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.
- 2Basic
- Practice
- 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.
- Outcomes
- IT is seen as the culprit and the saviour in so far as they respond quickly.
- Their demand for additional resources may raise more questions.
- Metrics
- # of open data issues.
- Trends in # of open data issues.
- Time to resolve data issues.
- # of batch or real-time synchronization processes.
- Practice
- Form cross-functional teams to start working on the design of a master data management solution.
- Outcome
- A realization that the solution resides or lies with the organization as a whole and not just IT changes attitudes.
- Metric
- % of master data elements defined organization-wide.
- 3Intermediate
- Practice
- Identify the value of master data management and provide funding for improvement projects.
- Outcome
- The value proposition of good master data management is articulated and pursued by both business and IT.
- Metrics
- % of data management activities where value has been measured.
- # of open data issues.
- Trends in # of open data issues.
- Time to resolve data issues.
- # of batch or real-time synchronization processes.
- Practice
- Use cross-functional teams to work on the design and maintenance of a master data management solution.
- Outcome
- Management actively encourage participation in cross functional teams to solve data issues.
- Metric
- % of master data elements defined organization-wide.
- Practice
- Analyse data issues and identify root causes.
- Outcome
- Understanding increases on the root causes of data issues and their cost.
- Metrics
- # of one-off or bespoke data fixes.
- % of data issues with identified root causes.
- 4Advanced
- Practice
- Provide data to the business as a service, based on the master data management data layers and services it provides.
- Outcome
- Data quality improves and metrics support this.
- Metrics
- # of open data issues.
- Trends in # of open data issues.
- Time to resolve data issues.
- Practice
- Review the master data management architecture to ensure a best fit solution (that may include reuse) for any new data requests.
- Outcome
- The business relies on master data management services for existing needs and can readily identify opportunities that the data service platform offers.
- Metrics
- % of master data elements defined organization-wide.
- # of batch or real-time synchronization processes.
- # of one-off or bespoke data fixes.
- 5Optimized
- Practice
- 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.
- 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.
- Metrics
- # of open data issues.
- Trends in # of open data issues.
- Time to resolve data issues.
- Practice
- Eliminate or automate data synchronization issues and phase reporting schedules to take account of synchronizations as necessary.
- Outcomes
- Data synchronizations (where necessary) are dynamic and automated.
- Reports are consistent and offer a common view of data across all levels.
- Metrics
- # of batch or real-time synchronization processes.
- # of one-off or bespoke data fixes.
- % of master data elements defined organization-wide.
- % of master data management processes that are automated (as appropriate).