UK02308 1-FMM UK-LON

Standardize, Parse Match w reference data, Address Ver.

EDQ

This record contains two different customers

EDQ can generate a new record from the original one

Cleansed data

Bad data

46

Step 3

ODI Loads DIM_CUSTOMERS

(Slowly Changing Dimension)

DIM_CUSTOMERS

Name Address City Postcode Country Account_num Acct_rep Territory Acct_Status Clrecid

Dr Simon

Brennan

11 Abottsford

Avenue London N153BT UK UK02306 1-DDE UK-LON CLOSED U21015

Miss Kylie

Brennan

11 Abottsford

Street London N153BT UK UK02307 1-FMM UK-LON ACTIVE U21018

Dr Simon

Brennan

11 Abottsford

Ave London N153BT UK UK02306 1-FMM UK-LON ACTIVE U21016

Karen

Brennan

11 Abottsford

Ave London N153BT UK UK02308 1-FMM UK-LON ACTIVE U21017

STG_VALID_CUSTOMERS


Dr Simon

Brennan 11 Abottsford Ave London N153BT UK UK02306 1-DDE UK-LON

Miss Kylie


Dr Simon


Karen Brennan 11 Abottsford Ave London N153BT UK UK02308 1-FMM UK-LON

Cleansed data

Inserted data

Updated data

ODI Loads using SCD Type2 IKM:

1) update & close old record

2) create new one ACTIVE



CUSTOMER LOGO


Name

Title, Company Name

blogs.oracle.com/IMC


Partner Webcast – Oracle Enterprise Data Quality for ODI: The importance of Quality in Data Integration - 30 May 2013

Technology

exabytes of data

good data

oracle andor

data quality tools

data quality software

aberdeen groupnew data

data governance initiatives

duplicates inproduct