In the previous post, Master Data as an Asset, I noted the importance of handling your master data with the respect it deserves—that is, as a valuable asset. The first step to improving your organizations master data is to identify your maturity level. After all, we need to learn how to walk before we can run. Hence, your master data processes requires incremental improvements to achieve desired maturity.
We can classify master data maturity into five major levels, as shown in the diagram below:
Here’s how we define each level:
- Tribal: This primitive level is characterized by ad-hoc and manual entry of master data. Often there is a lack of documented processes and standards and of formalized organization.
- Developmental: In this stage master data processing begins to take shape. Here, data standards are informally shared and request forms are typically either paper-based or sent via ad-hoc emails. Data applications, including the use of tools for mass master data updates and maintenance, are managed at a departmental level. Also, departments are usually siloed from each other, with data focus taking on a “transactional” nature.
- Reactive: The Reactive level includes mid-tier data governance processes, with data standards being documented and centrally managed in a document repository or database. Specific data governance projects, such as integrated master data workflow creation and maintenance, are funded and staffed. In this stage business rules are created and documented. Policies and procedures for creating and maintaining data are defined and documented.
- Established: A handful of companies have tackled their critical master data challenges head on. However, there’s always room for improvement. At the Established level of master data maturity they have integrated data standards, documented change-management procedures, and have a documented data strategy with well-defined quality metrics. With active data management, they leverage workflow dashboards, identify process bottlenecks, and monitor service levels and related key performance indicators to ensure consistent quality and productivity.
- Optimized: Organizations in the top 1% of the master data maturity pyramid belong to the Optimized category. These companies have a state-of-the-art data management platform that leverages optimized workflows for all types of master data. Such a platform, along with its underlying infrastructure, spans all use cases and supports a collaborative environment where both transactional and support documentation is maintained for flexible reference, auditing purposes, business-centric metrics, and proactive service-level management. These companies apply continuous improvement techniques, focusing on the Lean philosophy to increase efficiency and eliminate waste.
A company’s level of master data maturity doesn’t depend on size or economic status. At Winshuttle, we frequently come across large, global enterprises that are stuck in the primitive stages of master data processing. A company at the Tribal level of master data governance is unlikely to jump to the Optimized level in a single step. Achieving this goal requires not only appropriate aim across the organization, but also the right tools.
What level of maturity is your organization? How are you planning to increase your company’s master data maturity level? In our next blog we’ll discuss how Winshuttle has helped many companies accelerate their master data maturity. If you would like more information please reach out to email@example.com.
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