Data Methodology

1. Data Methodology Camille C. Alvarez December 6, 2010

2. Agenda • What is Data Conversion? • Data Conversion Methodology • Data Conversion Sequence • Data Methodology Controls • Data Wrap Up • Questions

3. What is Data Conversion?

4. What is Data Conversion

5. Data Conversion Objects • Master Data – a collection of information about a person or an object, e.g. a cost object, vendor, or G/L account. Generally for end users, master data is reference data that you will look up and use, but not create or change • For example, a vendor master record contains not only general information such as the vendor's name and address, but also specific information, such as payment terms and delivery instructions. • Transactional Data - data related to a single business event such as a purchase requisition or a request for payment. • For example, when you create a requisition, ERP creates an electronic document for that particular transaction. SAP gives the transaction a document number and adds the document to the transaction data that is already in the system. • Historical Data - Data from previous time periods, in contrast to current data. Historical data is used for trend analysis and for comparisons to previous periods. • We will not be loading any historical data (except for monthly GL Balances).

6. Data Methodology and Process And Controls

7. Deployment Activities Process Overview Prepare Country“Self Preparation” PreliminaryAssessment VendorEngagement KnowledgeTransfer DeploymentSet-Up Deliver Maintain User Support& ApplicationMaintenance PROJECT MANAGEMENT DESIGN(Fit/Gap) BUILD TEST DEPLOY SUPPORT DATA Migration CHANGE MANAGEMENT 1 month 6-7 months

8. Organization Structure Data Methodology • Core Functional Team • Finance • Procure to Pay • Supply Chain Execution • Order To Cash • Core Technical Team • Application Development • Data Conversion • Data Cutover • Infrastructure • Quality Assurance • Support • Wave/Deployment Team • Local Business Country • System Integrators Project Management Office Change Enablement

9. Core Data Conversion Technical Team

10. Data Methodology Framework

11. Data Migration Activities Content Content Process DATA MANAGEMENT (cross) • Data Cleansing • Data Cleansing Guidelines • Data Cleansing Execution and Validation • Data Mapping • Static / Dynamic objects list • Data Source list • Functional And Technical Data Mapping – Data transformation rules • Conversion Strategy (Manual vs. Automatic - Historical depth) • Data Migration Realization Plan • Data Migration Realization • Legacy data extraction and transformation • Data Construction • Data Conversion tools Enhancement / Realization • Data Conversion tools Unit Test • Data Conversion Mock Testing • Mock 1 Data Conversions • Mock 2 (UAT) Data Conversion • Data Conversion during Deploy Stage • Data Conversion Plan Finalization • Go-Live Simulations • Data Conversion Productive Run • Data Validation and Sign-Off from Business Users • Data bugs fixing [during SUPPORT]

12. Data CleansingPreparation/Fit Gap/Build Phase • In order to have proper data to be extracted, transformed and uploaded it will be necessary to perform a Cleansing Activity • During the Preparation Stage the Data Cleansing Activity have to be launched. A Specific Document prepared by the Enterprise Data Management Office, that explain the rationales to be used to cleanse the main master data, will be distributed to the LPO within the Wave Communication Package. • The Business will be requested to prepare the metrics of the cleansing activities that have to be performed. • In the context of the preliminary Assessment specific Data Cleansing Sessions will be scheduled, in order to clarify any doubt related to the cleansing activities and to agree a cleansing plan. Data Cleansing Preparation Data Cleansing Execution • The cleansing Activities that can be performed on Impulse will be launched immediately. The Data that can’t be cleansed in legacy will need to be identified, tracked and treated during the input files preparation. This activity have to be performed before the end of the Build Phase Data Cleansing Validation • After the Data Cleansing Execution the Data Cleansing Metrics Have to be finalized and validated by the Enterprise Data Management Office. Validated Data can be used to prepare Mock testing

13. Data MappingFit Gap / Build Phase • The first Mapping activity that will be conducted is the Data Object Mapping. The actual list of data object, provided by the Core Data Conversion Team, is analyzed by SI and LPO, in order to identify any possible Gaps. • For Each object it will be necessary to assess the Data Source • For Each Object it will be necessary to assign a business responsible that will be the primary contact for the SI for all the Data activities that concern this object • For each object it will be necessary to define the data conversion strategy (Manual versus Automatic, Conversion Tier) • Each difference between the actual list and the analysis will be submitted for approval to Core Data Conversion Team Data Object Mapping Functional Data Mapping • For each data migration object, a field functional data mapping must be performed. An ERP Field list will be provided by the Core Data Conversion Team. The Legacy reference for this field (Source, field description, Screen name and Screen field name) must be identified and indicated Technical Data Mapping • For all Legacy fields indicated it will be necessary to indicate Legacy field and table name, field format and length, in order to identify any possible inconsistency between source and destination

14. Data Migration RealizationBuild Phase • Following the results of the data Mapping, the Core technical team will upgrade the data conversion tools Data Conversion Tools Enhancement Data Construction Preparation • As Well, the Core Data Conversion Team will provide to the Local Business the guideline to prepare the “Data Construction” files. A first example of these DC files needs to be provided early to perform the Data Conversion Tools Unit test. Local Business will be supported by the development team in preparing these files Data Conversion Unit Test • After the data conversion tools enhancement, Unit test of Extraction, transformation and Upload needs to be performed. • The Upload test will be performed in the development environment, in a dedicated client. • Deployment team can be requested to support the validation of these test

15. Data Conversion Mock TestingTesting Phase • As soon as the Test Environment will be available the Mock Test 1 will be launched. After the Environment preparation, the manual configuration execution and validation, the Core Data Conversion Team will perform a data conversion test. • Likely the scope of this conversion will be the totality of the static data and anticipate the production volume of the Transactional Data • These Conversions will be validated following the guidelines described in appendix 2 • This Conversion test will be tracked on defect system such as HPQC • The Mitigation plan for the defects will establish Roles and Responsibility of the involved functions Mock Conversion 1 • After the execution of the mitigation plan for the defect raised during Mock 1 a second Mock will be executed with the same principle already described for the Mock 1 • This second mock must be scheduled in order to be able to use Mock 2 converted data for IST2 Mock Conversion 2

16. Data Migration Productive RunDeploy / Support Phase • Within the Cutover Plan, a detailed Data Conversion Plan needs to be produced. This Plan needs to detail the Conversion Sequences, the Manual Conversion Scheduling and the data validation. • Conversion Schedule, Conversion execution responsible and Data Validation Responsible needs to be indicated. • Final Legacy Extracts, Data freezing schedule and Dual Maintenance Rules needs to be finalized and approved Data Conversion Plan • The Final Data Conversion Plan will be tested within the Go-Live Simulation. The procedure for the data conversion preparation, execution and validation are the same already indicated for Mock 2 Mock Conversion 3 (Go Live Simulation) • After the Go Decision, the cutover execution happen. Within the cutover execution there’s the data migration productive run, that will follow the final data conversion plan and will be submitted to the same validation process as the Go Live Simulation. • After the productive run, the Enterprise Data Migration Office will starts the Data Quality enhancement Data Conversion Productive Run

17. Data Conversion Sequence Diagram

18. Data Methodology Controls

19. Data Wrap Up • Data Methodology provides the following: • Follows and supports the SDLC • Promotes execution and an engagement model • Wave, Core, and local business • Sets expectations and support data conversion activities • Establishes deliverables and metrics • Roles and Responsibilities are established in each of their data activity phase for accountability, consultation, or information • Provides a process guideline for policies and procedures controls for audit purposes

20. Questions

21. Thank you!!!

22. References • Detail to Data Migration Methodology. • http://im-inside-world/is/isprivate/projectshome/IMGlobalPrivateprojwksp/Project%20Documents/Deployment%20Methodology/IMG_CP_Data%20Migration%20Methodology.pptx • Data Conversion Controls for Awareness