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MDM Strategies for the Global 10,000
Atul PatelDirector MDM
SAP Asia Pacific & Japan
SAP AG 2007, Data Unification / 2
SAP AG 2007, Data Unification / 3
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
$
£
¢
€
¥
$
SAP AG 2007, Data Unification / 4
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
SAP AG 2007, Data Unification / 5
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
SAP AG 2007, Data Unification / 6
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
SAP AG 2007, Data Unification / 7
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
SAP AG 2007, Data Unification / 8
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
SAP AG 2007, Data Unification / 9
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
SAP AG 2007, Data Unification / 10
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 AG 2007, Data Unification / 11
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
SAP AG 2007, Data Unification / 12
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
SAP AG 2007, Data Unification / 13
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 AG 2007, Data Unification / 14
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
SAP AG 2007, Data Unification / 15
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
SAP AG 2007, Data Unification / 16
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
SAP AG 2007, Data Unification / 17
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
SAP AG 2007, Data Unification / 18
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?
SAP AG 2007, Data Unification / 19
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
SAP AG 2007, Data Unification / 20
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
SAP AG 2007, Data Unification / 21
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
a S
tew
ard
Bus
ines
s D
ata
Ste
war
dB
usin
ess
Dat
a C
usto
dian
Oth
er C
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
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
Business Unit 2Leadership
Business Unit 3Leadership
Support ServicesLeadership
Major IssuesSummary Metrics
AuthorityBusiness Goals
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
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
Core Business Processes• Procure to pay• Plan to fulfill• Order to cash
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
Business Unit 2Leadership
Business Unit 3Leadership
Support ServicesLeadership
Major IssuesSummary Metrics
AuthorityBusiness Goals
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
SAP IndustrySolution
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
AuthorityBusiness Goals
Business Unit 1Leadership
Business Unit 2Leadership
Business Unit 3Leadership
Support ServicesLeadership
Major IssuesQuality Metrics
Authority & FundingBusiness Goals
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
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
Company Background
Formalizing Data Quality
What is Master Data?
Data Modeling Approach – Tops Down
Physical ImplementationSummary/Q&A
SAP AG 2007, Data Unification / 40
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
Formalizing Data Quality
What is Master Data?
Data Modeling Approach – Tops Down
Physical ImplementationSummary/Q&A
SAP AG 2007, Data Unification / 42
Formalizing Data Quality
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
SAP AG 2007, Data Unification / 43
Formalizing Data Quality
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
Formalizing Data Quality
What is Master Data?
Data Modeling Approach – Tops Down
Physical ImplementationSummary/Q&A
SAP AG 2007, Data Unification / 45
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
Formalizing Data Quality
What is Master Data?
Data Modeling Approach – Tops Down
Physical ImplementationSummary/Q&A
SAP AG 2007, Data Unification / 47
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
SAP AG 2007, Data Unification / 48
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
SAP AG 2007, Data Unification / 49
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
Formalizing Data Quality
What is Master Data?
Data Modeling Approach – Tops Down
Physical Implementation
Summary/Q&A
SAP AG 2007, Data Unification / 51
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
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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
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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
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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
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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
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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
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Why SAP MDM ? - Proven Solution
Over 500 active installations
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Why SAP MDM ? - Proven Solution