When it comes to implementing a master data strategy, taking a phased approach is crucial for success. Just as you wouldn't run a marathon without training, transforming your data architecture requires careful planning and execution. This article explores the importance of global entity reference data and the benefits it brings to large-scale data operations. Additionally, it emphasizes the significance of a simplified and nimble data design to accommodate future changes.
Taking a Phased Approach: Changing your data structure on a large scale necessitates a robust assessment of the potential impact. Understanding where the data is currently being used and identifying potential risks is key to mitigating any issues that may arise. Once risks have been cataloged and addressed, it is essential to prioritize the areas of your business that require the most immediate attention. This could be your sales organization or your manufacturing function with strict supplier audits. Each area will require a tailored approach and specific metrics to measure success. By implementing changes in a phased manner, you provide faster time to value for your business, while providing your team with the flexibility to address any unforeseen challenges efficiently and establish a standardized template for future rollouts.
The Role of Global Entity Reference Data: While a data transformation effort may require a significant manual effort, it is important to focus on improving data automation within your organization. Global entity reference data plays a crucial role in large-scale data operations by providing a trusted independent record for matching, validating, enriching, and maintaining your first-party data. As your operations scale, leveraging global entity reference data enables your teams to reduce manual checks and stay informed about critical business changes within your company's portfolio. This leads to increased efficiency and accuracy in data management, while unlocking actionable insights made visible through data enrichment.
Designing for Simplicity and Scalability: In a world where data continues to grow exponentially, simplicity in data design is paramount. Data is most powerful when it is operationally functional. Therefore, it is essential to design your data model to be nimble and adaptable, minimizing the need for costly transformations in the future. Start with a simplified structure for uniquely managing organizations, locations, individuals and relationships. This master data will make up the “golden records” for customers, suppliers and partners in your MDM system that will be shared across the enterprise. Your MDM system can generate unique IDs for the master data and maintain cross-references to unique IDs from your Global Entity Reference Data, as well as record IDs from systems across your enterprise. This master data strategy allows for more flexibility to handle the potential impact of large-scale changes to your enterprise data model, especially if your company plans to acquire new technology, assets, or other companies in the short to medium term. By prioritizing simplicity and scalability, you can future proof your data architecture and avoid unnecessary disruptions.
Implementing a successful master data management strategy requires a phased approach, careful consideration of potential risks, and a focus on automation and simplicity in data design. Global entity reference data plays a vital role in large-scale MDM projects.
No matter where you are on your master data management journey, we can help. To learn more, visit www.moodysanalytics.com/master-data
