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:
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.
Necessary cookies are absolutely essential for the website to function properly. These cookies ensure basic functionalities and security features of the website, anonymously.
|cookielawinfo-checbox-analytics||11 months||This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".|
|cookielawinfo-checbox-functional||11 months||The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".|
|cookielawinfo-checbox-others||11 months||This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.|
|cookielawinfo-checkbox-necessary||11 months||This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".|
|cookielawinfo-checkbox-performance||11 months||This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".|
Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features.
Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.
Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc.
Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. These cookies track visitors across websites and collect information to provide customized ads.
Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet.