In recent years, there has been a growing awareness among organizations with regards to their data and the role it plays in the success or failure of their most critical business functions. Some of this recognition is driven by regulation, others by IT advances such as cloud technologies and big data, while others still by the desire to have high quality trusted data.
This shift in mindset is evidenced by the change in technology budgets from a concentration on hardware and infrastructure purchases, towards leveraging and making the best use of corporate data assets. In line with this has been the rise in popularity of Master Data Management (MDM) systems.
Used in the management of critical shared data entities such as security, product, or client master, MDM, when properly implemented, can form the cornerstone of an organization’s Enterprise Data Management (EDM) framework.
The goals of MDM are to identify, validate, and resolve data issues as close to source as possible, whilst creating a “Gold Copy” master dataset for downstream consumption. MDM provides many benefits, and when implemented correctly can ensure consistency, completeness, and accuracy of core shared data sets. But MDM is not the silver bullet of providing enterprise data quality. At its core, MDM manages just a single (though important) area of an organization’s data universe – namely, business entities. Looking a little deeper into an organization’s data use, we find that many business and technology functions rely on a mixture of operational data, reference data, metadata, and audit information, in addition to the master data, with the quality of each being equally important. MDM does a commendable job of ensuring the shared master data is managed correctly and is fit for purpose; however, MDM does not represent a full Data Governance or EDM program. Quality is only part of the data equation, whereas organizations need a broader view and transparency into the data they plan on using for critical decisions, and this is something that MDM systems are simply not adept at.
Organizations seeking a comprehensive data management strategy need to address some additional capabilities:
MDM’s myopic focus makes it impossible to address these areas across the organization’s broader spectrum of data, and highlights the key differentiators and importance of Data Governance to the organization.
Proper governance sits on top of MDM, data movement, or data warehouse initiatives, and ensures active participation of the business in the definitional, sourcing, quality, and accountability perspectives.
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