What are Analytics-Ready Modular Plug-and-Play Datasets?

Modular plug-and-play datasets are compatible datasets designed specifically to be directly interoperable with each other. Any of the compatible modular datasets can be seamlessly combined, on-demand, to retrieve whatever data is requested. These modular datasets, alone or in dynamic combinations, are also designed to provide all the functionality of a data warehouse.

We have determined that siloed datasets simply lack the master data and metadata commonality needed to directly share their data with other datasets. Our data products, Maxxphase Data Compatibility Standards, are non-invasively added to each disparate dataset to provide the master data and metadata commonality making the dataset compatible. When a dataset is encapsulated with a series of Data Compatibility Standards, the dataset becomes modularized. All direct data interoperability between compatible datasets is through the Data Compatibility Standards. 

Businesses now have direct access to whatever data they need, no matter what compatible datasets are involved. Beyond that, there are no programmed data flow dependencies among datasets. All datasets remain independent, which results in amazing agility not currently seen without plug-and-play functionality. No data flow datasets greatly reduce the resources needed for development and maintenance while increasing data trust. 

 Plug-and-Play Modularity

Dataset modularity begins with Compatible Data Modeling – an enhanced form of conventional data modeling. Compatible data models are encapsulated within a "shell" of Data Compatibility Standard data entities. This shell of standardized data entities is added non-invasively to any conventional data model as an enrichment to become a compatible data model. 

Plug-and-play features are added to a compatible data model using our patented peer entity relationships. Unlike static foreign key relationships, peer entity relationships are dynamically formed and dissolved as needed.  

Compatible data models are modular with plug-and-play relationships.  Each modular data model can be consolidated and integrated with any other modular data model. When a modular data model is instantiated as a dataset, that dataset is also modular and readily integrates with other modular datasets. Our patented modular data models and modular datasets are new to IT.

Maxxphase Data Compatibility Standards are dimensional. As such, each compatible dataset is given the functionality of a data warehouse. Since these Data Compatibility Standards also provide direct dataset interoperability, data warehouse functionality is provided to compatible datasets alone or in dynamic combination. Data Compatibility Standards make our modular plug-and-play datasets analytics-ready.

Business and IT Agility

Ever had a problem onboarding a new data system or retiring an outdated data system? The difficulty emanates from disparate data systems bound together with hand-crafted data flow software needed to integrate their data. These data systems and their data integration software have been named the Data Integration Hairball because of the complexity of their interdependence. This siloed data architecture is stagnant because of the difficulty and expense of making changes.

Plug-and-play data systems feature no code-standardized data junctions between compatible modular datasets. These standardized data junctions do not require nor want data movement, data preparation, or data transformations. Developing and maintaining modular plug-and-play datasets is very efficient, cost-effective, and flexible compared to any Data Integration Hairball.

These compatible modular datasets are components that spontaneously form a single consistent Compatible Data Fabric. Since the direct data junctions are standardized, any compatible data systems can easily be added or removed from the Compatible Data Fabric. Plug-and-play functionality creates a compatible data architecture that is very agile.

Please contact us with your Modular Plug-and-Play Dataset questions!