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The most common reasons are a lack of C-level sponsorship, a “technology-first” approach without a governance model, a lack of a clear definition of data ownership, a “big bang” implementation, and the absence of measurable quality goals. MDM projects almost never fail because of the technology—they fail due to organizational and political factors.

With a structured approach, noticeable improvements can often be achieved in one initial domain within three to six months. Company-wide, sustainable data quality governance across multiple domains is an ongoing process with no fixed end date—featuring clear milestones rather than a one-time completion.

With the Agile Data Manager, Goldright offers a low-code platform that embeds validation rules, required fields, and approval workflows directly at the data source—including native version history. This makes data quality measurable and continuously manageable, rather than requiring a one-time cleanup.

No. Without a continuous process and monitoring, even a freshly cleaned dataset will deteriorate again within a few months because new employees, systems, and processes constantly introduce new inconsistencies. Data quality is an operational mode, not a project.

The duplicate rate measures the proportion of duplicate records for the same business object. It falls under the dimension of uniqueness and is usually the most costly quality defect: For example, a duplicate supplier record leads to duplicate payments. Industry benchmarks show duplicate rates of 10 to 30% in the absence of formal data quality processes.

Operational responsibility lies with the data owners in the business units, while strategic oversight typically falls to the Chief Data Officer (CDO). IT provides the technical platform. This three-way division forms the backbone of any effective data quality governance.

Data quality is the umbrella term for the quality of all types of data. Master data quality refers specifically to master data—that is, customer, supplier, product, and employee data that rarely changes and is used by many processes. Because master data is used so widely, quality issues there have a particularly significant impact.