Getting Your Data Ready for SAP S/4HANA
AI智能总结Getting Your Data Ready for SAP S/4HANA
Key Statistics
- IDC Prediction: Over 59 zettabytes of data will be created, captured, copied, and consumed in 2020.
- Future Data Production: The amount of data produced over the next three years will exceed the total data produced in the past 30 years.
Importance of Data Management
- Data as Currency: Data is recognized as the currency of the future, with enterprises evolving to manage and extract relevant data for improved planning and optimized business processes.
- Role of CDO: The role of a Chief Data Officer (CDO), reporting directly to the CEO, is becoming increasingly common, with 63% of firms having a CDO.
Key Trends
- Data Governance: Revival of data governance programs, including master data management initiatives to provide curated, high-quality, and trusted data.
- Cloud Migration: Enterprises are adopting cloud solutions to address these trends.
Challenges and Best Practices
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Selective Data Transition
- Example: A large American CPG company chose a selective shell copy approach for migrating only their domestic business to SAP S/4HANA.
- Approach: Migration of relevant business data with flexibility to adapt data and processes in one step, allowing for redesign of business processes while retaining historical data.
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Data Quality and Volume Analysis
- Importance: Extensive analysis of proposed data models in SAP S/4HANA is crucial to foresee the impact of data quality and volume before migration.
- Example: An American water management agency performed a large-scale analysis to identify and cleanse data before moving it to SAP S/4HANA.
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Server Sizing
- Recommendation: Determining server sizing through a data sizing exercise, with preference given to over-sizing servers to accommodate future needs.
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Change Management
- Education: Educating business users on changes and their operational impact to gain their support.
- Example: The water management company encouraged business users to familiarize themselves with the new system before its launch.
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Testing
- Comprehensive Testing: Ensuring thorough testing by impacted business units to eliminate potential issues post-go-live.
- Example: Insufficient testing at a US water management agency led to several issues during the migration to SAP S/4HANA.
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KPI-Centric Approach
- Data Quality Roadmap: Establishing processes and governance controls at the data ingestion layer, regular de-duplication and cleansing, and examining processed data against KPIs and specific business outcomes.
- Example: A multinational tire manufacturer focused on integrating SAP and non-SAP data to deliver customer-centric insights.
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Data Services Organization
- Best Practice: Setting up a data services organization to understand data usage, integration with business processes, and maintaining data quality.
- Example: A North American fertilizer company established a data services organization as part of its SAP S/4HANA implementation.
Conclusion
Successful transition to SAP S/4HANA requires careful planning, investment, and alignment with business outcomes. Enterprises must consider data preparation, quality, and governance, alongside effective change management and comprehensive testing to ensure a smooth transition.
