Mastering Data Management

Use MDM as a tool to evaluate and fix your data quality issues.

In my previous post, we examined the confusion between data quality and MDM. Today we’ll discuss how to effectively leverage MDM as a tool or technique to prioritize data quality problems.

Data quality is a problem for almost every enterprise. The sheer number and variety of data quality issues makes it difficult even to list and prioritize them. This may result in analysis-paralysis scenarios when the needs and requirements for a data quality program are discussed for years and the program fails at inception.

Master Data Management resolves this uncertainty problem by clearly stating its focus. MDM claims that some entities (master entities) are more important than others since they are widely distributed across the enterprise, reside and are maintained in multiple systems and application silos.

Master entities are critical for multiple business and operational processes. While these entities and associated data domains may comprise only 3-7% of an enterprise data model, their significance from the data quality perspective is disproportionally high.

Bringing master data into order often solves 60-80% of the most critical and difficult to fix data quality problems.

In essence, MDM is a great way to prioritize data quality problems and focus resources properly to maximize the return on a data quality effort. While data quality priorities vary from system to system, MDM helps align these priorities across the enterprise.

Expressed differently, MDM is an efficient approach to addressing enterprise data quality problems, enabling organizations to cherry pick their biggest data quality and data management obstacles.

Party, Product and Location top the list of choices of what most companies recognize as their master data.

The terms “Party” and “Product” have a variety of industry specific variants. These entity types are not just widely distributed across application silos but also represented by high volumes of records, which creates additional challenges.

The quality of reference data is oftentimes addressed as part of MDM-centric data quality programs too. Reference data include entities with fewer record counts than in traditional MDM entities. Reference data deals with categories, types, classifications, and other “pick lists” defined by the business and/or data governance. Many MDM-centric data quality programs address the reconciliation of code semantics

In my next post, we’ll dive into the specifics of effectively using MDM within your data quality processes. This post is now live.