Master Data consist of the important enterprise “nouns” in an organization, such as Customer, Part, Account, and Product. Margaret Rouse defines Master Data Management (MDM) in “Building an Effective Data Governance Framework” as:
“…a comprehensive method of enabling an enterprise to link all of its critical data to one file, called a master file, that provides a common point of reference. When properly done, MDM streamlines data sharing among personnel and departments. In addition, MDM can facilitate computing in multiple system architectures, platforms and applications… “
Master data, by its very nature, is strategic to the organization. If you are contemplating the use of Master Data Management for key enterprise data, what do you need to consider to ensure a successful rollout? You will need to develop an implementation strategy that considers all of the disparate data consumers and their use cases to be successful. Dr. Ann Marie Smith in “Priming a Master Data Management Strategy for Success”, defines three key success factors, all strategic:
- Develop the MDM program as part of an enterprise information (or data) management initiative
- Develop the MDM program with the support of a formal enterprise data governance program
- Develop the MDM program in conjunction with a data quality program
MDM implementation strategies include all of the standard components of a strategy: people, process, and tools. For tooling, the MDM system will need to satisfy the organization’s requirements for initial and maintenance pricing, compatibility with other enterprise systems, and the organization’s ability to implement and support the system. Like most enterprise technologies, MDM systems tend to be expensive to purchase and maintain, so using a hosted MDM-as-a-Service could be an option. Cross-functional processes will be required to manage data sourcing and consumption, cadence of data updates, and most importantly, overall on-going data governance. Roles will need to be defined and filled for MDM implementation and support, and data governance.
MDM projects pose many unique challenges. Because Master Data supplies the enterprise with governed data, project managers need to coordinate development and testing with all consuming systems. If a formal enterprise data team doesn’t exist, the MDM project team will be responsible for soliciting initial and on-going data requirements from the downstream system maintainers. MDM quality assurance is complicated by the wide-ranging source and target system integration demands. In addition to functional testing, extensive end-to-end testing is necessary to include the entire life cycle of data creation, update, and disposition for all source and consuming systems. Another project management challenge is synching MDM development with releases of downstream systems. The consuming systems must update their interfaces prior to the rollout of any new MDM version.
In summary, MDM projects have enterprise impacts and must consider the needs of all consuming systems. To be successful in the long run, MDM must have enterprise-level stakeholders, and formal data quality and governance processes must be instituted.
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