Data Quality Management (DQM) and Master Data Management (MDM) are overlapping disciplines within data management. An obvious reason for this overlap is that most data quality challenges are found in master data. Prominent examples are duplicates in customer master data, inaccurate location master data and incomplete product information.

In this spectrum we have three kinds of tools on the market:

1. Independent data quality tools that are mainly emphasizing on these capabilities:
   • Data matching with the aim of identifying duplicates and making a link between two or more data records that describes the same real-world entity.
   • Data profiling with the aim of identifying and quantifying data quality issues as anomalies, inconsistency and incompleteness.
2. Data quality tools offered under the same brand and packaged together with MDM tools and other data management tools in a data management suite.
3. Data quality capabilities as data matching and data profiling built into (extended) MDM platforms.

The results (golden records) from independent data quality tools can be stored and maintained in the master data part of business applications as ERP and CRM systems – or a separate MDM platform.

The ability to settle a more complex result of for example deduplication, as explained in the post Three Master Data Survivorship Approaches, can drive the requirement of which of the above-mentioned tools that will serve your organization best.

This list welcomes all the mentioned DQM offerings. Check out the current list here.