Book Image

RISE with SAP towards a Sustainable Enterprise

By : Adil Zafar, Dharma Alturi, Sanket Taur, Mihir R. Gor
Book Image

RISE with SAP towards a Sustainable Enterprise

By: Adil Zafar, Dharma Alturi, Sanket Taur, Mihir R. Gor

Overview of this book

If you’re unsure whether adopting SAP S/4HANA is the right move for your enterprise, then this book is for you. This practical and comprehensive guide will help you determine your next steps toward building a business case, while preparing you for all the possible scenarios and enabling you to make informed decisions during implementation. RISEwith SAP toward a Sustainable Enterprise is packed with clear and detailed advice, including a run-through of what it takes to design the landscape using RISE with SAP. As you go through the chapters, you’ll get a solid understanding of precisely what services are available (such as Process Discovery, data migration, the fit-to-standard approach), and which scope items on RISE with SAP should be considered, allowing you to make the most of RISE with the SAP-based model. Finally, you’ll get an overview of different industry-based use cases and how they can be brought to reality with the platform that’s set up on the RISE with SAP offering. By the end of this book, you’ll be able to build a detailed business case to determine if RISE with SAP is the right transformation engine for you, along with a clear idea of optimized landscape design on RISE with SAP that addresses the pain points for your implementation and support activities.
Table of Contents (18 chapters)
1
Part 1: Overview
5
Part 2: The Journey with RISE with SAP
11
Part 3: The Way Forward: The Art of Possible

Understanding the importance of data governance and data quality

In this section, we focus on the importance of data governance and data quality. In order for any S/4HANA implementation to be successful, having data of the highest quality is very important. Maintenance of quality needs to be embedded both as part of the data migration as well as in pre- and post-data migration activities to avoid system usage issues. Data quality is defined by how effectively the data supports the transactions and decisions needed to meet an organization’s strategic goals and objectives. In order to have a continuous data quality framework set up, you need to consider the following:

  • Data assessment: Once we understand the data sources that are needed to drive the transformation, we need to profile the source, using the data-first approach mentioned earlier in this chapter to establish what data can be used and its quality to drive this analysis. This includes data re-duplication and...