<Insert Picture Here> Master Data Management

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Master Data Management

김 대준, 상무

MDM Issues

‰

Lack of Enterprise-level data code standard

¾ Region / Business Function

‰

Lack of data integrity/accuracy

¾ Difficulty of daily-based Data Quality management

‰

Difficulty of managing lots of interfaces

‰

Lack of Enterprise Data Governance Framework

‰

Need of collaboration with business partners

Key considerations when engaging into MDM projects

‰

How does MDM fit into my enterprise application footprint?

‰

How do I solve the data quality challenge?

‰

What should be my MDM vision / end-state?

‰

How do I get there? Where do I start? How can I phase my

deployment?

‰

What MDM style do I implement? How will my choice today impact

my ability to evolve?

‰

How can I support apparent conflicting MDM requirements across

Child Objects Handsets CDRs POS Rate Plan Pricing NRC Usage Attributes Meter reading Catalog Tax Account Tax Return Curriculum Classes Case Learnings Value Policies Behavior Credit Score Terms Contracts Risk Loyalty Points Orders Service Request Campaign Offers/Responses Tuition Financial Account

Fragmented data in typical enterprise

topology…

Ever proliferating island of information

…in disparate applications covering

multiple channels, divisions & functions

…duplicated, incomplete, inaccurate,

¾

Key enterprise processes based on

unclean / incomplete data (marketing,

sales, service & customer retention

activities, regulatory compliance, new

product introduction,…)

¾

Analytics validity in question

¾

Error prone Integration

¾

Slow enterprise agility and

Enterprise MDM solution where information is

available as a service to operational & BI systems

BI/DW BI/DW Partner Fusion Apps Web site SFA Call Center ERP 1 _{SCM HR} Fusion Apps Legacy

Middleware / Application Integration Architecture

ETL ETL

Real-time / near real-time Master Data

MDM

MDM

Real-time / near real-time Master Data

MDM provides the ability to…

¾

Consolidate/Federate master &

shared information into one place

¾

Cleanse, de-duplicate and

Enrich data centrally

¾

Distribute data as a single

point of truth as a service to

consuming applications,

enterprise business processes

and decision support systems

Components of a Comprehensive

MDM Strategy

Alternate Hierarchies, Reporting, Analysis

Master Data Management

Product Master DQ Industry Custom ERP Other… Billing CRM Legacy

Integration Bus

Extract Transfor

m

Load (ETL)

Business Applications

Oracle MDM in an Enterprise Architecture

Customer Master DQ

Business

Users

Data

Stewards

Oracle MDM provides the ability to…

¾Consolidate/Federate master & shared information into one place

¾Cleanse, de-dup and enrich data centrally

¾Distribute data as a single point of truth as a service to consuming applications,

enterprise business processes and decision support systems

Real time/ Batch

Master Data ETL

Oracle Enterprise Master Data Management

Customers

Oracle Fusion Middleware

Products

Suppli

e

rs

Oracle

Product

Hub

Oracle

Customer

Hub

Operational

MDM

Analytical

MDM

Oracle | Hyperion

Data Relationship

Management

Data Governance & Compliance

Requests, Approvals, Workflow, Audit, Risk Management

Financials

Analytical

Entities

MDM Differentiation – Hyperion Data Relationship

Management

Oracle Customer and Product Hub

Hyperion DRM

Entities

Customer, Supplier, Product

Ledger (Chart of Accounts, Cost

Centers, Legal Entities), Others

Schema

Deep vertical schema

Data model agnostic

Mode

Operational MDM

Operational & Analytical MDM

Typical User

Interaction

Lights Off (high level of automation)

Lights On (interactive - business

user tool)

Typical User

Data Steward

Financial / LOB Business User

Strengths

• Build golden record of customer

• Cleanse and de-duplicate party data

• Survivorship rules

• Data quality

• Audit Capability

• Pre-built customer data management processes

• Robust and flexible party data model

PRODUCT

• Build golden record of product

• Standardize product descriptions

• Push standardized product definitions from PLM to multiple global systems

• Leverage clean data to consolidate buying power

CHART OF ACCOUNTS, COST CENTERS, LEGAL ENTITIES

• Advanced Hierarchy Management

• Business User interaction

• Versioning Control

• Audit Trail (who moved what where when)

• Inheritance, derivation, and default attributes

MDM

Processes & Policies

Billing / Service Account Service Items

Cust. Account Contacts Relationships

Schema

… Hierarchies

Source 1

Souce 2

Source 3

Data Cleansing

Data Matching

Data Cleansing Data Cleansing

ETL

Transformation & load

Data Quality Rules Services Survivor-ship Audit/ History Data Cleansing Data Matching Enrich Cross-ref Merge/ Unmerge Security Visibility Privacy

Integration Web Services Source System Mgt Publish & Subscribe Pre-built processes

Data Admin

Consuming Apps: Marketing, Sales,

Order Fulfillment & Provisioning,

Contracts, Support, Financials, Billing,

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Understand the Different Architectural Styles of MDM

Coexistence

Style

Registry

Style

Transaction

Style

Matches and links to

create a "skeleton"

system of record.

Physically stores the

global ID, links to data

in source systems and

transformations.

Mainly for

Real-Time

Central Reference

Matches and physically

stores the up-to-date

consolidated

view of master data.

Central authoring of

master data.

Acts as System of

Record to Support

Transactional Activity

Matches and physically

stores a consolidated

view of master data.

Publishes the

consolidated view. Not

usually used for

transactions, but could

be used for reference.

For Harmonization

Across multiple

sources and recipients

Matches and physically

stores consolidated

view of master data.

Updated after the event

and not guaranteed up

to date.

Consolidation

Style

For Reporting,

Analysis and

Central Reference

Updates Source data

No Update of Source

Variations in: 1. Degree of physical instantiation of master data

2. Nature and latency of interaction between the hub and spokes

3. Where master data is authored

4. Suitability for transactional and analytical applications

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Sample Phasing

Acct Billed Details

Acct Billed Details

Dispute Contact Dispute Contact Preferred Account Preferred Account Service Base Service Base LPS LPS ICMS - LL Base Information Base Information JWALK GUI 902 905 907 909 FMS ODS

Comptel PPAS TAPIN DOC1

EDW

OL

RA

Phase 2c: MDM feeds

downstream systems in batch Phase 2b: Real time DQ

Phase 2b: Real time MDM feed

Phase 2d: MDM master for interactions

Business Process Integration

Phase 2a: Pull / Push MDM data

MDM

Service

Service Marketing _Marketing

CRM

Phase 1: Batch Feed

Data Migration & Quality

Sample Phasing

902 905 907 909

FMS ODS

Comptel PPAS TAPIN DOC1

EDW

OL

RA

Phase 2c: MDM feeds

downstream systems in batch

Business Process Integration

Phase 2a: Pull / Push UCM data

MDM

Service

Oracle MDM

Enterprise MDM solution that provides information as a Service to

operational systems

Real-time / near real-time Master Data

MDM

MDM

Real-time / near real-time Master Data

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Oracle Best Practice - Data Governance Framework

Policies Processes Organization

Data Governance Framework Data Definition Data Control Data Quality

•Data Governance needs three interdependent components;

•Master Data Mgt. Policy : data governance processes must be followed •Master Data Mgt. Process : CRUD, communication, conflict resolution •Master Data Mgt. Organization : Data Owner, Data Steward

•Scopes of Data Governance Framework •Defining the data entities & attributes •Defining the Object naming standard

•Defining the data entry standard (including validation & verification rule) •Defining data access and security policy

•Data retention criteria (Survivorship rule) •Defining business rules

•Defining data quality metrics

•Define the metrics of data quality

•Define DQ procedures (data duplication, identification, and resolution, merge/unmerge)

•Define reports to measure quality metrics

Data Governance Strategy – case study

Design and implementation of the governance model is a phased approach leveraging best practices, lessons learned from past efforts, and Key Data Entity (KDE) design teams comprising of business, IT, and vendor SMEs.

Pre-Deployment Deployment Post Deployment

High Level Design

High Level Design Detailed DesignDetailed Design BuildBuild

Data Governance Strategy Data Governance Strategy Detailed Governance Operating Model Policy Policy Governance Structure Governance Structure Governance Process Governance Process Roles and Responsibilities Roles and Responsibilities Implementation Plan & Communication Strategy Implementation Plan & Communication Strategy • Identify key data

entities for governance scope

• Stakeholder identification and engagement • Develop & ratify

Governance framework • Develop and ratify

Governance process

• Define data controls and data policy requirements • Identify change management considerations • Develop detailed operating model • Ratify NetApp Governance Model • Determine data mgmt

processes and tools • Establish data controls in

data mgmt processes • Identify data stewards and

owners for key data entities • Prepare proposal for data

governance model for key data entities Governance Framework Cutover Cutover Governance Framework Governance Framework Governance Scope Assessment Governance Scope Assessment Governance Best Practices Governance Best Practices Governance Model Governance Model Policy Policy Governance Structure Governance Structure Governance Process Governance Process Roles and Responsibilities Roles and Responsibilities KDE Governance Design KDE Governance Design DG Design Team

DG Design Team KDE Design TeamKDE Design Team

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Oracle MDM Vision

Oracle Fusion Middleware / MDM Foundation

Data Integration Business Process Orchestration Events & Rules Engine Identity Management Data Quality & Enrichment Hierarchy Management Audit & Change Management Profiling & Correction Registry Service Metadata Management Retail & CPG High Tech & Manuf. Telco & Utilities Financial Services Public Sector MDM Implementation Best Practices Data Governance Model Enterprise Business Processes

Master Data Management Solutions

Operational Systems External Applications SAP PeopleSoft Oracle EBS Siebel Legacy Web Apps Customer Supplier

Enterprise Schema & Shared Services

Product _Location

Organization

Financi

a

ls

Master Data Management Applications

Learning from our 500+ customers

Oracle MDM – The key differences

1

Toolkit vs. Application with easy extension abilities

2

Implementation know how

3

Leader in Party, Product and Finacial Hubs

4

Large scale deployments

Contact Reference

•

김대준 – 상무, MDM Solution Architects

•

Email:

[email protected]

•

Phone: 011-295-9046

•

김태호 – Principal Consultant, MDM Solution

•

Email:

[email protected]

•

Phone: 011-752-0719

Oracle MDM Solutions (Data Hubs)

http://www.oracle.com/data_hub/index.html

Customer Data Management