Can combining master data management (MDM) projects with initiatives such as business intelligence and business...
process management programs increase the ROI of both investments? And if so, do you have tips on how to effectively combine them?
Master data management efforts are major initiatives in their own right. They require significant investments in human resources (project managers, MDM and data architects, tool specialists, data analysts, etc.) and in MDM software and supporting hardware. A big budget and ample amounts of time are needed to successfully implement and then sustain a foundational MDM framework and subsequent development projects.
However, many companies launch or expand MDM projects to address the needs of other information disciplines, such as business intelligence. A successful BI program requires that the organization have a well-managed MDM environment so that the right data -- clean, accurate and consistent -- can be applied for analysis and decision making. Poor master data management has doomed many BI efforts.
Combining MDM and BI, therefore, can provide increased business value in managing data and information, if done properly. An MDM architecture must be designed so that the BI architecture is in harmony with it, not in collision. MDM projects require enterprise data governance and enterprise metadata management, and so does successful BI -- these disciplines should be established before a joint MDM and BI effort is launched. Additionally, successful master data management and business intelligence rely on a robust enterprise data architecture, so the development of a strong conceptual data model with defined and approved subject areas and critical analytical metadata would also contribute to the success of a combined program.
The ROI of a combined MDM and BI initiative can be measured in the value returned from improved understanding of critical common data that can be used as the foundation for different forms of analysis, and in the increase in the speed of decision making enabled by the data analysis.
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