Oracle Data Integration€¦ · •Inbound customer flows were creating duplicate records...

Oracle Data Integration

Cloud Data Quality: Lessons Learned from Oracle’s Journey to the Sales CON9595

Martin Boyd Senior Director Product Strategy, Oracle Ravi Mikkilineni CRM Data Management, Oracle

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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.

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Oracle Data Integration Core Business Use Cases

Oracle OpenWorld 2014 4

Analytics & Business Intelligence • Big Data Integration & Governance • Data Warehouse / Mart Integration

IT Data Integration & Governance • Enterprise Shared/Common Data Services • Move, Migrate, Transform, Profile, Cleanse,

Glossary and Metadata Management Business Continuity • Active-Active for Maximum Availability • Zero Downtime Migrations

24 x 7 x 365

Cloud

On Premise

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NoETL Engine 100% Native Data

Transformation

Data Integrator

Big Data Preparation

GoldenGate

Data Quality

Data Service Integrator

Metadata Management

Non-invasive CDC, Realtime streaming

data delivery

Profile, Cleanse, Match, and

Remediate Data

Prepare, Secure, Enrich and Publish Unstructured Data

Catalog, Trace and View Models across

the Enterprise

Federate Data Across DBs, Services

and Applications

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NoETL Engine 100% Native Data

Transformation

Data Integrator

Big Data Preparation

GoldenGate

Data Quality

Data Service Integrator

Metadata Management

Non-invasive CDC, Realtime streaming

data delivery

Profile, Cleanse, Match, and

Remediate Data

Prepare, Secure, Enrich and Publish Unstructured Data

Catalog, Trace and View Models across

the Enterprise

Federate Data Across DBs, Services

and Applications


Data Quality Management is more critical than ever


Data as a Capital Asset

Data Stewards

Business Control

Chief Data Officer

DATA GOVERNANCE

Operational Data Stores

Link to Big Data

Link to Social Data

Cloud Apps

MASTER DATA MANAGEMENT

Anti Money-Laundering

REGULATORY COMPLIANCE

Self Service

Data Lakes Cloud BI

BUSINESS INTELLIGENCE Basel &

Solvency III

Know Your

Customer

ENTERPRISE DATA QUALITY MANAGEMENT

Case Management KPIs Configuration


Oracle Enterprise Data Quality

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

Enterprise DQ Platform

Market-leading usability for all types of data

Unparalleled time-to-value

High performance engine

Out-of-the-box global knowledge-base

Foundation for governance program

8 Oracle OpenWorld 2014


Agenda

Introduction

Challenges

Approach

Solution

Results

1

2

3

4

5


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Oracle Sales Cloud

• 450k Opportunities/year

• 1.5 Million Leads/year (Marketing + Partner)

• 72k Territories

• Customer Data Management team: 40 FTE

Oracle on Oracle Sales Cloud

1. One of the largest cloud CRM implementations in the world

2. Mission-critical for Oracle

• 30k Users

• ~2 Million Customer accounts (Sales Accounts + Prospects)

• ~2.4 Million Addresses

• ~3 Million Contacts

Copyright © 2015, Oracle and/or its affiliates. All rights reserved. | Oracle OpenWorld 2014 11

Garbage -in

Garbage -out

But even with the BEST Cloud CRM…


Data Quality Challenges

• Good customer data is central to driving up revenue, improving customer satisfaction and understanding your business

• We had several challenges with our data undermining CRM effectiveness:

• Inbound customer flows were creating duplicate records

• Confusion for users in selecting the correct record to use for a transaction

• End to end process flows failed due to inconsistent or incorrect attribution delaying processes such as lead flow, quoting, order booking, shipping and invoicing

• Incomplete view of our customer interactions

• Less targeted sales and marketing

• Legal and legislative compliance issues


Benefits of a Data Quality program for CRM

• Improved visibility opening up cross sell and up sell opportunities

• Better targeted marketing

• A more complete view of the issues our customers are facing

• Improving customer satisfaction by ensuring their transactions move quickly and accurately

Revenue Cost

• Increased automation based on richer and more accurate records

• Less duplication of work such as sales rep assignment, credit and risk management, cleansing activities, agreements

Bottom-line: High quality data (complete, standardized, verified, de-duplicated) is a core requirement for CRM


Oracle Confidential 14

Understanding the Problem

• Initial analysis was based on 2 strategic customers

• Duplication was far greater than reported, > 50% for large customers

• Automatic duplicate identification was an issue because of:

• Incomplete or inaccurate customer names, for example Oracle, Oracle ECE, Oracle Corp, Oracle East Central Europe Limited

• Different entities of the same company at the same address:

• Difficulty comparing addresses with differing text i.e. CA and California

• Synchronizing customers between systems had a high error rate

• Needed manual intervention to correct issues – for example due to badly formed address data

Oracle East Central Europe Limited Company Registration number 335354. Registered office is at East Point Business Park, Fairview Dublin 3.

Oracle EMEA Limited Company Registration number 158973. Registered office is at East Point Business Park, Fairview Dublin 3. VAT Registration Number IE 6556973 V


Master Data Management Program and Approach

• The Customer MDM program was developed to solve the fragmented and inconsistent data in Sales Cloud and its associated systems.

• No additional resources were required. Existing resources were focused on delivering a solution for the root of the issues rather than the symptoms

• Project team:

• Oracle Applications Labs – Solution design and implementation

• Businesss LOB stakeholders – including Sales, Marketing, Finance and Support – Implement business process changes, testing and signoff

• Executive Steering committee – Solution and business process approval


Solution

• Needed a consistent and highly automated data quality process which could be integrated into each system flow.

• Needed to be fast enough for real time integration

• Have enough capacity for bulk cleaning existing records, and for batch creation

• Flexible enough to determine where a manual review was required

Validate Cleanse Enrich Match Report Maintain


Data Quality Infrastructure

Enterprise Data Quality Layer

Address Verification

DnB Enrichment

Party Matching

ERP

(Billing)

Sales Cloud (CRM)

Data Validation

Store Systems

B2B Partners

Marketing Systems

M&A Data Migrations

End User


Validation

• Validate attributes are correct at the point of entry

• Ensure consistent standards are applied across all inbound sources

• Sensitive to transaction type and required velocity

• Notification of errors and warnings Data Object Requirement Error or Review?

Customer Name • Always Required

• In specific countries, such as Japan, ensure both English and local language names are

provided

Error

Address • Check for minimum required fields – address1, city, country, zip etc. Error

Contact • Always Required, needs either phone or email Error

Tax Data • Always required for specific countries Error / Review

Configurable



Address Cleaning

• Address validation & standardization

• Critical component of the solution

• Standardization improves matching rates by ~30%

• Clean Addresses Improve automation of geography based processes

• Sales Rep assignment, tax calculation, export control compliance and shipping

• Accurate Geo values enable customer integration across systems with different address standards

• Local Language Addresses

• Many countries require addresses both in local language and English.

• Automation to provide translated address significantly reduces the need for manual intervention



Address Cleaning

• GEO co-ordinates

• Required to support Field Service, Warehousing and Logistics processes.

• Foundation for mobile applications


Input Data: "300 Berry #1210 SF California"

PremiseNumber

ThoroughfareName

SubPremise

Locality

AdministrativeArea

PostalCode

300

Berry St

Unit 1210

San Francisco

CA

94158-1670

Format

300 BERRY ST UNIT 1210

SAN FRANCISCO CA 94158-1670

USA Geocode

Latitude 37.775837

Longitude -122.39557


Enrichment

• Match inbound records against sourced data, such as Oracle DaaS service

• Provide Industry, Company size, Hierarchy Information

• Additional information used to improve duplicate detection


Single Match

CREATED_MODULE CUSTOMER_NAME ADDRESS1 ADDRESS2 CITY STATE COUNTY POSTAL_CODE COUNTRY ORG TAX_PAYER_IDTAX_REG_NUMBER

Webreg Amcom Softw are 10400 Yellow Circle Dr. Suite 100 Eden Prairie MN Hennepin 55343 US 1001

PARTNER_STORE Life Quotes, Inc. 8205 S Cass Ave Ste 102Suite 102 Darien IL Dupage 60561 US 1001

XML_EDI CORNING INC. 21 Lynn Morse Drive PAINTED POST NY 14870 US 1001

DIRECT_STORE Jefferson Contracting, Inc.9390 Middlew ay Pike Charles Tow n WV Jefferson25414 US 1001

MATCHSCORE DUNS_NUMBER BUSINESS_NAME ST_ADDRESS1 ST_ADDRESS_2CITY_NAME STATE POSTAL_CODE COUNTRY_NAMESTATE_PROVINCE_NAMECOUNTY_NAMEURL

88 435545535 Amcom Softw are, Inc. 10400 Yellow Circle Dr Eden Prairie MN 553439153 USA Minnesota Hennepinw w w .amcomsoft.com

95 676585856 Life Quotes, Inc. 8205 S Cass Ave Ste 102 Darien IL 605615319 USA Illinois DuPage w w w .insure.com

90 976545644 Corning Incorporated 21 Lynn Morse Dr Painted Post NY 148709198 USA New York Steuben

93 564353456 Jefferson Contracting, Inc9390 Middlew ay Pike Charles Tow nWV 254144996 USA West VirginiaJefferson

Input

Output


Matching

• Needed a fast flexible matching solution that could:

• Provide both real time matching and batch processing capabilities

• API’s that could be integrated into every flow

• Ability to configure match rules specific to different systems

• CRM: Allow significant variation in name and consider geography to support sales territory assignment

• Finance: Higher accuracy in naming, match only within the same Oracle business unit, consider tax identifiers

• Recognize standard synonyms and account for typos and other errors

• Provide confidence score to allow records to be routed for manual review



Matching


DUNS Number (Exact Match)

Party Name (Exact Match)

Address Line1 (Exact Match)

Postal Code (Exact Match)

Country (Exact Match)



Address Line1 (Fuzzy Match)





Address Line1 (Exact Match)


100

99

98

Match Score

Party Name (Fuzzy Match)

Address Line1 (Fuzzy Match)





Party Name (Fuzzy Match)



76

75

74


Reporting

• Move form reactive to proactive maintenance

• Profile data frequently to detect attribution issues

• Allow integration with issue notification systems

• Provide key metrics



Results – Account Reductions, While Business Growing

0.0

0.1

0.2

0.3

0.4

0.5

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0.7

0.8

0.9

1.0

FY12 FY13 FY14

Vo

lum

e

Finance – Active Accounts

Accounts

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Jun-12 Oct-12 Feb-13 Jun-13 Oct-13 Feb-14 Jun-14 Oct-14 Feb-15

Vo

lum

e

Sales Accounts – Active vs. Total

Total Pre EDQ Post EDQ Predicted

34% Reduction 44% Overall

Reduction

+40% Annual Growth – Pre EDQ


EDQ for Oracle Sales Cloud – Results

• Targeted marketing with 70% leads assigned to Preexisting Sales Accounts

• 72%+ reduction in net new prospect creation from inbound sources

• Manual creation of accounts reduced by 80% - cost saving and time reduction

• Reduction in >45K per annum manual interventions required to correct address issues etc

• 85% of all Sales Accounts are integrated to Finance resulting in improved transaction velocity


Questions and Answers

Oracle OpenWorld 2014 27


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