Master data management (MDM) is a discipline in which business and information technology collaborate to ensure the uniformity, accuracy, stewardship, semantic consistency, and accountability of the enterprise's official shared master data assets.
As with other Extract, Transform, Load-based data movements, these processes are expensive and inefficient, reducing return on investment for a project.
One of the most common problems for master data management is company growth through mergers or acquisitions.
Ideally, database administrators resolve this problem through deduplication of the master data as part of the merger.
Another problem involves determining the proper degrees of detail and normalization to include in the master data schema.
The stakeholders of such systems may be forced to build a parallel network of new interfaces to track the onboarding of new hires, planned retirements, and divestment, which works against one of the aims of master data management.
It has the objective of providing processes for collecting, aggregating, matching, consolidating, quality-assuring, persisting and distributing master data throughout an organization to ensure a common understanding, consistency, accuracy and control,[4] in the ongoing maintenance and application use of that data.
The benefit of this model is its conceptual simplicity, but it may not fit with the realities of complex master data distribution in large organizations.
If it is required, then the solution implemented (technology and process) must be able to allow multiple versions of the truth to exist but will provide simple, transparent ways to reconcile the necessary differences.
Often, solutions can be found that retain the integrity of the master data but allow users to access it in ways that suit their needs.