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MDM Strategies for the Global 10,000 Atul Patel Director MDM SAP Asia Pacific & Japan

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Presentation on theme: "MDM Strategies for the Global 10,000 Atul Patel Director MDM SAP Asia Pacific & Japan"— Presentation transcript:

1 MDM Strategies for the Global 10,000 Atul Patel Director MDM SAP Asia Pacific & Japan

2  SAP AG 2007, Data Unification / 2

3  SAP AG 2007, Data Unification / 3 Setting the Stage: The Costs of Dirty Data have never been higher ProcurementLogisticsWarehousePlanningFulfillmentMarketing Invoice Queries Lacking Inbound Visibility Late Delivery Scanning Queries Ineffective Store Supply Manual Processes Inability to respond to market Out of Stocks Weakened Loyalty Lost Revenue Analysis Paralysis Wood for the trees Wrong Promotions Spending too much/ Inefficient practices Ineffective Supply Chain $ £ ¢ € ¥ $

4  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

5  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 configuration s 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

6  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 Multiple Products Duplicate Suppliers Duplicate FI Accounts Lack of Customer Visibility Employee Attrition Overlap in Sales Orgs Post M & A Impact

7  SAP AG 2007, Data Unification / 7 The New Integration Challenge Disparate 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 Application Server Portal Business Intelligence Messaging Security Master Data Mgmt Enterprise Integration CRM SRM ERP

8  SAP AG 2007, Data Unification / 8 Bad Master Data hinders process innovation since 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 60611 Part: 2574 SRM Part: 8975 VENDOR: ABC123 Logistics VENDOR: XYZ456 YOUR VALUE CHAIN ERP Jane Peters 199, 3 rd Street Palo Alto, CA Part: B7521

9  SAP AG 2007, Data Unification / 9 Costs and Complexity increase over time As 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 Management Doing business is expensive Data Warehousing One-off cleansing M & A Outsourcing New product launch

10  SAP AG 2007, Data Unification / 10 Consolidation Ensure consistent master data across systems Managing Master Data Actively Is Imperative to ensuring optimal process innovation Harmonization Cleanse and distribute across entire landscape Central Management Create consistent master data from the start, centrally Data Quality Time New Product Launch Master Data Management Improve data quality in steps M&A Outsourcing Consolidation Harmonization Central MDM Data Quality Time Without Consolidation Doing business is expensive

11  SAP AG 2007, Data Unification / 11 SAP NetWeaver – A Strategic Platform for Enterprise SOA Master 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

12  SAP AG 2007, Data Unification / 12 Master Data Management with 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

13  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 Integration One view of customer information anytime anywhere Analysis Jane Peters 199, 3 rd Street Palo Alto, CA 94304 Jane Peters Smith 4418 North St. Chicago, IL 60610

14  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 Unification Business 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 Doe 12A34213-12-1234 Customer #SSN #Name

15  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 Intelligence Deliver unique insights with an integrated platform + TRANSACTIONAL DATA = MASTER DATA BUSINESS INSIGHT

16  SAP AG 2007, Data Unification / 16 CONSOLIDATING HAS NEVER BEEN EASIER Consolidate, harmonize and centrally manage master data Instance Consolidation from R/3 and other sources Direct ODBC System Access, extract flat files, 3 rd 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 3 rd 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

17  SAP AG 2007, Data Unification / 17 CONSOLIDATING HAS NEVER BEEN EASIER Consolidate, 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

18  SAP AG 2007, Data Unification / 18 IDENTIFY SUPPLIER TAKE ORDER MANAGE CUSTOMER VERIFY 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?

19  SAP AG 2007, Data Unification / 19 First step to enterprise SOA Accelerate 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

20  SAP AG 2007, Data Unification / 20 First step to enterprise SOA Accelerate new business processes with accurate master data WEB SERVICES - SOA Chaos  Manually built  Not guaranteed to work  No governance Delete from database Rollback inventory Cancel Shipment Cancel Invoicing Send Notification Adjust Planning Notify Suppliers ENTERPRISE SOA Integrity  Business semantics  Productized  Unified repository Cancel Order

21  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

22 Governance was identified as the top data issue Deployment Standards Quality Architecture Governance % of overall responses, n-94 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

23 5 Steps to Operationalize Governance Assess 1. Define Value Proposition 2. Engage Stakeholders 3. Integrate Best Practices 5. Manage Transition 4. Execute Best Practices

24 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

25 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

26 4. Prepare to use Data Governance Best Practices Schedule participation by IT and Business Stakeholders and Subject Matter Experts Development sequence I.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

27 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 Resources Focus on Operational Data Governance from the start

28 Data management projects are strategic but complex Business need Commitment to change Strategic Operational Requires commitmentEssential but hard ImpactingLow-hanging fruit Regulatory compliance Single sign-on (SSO) Internal self-service Shared services Master data management Business insight Operational dashboards Knowledge management Business process improvement External self-service Forecasting and planning High Low Source: Governing Enterprise SOA on SAP NetWeaver, © 2005 Forrester Research, Inc. All rights reserved.

29 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

30 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 a Key to Data Quality

31 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 CAPABLE PROCESSES COMPLETE SOLUTION

32 Lean Governance Model Data Trustees Material, Customer Vendor, 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 Enterprise Standards Direction, Accountability Domain Standards Processes CREATE new record MAINTAIN current record SEARCH existing records ARCHIVE obsolete record Processes GOVERN Architecture AUTHORIZE Standards ASSIGN Accountability MONITOR Quality

33 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 Models are federated

34 Data PositionScopeRoles & Responsibilities Data TrusteeData 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 StewardData type across all businesses-Leads cross-business definition of data standards, rules, hierarchy; -Data quality leadership -Cross enterprise data domain expertise Business Data StewardData 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 CustodianData 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

35 Consensus Stds Metrics Recommended Data Governance Structure Data Trustee Material – Customer – Vendor – Enterprise Data Stewards Material - Customer – Vendor – BU 1 Data Steward BU 1 Data Custodians BU 2 Data Steward BU 2 Data Custodians BU 3 Data Steward BU 3 Data Custodians Authority Business Goals Business Unit 1 Leadership Business Unit 2 Leadership Business Unit 3 Leadership Support Services Leadership Major Issues Quality Metrics Authority & Funding Business Goals Staffing notes: 1.Redeployment of existing resources 2.Official recognition of existing “de facto” assignments 3.Business determines number of Owners and Custodians based on Data volume and value

36 OptionDescriptionKey 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.

37 Master Data Management at Intel Jolene Jonas SAP MDM Product Manager SAP Data Architect

38 Company Background Formalizing Data Quality What is Master Data? Data Modeling Approach – Tops Down Physical Implementation Summary/Q&A

39 Company Background Formalizing Data Quality What is Master Data? Data Modeling Approach – Tops Down Physical Implementation Summary/Q&A

40  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

41 Formalizing Data Quality What is Master Data? Data Modeling Approach – Tops Down Physical Implementation Summary/Q&A

42  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

43  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

44 Company Background Formalizing Data Quality What is Master Data? Data Modeling Approach – Tops Down Physical Implementation Summary/Q&A

45  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

46 Company Background Formalizing Data Quality What is Master Data? Data Modeling Approach – Tops Down Physical Implementation Summary/Q&A

47  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

48  SAP AG 2007, Data Unification / 48 Pre- Enterprise Commodity Data Model Reporting SAP CRS Material Master(CIM) Material Group=Commodity Tax Man Spends Analyst Spends Manager Material Planner Summarize by taxable area Planning Categories Summarized Grouping Lowest Detail One Term, Many Definitions

49  SAP AG 2007, Data Unification / 49 Enterprise Driven Commodity Data Model Reporting SAP CRS Material Master(CIM) Material Group=Commodity Detail New Commodity Hierarchy Tax Man Spends Analyst Spends Manager Material Planner Summarize by taxable area Summarized Grouping Commodity plus Hierarchy Detail Commodity Report Commodity Gatekeeper Controlled Entry Single Definition per Term

50 Company Background Formalizing Data Quality What is Master Data? Data Modeling Approach – Tops Down Physical Implementation Summary/Q&A

51  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 Component MDM – Master Data Management Determining Best Fit for Record of Origin

52  SAP AG 2007, Data Unification / 52 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

53  SAP AG 2007, Data Unification / 53 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

54  SAP AG 2007, Data Unification / 54 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

55  SAP AG 2007, Data Unification / 55 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

56  SAP AG 2007, Data Unification / 56 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

57  SAP AG 2007, Data Unification / 57 Why SAP MDM ? - Proven Solution Over 500 active installations

58  SAP AG 2007, Data Unification / 58 Why SAP MDM ? - Proven Solution

59 Thank You. Atul Patel Director MDM SAP Asia Pacific & Japan

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