mdm strategies for the global 10,000 atul patel director mdm sap asia pacific & japan...

MDM Strategies for the Global 10,000

Atul PatelDirector MDM

SAP Asia Pacific & Japan

[email protected]

SAP AG 2007, Data Unification / 2


Setting the Stage: The Costs of Dirty Data have never been higher

Procurement Logistics Warehouse Planning Fulfillment Marketing

InvoiceQueries Lacking

Inbound Visibility

Late Delivery

ScanningQueries

IneffectiveStore

Supply

ManualProcesses

Inabilityto respondto market

Out of Stocks

WeakenedLoyalty

Lost Revenue

AnalysisParalysis

Woodfor thetrees

WrongPromotions

Spending too much/Inefficient practices

IneffectiveSupply Chain

$

£

¢

€

¥

$


Master Data Problems Need to be Addressed 93% experienced data management issues during their

most recent projects 51% do not see data as a strategic corporate asset(Source: ASUG-SAP EDM Data Governance Survey, 2006)

While data management has an immense impact, awareness is an issue

50% of enterprises surveyed maintain master data separately in 11 or more source systems (Source: Tower Group)

“Fortune 1000 enterprises will lose more money in operational inefficiency due to data quality issues than they will spend on data warehouse and CRM initiatives.”

(Source: Gartner)

Data Management is often identified as the root cause of problems in process improvement projects

(Source: ASUG-SAP EDM Survey 2006) ‘We found that 40% of the orders were getting stuck at

some point, because of mismatched master data’ - Roderick Hall, Senior project manager, Ericsson

Executives at Swedish telecom equipment maker Ericsson thought its various global subsidiaries were being serviced by nearly 200,000 vendors, but that number was brought down to about 130,000 after eliminating duplicate entries through the use of a master data management (MDM) project

Customers have experienced huge

benefits by solving data issues

Analysts are in agreement


Master data defines both the material, vendor and customer and how they will behave in the system

Conditional data applies only in specific situations (if this cust. and material then this price)

Transactional data depends on conditional data and master data

Enterprise reporting lucidity depends on transactional activity

Defines your system and the limits of all elements

Material, customer, vendor

Pricing, document routing

Purchase orders, sales orders

P&L, Sales reports, inventory

Profit centers, Cost centers, Plant configurations

Examples

Key Reference Data

Master Data

Conditional Master Data

Transactional Data

Reporting

Static

Stable

Master Data is the Strength of the Data Foundation that Runs Your Enterprise


2006 M & A’s equaled ~ $3.9 Trillion .M & A’s happen all the time and significantly worsen the data problem

56 % surveyed said acquisitions were key to guarantee the profitability

IT operations and application delivery are the least successful IT factors

23 % of acquisitions failed to recoup costs

Source: Accenture / Economist Intelligence Unit 2006 Global M&A Survey, Business Week Study

$2.00$2.80

$3.9

2004 2005 2006

M & AValue

MultipleProducts Duplicate

Suppliers

DuplicateFI

Accounts

Lack ofCustomerVisibility

EmployeeAttrition

Overlapin

SalesOrgs

PostM & AImpact


The New Integration ChallengeDisparate technologies do not support process innovation

Inflexible, slows process change “Hardwired” process

IT silos can’t meet LOB needs IT silos prevent delivering composites

Costly to maintain, ties up budget Exponential # of integrations

No cohesive master data

ApplicationServer

PortalBusiness

Intelligence

Messaging

Security

Master Data Mgmt

Enterprise Integration

CRM

SRM

ERP


Bad Master Data hinders process innovationsince every department has a different version of it

Master data is data about your customers, products, suppliers etc.

M & A’s are worsening the problem

Call Center

Jane Smith 4418 N. Str.Chicago, IL

60611Part: 2574

SRM

Part: 8975

VENDOR:ABC123

Logistics

VENDOR:XYZ456

YOUR VALUE CHAIN

ERP

Jane Peters199, 3rd StreetPalo Alto, CA

Part: B7521


Costs and Complexity increase over timeAs business events continue to impact the data

57% of marketing content work was to mitigate errors

40 % orders getting blocked due to master data problems

$6 billion Maytag merger

Data Quality

Time

Without Master Data ManagementDoing business is expensive

Data Warehousing One-off

cleansing

M & A

Outsourcing

New product launch


ConsolidationEnsure consistent master data across systems

Managing Master Data ActivelyIs Imperative to ensuring optimal process innovation

HarmonizationCleanse and distribute across entire landscape

Central ManagementCreate consistent master data from the start, centrally

Data Quality

Time

New Product Launch

Master Data ManagementImprove data quality in steps

M&A

Outsourcing

Consolidation

Harmonization

Central MDMData

Quality

Time

Without ConsolidationDoing business is expensive


SAP NetWeaver – A Strategic Platform for Enterprise SOAMaster Data is an integrated capability of the Platform

SOA Provisioning Stable, scalable core Open, standards-based Service-enabling

processes, information, events

Composition Environment Fast paced “edge” of the business Don’t just code – compose! Lean consumption


Master Data Managementwith SAP NetWeaver

Compose cross application processes in SOA with consistent master data

Infinitely configurable schema options

Support consolidation, harmonization, central mgmt

Pre-packaged IT and business scenarios

500+ customers

Manage Any Master Data


Jane Smith

4418 N. Str.Chicago, IL 60611

Extensive matching framework

Provides web services to customer data access

SAP & Non-SAP integration

Customer Data IntegrationOne view of customer information anytime anywhere

Analysis

Jane Peters

199, 3rd StreetPalo Alto, CA 94304

Jane Peters Smith

4418 North St.Chicago, IL 60610


SAP NetWeaver MDM – CDI Summary

Share a single view of customers across various business applications

Capabilities such as matching, standardization, and survivorship

Business Partner data model supporting B2B & B2C interactions

Pre-integrated with SAP NetWeaver, including CDI-specific Web services

Interfaces to third-party data quality tools and content providers

High performance scalability and performance

Data UnificationBusiness Scenarios

Rich Product Content Management

Global Data Synchronization

Customer Data Integration

Example: Customer Record create once in MDM

Distributed everywhere where required

Oracle SAP Legacy

John Doe12A34 213-12-1234

Customer # SSN #Name


Understand your most profitable products, best customers and cheapest/reliable vendors

Gain insights by integrating transactional data from heterogeneous systems with master data for analysis

Improved Business IntelligenceDeliver unique insights with an integrated platform

+

TRANSACTIONAL DATA =MASTER

DATA

BUSINESS INSIGHT


CONSOLIDATING HAS NEVER BEEN EASIERConsolidate, harmonize and centrally manage master data

Instance Consolidation from R/3 and other sources

Direct ODBC System Access, extract flat files, 3rd party application data, XML sources, many more..

Single pass data transformation, Auto-mapping, Validation Rules, Exception handling

Business Users can define matching rules, complex matching strategies, conduct data profiling, enrich data

Data Enrichment Controller to use 3rd party sources like Trillium, D & B and other partners for address completion, company validation and enriching data

Search and compare records, identify sub-attributes for consolidation in sub-second response times

Merge Records seamlessly, tracking source systems with built in key mappings

Leverage out of box data models for consolidated data


CONSOLIDATING HAS NEVER BEEN EASIERConsolidate, harmonize and centrally manage master data

Leverage built in workflow to manage compliance process, ensure administrators can validate imported records

Enforce data governance through user roles, security, workflow, audits to prevent future data problem

Syndicate master data in XML or to any SAP or non-SAP applications

Works with SAP and non-SAP distribution technologies for easy fit in heterogeneous environments

Centrally manage master data

Leverage validation rules to enforce data integrity

Manage rich content set and relationships associated with master data record


IDENTIFY SUPPLIERTAKE ORDER MANAGE CUSTOMERVERIFY AVAILABILITY

Why Customers are choosing SAP ?One solution for ALL master data in your industry specific process

SAP NetWeaver One master data solution for all business processes

Who is my customer?

Do I have the right product?

Who is my best vendor?

Which employee should we assign to?


First step to enterprise SOAAccelerate new business processes with accurate master data

Unify any data

Unify customer, product, employee, supplier and user defined data with one solution to build robust business processes

Industry insights

Supports 1Sync (UCCnet, Transora), configurable for other industries

Easy deployment

Pre-built data models, mappings and iViews


First step to enterprise SOAAccelerate new business processes with accurate master data

WEB SERVICES - SOA

Chaos

Manually built Not guaranteed to work No governance

Delete fromdatabase

Rollbackinventory

CancelShipment

CancelInvoicing

SendNotification

AdjustPlanning

NotifySuppliers

ENTERPRISE SOA

Integrity

Business semantics Productized Unified repository

CancelOrder

CancelOrder


Master Data Management is much more than Software

Internal process, controls and politics are the hardest part

Reduces organizational risk and critical to CFOs for the snapshot of all related information!

Governance

Internal Standards

Change Management

Data Stewardship

Business Processes

Privacy and Compliance

Local vs. Global Issues

Methodologies

Governance was identified as the top data issue

87

67

61

39

11Deployment

Standards

Quality

Architecture

Governance

% of overall responses, n-94

67

54

43

Unclear data roles and responsibilities

Lack of or conflicting data processes

Data processes not capable or fully developed

Unclear data roles and responsibilities is the key governance issue% of overall responses, n=94

5 Steps to Operationalize Governance

Assess1. Define Value Proposition

2. Engage Stakeholders

3. Integrate Best Practices

5. Manage Transition

4. Execute Best Practices

1. Define Value Proposition• Data required for project scope

• Value requirements and relevant data quality Data Governance Scope Template

2. Engage Stakeholders

• Executive Sponsors

• Enterprise Data Stakeholders

• Business Data Stakeholders

• IT Data Stakeholders

• Data Process Owners Data Governance Policy, Position Descriptions

3. Integrate Governance Best Practices into Project Methodology• Standardized data sections of project deliverables

• Data roles and responsibilities in project organization

• Establish data architecture & standards

• Project data quality KPIs established

• Project data quality techniques established Data Governance Policy, Data Governance Scope

Template, Work Plan to Operationalize Data Governance

4. Prepare to use DataGovernance Best Practices• Schedule participation by IT and Business Stakeholders

and Subject Matter Experts

• Development sequenceI. Process

II. Domain

III. Design

IV. Prototype

• Build and test EDM infrastructure and automation

• Qualify data process capability Business Data Governance Processes, Enterprise Data

Governance Processes, Recommended Operational Data Governance Metrics, Work Plan to Operationalize Data Governance

5. Transition to Operational Data Governance

DATA ORGANIZATION Project Sustaining

PROCESS Qualification Continuous Improvement

QUALITY METRICS Transformation Production

EDM MODEL Design Drive out variability

Core Data Governance Team

Operational Data Governance Team

Additional Project Data ResourcesFocus on OperationalData Governance from the start

Data management projects are strategic but complex

Business need

Commitment to change

StrategicOperational

Requires commitmentEssential but hard

ImpactingLow-hanging fruit

• Regulatorycompliance

• Single sign-on (SSO)

• Internalself-service

• Shared services

• Master datamanagement

• Businessinsight• Operational

dashboards

• Knowledge management

• Businessprocess improvement

• Externalself-service

• Forecastingand planning

High

Low

Source: Governing Enterprise SOA on SAP NetWeaver, © 2005 Forrester Research, Inc. All rights reserved.

Standard Master Data Maintenance (Change/delete/archive) Process

Ent

erpr

ise

Dat

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ard

Bus

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s D

ata

Ste

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dB

usin

ess

Dat

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usto

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Oth

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ompa

ny

Per

sonn

elD

ata

Tru

stee

Request master data maintenance with appropriate documentation about business

need

Metrics indicate need for master

data maintenance e.g. inconsistent payment terms in

the system of reference

Business trigger e.g. Merger of existing

customers

Last Revised:

Author: SAP EDM Date Created: 4/20/2007

Filename:Standard Data Maintenance Process

4/20/2007 10:08:16 AM

Project: Enterprise Data Management

Master data analysis in the system of record and system of reference e.g. run quarterly recon/analysis report in MDM

and ECC

Perform impact analysis on transactional data e.g. how many

open Purchasing documents need to be retrofit/deleted as per the change? How many

finance reports are impacted?

Approve change based on the information

provided?

Request more

analysisNo

Perform impact analysis on

business partners e.g. corrected

payment terms?

Is this a change

request?

Yes

NoA

BYes

Data Quality Results From Capable Data Processes

Operational processes• Same for all master data• Minor variations in routing and approvals by data type and domain• Qualified and continuously improved

Organization• Clear roles and responsibilities• Compliance with standards a “condition of employment”.• Data and process metrics impact personnel performance grade

Technology• Web Enabled User Interface• Automated enforcement of stds• Automated workflow• Common platform for all domains• SAP ECC or MDM as System of Record

Effective Data Governance includes:• People (IT and Business Stakeholders)

• Processes (Enterprise and Operational)

• Framework for engaging business and IT data quality stakeholders over the long run

• Implementation deployment based•SAP Roadmap •Business Value•Business Risk

Data Governance is aKey to Data Quality

Process Essentials

Operational Focus

Enterprise Focus

CREATE new record

MAINTAIN current record

SEARCH existing records

ARCHIVE obsolete record

GOVERN Architecture

AUTHORIZE Standards

ASSIGN Accountability

MONITOR Quality

• Consistent processes across domains• Steward and Custodian assignments

by domain• Standard processes a key component

for service oriented architecture

• Data quality is the goal• Business data processes

are the key – invest resources to get these right

• Data governance processes are a tool - functional, not elaborate

Where, When & How the data is Entered Stored Transmitted Reported

ENTERPRISEDATA

MANAGEMENT

ARCHITECTURE Where, When, How & Why

the business uses the data

Data Standards• SAP• Legacy• eCommerce

Core Business Processes• Procure to pay• Plan to fulfill• Order to cash


ManagementReporting

ManagementReporting

Operations

STANDARDS Common data definitions Schemas (hierarchies and groupings)

to support business functional needs Data Standards for

Content Accuracy Format Timeliness

QUALITY Data metrics based on

standards 6 Sigma methodologies for

data process design Data process performance

metrics Metric alignment with data

Roles & Responsibilities

THD Standard Data Deletion Process

Subj

ect M

atte

r Ex

pert

Busin

ess

Data

O

wner

Busin

ess

Data

Ch

ampio

nO

ther

Hom

e De

pot

Data

Cha

mpio

n

Yes

No

Start

Initiate SES for data deletion

Delete data & notify company data steward

Verify correct data deleted

Deletion request

approved?

Consult with business data owners and requestor to

determine “special” requirements

Last Revised:

Author: Lyndsi Caracciolo Date Created: 11/9/2005

Filename:THD Standard Data Deletion Process v2.vsd

11/11/2005 4:25:23 PM

Project: Enterprise Data Management Strategy

Metrics indicate need to delete

data

Determine exact data to be deleted

A

Create and verify recovery media

copies

Create Special Processing Instructions

Request data to be deleted

Notify Requestor End A

End

GOVERNANCE Strategic Data Policy Data management segmentation Best Practice data processes Data Ownership Roles & Responsibilities Change Management

Consensus Stds

Metrics

Data Trustee

Material –Customer –Vendor –

Data Stewards• Material -• Customer –• Vendor –

BU 1Data Owner

BU 1Data Custodians

BU 2Data Owner

BU 2Data Custodians

BU 3Data Owner

BU 3Data Custodians

AuthorityBusiness Goals

Business Unit 1Leadership



Support ServicesLeadership

Major IssuesSummary Metrics


Staffing notes:1. Redeployment of existing resources2. Official recognition of existing “de facto” assignments3. Business determines number of Owners and Custodians based on Data volume and value

DEPLOYMENT Business Intelligence Integration Data embedded in IT methodology Data metrics Change Management Data process improvement Standard data services

MDMMDM

SAP UIOPTIONS

SAP UIOPTIONS

SAP IndustrySolution


Web Site

Specialty

Distribution

Services

Partners

Regulatory

Legacy 1

Legacy 2

Legacy 3

SAP B/WSAP B/W

ExternalSourcesExternalSources

SelfService

SelfService

Where, When & How the data is Entered Stored Transmitted Reported

ENTERPRISEDATA

MANAGEMENT

ARCHITECTURE Where, When, How & Why

the business uses the data

Data Standards• SAP• Legacy• eCommerce



ManagementReporting

ManagementReporting

Operations

STANDARDS Common data definitions Schemas (hierarchies and groupings)

to support business functional needs Data Standards for

Content Accuracy Format Timeliness

QUALITY Data metrics based on

standards 6 Sigma methodologies for

data process design Data process performance

metrics Metric alignment with data

Roles & Responsibilities

THD Standard Data Deletion Process

Subj

ect M

atte

r Ex

pert

Busin

ess

Data

O

wner

Busin

ess

Data

Ch

ampio

nO

ther

Hom

e De

pot

Data

Cha

mpio

n

Yes

No

Start

Initiate SES for data deletion

Delete data & notify company data steward

Verify correct data deleted

Deletion request

approved?

Consult with business data owners and requestor to

determine “special” requirements

Last Revised:

Author: Lyndsi Caracciolo Date Created: 11/9/2005

Filename:THD Standard Data Deletion Process v2.vsd

11/11/2005 4:25:23 PM

Project: Enterprise Data Management Strategy

Metrics indicate need to delete

data

Determine exact data to be deleted

A

Create and verify recovery media

copies

Create Special Processing Instructions

Request data to be deleted

Notify Requestor End A

End

GOVERNANCE Strategic Data Policy Data management segmentation Best Practice data processes Data Ownership Roles & Responsibilities Change Management

Consensus Stds

Metrics

Data Trustee

Material –Customer –Vendor –

Data Stewards• Material -• Customer –• Vendor –

BU 1Data Owner

BU 1Data Custodians

BU 2Data Owner

BU 2Data Custodians

BU 3Data Owner

BU 3Data Custodians






Major IssuesSummary Metrics



DEPLOYMENT Business Intelligence Integration Data embedded in IT methodology Data metrics Change Management Data process improvement Standard data services

MDMMDM

SAP UIOPTIONS

SAP UIOPTIONS



Web Site

Specialty

Distribution

Services

Partners

Regulatory

Legacy 1

Legacy 2

Legacy 3

SAP B/WSAP B/W

ExternalSourcesExternalSources

SelfService

SelfService

CAPABLE PROCESSES

COMPLETE SOLUTION

Lean Governance Model

Data TrusteesMaterial, CustomerVendor, Plant, etc.

Authority, Goals, Funding, Accountability

Data Custodians(Finance, Mfg, Marketing,

HR, Engineering, etc.)

Business Data Stewards

(Finance, Mfg, Marketing, HR, Engineering, etc.)

Shared Service

Enterprise Data Management

Support Team

Operational Focus

Enterprise Focus Leadership Team

(Finance, Mfg, Marketing, HR, Engineering, etc.)

Coordination

EnterpriseStandards

Direction, Accountability

DomainStandards

Processes

CREATE new record

MAINTAIN current record

SEARCH existing records

ARCHIVE obsolete record

Processes

GOVERN Architecture

AUTHORIZE Standards

ASSIGN Accountability

MONITOR Quality

Spectrum of Governance Options

“Federated”“Totally Centralized”

Architecture

Organization

Processes

Maintenance & Quality

Characteristics

• Deep skills for advanced needs• Rapid problem resolution• Larger prioritization queue• Local dependencies on central

group (timezones, legal)• High resource efficiency • “Guarantees” global visibility

• Accommodates local needs in timely response

• Tighter alignment with business governance

• Weakens standards enforcement• Slower to respond to enterprise

needs• Risk of creating duplicate data• Risk of losing global visibility

• Rapid response to local needs

• Ownership aligned with individual business organizations

• Starting point for newly acquired companies

• Reporting and terminology in specific business vernacular

Standards

“Totally Decentralized”

All successful Data Governance Modelsare federated

Data Position Scope Roles & Responsibilities

Data Trustee Data type across all businesses - Executive responsible for ensuring consensus data standards that are best for the company are set and enforced

- Provides authority to Data Stewards and Data Leads to enforce standards

- Keeps CIO and senior management informed major data issues or initiatives

Enterprise Data Steward Data type across all businesses - Leads cross-business definition of data standards, rules, hierarchy;

- Data quality leadership- Cross enterprise data domain expertise

Business Data Steward Data within a business unit - Owns local execution of enterprise data processes- Represents Business Unit in cross-business

definition of global data standards, rules, hierarchy, metrics.

- Enforces global data rules and standards within business unit using data metrics

- Accountable to Data Trustee for data quality

Business Data Custodian Data for a specific operational unit or component(Examples: software supplier data, local site contracts data, capital asset data for a site)

- Owns operational data processes- Accountable for data quality of data processes- Initiates and conducts quality improvement efforts

Recommended Data Governance Positions

Consensus Stds

Metrics

Recommended Data Governance Structure

Data Trustee

Material – Customer –

Vendor –

Enterprise Data Stewards• Material - • Customer –• Vendor –

BU 1Data Steward

BU 1Data Custodians

BU 2Data Steward

BU 2Data Custodians

BU 3Data Steward

BU 3Data Custodians






Major IssuesQuality Metrics

Authority & FundingBusiness Goals


Option Description Key Pros & Cons

Before Enterprise Application

Initiative

• Perform an assessment of Enterprise Data Management practices

• Develop a comprehensive Enterprise Data Management strategy that spans across the enterprise and across Data Domains

• Launch an Enterprise Data Management program• Stand up an Enterprise Data Management

Governance Organization

• PROS – Activities and design documents can be reused for the Enterprise Application Initiative, EDM Strategy becomes input and direction to Blueprint phase, EDM Strategy can be comprehensive and enterprise-wide which spans beyond the scope of the Enterprise Application Initiative

• CONS – Approach requires resources before the Enterprise Application initiative, Initiative may repeat work already done by EDM Strategy team if strategy deliverables are not specifically carried through into project planning and execution

During Enterprise Application

Initiative

• Develop the Enterprise Data Management Strategy during early Blueprint

• Launch Enterprise Data Management program as part of the Enterprise Application rollout

• Stand up a Data Management Board for the Initiative that will evolve into a Data Governance Organization

• Build Enterprise Data Management into the Enterprise Application Deliverables

• PROS – Can use resources and momentum of large project to affect change in data management at the same time, can validate EDM strategy during the project

• CONS – EDM Initiative resources can be diverted to Data Conversion and Interfaces deliverable production as functional resources get diverted to process based deliverables, Project deadlines take precedence over execution of the EDM Strategy objectives.

After Enterprise Application

Initiative Go-Live

• Emphasize importance of Enterprise Data Management during the Enterprise Application project.

• Begin an Enterprise Data Management program by deploying resources that become available when Enterprise Application project is complete.

• PROS – Much of what is needed for Enterprise Data Management may have been created in the Enterprise Application Initiative already, resources will now be open for an EDM Project

• CONS – May miss window for change as Data Standards, System Design and processes are frozen, ability to affect change and design for an EDM Program are constrained, Organizations are reluctant to go back and change processes right after an Enterprise Application rollout

When to start an EDM Program

Best Practice is to implement the Enterprise Data Management Strategy as part of the Enterprise Application Initiative, with key EDM activities staged in slightly in advance of the ERP project implementation activities.

Master Data Management at Intel

Jolene Jonas

SAP MDM Product Manager

SAP Data Architect

Company Background

Formalizing Data Quality

What is Master Data?

Data Modeling Approach – Tops Down

Physical ImplementationSummary/Q&A


Intel is the world's largest chip maker, and a leading manufacturer of computer, networking and communications products. Founded in 1968, first microprocessor shipped 1971

Worldwide Presence 124 Offices in 57 countries 97,000 employees + 39,000 Contingent workers Over 450 products & services 2005 revenues $39 billion Information Technology Group

– 6,469 Employees + 2,254 Contingent workers– 79 IT Sites in 27 countries– 26 data centers all running Intel® architecture-based servers

SAP* since 1996, key of our ERP implementation– Centrally-located infrastructure – Distributed implementation by business functions– Future: Replatforming SAP and moving to SOA*

* SOA – Service Oriented Architecture

Company Background

Company Background







Effort began in 2001

Elevated awareness corporate wide Data is an asset

– Systems are temporary but Data lasts forever

Quantified impact of poor data, the pain of poor Master Data– Per Data Quality Experts - assume 10% error rate due to poor quality

– High TCO*- 25+ Customer Apps all doing same work

- No single place where Customer is created- Lack of an integrated view

Formed an Information Quality Organization Message given tops down

Targeted training classes– Management and detail level

TCO – Total Cost of Ownership



Defined data quality goals: Single terms/definitions - One language Single Record of Origin for Configuration and Master Data Increase reuse Monitors & audits to track improvement Streamline business processes

Standards & Governance: Data Architects

– Lead Data Architect per subject area- Finance, Location, HR, Customer, Supplier, Item

– Owns standards, governance, project deliverables– Defined a Data Model driven approach for development

Business gatekeepers– Focused Change Control Boards

Company Background






First - What is Master Data?

Includes Master Data & Config

Persistent (lifecycle outside a single business process) Has a CRUD* process outside of the business processes where consumed

Definition independent of other data– i.e. Item is Master Data, BOM is not as it is dependent on Item

Highly reused – (Used in more than one business process)

Primarily created for use in other processes

* Create, Read, Update, Delete

Company Background






Tops Down Approach to Data

First - Define the conceptual layer Sets the foundation, the business framework Brings Intel to one data dictionary

– Single terms and definitions

Second – Seed the logical layer from the conceptual Reuses approved conceptual entities Adds all the facts/attributes, business data rule Grows as new needs are identified Acts as blueprint for physical design Services being designed based on the model

Third - Use logical model to “seed” the physical models Ensures reuse of approved entities and attributes Physical representation of the applications Why?

– Links application speak to Intel speak– Roadmap for enhancements/integration/reuse– Impact analysis


Pre- Enterprise Commodity Data Model

Reporting

SAP CRSMaterial Master (CIM)

Material Group = Commodity

Tax Man

Spends Analyst

Spends Manager

MaterialPlanner

Summarize by taxable area

Planning Categories

Summarized Grouping

Lowest Detail

One Term, Many Definitions


Enterprise Driven Commodity Data Model

Reporting

SAP CRSMaterial Master (CIM)

Material Group = Commodity DetailNew Commodity Hierarchy

Tax Man

Spends Analyst

Spends Manager

MaterialPlanner

Summarize by taxable area

Summarized Grouping

Commodity plusHierarchy

Detail CommodityReport

Commodity Gatekeeper

Controlled Entry

Single Definition per Term

Company Background




Physical Implementation

Summary/Q&A


Intel Master Data Direction

Finance data

Currently using SAP R/3 as single Record of Origin– Minimal gaps

– Meets business need

Therefore – move to SAP ECC^

Location data SAP R/3 works well

– But has data gaps- Effective dating, status codes, type codes

Therefore – move to SAP ECC

Build out SAP NetWeaver MDM to close data gaps– Utilize SOA to glue them together

ECC – Enterprise Central ComponentMDM – Master Data Management

Determining Best Fit for Record of Origin


Intel Master Data Direction

Item (Material Master) & Commodity Currently use R/3 as authorized Record of Origin

Large gaps in data & business rules

Therefore, targeting Record of Origin as SAP NetWeaver MDM

Customer/ Supplier Currently use R/3 for Direct Customer and Supplier

– Indirect Customers in many other apps

Building out mySAP CRM and SRM in 2007

Long term goal is SAP NetWeaver MDM as Record of Origin

Integrated SAP Netweaver BI Distribution from authorized Record of Origin only

– Requires controlled distribution attribute by attribute

Requires strict control of Master Data number ranges

Determining Best Fit for Record of Origin

*ROO – Record of Origin – Single point of create for unique identifier


SAP NetWeaver MDM will run on Intel® Architecture

Certified on 64-bit Intel® Xeon® processor

Benefits Premier performance, scalability, and the highest reliability at a fraction of the

cost of proprietary systems

Integrated, advanced RAS features for highest standards of system availability and uptime

Greater range of optimized solutions than proprietary platforms support, at a lower cost

Optimized SAP solutions to run best Intel architecture via massive Intel and SAP engineering investment


SAP NW MDM Live at Intel since Nov 2006

Started with our logical data models

Built our own physical data model due to Intel specific needs MDM plugged into existing infrastructure

– Redundant applications will be phased out over time as in-house expertise is gained with new application

– Allows us to identify gaps and work with SAP for closure

1.8m Materials, 180K Suppliers = ~$10-15Bn spend, 6m Customers

2007/2008 will see further rollout of MDM to business applications

Collaborating with SAP on a Master Data Service/xApp Get Supplier, Search Supplier

Leverages MDM Web Services delivered in latest release– 6 week effort

OOB – Out Of Box


Lessons Learned

Being an early adopter has benefits Strong influence on SAP strategy for central maintenance

– Customer champion on the Influence Council

Many product enhancements at Intel request

Alignment with SAP SOA team on a Master Data Service

Very strong support from SAP enabling our success

Go with SAP data model More complete integration back to core SAP

Extend what is delivered


Summary: ROI savings estimated at $10-18m

Benefits of a Data Model Driven Approach Grounds Intel on common language

Ensures fully integrated, reusable design

Provides consistent blueprint to development community

Reduces Total Cost of Ownership (TCO) through Record of Origin– Cost Avoidance - reduction in applications (infrastructure and headcount)

Delivers better data quality

Must have management buy-in to succeed

SAP NetWeaver MDM has a key role in Master Data Management Both as an Record of Origin and Record of Reference


Why SAP MDM ? - Proven Solution

Over 500 active installations


Why SAP MDM ? - Proven Solution

Thank You.

Atul PatelDirector MDM

SAP Asia Pacific & Japan

[email protected]

mdm strategies for the global 10,000 atul patel director mdm sap asia pacific & japan...

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