1. Application Consolidation for Enterprise Applications (ERP/CRM) Rick L Miller, WW Program Manager, IBM Information Management

2. Enterprise Applications Support Core Business Processes Highly interdependent applications Support critical business processes like order to cash Require complex deployments, data migrations and upgrades Inherently risky and prone to failure SAP Component (ERP) SAP Component (SCM) SAP Component (BI/BW) SAP XI/PI ESR SAP Enterprise Portal SAP MDM

3. The Problem Continually growing islands of information coupled with lack of governance processes increase the challenges of consolidating applications AIIM & Accenture Surveys, 2007 47% of users dont have confidence in their information 42% of managers use wrong information at least once a week 59% of managers miss information they should have used Uncertainty Creates Risk What are the right data sources? What is the state of the legacy data? Are we simply moving our data quality issues? What legacy data is missing for the new environment? How can we build accurate project specifications to limit risk? How do we keep data from becoming the critical path?

4. Source: The Gartner Group – Corporate Integration Projects Data sufficient for legacy systems, but not sufficient for SAP Lack of documentation of existing systems Poor quality of data in source systems Multi-brand complexity Errors detected late in project lifecycle leading to re-work and delays Typical Risk to Data Migration Projects 83% of migration initiatives either overrun their budgets or fail outright

5. Data integration activities will consist of 15 – 30% of total project spend Between 20 and 40% of your implementation expenses should be allocated to data migration 1 1 Direct from SAPs Legacy System Migration Workbench (LSMW) manual

6. Data integration activities will consist of 15 – 30% of total project spend Data Integration/Migration Activities 30% Understanding Source Data 40% Cleaning, Standardising Harmonizing, Management 30% Conversion, Loading, Interfaces, Connectivity DeliverDiscoverPrepare Largely manual effort on small percentage of data. Some manual coding can review all data. This effort is the most unpredictable. The work can vary greatly depending on condition of data, however it is always the largest piece of work in the data initiative. Largely manual effort on 100% of data. This can mean dozens of persons cleaning source systems manually to correct and augment data and manually aligning records to MRD. Some manual coding can reduce the manual effort. Coding transformations and loads. Traditionally this effort is plagued with problems related to data quality and it can easily be pulled by necessity into the Cleaning, Standardising and Harmonising area causing timing and budget problems.

7. Data integration activities will consist of 15 – 30% of total project spend Data Integration/Migration Activities 30% Understanding Source Data 40% Cleaning, Standardising Harmonizing, Management 30% Conversion, Loading, Interfaces, Connectivity DeliverDiscoverPrepare Largely manual effort on small percentage of data. Some manual coding can review all data. This effort is the most unpredictable. The work can vary greatly depending on condition of data, however it is always the largest piece of work in the data initiative. Largely manual effort on 100% of data. This can mean dozens of persons cleaning source systems manually to correct and augment data and manually aligning records to MRD. Some manual coding can reduce the manual effort. Coding transformations and loads. Traditionally this effort is plagued with problems related to data quality and it can easily be pulled by necessity into the Cleaning, Standardising and Harmonising area causing timing and budget problems. Reality is 70% of the activity required to be successful with the data migration occurs here

8. Data integration activities will consist of 15 – 30% of total project spend Data Integration/Migration Activities 30% Understanding Source Data 40% Cleaning, Standardising Harmonizing, Management 30% Conversion, Loading, Interfaces, Connectivity DeliverDiscoverPrepare Largely manual effort on small percentage of data. Some manual coding can review all data. This effort is the most unpredictable. The work can vary greatly depending on condition of data, however it is always the largest piece of work in the data initiative. Largely manual effort on 100% of data. This can mean dozens of persons cleaning source systems manually to correct and augment data and manually aligning records to MRD. Some manual coding can reduce the manual effort. Coding transformations and loads. Traditionally this effort is plagued with problems related to data quality and it can easily be pulled by necessity into the Cleaning, Standardising and Harmonising area causing timing and budget problems. Estimates are often based on this

9. Data integration activities will consist of 15 – 30% of total project spend Data Integration/Migration Activities 30% Understanding Source Data 40% Cleaning, Standardising Harmonizing, Management 30% Conversion, Loading, Interfaces, Connectivity DeliverDiscoverPrepare. 75% Business 50% Business 25% Business 75% IT50% IT 25% IT There is a more significant involvement by the business users in the up front activities. Not understanding the roles and responsibilities required can negatively impact the project.

10. Data Quality Issues/Risks Poor quality data from source systems Duplicate data Irrelevant data Embedded business rules Data sufficient for legacy systems not sufficient for ERP Data gaps require augmentation strategies Error detection and reprocessing is iterative and time consuming Critical data issues often are not found until late in Realization and Final Prep Projects delayed due to data quality problems Inability to load data Data attributes insufficient to execute cross functional business processes Multiple change requests required to deal with data issues Decisions to go with what we have and clean up the mess later Data migration issues lead to Inaccurate reporting Customer satisfaction suffers Poor system adoption

11. Incorrect Data Loaded Correctly into SAP Customer Master Data Duplicate data Irrelevant data Lack of address validation or verification

12. Vendor Customer G/L G/L Account Material SAP Master Data in Business Flow Sales Order MRP Planning MPS Planned Order Purchase Requisition Production Order DeliveryBilling Customer Payment Purchase Order Invoice Receipt Vendor Payment Goods Issue Goods Receipt Goods Receipt Goods Issue Product Costing Profitability Analysis RawFinished SAP Integration Requires High Quality Data

13. Project Lifecycle Traditional Approach InfoSphere Approach 70 50 30 10 90 110 Benefits of This Approach Reduce risk – surface and address issues early Maximize value from Subject Matter Expert resources Lower cost than traditional approach AnalysisMappingSystem Test LoadPost Cutover Relative $cost of fixing error/issue Relative Effort Best Solution Is To Fix Problems Early Find problems early, reduce pain (risk, $, time) later

14. Application Consolidation with InfoSphere Benefits vs. Hand-coding and services-only solutions Discover Implement and Monitor Deliver Prepare 50 – 90% Savings 50 – 90% Savings 40 - 60% Savings Benefits 45 to 80% of total integration cost savings in the initial project Ensures trust for timely Go-Live and Production Deployments >75% ongoing project savings for new implementations/waves Ensures lasting data quality 73 – 90% Savings

15. Enabling Trusted Information Analyze Source Systems Before Blueprint Eliminates surprises Provides an objective basis for planning Minimizes re-work by increasing accuracy of specifications Identifies data strengths and weaknesses Quantifies data quality impacts pre-transformation 1 Discover Employ Ongoing Data Quality Process Monitor critical business rules Trend data quality over time to identify problem areas Assess Data Quality periodically 5 Support Apply to Blueprint Design Workshops Provides enterprise standardization Identify matched records across or within sources Survive the best possible record sets for target Analyze free form text and split domains Identify embedded business logic Apply data correction and augmentation 2 Design Maintain Data Quality Through Controls Apply end user controls for standardization, validation and augmentation Apply standardization, validation and augmentation in master data synchronization strategy Apply standardization, validation, augmentation to operational interfaces 4 Governance Apply Results to Realization of Data Integration Provides transformation logic (mapping tables) Identifies faulty logic & aids acceptance testing Captured business rules for reuse Apply data correction and validation in Batch and Real Time 3 Delivery

16. SAP Provisioning Environment DATA HARMONIZATION LEGACY SOURCES DATA ALIGNMENT STAGING AREA PROVISIONING AREA ALIGNMENT AREA GAP REPORTS DATA EXTRACTION IDA, DS IA, QS Cognos FT, DS QS, DS BG, MWB Substantial acceleration for SAP migrations & consolidations

17. CMC needed a single view of its customers to understand their business across all divisions. CMC was unable to rapidly and dynamically analyze their global manufacturing capacities. As a result, they could not make optimal decisions on where to fulfill demand or take advantage of manufacturing techniques such as Global Available-to-Promise (ATP). CMC needed to adequately leverage global spend across all divisions with their suppliers. Challenge CMC has invested in SAP software to help them create common business processes and reduce the total cost of ownership of their information technology assets. CMC purchased IBM Information Server to rationalize the data that will be loaded into SAP and create a common view of data globally. InfoSphere Solution Benefits Merged divisional silos of data into a single view of customer. CMC will use IBM Information Server to rationalize the data being migrated from legacy systems into SAP. Eliminated manual processes, allowing CMC to utilize IBM Information Server coupled with the IBM data integration methodology to accelerate and standardize the migration process Completed first 2 migrations on- schedule. Aggressive 3 month rapid migration cycle through 2010 Commercial Metals Company Global Consolidation of 200+ locations into a single SAP Instance

18. ERP Data Integration Realities Moving to ERP will not make data quality issues go away, it will expose them ERP will allow data to be loaded that is either not relevant or will load, but is not clean As data is loaded into ERP extensive validations are performed (for example a full Customer Master object has over 400 validations) ETL tools alone do not provide support for all necessary data integration activities. This is more than a mapping activity. Legacy data must be recast into the ERP load formats and only have a fraction of required attributes to run ERP business processes Project milestones are often delayed due to ERP validation errors. This represents a lot of extra development time and project risk Loading data correctly into ERP is not the big issue, loading correct data correctly into ERP is the real issue

19. Accurate Assessment & Design of Business Processes Accelerated Data Migration Ensuring on-time Go-Lives Repeatable & reusable infrastructure for on-going legacy system interfaces Enforced Data Quality & Governance Establishing Enterprise Master Data integration InfoSphere Benefits in an SAP Enterprise Enhancing the bottom line by simplifying & reducing implementation cycles and ensuring sustained value

20. Thank you! For more information please visit: http://www.ibm.com/software/data/infosphere/