CON8899_Boyd-Oracle EDQ OOW 2013 - Overview and Roadmap ...

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Oracle Enterprise Data Quality

Overview and Roadmap

Martin Boyd


Senior Director, Product Strategy

Mike Matthews


Director, Product Management


Copyright © 2013, Oracle and/or its affiliates. All rights reserved.

3

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.



Copyright © 2013, Oracle and/or its affiliates. All rights reserved.

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Program Agenda


Why Care About Data Quality and Governance?


Oracle Enterprise Data Quality


Roadmap and Demonstration


Copyright © 2013, Oracle and/or its affiliates. All rights reserved.

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“Ultimately, poor data quality is like dirt on the windshield. You may be able
to drive for a long time with slowly degrading vision, but at some point, you
either have to stop and clear the windshield or risk everything.”



Ken Orr, The Cutter Consortium

Copyright © 2013, Oracle and/or its affiliates. All rights reserved.

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Companies

Individuals

Data Changes in the Real
-
World

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


Master data changes at a rate of 2% per month

Products


On average 20% duplicates in
product data


90% product introductions fail


Retailers loose $40B or 3.5% of
total sales lost each year due to
item master inaccuracy


60% of all invoices will have an
error


Companies with global data Sync
will realize 30% lower IT costs

In one hour…

In one hour…

In one year…

6


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

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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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Typical Customer/Party Data Issues

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

N
icknames and
aliases


Chris


Christine, Christopher, Tina

U
npredictable
use of initials


John Alan Smith, J A Smith

N
oise


Full stops, dashes, slashes, titles,
apostrophes

Transposed
characters


Johnson,
Jhonson

A
bbreviations



Wlm
/William, Mfg/Manufacturing

L
ocalization



Stanislav

Milosovich



Stan Milo

Truncations


Credit Suisse First
Bost

I
naccurate 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

Copyright © 2013, Oracle and/or its affiliates. All rights reserved.

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Typical Product/Item Data Issues

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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Putting your Data to Work

Common Data Quality Use Cases

System Consolidation/Migration


Enforce new system standards on
legacy data

Compliance


Drive consistent data and processes
to meet regulatory requirements
(
watchlist

screening, anti
-
money
laundering, tax compliance, etc.)

Application Enablement


Clean
-
up and govern application
data (CRM, HR, PLM, Retail
search, etc.)

Business Intelligence Enablement


Enforce BI standards on disparate
data

MDM Enablement


Verify, standardize, match and
and merge data from disparate
sources

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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

Govern

Your
Data

Your Experts

Current
issues,
gaps,
errors

B
usiness &
data
s
tandards

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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/ETL

Non
-
DQ/MDM
-
aware Apps

DQ/MDM
-
aware Apps

Web
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

Target
system DQ
metrics

‘Gold’
data

Apply data standards

Source
system DQ
metrics

DQ
process
metrics

Copyright © 2013, Oracle and/or its affiliates. All rights reserved.

16

Program Agenda


Why Care About Data Quality and Governance?


Oracle Enterprise Data Quality


Roadmap and Demonstration


Copyright © 2013, Oracle and/or its affiliates. All rights reserved.

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Modernization

MDM

SOA

Big Data

Oracle Data Integration

Complete Offering for Enterprise Data Integration



Complete and best
-
of
-
breed
approach for
enterprise data
integration


Maximum performance
with
lower TCO, ease of use and
reliability


Certified for leading
technologies to deliver
fast
time to value

Oracle Data Integrator

Oracle
GoldenGate

Oracle Enterprise Data Quality

Oracle Data Service Integrator

OLTP

Applications

Legacy

Unstructured

Synchronization

Custom

BI

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Enterprise Data Quality


Process metrics


Quality metrics


Case Management


Remediation


Party (individuals,
households) match


Entity match


Semantic (category)
match


Statistical match


Match review


Merge/survivorship


Global parse


Category parse


Extract


Transform


Address verification &
geocoding


Substitute


Enrich


Classify


Statistics


Patterns


Phrases


Duplicates


Completeness


Max/min values

Profile

Standardize

Match

Govern

Quickly understand data content

Drive conformance to standards

Identify & merge duplicates

Monitor effectiveness & resolve problems

Common Access/UI

Enterprise DQ Platform

Enterprise DQ Cloud Services


Packaged cloud services for cloud applications

Enterprise DQ
Matching Cloud
Service

Enterprise DQ
Address Verification
Cloud Service

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Enterprise Data Quality

Broadest

DQ offering


Best of breed capabilities for both
Party Data
and
Product
Data


Profiling, standardization, matching, case management,
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


Pervasive operation
for
enterprise quality governance


Within legacy systems and MDM Hubs


As part of migration/system load


On data entry/capture


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

Common Access/UI

Enterprise DQ Platform

Enterprise DQ Cloud Services

Enterprise DQ
Matching Cloud
Service

Enterprise DQ
Address Verification
Cloud Service

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EDQ Web Services

Enforce common DQ standards across the enterprise

Common
Services

Applications

App 1

App 2

App 3

Library of
enterprise
standard DQ
services


Any EDQ process may
be called as a real
-
time
web service


Call any process from
any application to

1.
Enforce common
standards

2.
Minimize
architectural
changes

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Case Management for Governance

Usage


Cases/alerts are assigned a work queues and a priority


Data specialists sign in and review/resolve issues


Management reports allow monitoring of work queues
and productivity


Helpful for

o
One
-
time cleanse/migration

o
Ongoing governance program

Features


Hierarchical Case/alert functionality


Configurable Workflows


Automatic prioritization of cases/alerts


Timers


Email Notification Support


Comprehensive audit trail


Immediate ad
-
hoc reporting


Review and resolve exceptions from the DQ process

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Data Prep for System Migration/Implementation

EDQ Process

Apps and hubs

Governance and Case Management to ‘Perfect’ Data


DQ Insight (Dashboard)


Reporting


Trend Analysis


Case Management


Workflow


Remediation


Legacy Data

‘Fit for Purpose’ Data

Copyright © 2013, Oracle and/or its affiliates. All rights reserved.

23

Program Agenda


Why Care About Data Quality and Governance?


Oracle Enterprise Data Quality


Roadmap and Demonstration


Copyright © 2013, Oracle and/or its affiliates. All rights reserved.

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EDQ Investment Areas

Integrated
DQ &
Governance

Integrated best
-
in
-
class Customer and
Product DQ

Expand Governance
to include
operational
confidence reporting

Integration
Across
Oracle

Deeper Siebel
Integration

Out
-
of
-
the
-
box DQ
for Fusion Apps

Metadata integration
with ODI

Endeca, ATG,
EBS…

Cloud/
SaaS

SaaS deployment
for Fusion Apps

Full clustering and
elastic provisioning
support

Cloud DQ Services

Global Rules
& UI

Global Identity
Resolution

DQ Rules and
Reference Data for
major locales

Additional UI
Localizations

Advanced
Techniques

Statistical parsing &
classification

Statistical outlier
detection

Entity identification
& extraction for Big
Data

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EDQ
in the Cloud

Cloud Data Services powered by EDQ


Providing data enhancement services in the Oracle Cloud


Uses EDQ as the matching engine and to ensure reference

data quality

EDQ in Fusion Apps


EDQ to be deployed and used by Fusion Apps


Leveraging Oracle DB and FMW cloud support

EDQ in Managed Cloud


Growing number of customers already choosing to run full service EDQ in the Oracle Managed Cloud

EDQ powering Partner Cloud Offerings


Kaygen partnering with Oracle to deliver Data Governance in managed cloud with EDQ


Several others following suit


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EDQ for Fusion Applications


Fusion Applications
Integration (Fusion R9)


EDQ deployed in Fusion
Apps as the attached DQ
engine


Advanced Search, Duplicate
Prevention, Master Data
Matching


Address Verification and
Cleaning for all countries

Profile

Standardize

Match

Govern

Quickly understand data content

Drive conformance to standards

Identify & merge duplicates

Monitor effectiveness & resolve problems

Common Access/UI

Enterprise DQ Platform

Enterprise DQ Cloud Services

Enterprise DQ
Matching Cloud
Service

Enterprise DQ
Address Verification
Cloud Service

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EDQ 11
-

Major New Features


Case Management Expansion


Instant reports on high volume data


Aggregated reports (e.g. activity by period, priority, etc.)


Improved case search and filter


Expanded workflow options


Reference Published Processors


Enables development of ‘locked’ IP to extend EDQ


Full reuse and upgrade of processors across processes/projects


UI Localization to 9 Languages


Chinese, Japanese, Korean, Brazilian Portuguese, French, Italian, German,
Spanish, English

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EDQ 11
-

Improving Productivity


New Job Manager


User
-
defined job layouts and canvas notes


‘Blocking’ triggers allow jobs to be called within jobs with
execution control


Additional externalization options


New Process Canvas


Improved canvas usability and multi
-
language support


Browser
-
based Web Service Tester


Faster testing of EDQ Web Services

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EDQ 11


Other Changes


Oracle Universal Installer


Automated installation process for all platforms


Fusion Middleware Integration


Enables use of WebLogic OPSS for security and authentication


Uses FMW Audit Control to capture key configuration changes


Automated Results Purge capability


Support for Subversion 1.7


Array support in Data Interfaces


Multi
-
attribute data type converters


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EDQP 11
-

Major Features


New Integrations


Connector for Endeca Guided Navigation


Integrated with Agile PLM 9.3.2


Statistical Matching Module
(StatSim)


Quick Rules Free Configuration


Match or classify verbose semi
-
structured
data


Integrated with Governance Studio


Remediation Capabilities


Provides List of Values for Data
Enrichment


Integrated with AutoLearn Workflow


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EDQP Drives Endeca Navigation

Integrated Data Quality:


Populate



Identify, extract and standardize product
dimensions & properties


Integrate



Automatically create required dimensions
within Endeca (avoid manual dimension setup)

Endeca
Engine

EDQP

Client
Browser

Data
Source

Data
Source

Data
Source

Improved data
improves user
experience


Standardize data structure


Standardize data values


Integrated directly into Endeca pipeline

Endeca
Load

Data Preparation

EDQP ‘pushes’ required metadata
into Endeca to create required
navigation dimensions

PIM or any other
data source

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EDQ 12c


Data Quality Governance II


Integrated Semantic Data Engine (EDQ
-
P)


Full WebLogic Server Clustering support


Shared config for multiple EDQ servers


Session balancing and failover


Active
-
Active Case Management


Oracle Access Manager integration


Hadoop Connectivity


Fully automatable Reference Data Generation



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Data Governance with EDQ

Single DQ environment

DQ Engine

Data sources

Real
-
time checks

Apps and hubs

Enabling People and Process with Technology


DQ Insight (Dashboard)


Reporting


Trend Analysis


Case Management


Workflow


Remediation


Current capabilities to
be enhanced and
combined into a new
cloud
-
enabled DQ
Governance UI

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EDQ Application Integration


Fusion Applications


Deep integration in progress; planned for Fusion R9 release


Siebel CRM and UCM


Deep integration in place using services architecture; more stable,
performant, functional and scalable than 3
rd

Party or OEM integrations


EBS



Template connectors available for common integrations (customer/party, etc.)


Salesforce.com



Template connectors available for batch cleansing


Oracle Product Hub; Fusion Product Hub


Deep integration for batch and real
-
time load


ENDECA

(Oracle Commerce)


Data cleansing and metadata sync to streamline managing complex
product data schemas for eCommerce


Agile PLM


Template connectors for batch and real
-
time validation, BOM validation and BOM sync



Enabling Applications with Data Quality Services


Application owners are painfully aware of the impact & costs of poor data


EDQ is investing heavily in providing out
-
the
-
box Application DQ solutions

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Demonstration

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EDQ 11


Reference Published Processors


EDQ enables the construction, publication and packaging of
domain
-
specific processors that can be packaged and
reused on any EDQ server


Functionality has now been extended to support use by
reference as well as copy, allowing the construction of
locked and upgradeable processors in many projects


EDQ Solution Development are already using this to build a
set of reusable processors for customer data in various
locales


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Reference Published Processors

1.
Construct a new processor by configuring
a chain of processors

2.
Configure how to expose inputs and
options

3.
Reference data may be packaged inside
the processor or ‘expected’ by it

4.
Add a family and custom icons

5.
Publish to the server:

-

Choose ‘Template’ or ‘Reference’

6.
Package to create formal processor packs




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EDQ 11


Case Management Improvements


Instant searching and operational
reporting for large data volumes


Aggregations in reporting


Search on history


Find all cases commented on by me


Find all cases transitioned in
workflow by me


Use negative search logic


Find all cases where value is NOT x



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39

EDQ 11


New Job Manager


User
-
specifiable layouts


Externalization of all major
configuration points to
allow dynamic overrides


Improved UX and
performance


Improved trigger controls


Canvas notes



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EDQ 11


Additional Localizations


All UIs available in:


US English


French


Italian


German


Spanish


Chinese


Japanese


Korean


Brazilian Portuguese


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41

Q&A

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Join the Data Integration and MDM Community

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LinkedIn

YouTube

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dataintegration

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mdm


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