Master Data Management
Define and maintain one or more master datasets, and synchronize them across relevant processes and systems. Define the data patterns and the quality standards to which data must conform in each stage of its life cycle.
Improvement Planning
Practices-Outcomes-Metrics (POM)
Representative POMs are described for Master Data Management at each level of maturity.
- 1Initial
- Practice
- Maintain local and function based master data or application specific master data.
- Outcomes
- Data is typically being duplicated and different versions of the “truth” exist in different systems.
- The problem is typically not being addressed and is getting worse.
- Metrics
- # Separate databases.
- # Cross-databases consistency checks.
- % Master data elements defined organization-wide
- 2Basic
- Practices
- Analyse data complaints and identify and run small data synchronization exercises that are unique to solving a specific problem with a specific set of data sources.
- Form cross-functional teams and start working on the design of a master data management solution.
- Outcomes
- IT is seen as the culprit and the saviour in so far as they respond quickly.
- Their demand for additional resources raises more questions.
- A realization that the solution resides or lies with the organization as a whole and not just IT, is changing attitudes.
- Metrics
- # Open data issues.
- # Trend of # Open data issues.
- # Time to resolve data issues.
- # Databases.
- # Batch or real-time synchronization processes.
- % Master data elements defined organization-wide.
- 3Intermediate
- Practices
- Identify the value of master data management and provide funding for Improvement projects.
- Use cross-functional teams to work on the design and maintenance of a master data management solution.
- Analyse data issues and identify root causes.
- Outcomes
- The value proposition of good master data management is articulated and pursued by both business and IT.
- Management actively encourage participation on cross functional teams to solve data issues.
- A fuller understanding of root causes of data issues and the cost of these issues is emerging.
- Metrics
- # Open data issues.
- # Trend of # Open data issues.
- # Time to resolve data issues.
- # Databases.
- # Batch or real-time synchronization processes.
- # One-off or bespoke data fixes.
- % Master data elements defined organization-wide.
- 4Advanced
- Practices
- Provide data to the business as a service based on the master data management data layers and services it provides.
- Review of master data management architecture to ensure a best fit solution (that may include re-use) for any new data requests.
- Outcomes
- Quality is improving and metrics support this.
- The business relies on master data management services for existing needs and can readily identify opportunities that the data service platform offers.
- Metrics
- # Open data issues.
- # Trend of # Open data issues.
- # Time to resolve data issues.
- # Databases.
- # Batch or real-time synchronization processes.
- # One-off or bespoke data fixes.
- % Master data elements defined organization-wide.
- 5Optimized
- Practices
- Couple master data management with service technologies to provide a flexible data platform upon which the business is supported and allowed to evolve and grow.
- Eliminate or automate data synchronization issues and phase reporting schedules to take account of synchronizations as necessary.
- Outcomes
- Data as a service is a reality and provides a platform for new business.
- The business is dynamic and flexible in its use of data.
- Data synchronizations (where necessary) are dynamic and automated.
- Reports are consistent and offer a common view of data across all levels.
- Metrics
- # Open data issues.
- # Trend of # Open data issues.
- # Time to resolve data issues.
- # Databases.
- # Batch or real-time synchronization processes.
- # One-off or bespoke data fixes.
- % Master data elements defined organization-wide.