Expert Session - Migration -

Philippe Despierre & Jan Van Vlaenderen - 27/05/2021 Short introduction

Jan Van Vlaenderen Philippe Despierre

- Started career as a SCM - Started career as a FI consultant consultant in the domain of MM, - Community lead financial WM and QM solutions since 2018 - lead in projects - Data addict since 2009

2 Agenda

Inform on a data migration strategy Inform on ETL methodology, based on Best Practises Learn some highlights on Learn what’s on the LTMC tool by SAP S/4HANA release 2020

3 Data Migration Strategy - Purpose and Scope What is “data migration” ?

According to Wikipedia Data migration is the process of transferring data between storage types, formats, or computer systems. It is a key consideration for any system implementation, upgrade or consolidation.

BUT… the goal of a data migration project is not simply to move and transform data from one system/format to another; it is to ensure that the moved data is of high quality, is fit-for-use, and supports the underlying business processes and operational goals of the organization!

Data migration is necessary It is one of the key cross stream tasks during a transition. No data, no Go-live. Unfortunately, it is often underestimated, it is not just ‘pressing a button’. Understand your data migration requirements early and plan for it accordingly. Data migration is 90% preparation.

5 Data Migration Approach

Preparation

Creation of data migration strategy document Identification of objects and scope finalization Deliverables Data extract and upload methodology (auto, semi-auto, Expertum DM Strategy Document manual) Object scope list Identify dependencies Functional design per object Creation of data migration schedule Data Migration Schedule Effort estimate Resource Plan Resource requirements Functional design per object Signoff functional design

6 Approach - ETL based on Best Practises

Legacy Data Target Loading Environment Environment

Performance Dashboards and Analysis Business Reporting

Pre-Built load Flat Files/ routines for SAP Excel Objects Files

Validate Cleanse Transform & Load Databases Name Parsing Business Transform IDocs Address Parsing Validation Rules Extract & Data into SAP & Correction Profile structure Automatic SAP Material/Product Config SAP Parsing Validation Applications Matching SAP Configuration Reconciliation Extraction

XML 7 Approach - the bigger picture

8 Approach - ETL based on Best Practises

Load Transform Unit testing Extract • Creation of data upload tool • Transform extracted data in • Creation of extract tools • Finalizing upload sheet format upload datasheet as per the Technical creation load tools • Extract restricted data • Upload of representative data

Unit Test Unit transformation rule • Deliver report Technical test

Load Extract Transform Trial Migration 1 • Finalization of data upload • Finalization of extract tools • Transform extracted data in I tool • Extract restricted but upload datasheet as per the • Upload transformed testing representative rule • Delivery report

Limited data sets Migration Trial

Transform Extract Load Trial Migration 2 • Transform extracted data in

II • Extract data for all erroneous • Upload of extracted data upload datasheet as per the objects • Deliver report User Acceptance Testing transformation rule Quality data sets Migration Trial

Higher volumes Transform Extract Load • Transform extracted data in • Extract data for all the object • Upload of extracted data upload datasheet as per the (partial) • Deliver report Dry run run Dry transformation rule Dress Rehearsal for golive Transform Extract Load • Transform extracted data in • Extract complete data after • Upload of complete data set upload datasheet as per the Final Data Migration freeze • Deliver report transformation rule / delta / • Sign-off • Sign-off

Golive • Sign-off Final Migration Migration Final

9 Data Migration - ETL methodology based on Best Practises ETL - KEY step 1 : Extraction

V Data Discovery A Identify master data Step 1 Different applications L Extraction Data Cleansing I Data purging – obsolete data deleted or marked for deletion D Data corrections

• Identify and correct in current legacy A • Data accuracy (net weight, material descriptions, … ) are vital T Validation of the cleansed data – prior I Can be automatic, semi-auto or manual Repetitive and successive runs improving the accuracy O and completeness of the extracted data N

11 ETL - KEY step 2 : Transform

V Local data mapping A From local source to destination SAP system Identify those for which no origin was found L Data harmonization Identification of ‘global’ data (ie extending of existing data) I Data conversion D Convert legacy system data to meet the business requirements Step 2 Transform A Business rules will be applied Can be automatic, semi-auto or manual T Enrichment Enrich the inexistent data in source system (default value, I manually, …) O Validation Validate the content of input files N Business and system validation rules will be applied

12 ETL - KEY step 3 : Load

V Data from the repository (or manual data sheets) will be uploaded A based on rules and (standard) upload tools.

L Progressive approach Master data or called static data migration (material master, I business partners, …) Data migrated over and extended period of time D Change-over approach Transactional data or called dynamic data migration (stock, open A sales orders, …) T Data migrated during a short cutover period (e.g. weekend)

I Data upload methodology depends Step 3 Quantity of the data O Load Availability of the data Complexity of the data N Importance for business flows

13 Validation during different phases

Validation is a crucial step in the full process. Based on a variation of business and/or system rules, data will have to be validated and signed off after extraction (confirm data profiling is correct), transformation (confirm data is converted to the agreed values) and after load (confirm data is loaded as expected, in the required quality – functionally and technically) Tracked in planning – defect logging and handling Extract Transform Load

Legacy Conversion SAP systems environment Data Data Data validation validation validation

14 Data Migration Activities - Roles Team structure

Data Migration Team

Core (Local) Functional Business Team Team

16 Team structure

Data Migration Team

Customer, Expertum, DM Manager

Core (Local) Functional Business Team Team

17 R&R - Data migration team

The deliverables expected Data Migration Strategy Data Migration Plan

Data Data load tools (Upload procedures) - with dev team Migration Team Data migration load template Data load

Customer, Expertum, DM Manager

18 R&R - Functional core team

The deliverables expected Configuration upload environment Functional specification for data load tool Data migration mapping templates for data structures Data validation

Customer, Expertum, DM Manager

Core Functional Team

19 R&R - Functional core team

The deliverables expected Source data cleansing (Local) data mapping rules (Local) data extraction, cleansing, profiling, and validation tools Country / Site specific data migration mapping Data validation Customer, Expertum, DM Manager

(Local) Business Team

20 Key topics to take “home”

No data, no system

Preparation is key within a data migration track

Involvement of each, cross functional, department of the company is required Whether it is IT, business, developer, key user…. You will need them to make it a success story !

21 Some highlights on Data Cleansing Functional Data Migration approach

Any SAP S/4 System

Extract Load

Transformation Engine Functional Data Migration approach

Scoring Transformation

Any Cleansing System Source Table 1

Base Dependency table 1

Source Target Table n Trans- Model Base formed table x data

Source Table 2 Check tables Other Sources Transformation Engine What’s on the LTMC tool by SAP S/4HANA release 2020 To conclude

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26 Thanks for listening! Any questions?

Philippe Despierre Jan Van Vlaenderen Community lead finance Project lead - MDM

[email protected] [email protected] +32 496 808 943 +32 478 67 47 91

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