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.

However, issues with data quality, classification, and reconciliation may require data transformation. As with other Extract, Transform, Load-based data movements, these processes are expensive and inefficient, reducing return on investment for a project.

As a result of business unit and product line segmentation, the same entity (whether a customer, supplier, or product) will be included in different product lines. This leads to data redundancy and even confusion.

For example, a customer takes out a mortgage at a bank. If the marketing and customer service departments have separate databases, advertisements might still be sent to the customer, even though they've already signed up. The two parts of the bank are unaware, and the customer is sent irrelevant communications. Record linkage can associate different records corresponding to the same entity, mitigating this issue.

One of the most common problems for master data management is company growth through mergers or acquisitions. Reconciling these separate master data systems can present difficulties, as existing applications have dependencies on the master databases. Ideally, database administrators resolve this problem through deduplication of the master data as part of the merger.

Over time, as further mergers and acquisitions occur, the problem can multiply. Data reconciliation processes can become extremely complex or even unreliable. Some organizations end up with 10, 15, or even 100 separate and poorly integrated master databases. This can cause serious problems in customer satisfaction, operational efficiency, decision support, and regulatory compliance.

Another problem involves determining the proper degrees of detail and normalization to include in the master data schema. For example, in a federated Human Resources environment, the enterprise software may focus on storing people's data as current status, adding a few fields to identify the date of hire, date of last promotion, etc. However, this simplification can introduce business-impacting errors into dependent systems for planning and forecasting. 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.

Master data management is enabled by technology, but is more than the technologies that enable it. An organization's master data management capability will also include people and processes in its definition.