MDM Strategies for the Global 10,000

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

Setting the Stage: The Costs of Dirty Data have never been higher
Procurement Logistics Warehouse Planning Fulfillment Marketing $ Invoice Queries Manual Processes Analysis Paralysis Lacking Inbound Visibility Out of Stocks Scanning Queries Spending too much/ Inefficient practices Wood for the trees Inability to respond to market Weakened Loyalty Late Delivery Ineffective Store Supply Wrong Promotions Global Enterprises looking to enterprise service-oriented architecture (SOA) to support rapidly changing business needs for innovation and growth. They are looking at SOA Framework: • Providing a foundational capability to manage any master data type within the context of an industry process • Supporting business strategy to enable a flexible and powerful IT architecture, • Accelerating and simplifying the deployment of customer, product, supplier and others kinds of master data Ineffective Supply Chain Lost Revenue $ €

Master Data Problems Need to be Addressed
While data management has an immense impact, awareness is an issue 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) 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) Analysts are in agreement 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

Conditional Master Data
Master Data is the Strength of the Data Foundation that Runs Your Enterprise Examples P&L, Sales reports, inventory Enterprise reporting lucidity depends on transactional activity Reporting Purchase orders, sales orders Transactional data depends on conditional data and master data Transactional Data Pricing, document routing Conditional data applies only in specific situations (if this cust. and material then this price) Conditional Master Data Material, customer, vendor Master data defines both the material, vendor and customer and how they will behave in the system Master Data Stable Profit centers, Cost centers, Plant configurations Defines your system and the limits of all elements Key Reference Data Static

2006 M & A’s equaled ~ $3. 9 Trillion
2006 M & A’s equaled ~ $3.9 Trillion . M & A’s happen all the time and significantly worsen the data problem Lack of Customer Visibility 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 Multiple Products Duplicate Suppliers Post M & A Impact Duplicate FI Accounts Overlap in Sales Orgs Employee Attrition Source: Accenture / Economist Intelligence Unit 2006 Global M&A Survey, Business Week Study

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 CRM Enterprise Integration Portal Business Intelligence Master Data Mgmt Security ERP SRM Messaging Application Server

Bad Master Data hinders process innovation since every department has a different version of it
YOUR VALUE CHAIN SRM Part: 8975 VENDOR: ABC123 Master data is data about your customers, products, suppliers etc. M & A’s are worsening the problem Logistics VENDOR: XYZ456 ERP Jane Peters 199, 3rd Street Palo Alto, CA Part: B7521 Call Center Jane Smith 4418 N. Str. Chicago, IL 60611 Part: 2574 Your Trading Partners: Trading partners perpetuate their own versions of master data. How do you share data?

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 Without Master Data Management Doing business is expensive Data Quality New product launch M & A Outsourcing Data Warehousing One-off cleansing Time

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

SAP NetWeaver – A Strategic Platform for Enterprise SOA Master Data is an integrated capability of the Platform Composition Environment Fast paced “edge” of the business Don’t just code – compose! Lean consumption SOA Provisioning Stable, scalable core Open, standards-based Service-enabling processes, information, events 1st composition platform … for Business Process Experts Evolutionary path … to Enterprise SOA leveraging enterprise services Ecosystem co-innovates … on a single platform

Master Data Management with SAP NetWeaver
Manage Any Master Data 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

Customer Data Integration One view of customer information anytime anywhere
Jane Peters Smith Analysis 4418 North St. Chicago, IL 60610 Jane Peters Jane Smith Extensive matching framework Provides web services to customer data access SAP & Non-SAP integration 4418 N. Str. Chicago, IL 60611 199, 3rd Street Palo Alto, CA 94304

SAP NetWeaver MDM – CDI Summary
Data Unification Business Scenarios Rich Product Content Management Global Data Synchronization Customer Data Integration 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 Example: Customer Record create once in MDM Distributed everywhere where required John Doe 12A34 Customer # SSN # Name Oracle SAP Legacy

Improved Business Intelligence Deliver unique insights with an integrated platform
BUSINESS INSIGHT = MASTER DATA TRANSACTIONAL DATA + 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

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, 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 SAP is the only vendor who offers a single solution for managing all master data object types. By adding business context to MDM, we deliver customer data integration, product information management, global data synchronization, and global spend analysis. 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 EASIER Consolidate, harmonize and centrally manage master data
Leverage validation rules to enforce data integrity Manage rich content set and relationships associated with master data record 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 SAP is the only vendor who offers a single solution for managing all master data object types. By adding business context to MDM, we deliver customer data integration, product information management, global data synchronization, and global spend analysis. 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

Do I have the right product? Which employee should we assign to?
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 TAKE ORDER VERIFY AVAILABILITY IDENTIFY SUPPLIER MANAGE CUSTOMER Who is my customer? Do I have the right product? Who is my best vendor? Which employee should we assign to?

Insight & Productivity Consolidation & Control
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 Flexibility & Speed Insight & Productivity Consolidation & Control

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

Master Data Management is much more than Software
Internal process, controls and politics are the hardest part Governance Internal Standards Change Management Data Stewardship Business Processes Privacy and Compliance Local vs. Global Issues Methodologies Reduces organizational risk and critical to CFOs for the snapshot of all related information!

Governance was identified as the top data issue
Participant Profile By spend Category Pie Chart: Supplier Spend Profile, Q#1 Do 3 summations: one for each of the 3 categories (direct, indirect, services) across participating companies Calculate percentage Direct Materials % = (Sum of Direct Materials Spend across participating companies/sum of direct+indirect+services spend across all participating companies) Indirect Materials %: (Sum of Indirect Materials Spend across participating companies/sum of direct+indirect+services spend across all participating companies) Services %: (Sum of Services Spend across participating companies/sum of direct+indirect+services spend across all participating companies) By spend (Purchased Cost) chart Supplier Spend Profile, Q#1 (first text box) Bucket each participating company’s total spend into 3 buckets > 10,000 million Between 5,000 and 10,000 million Less than 5,000 million By spend focus chart For each company, calculate percentage of spend on direct, indirect and services Direct spend %: Direct spend/(direct+indirect+services spend) Indirect spend %: Indirect spend/(direct+indirect+services spend) Services spend %:Services spend/(direct+indirect+services spend) If Direct spend % is greater than 75%, bucket into “Direct Materials Focus” category If Indirect spend % is greater than 75%, bucket into “Indirect materials Focus” category If Services spend % is greater than 75%, bucket into “Services Focus” category If neither direct spend %, indirect spend % or services spend % is greater than 75%, then put in “Balanced Spend Mix” category Chart reflects number of company in each of the 4 buckets Governance was identified as the top data issue % of overall responses, n-94 Governance Unclear data roles and responsibilities is the key governance issue % of overall responses, n=94 Architecture Unclear data roles and responsibilities Lack of or conflicting data processes Quality Data processes not capable or fully developed Standards Deployment

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

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 Data Governance Best Practices
Schedule participation by IT and Business Stakeholders and Subject Matter Experts Development sequence Process Domain Design 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
Core Data Governance Team Focus on Operational Data Governance from the start Additional Project Data Resources EDM MODEL Design Drive out variability DATA ORGANIZATION Project Sustaining PROCESS Qualification Continuous Improvement QUALITY METRICS Transformation Production Operational Data Governance Team

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

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

Data Governance is a Key to Data Quality
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

Process Essentials Enterprise Focus 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 GOVERN Architecture AUTHORIZE Standards ASSIGN Accountability MONITOR Quality COMPLETE SOLUTION CREATE new record MAINTAIN current record SEARCH existing records ARCHIVE obsolete record Consistent processes across domains Steward and Custodian assignments by domain Standard processes a key component for service oriented architecture Operational Focus CAPABLE PROCESSES

Lean Governance Model Enterprise Focus Operational Focus Processes
Leadership Team (Finance, Mfg, Marketing, HR, Engineering, etc.) Processes GOVERN Architecture AUTHORIZE Standards ASSIGN Accountability MONITOR Quality Authority, Goals, Funding, Accountability Data Trustees Material, Customer Vendor, Plant, etc. Coordination Direction, Accountability Shared Service Enterprise Data Management Support Team Domain Standards Business Data Stewards (Finance, Mfg, Marketing, HR, Engineering, etc.) Processes CREATE new record MAINTAIN current record SEARCH existing records ARCHIVE obsolete record Enterprise Standards Data Custodians (Finance, Mfg, Marketing, HR, Engineering, etc.) Operational Focus

Spectrum of Governance Options
All successful Data Governance Models are federated “Totally Centralized” “Federated” “Totally Decentralized” Architecture Standards Organization Processes Maintenance & Quality 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 Characteristics

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

When to start an EDM Program
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 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

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

Company Background 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

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

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

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

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
One Term, Many Definitions Material Planner Tax Man Summarize by taxable area Planning Categories SAP CRS Material Master ( CIM ) Reporting Material Group = Commodity Lowest Detail Spends Manager Spends Analyst Summarized Grouping

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

Intel Master Data Direction
Determining Best Fit for Record of Origin 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

Intel Master Data Direction
Determining Best Fit for Record of Origin 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 *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 Patel Director MDM SAP Asia Pacific & Japan

MDM Strategies for the Global 10,000

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