Liliendahl on Data Quality

As examined in a previous post with the title Data Fabric and Master Data Management, the use of the knowledge graph approach is on the rise.

Utilizing a knowledge graph has an overlap with Master Data Management (MDM).

If we go back 10 years MDM and Data Quality Management had a small niche discipline that was called (among other things) entity resolution as explored in the post Non-Obvious Entity Relationship Awareness. The aim of this was the same that today can be delivered in a much larger scale using knowledge graph technology.

During the past decade there have been examples of using graph technology for MDM as for example mentioned in the post Takeaways from MDM Summit Europe 2016. However, most attempts to combine MDM and graph have been to visualize the relationships in MDM using a graph presentation.

When utilizing knowledge graph approaches you will be able to detect many more relationships than those that are currently managed in MDM. This fact is the foundation for a successful co-existence between MDM and knowledge graph with these synergies:

• MDM hubs can enrich knowledge graph with proven descriptions of the entities that are the nodes (vertices) in the knowledge graph.
• Additional detected relationships (edges) and entities (nodes) from the knowledge graph that are of operational and/or general analytic interest enterprise wide can be proven and managed in MDM.

In this way you can create new business benefits from both MDM and knowledge graph.