ENSURING A SUCCESSFUL SAP DATA MIGRATION

Transcription

1 ENSURING A SUCCESSFUL SAP DATA MIGRATION Presented By EXPEDIEN & KENNAMETAL Align Data Strategy With Your Business Goals

2 Speakers Eric Stridinger, Global Data Management/EIM Lead/Manager, Kennametal Jeff Coombs, SVP, Marketing Expedien Shibajee D Gupta Lead EIM Consultant, Expedien Contact info@expedien.net

3 Agenda Introduction SAP Data Migration Kennametal Success Story Q&A

4 Expedien, An Overview Enterprise Data Management Solutions & Services Provider Firm strong team of professionals with over 1500 person years of rich experience in Enterprise Data Management and SAP. Founded in 2003, headquartered in Houston TX with other offices in Santa Clara and Noida, India. For more information, please visit 4

5 Expedien Services EDM Data Quality Data Migration Data Integration Master Data Management Data Governance Data Warehousing Business Intelligence SAP Functional & Technical Consulting SAP Portal 5

6 Data Migration Start to Evolution Endless Data Consumption with Confidence Business Intelligence MDM/Data Governance - EILM ERP Implementation Data Migration Legacy Data

7 Business Value BVIA Enrichment Current State Integrated State Evolution BV Master Data BV BV

8 Expedien Rightway Enterprise Information Life Cycle Management (EILM) Methodology Engagement Scope Engagement Business Scope Case Readiness Assessment Information Collection Roadmap & Strategy Roadmap & Governance Strategy Strategy Technology & EDM Components Selection Data Assessments Quality Enrichment Rules Business Value Integration Data Quality Metrics Definition Plan, Design & Implementation Plan, Design & Implementation Data Integration Data Integration Migration and BV Validation Data Migration Data Quality Validation Data Quality MDM Validation & Metadata Management MDM & Metadata Enterprise Management Data Warehouse Enterprise Data Warehouse Data Reconciliation & Acceptance Business Value Delivery Business Value Enterprise Delivery BI Enterprise Balance BI Scorecards Balance Management Scorecards Dashboards Operation Dashboards OLAP Reporting Expedien Knowledge Repository/Accelerators Evolution Evolution Knowledge Transfer Knowledge Reusable Transfer Components Reusable Competency Business Components Center Competency Data Center Retention & Archival Data Governance Special Customer Need Data Life-Cycle Management Business Process Data Quality Data Migration Data Retention & Archival

9 Five Steps Data Migration Landscape and Process Data Migration PMO and Steering Committee Establishment Legacy to SAP ECC 6.0 Data Migration Legacy Systems and SAP 4.6 Identify Data Candidates DQ Assessment Standardization, Matching and Ranking DQ Rules Definition Data Business Values Analysis Define Key Quality Indicators Define Legacy to Object Mapping Infrastructure Setup DQ Rules Build Define Progress Scorecards Build ETL Maps Unit Test by Object Data Business Values Enrichment DQ Rules Validation Monitor Progress Modify ETL Maps Capture and Reprocess Drop outs Data Business Values Validation DQ Rules Monitoring Check Data load threshold Prepare Data for Test Cycle Prepare for post load reconciliation Data Business Values Publishing Load Method Decision Point IDOC OR BAPI LSMW ABAP or Other Methods SAP ECC 6.0 Platform - ETL/Data Quality such as SAP Business Objects Data Services and Information Steward Standard RDBMS SAP Configuration Data Reconciliation

10 Five Steps Data Migration Load Process Cutover 1 Cutover 2 Cutover 3 Cutover PRD SAP ECC 6.0 SAP ECC Dev SAP ECC T01 SAP ECC T02 SAP ECC T03 SAP ECC PRD Data Cleansing Cycles 1st Dry Run 2nd Cycle 3rd Cycle 4th Cycle 5th Cycle Partially Cleaned Master Data Object Cleansed Master Data Object

11 Five Do s of Data Migration Project Management Start early. Setup a strong PMO and define progress metrics and scorecards. It is very easy to underestimate the data migration aspect of an SAP implementation. Legacy Data Light the fire. Identify and prioritize all possible legacy systems and kick off DQ Assessment upfront. Data Quality Define Data Quality rules from a repeatable framework perspective. Data Migration Team Should participate actively in the business process workshops during blue printing Load Methods Distinctly categorize the load methods by objects upfront. This eliminates considerable downtime once the data sets are ready.

12 Five Don ts of Data Migration Project Management Do not over scope the project. Scope the project optimally. Biting off more than the chewable capacity can result in serious negative impacts, e.g. cost and time over runs. Legacy Data Do not underestimate the number of legacy data systems. There could be random spreadsheets and mini database sitting on standalone computers. Data Quality Do not overestimate the legacy data quality and bypass Data Quality Assessment steps. It doesn t hurt to profile good quality data. Data Migration Team Do not over engineer the build and test process. This can result in resource burn out in the long run. Make sure the mapping documentation has the absolute critical data elements passing through the quality gates. Load Method Do not overlook the historical data load strategy. Identify the strategy and then plan the load process and method keeping the data dependency in view.

13 Introduction: Kennametal Kennametal is a $2.5+ B manufacturing company, pioneer in tooling solutions, engineered components and advanced materials. Due to the nature of its business Kennametal often goes through Merger and Acquisitions, that results into integration of legacy systems into the SAP ECC system of record. ERP Project Goal/ Charter: Project Rapid Implement Business Transformation Upgrade SAP R3 4.6 system to ECC 6.0 to streamline global processes/data Implement SAP new dimension products such as PLM, SCM, SRM, HCM and CRM Drive towards 90% Standard Processes Move towards a Customer Centric Model Need for Data Governance Future Mission for a repeatable Data Quality Assessment and Data Migration process

14 Criticalities: Kennametal - Data Strategy Integration of non SAP Data Multi-Language Data Conversion Business Transformation for Materials and Inventory Management Build a reusable / repeatable conversion process Huge dependency on success factors Enrichment: Plant and Storage location naming convention Plant and Storage location re-alignment Material Grouping modification Material ID and D&B number integration Value Addition: Home grown MDM process Inventory valuation of aged Material and identification for better housekeeping as a positive fallout Foundations for Data Governance Establishment of an infrastructural and process framework by Expedien with expert knowledge transfer to make Kennametal self sufficient in the long run.

15 Kennametal - Achievements Results: Go to market milestone was achieved Legacy systems decommissioned System downtime dropped reasonably IT staff changes to focus on the future Critical Master Data consistency and accuracy Foundation for Data Governance and overall EDM strategy Bottom line Benefits: Increased sales and revenue - Single view of customer = Improved targeted marketing. - Ability to up-sell and cross-sell Significant cost reductions - Accurate product & catalog information Call center productivity increased considerably

16 Recap It is crucial to have resource mix of business and technology in projects like this. The Landscape and processes should be of repeatable nature so that future data migration will be a little easier every time. Data Quality efforts including measurement against data quality metrics and continuous monitoring A must for successful data migration. Take data migration as an opportunity to build foundation for data governance and master data management. It is absolutely must to add right resources in right spots of the project and also make sure the resources have the right skill sets as such projects do not have any room for newbies.

17 Questions and Answers Thank You! Please send your questions to