Copyright © 2015, Oracle and/or its affiliates. All rights reserved. |

Safe Harbor Statement

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.

Oracle Confidential 1

Copyright © 2016, Oracle and/or its affiliates. All rights reserved.

Data Quality for the Cloud: Enabling Cloud Applications with Trusted Data

Martin Boyd, Senior Director, Oracle Erik Lavin, Senior Manager Analytics Beckman Coulter September, 2016

Data Integration Solutions – Five Core Capabilities

1. Business Continuity DATA ALWAYS AVAILABLE

2. Data Movement DATA ANYWHERE IT’S NEEDED

3. Data Transformation DATA ACCESSIBLE IN ANY FORMAT

4. Data Governance DATA THAT CAN BE TRUSTED

5. Streaming Data DATA IN MOTION OR AT RES

Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 4

Eight Core Products

Cloud or On-Premise

Five Cloud Solutions

1. Data Migrations REPEATABLE PROVEN DATA MIGRATION TOOLS

2. DW Integration ENABLE CLOUD DATA WAREHOUSE STRATEGIES

3. Dev & Test Cloud OPERATE DEV-TEST DB’S IN CLOUD

4. Data High Availability DATA RELIABILITY AT 99.999% SERVICE LEVELS

5. Heterogeneous Cloud BEST OF BREED FOR AMAZON AWS ETC.

Copyright © 2015, Oracle and/or its affiliates. All rights reserved. |

Simple to Use

High Performance Matching

Powerful DQ Rules

Oracle Enterprise Data Quality Oracle Enterprise Data Quality establishes trusted business data by providing a foundation for data profiling, data standardization, match and merge capabilities and data cleansing.

Profile, Standardize, Match, Merge and Cleanse your data

DW MDM

Apps ETL

Health check for your data; quick & easy profiling and

cleansing

Intuitive business user

friendly toolkit for quality

rules

Suitable for high

performance Applications or

Master Data

Copyright © 2015, Oracle and/or its affiliates. All rights reserved. |

Can We Trust Our Data?

Oracle Big Data Governance 7

“We have a great BI system, but…”

“We have a new cloud CRM system but…”

“We need to report to outside regulators but…”

“We have a sophisticated data warehouse, but…”

“…can we trust our data?”

Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 8

We All Need to Trust Our Data Without trusted customer data:

• Sales/marketing cannot drive sales campaigns • Customer service cannot deliver high quality customer service • Compliance officer cannot guarantee they are not doing business with

terrorists, complying to taxation rules, etc [financial services]

Without trusted supplier and product data: • Procurement cannot enable strategic procurement to lower costs

Without trusted product data:

• Product data manufacturing cannot manage warehouse inventory • Product data eCommerce cannot enable catalog search • Product data operations cannot find warehouse parts

Without trusted financial data:

• Closing the books can take weeks of manual data reconciliation

Without trusted data of any type: • Business Intelligence/Reporting may be misleading and possibly

dangerous…

Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |

Your data may be your most valuable asset

Treat it wisely

Profiling

Business Rules

Deduplication

Standardization

Remediation

Enhancement

Copyright © 2015, Oracle and/or its affiliates. All rights reserved. |

What you would expect of EDQ

• Fast and highly scalable - proven with very large record volumes

• Single configuration UI - from the very beginning of the product

• Easily integrated in batch or real-time

• Works with virtually any type of data in any format or language

• UIs and Documentation available in 9 languages

• Available On Premise or in the Oracle Public Cloud

Copyright © 2015, Oracle and/or its affiliates. All rights reserved. |

What you may not know

• EDQ is the DQ engine powering a range of Oracle Cloud Services:

– Sales Cloud

– Customer Data Management

– Procurement Cloud

– Data as a Service for Sales

– Address Verification

• Customers can deploy on Oracle Cloud today and configure with all the flexibility of on prem deployments

• EDQ includes a highly flexible and fully integrated Case Management application

Copyright © 2015, Oracle and/or its affiliates. All rights reserved.

Data Quality/Governance Maturity Progression

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Tactical Single silo or process

Linked Apply same techniques and

rules at multiple points

Strategic Driving for consistent rules and application everywhere

• Ad-hoc implementation of rules for 1-off cleanup

• Little reuse/consistency between processes

Examples: • 1-time data cleanup for data

merge/migration • Transformation /cleanup for DW/BI • ‘Protect’ data in single repository, eg. CRM,

ERP

• Starting centralized/formal decision making around standards

• Matrixed organization structure with central center of excellence

• Working to implement consistent rules at multiple points

• Working to expand to multiple data domains (customer, product, supplier, financial etc.) and processes (data input, data import, load to application, etc.)

• Broad strategic initiative with top level sponsorship

• Formal decision making and responsibilities

• Drive ALL systems to enforce consistent standards

• Often involves MDM hubs for scale

• Will involve significant organizational change management

Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 13

Data Quality/Governance is an Iterative Process

Discovery – “What are the problems?”

• Profile content (EDQ)

• Profile data structure (OEMM)

• Catalog and prioritize issues

• Assign ownership for resolution

Rule Building – “What rules should we adopt?”

• Determine consistent definitions and business rules

• Determine how to standardize across system and how to resolve conflicts

• Build validation enforcement rules (EDQ)

• Manage reference data (EDQ, OEMM)

• Validate, standardize & transform processes (EDQ)

• Verify data flow lineage and transformations (OEMM)

Rule Enforcement – “Check data meets defined standards”

• Integrate data quality rules (EDQ) • Part of batch data movement

• Part of real-time data movement

• At point of data capture

• Case management for exceptions (EDQ)

Measure & Adjust – “how well it is going?”

• Compliance vs. non-compliance trends and metrics (EDQ, any BI environment)

• Analyze by data source, data type, etc.

• Identify areas for refinement and expansion

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Customer Data Management and Analytics at Beckman Coulter Case Study - Transformative Marketing Foray into Data Governance

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Who is Beckman Coulter

Beckman Coulter develops, manufactures and markets products that simplify, automate and innovate complex biomedical testing. More than 275,000 Beckman Coulter systems operate in both Diagnostics and Life Sciences laboratories on seven continents. For more than 80 years, our products have been making a difference in peoples’ lives by improving the productivity of medical professionals and scientists, supplying critical information for improving patient health and delivering trusted solutions for research and discovery.

Beckman Coulter develops, manufactures and markets products that simplify, automate and innovate complex biomedical testing. More than 275,000 Beckman Coulter systems operate in both Diagnostics and Life Sciences laboratories on seven continents. For mo

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Master Data Management Clean Up

16

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1 – 10 – 100 Rule

Data Management – Cost of Doing Business

Prevention

Correction

Failure

Cost to verify a record as it is entered

Cost to correct, cleanse and/or de-duplicate a record

Recurring cost to the business operating with the erroneous record

*Source: SiriusDecisions

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Before Customer Data Management Release 0.5 (2015Q2)

Information Architecture Sample Conceptual Architecture

Customer Data Management Prior to Release 1.0

Life Sciences

Diagnostics

Lack of single and complete 360View of Customer and Contact Information impacting

Marketing Effectiveness

Sales Effectiveness

Operational Inefficiencies for Order to Cash Process

Data Management Inefficiencies

Revenue Growth is hampered by inaccurate, incomplete, disparate data

Up/Cross-sell

Customer Experience

Lack of trusted, actionable Data and Analytics (BAMV)

Risk Management Impact

IT Project Risk

Regulatory Compliance Risk

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Tactical Project: Beckman.com Use Cases Enabled by CDM

1. Address Verification and Standardization There are a few solution options that could be leveraged

based on a EDQ and CDM footprint. Best practice includes verifying/cleansing addresses real

time before doing a duplicate/existence check for the web user

2. Search before create (checking if the record already exists and prompting user to select from matched records).

3. Compliance Watchlist screening for denied parties

4. Present “Next Best Offer” (Personalized Experience) to the Beckman.com visitors based on the identity and history from CDM

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MDM Status

Release 1: Complete Data Cleansing of multiple Data sources by Oracle

EDQ Result: Stand Alone Data Mart

Release 2: Complete Data Cleansing of multiple Data sources by Oracle

EDQ Data Management via Oracle CDM Cloud Result: Eloqua access of Clean Data overlay Analytics Automated cleansing of data

Release 2 and Beyond: Initiated

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Release 1.0 Marketing Data Mart Solution Recap – 1

The Universe: Sources Salesforce.com (Dx)

Eloqua

Oracle e-Business Suite (OBI)

CensusTrak

The Max IDN

Product Interest

Install Base

Opportunites

Territories Campaigns

Universe

X

Y

Z

X – Universe Y – BEC Basic Actionable Contacts Z – BEC Campaign Actionable Contacts

The Universe: Counts 1,353,814 Records

782,062 Contacts De-duplicated

528,409 Contacts w/ Country

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Release 1.0 Marketing Data Mart Solution Recap – 2

SalesforceDx

Eloqua

Oracle EBS

Oracle EDQFile Share

CensusTrak

The Max

User Load

OBIDW

MarketingData Mart

Start with EDQ to cleanse and merge records from existing systems

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Release 1: Marketing Data Mart Project Objectives Met

Project Objective:

Provide the Beckman Coulter marketing team with consolidated and cleansed view of leads, prospects and customers augmented with 3rd party market lists containing competitive information for marketing campaign segmentation, reporting, and analytics

Enabled Transformative Marketing Objectives by exceeding 2015 Q3 Target of 60% MV

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Release 2.0 Beyond the

Marketing DataMart:

Consume standardized, enriched, trusted

Customer/Contact information in critical Business Applications

Starting with

Eloqua Marketing Automation

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· Consolidate· Standardize· Cleanse· Share

Future Spokes (Release 3)

ORACLE Cloud CDM HUB

Pre-built integrations (Oracle)

Custom integrations

Pre-built integrations (SFDC)

Pre-built integrations (batch)

Customer Data Management Program – Information Management Release 2.0 Focused on Marketing Automation Only

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Customer Data Management Program – Business Alignment Release 2.0 Focused on Marketing Automation Only

Marketing Business Benefits – Post Deployment Review

o Automated feed of customer/contact universe records to Eloqua

o Scalability for use with M&A activity

o Scalability for use with EU and APAC teams

o Future foundation for Sales (SFDC), Business Ops (EBS), Regulatory etc.

o Customer/Contact 360View

o Governance of Marketing Attributes

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Beyond Release 2.0 (a.k.a Data Management for

Transformative Marketing Program)

Operational Customer Data Management

Governance supporting

Sales Effectiveness and

Digital Transformation

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Customer Data Management Program – Data Governance Roadmap for Enterprise Adoption -1

Establish Data Governance & Stewardship provide the right level of control and trust in data.

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Customer Data Management Program – Data Governance Roadmap for Enterprise Adoption -2

Source:

Data Management Maturity Model

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Business Enablement with Customer Data Management

31

Customer Hub – How OBI sees it!

We can now trust this data!

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Copyright © 2015, Oracle and/or its affiliates. All rights reserved.

Presen-tations on:

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Data Integration Solutions Program - tinyurl.com/DISOOW16

Demo Stations:

Hands-on labs:

Oracle Enterprise Metadata

Management

Oracle Enterprise

Data Quality

Oracle GoldenGate

Oracle Data

Integrator

Oracle Big Data

Preparation Cloud Service

Oracle Enterprise

Data Quality HOL7466

Oracle GoldenGate Deep Dive HOL7528

ODI and OGG for Big Data

HOL7434

Oracle Big Data Preparation

Cloud Service HOL7432

Middleware Demoground

- Moscone South

Big Data Showcase

- Moscone South

Database Demoground

- Moscone South

Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 34

Data Integration Solutions Program - tinyurl.com/DISOOW16

Monday, Sept 19 • Oracle Data Integration Solutions – Platform Overview and Roadmap

[CON6619 ] • Oracle Data Integration: the Foundation for Cloud Integration [CON6620 ] • A Practical Path to Enterprise Data Governance with Cummins [CON6621] • Oracle Data Integrator Product Update and Strategy [CON6622] • Deep Dive into Oracle GoldenGate 12.3 New Features for the Oracle 12.2

Database [CON6555]

Tuesday, Sept 20 • Oracle Big Data Integration in the Cloud [CON7472] • Oracle Data Integration Platform: a Cornerstone for Big Data [CON6624] • Oracle Data Integrator and Oracle GoldenGate for Big Data [HOL7434] • Oracle Enterprise Data Quality – Product Overview and Roadmap

[CON6627] • Self Service Data Preparation for Domain Experts – No Programming

Required [CON6630] • Oracle Big Data Preparation Cloud Service: Self-Service Data Prep for

Business Users [HOL7432] • Oracle GoldenGate 12.3 Product Update and Strategy [CON6631] • New GoldenGate 12.3 Services Architecture [CON6551] • Meet the Experts: Oracle GoldenGate Cloud Service [MTE7119]

Wednesday, Sept 21 • Data Quality for the Cloud: Enabling Cloud Applications with Trusted Data

[CON6629] • Transforming Streaming Analytical Business Intelligence to Business

Advantage [CON7352] • Oracle Enterprise Data Quality for All Types of Data [HOL7466] • Oracle GoldenGate for Big Data [CON6632] • Accelerate Cloud On-Boarding using Oracle GoldenGate Cloud Service

[CON6633] • Oracle GoldenGate Deep Dive and Oracle GoldenGate Cloud Service for Cloud

Onboarding [HOL7528]

Thursday, Sept 22 • Best Practices for Migrating to Oracle Data Integrator [CON6623] • Best Practices for Oracle Data Integrator: Hear from the Experts [CON6625] • Dataflow, Machine Learning and Streaming Big Data Preparation [CON6626] • Data Governance with Oracle Enterprise Data Quality and Metadata

Management [CON6628] • Faster Design, Development and Deployment with Oracle GoldenGate Studio

[CON6634] • Getting started with Oracle GoldenGate [CON7318] • Best Practice for High Availability and Performance Tuning for Oracle

GoldenGate [CON6558]

Copyright © 2015, Oracle and/or its affiliates. All rights reserved. |

Connect with Oracle Data Integration

@OracleDI

Blogs.oracle.com/DataIntegration/

Oracle Data Integration

Oracle Data Integration

Copyright © 2015, Oracle and/or its affiliates. All rights reserved.

Safe Harbor Statement

The preceding 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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