A clean core helps customers keep up with the competition and use SAP S/4HANA standard features whenever possible and provides an easier way to keep getting updates without breaking business-specific functionality.  During this podcast, Sue Waite, Senior Director for North American Center of Excellence, Business Technology Platform solutions at SAP, and David Sentance, Associate Partner and Head of Data & Analytics at Camelot Management Consultants, will discuss how enterprise data management practices help ensure a clean core as well as mitigate risk to your business transformation initiatives.

Speakers:

Host:  Corrie Birkeness

Additional Resources:

Building an Information Backbone for the Digital Enterprise: https://bit.ly/3wsYjVg

Accelerate Your Move to the Cloud with Trusted Data: https://bit.ly/4bWD7Gj

The Business Case for Clean Master Data: https://bit.ly/44LiZnR

SAP solutions for Data & Analytics: https://bit.ly/3IDy9kS

SAP solutions for Data Management: https://bit.ly/3UXbRBD

The Data Tower Setup in SAP S/4HANA Initiatives (Camelot ITLab): https://bit.ly/3WBxokG

Your Fast Forward S/4HANA Data Transformation (Camelot ITLab): https://bit.ly/4bjuPYO

Data Quality Health Check (Camelot ITLab): https://bit.ly/44J9cig

Data Analyzer (Camelot ITLab): https://bit.ly/3V2NnGb

Key Topics of Discussion:

Importance of a Clean Core: Discuss the significance of establishing and maintaining a clean core in data management, especially in the context of transitioning from classic ERP systems to cloud-based solutions like S/4HANA. Emphasize the benefits of having a clean core, such as improved efficiency, accuracy, and adaptability.

Dimensions of a Clean Core: Break down the five core dimensions of a clean core, including data cleanliness, composable approach, extensions, integration, and operational efficiency. Explain how each dimension contributes to the overall cleanliness and effectiveness of the core ERP system.

First Steps in Moving to the Cloud: Explore the initial steps organizations should take when transitioning to the cloud, focusing on foundational work related to data management and governance. Highlight the importance of assessing existing data challenges and developing a strategy for data migration and quality improvement.

Outcome-Driven Data Strategy: Stress the significance of adopting an outcome-driven data strategy before embarking on migration projects. Provide examples of business objectives, such as compliance, customer experience enhancement, or process optimization, and illustrate how these objectives inform the data strategy.

Source Data Assessments and Data Quality Improvements: Discuss the importance of conducting source data assessments to identify gaps, inconsistencies, and data quality issues. Explain how these assessments serve as a foundation for data quality improvements and help organizations avoid pitfalls during migration and implementation.

Techniques for Keeping Data Clean: Explore techniques and tools for maintaining data cleanliness, including accurate data capture, enrichment, and ongoing governance. Highlight solutions like SAP Master Data Governance (MDG) and data quality management micro-services for ensuring data integrity and accuracy.

Change Management in Master Data Governance: Address the organizational change management aspect of implementing master data governance solutions. Discuss the need for training, stakeholder engagement, and establishing data governance councils and stewardship roles to ensure successful adoption and maintenance of clean data practices.