Traditional Data Modeling
Conventional data modeling methods do not address designing datasets that are directly interoperable. As such, when conventional data models are instantiated, the resulting datasets are always disparate. These disparate datasets are often characterized as data silos because they lack the ability to share their data without data transformations. Maxxphase developed Compatible Data Modeling to fix this data modeling problem.
Compatible Data Modeling
Compatible Data Modeling is a relatively straightforward enhancement of conventional data modeling. Compatible data modeling utilizes new data modeling objects and additional data design methods to support designing modular plug-and-play directly interoperable datasets. Compatible Data Models themselves are modular plug-and-play. As such, these compatible data models can be combined or separated as needed.
Data Compatibility Standard entities are new data modeling objects that become the universal interface of every compatible data model. The Data Compatibility Standard data entities are designed as a non-invasive add-on to encapsulate conventional data models with standardized master structural metadata. These standard data entities modularize any data model adding the metadata commonality needed for direct dataset interoperability in the datasets instantiated from compatible data models.
A new peer entity relationship has been defined to dynamically join an unlimited number of Data Compatibility Standard entities across compatible data models. Data integrity enforcement for the peer entity relationships is a patented master data management-type method. This data integrity enforcement among compatible datasets ensures that compatible datasets will be dynamically plug-and-play.
When datasets are instantiated from compatible data models, these datasets are compatible modular plug-and-play. Compatible datasets can be blended on demand to provide whatever data and information is required.
In Conclusion . . .
Conventional Data Models and their design methods can only create disparate siloed datasets. Since siloed datasets are a recognized hindrance to IT and business, we recommend that disparate data modeling be replaced with Compatible Data Modeling as soon as possible.
