Application Consolidation for Enterprise Applications (ERP/CRM) Rick L Miller,

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

2. SAPEnterprisePortal SAPMDM SAPXI/PIESR SAP Component (ERP) SAP Component (SCM) SAP Component (BI/BW) 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

3. 47% of users don’t have confidence in their information 42% of managers use wrong information at least once a week The ProblemContinually growing islands of information coupled with lack of governance processes increase the challenges of consolidating applications • 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? 59% of managers miss information they should have used AIIM & Accenture Surveys, 2007

4. Typical Risk to Data Migration Projects 83% of migration initiatives either overrun their budgets or fail outright • 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 Source: The Gartner Group – Corporate Integration Projects

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 migration1 1Direct from SAP’s Legacy System Migration Workbench (LSMW) manual

6. Data Integration/Migration Activities Discover Prepare Deliver 30% Understanding Source Data 40% Cleaning, StandardisingHarmonizing, Management 30% Conversion, Loading, Interfaces, Connectivity Data integration activities will consist of 15 – 30% of total project spend Largely manual effort on small percentage of data. Some manual coding can review all data . 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. 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.

7. Data Integration/Migration Activities Discover Prepare Deliver 30% Understanding Source Data 40% Cleaning, StandardisingHarmonizing, Management 30% Conversion, Loading, Interfaces, Connectivity 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 Data integration activities will consist of 15 – 30% of total project spend

8. Data Integration/Migration Activities Discover Prepare Deliver 30% Understanding Source Data 40% Cleaning, StandardisingHarmonizing, Management 30% Conversion, Loading, Interfaces, Connectivity 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 Data integration activities will consist of 15 – 30% of total project spend

9. Data Integration/Migration Activities Discover Prepare Deliver 30% Understanding Source Data 40% Cleaning, StandardisingHarmonizing, Management 30% Conversion, Loading, Interfaces, Connectivity . 25% Business 50% Business 75% Business 50% IT 75% 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. Data integration activities will consist of 15 – 30% of total project spend

10. 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 Data Quality Issues/Risks

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

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

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

14. 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 Application Consolidation with InfoSphereBenefits vs. Hand-coding and services-only solutions Discover 50 – 90% Savings 50 – 90% Savings Prepare 40 - 60% Savings Deliver Implement and Monitor 73 – 90% Savings

15. 1 Discover 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 5 2 Support Design Employ Ongoing Data Quality Process • Monitor critical business rules • Trend data quality over time to identifyproblem areas • Assess Data Quality periodically 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 4 3 Governance Delivery 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 Batchand Real Time 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 Enabling Trusted Information

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

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. InfoSphere Solution Commercial Metals Company Global Consolidation of 200+ locations into a single SAP Instance Challenge 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 • 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.

18. 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 ERP Data Integration Realities

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

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