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Data about individual attributes are recorded for each component entity, and then the entities are linked together.

We recently had the opportunity to speak with the primary analysts and thought leaders covering the master data management space. The topic of our discussion was multi-domain master data management. I believe it’s fair to say that everyone agreed the term “multi-domain” has many meanings, with no clear consensus on the definition.

We used these discussions to introduce a different notion in the conversation – the notion of composite entities. Data about real-world objects, such as criminal incidents or insurance policies often spans multiple entities and cross multiple domains. For example, a criminal incident report might consist of data about one or more people, vehicles, weapons, locations or events.

To model this one complex object, relevant data is typically decomposed into multiple entities. Data about individual attributes are recorded for each component entity, and then the entities are linked together. Composite entities therefore are logical networks of entities, often spanning multiple domains, which together represent a complex object.

Composite entities can be a powerful mechanism that enables users and analysts to perceive their data in new ways. With composite entities, an enterprise can be empowered to perform superior methods of data analysis, including multi-directional analysis, cross-domain linking and historical analysis.

Multi-directional analysis
An analyst might be aware of an item of interest, such as a vehicle used in an incident, and he can call up the full composite entity representing the incident. Not only does the analyst now have a holistic view of the incident, but the analyst can traverse the data sets to see what other incidents the same vehicles or people were involved in.

The analyst can then pull up the contexts for these other incidents. He can compare dates, times, locations and other entities, perhaps even those that he didn’t realize were relevant until looking at them.

Cross-domain linking
Attributes of entities can be matched within the same domain (as is generally done in resolving an entity), but attributes can also be matched across domains in order to identify cross-domain associations.

For example, a person might be associated with a location based on similar, but not exact, address records for the two entities. Further, a vehicle associated to the same location then suggests an indirect association between that person and the vehicle.

Historical analysis
An analyst doesn’t merely see the various component entities that make up this complex object today, but he can see prior versions of history and thus recognize changes over time.

For example, if a composite entity represents the incidents of crime in a region, then this historical analysis enables an analyst to see increases or decreases in criminal activity over time. This sort of data analysis can be a powerful guide to risk management and planning for the future.

I truly believe composite entities enable enterprises to understand their data the way it was meant to be interpreted, as representations of real-world objects.

Multi-domain objects visually appear as networks and enable analysts to traverse data sets in any direction. By presenting complex objects as networks of entities, an enterprise can have the tools it needs to make the best decisions and gain the best insight possible.